Mathieu Danoy

Rachid Jellali

Yannick Tauran

Johanna Bruce

Marjorie Leduc

Françoise Gilard

Bertrand Gakière

Benedikt Scheidecker

Taketomo Kido

Atsushi Miyajima

Francoise Gilard

Fabrice Soncin

Yasuyuki Sakai

Eric Leclerc

Characterization of the proteome and metabolome of human liver sinusoidal endothelial-like cells derived from induced pluripotent stem cells

Keywords: Liver Sinusoidal Endothelial Cells, LSECs, human induced pluripotent stem cells, hiPSCs, proteomic, metabolomic

scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Liver sinusoidal endothelial cells (LSECs) are the most abundant nonparenchymal cells in the liver, form a very specialized microvasculature, and play an important role in hepatic functions and diseases (De Smedt et al., 2021;[START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF]. Those cells possess morphological features specific to the liver and differ from vascular endothelial cells by the lack of basal membrane and by the presence of large pores called fenestrae [START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF]. The fenestrae, clustered in sieve plates, allows bidirectional mass transfer between blood (mixture of arterial and venous content) and hepatocytes (transforming unit, [START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF]Gage et al., 2020).

LSECs also exhibit high endocytotic activity for macromolecular waste products, due to the presence of a high density of endocytic vesicles (Braet andWisse 2002, Poisson et al., 2017). Among other functions, LSECs contribute to the regulation of sinusoidal flow, to the maintenance of hepatic stellate cell quiescence, to liver regeneration, and are involved in hepatic complications such as fibrosis, cirrhosis, and hepatocellular carcinoma (Braet andWisse 2002, Poisson et al., 2017).

The lack of complex in vitro models including mature and functional human Liver Sinusoidal Endothelial Cells (LSECS) remains one of the bottleneck problems for the pharmaceutical industry to provide relatively low-cost toxicological data and predictive information on the effects of xenobiotics in complex hepatic models. In that regard, promising alternatives such as the differentiation and maturation of human-induced pluripotent stem cells (hiPSCs) have been developed. As such, several investigations have led to the differentiation of hepatocyte-like cells (HLCs) from hiPSCs [START_REF] Tayeb | Highly efficient generation of human hepatocyte-like cells from induced pluripotent stem cells[END_REF][START_REF] Danoy | Transcriptome profiling of hiPSC-derived LSECs with nanoCAGE[END_REF][START_REF] Kido | CPM is a useful cell surface marker to isolate expandable bi-potential liver progenitor cells derived from human iPS cells[END_REF]. In parallel, hiPSCs have also been used to provide a reliable source for other hepatic cells such as HSCs [START_REF] Coll | Generation of hepatic stellate cells from human pluripotent stem cells enables in vitro modeling of liver fibrosis[END_REF] and LSECs [START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF]. In this frame, hiPSCs have been differentiated toward an LSECs phenotype in which immunostaining coupled to Western blotting confirmed expression of important LSECs markers such as Stabilin-2 [START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF].

Transcriptome analysis with the nano Cap Analysis Gene Expression (nanoCAGE) method was then performed to further confirm the overall vascular commitment of the cells (upregulation of the APLN, LYVE1, VWF, ESAM, and ANGPT2 genes). The analysis of promoter motif activities highlighted several transcription factors (TFs) of interest during the LSECs differentiation from iPSCs (IRF2, ERG, MEIS2, SPI1, IRF7, regulatory network involving the relevant TFs, their target genes as well as their related signaling pathways was proposed [START_REF] Danoy | Transcriptome profiling of hiPSC-derived LSECs with nanoCAGE[END_REF]. In the present paper, to extend the characterization of the hiPSCs-derived LSECs produced by the differentiation protocol of [START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF], their proteome and metabolome profiles were investigated.

The panel of signatures and pathways highlighted by those analysis and their integration were then discussed with datasets from both in vivo and in vitro studies.

Material and methods

hiPSCs culture and differentiation protocol

The hiPSCs used in this study (454E2) were provided by the cell bank of the Riken BioResource Research Center (RIKEN BRC). 454E2 hiPSCs were cultured and differentiated following the protocol previously described by [START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF]. After differentiation, the cells were harvested and stored in liquid nitrogen. When required, the needed number of cells was seeded on plates (1.5 x 10 4 cells/cm 2 ) coated with fibronectin (20 µg/mL, Life Technologies) for 1 hour at 37°C, for further culture and proliferation for 9 days.

Metabolomic analysis

The global metabolomic profiling was performed with culture media (basal & used in culture, culture medium) using gas chromatography coupled to quadrupole mass spectrometry (GC-MS). Before the GC-MS analysis, an extraction step was performed on the culture media, according to the protocol previously described in [START_REF] Jellali | Metabolomics on a chip approach to study hepatotoxicity of DDT, permethrin and their mixtures[END_REF]. Samples were analyzed by Agilent 7890B gas chromatograph coupled with an Agilent 5977A mass spectrometer. Separation was achieved on a Rxi-5SilMS column from Restek (30 m with a 10 m Integra-Guard column -ref .

The data files obtained were analyzed using the AMDIS software (http://chemdata.nist.gov/mass-spc/amdis/). Peak areas were determined using Masshunter Quantitative Analysis (Agilent) and normalized to ribitol. Metabolite contents are expressed in arbitrary units. The complete protocol is detailed in Supp File 1.

Proteomic analysis

The protocol for the preparation of proteins preparation protocol has been previously described (Danoy et al., 2020, details in Supp. File 1). Briefly, the protein samples were digested using trypsin, and the peptides were separated into 5 fractions.

After speed-vacuum drying, fractions were solubilized in 10 µL of 0.1% TFA, 10% ACN.

Liquid chromatography and mass spectrometry analyses were performed on an U3000 RSLC nanoflow-HPLC system coupled to an Orbitrap fusion MS analysis (Thermo Fisher Scientific). The Maxquant software was used to identify and quantify proteins.

The database used was a concatenation of human sequences from the Uniprot-Swissprot database (Uniprot, release 2018-06) and an incremented list of contaminants. False discovery rate (FDR) was kept below 1% on both peptides and proteins.

Data statistical analysis

The multivariate statistical analysis of proteomic and metabolomic data were performed using XLSTAT software and MetaboAnalyst, a web-based platform for metabolomics data analysis [START_REF] Chong | MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis[END_REF]. Supervised Partial Least Squares-Discriminant Analysis (PLS-DA) was carried out to discover significant variations between the groups. The quality of the PLS-DA model was evaluated by the R 2 Y (fitting degree) and Q 2 (prediction parameter) values. The variable importance for the projection (VIP) parameter was used to select variables that had the most significant contribution in discriminating between both groups. VIP is a weighted sum of squares of the PLS weight that indicates the importance of each variable to the whole model.

In parallel, univariate statistics were performed with Student's t-test, and P-value less than 0.05 was considered statistically significant. Variables with significant changes in the groups (P-value < 0.05 and VIP > 1) were selected as discriminating variables.

Finally, MetaboAnalyst and Idep9.1/KEGG were used for pathway enrichment analysis of metabolomic and proteomic data, respectively.

Results

After differentiation, LSECs were plated and amplified until they reached the confluence. The typical morphologies observed are presented in Fig. 1A-B. Notably, the cells displayed a comparable morphology to their primary counterpart, cultured in similar conditions (Fig. 1C). They formed a confluent, cobblestone-like, monolayer with large nuclei typical of endothelial morphology.

Characterization of the proteome profile of hiPSCs-derived LSECs

The proteome analysis of undifferentiated hiPSCs vs hiPSCs-derived LSECs was performed on 3187 positively identified proteins (Supp. File 2) to assess the changes in protein expression through the differentiation. The comparison led to the identification of 2537 proteins differentially expressed with a p_value below 0.05. To verify that the differentiation engaged hiPSCs into a LSEC phenotype, the previously identified proteins were sorted by fold change. Within those proteins, the top-100 proteins over-expressed in hiPSCs-derived LSECs included several important endothelial markers such as PECAM1, vWF, CDH13, THBS1, ICAM1, ANGPT2, CDH5, ESAM, CLDN5, ITGA5, ITGA3, ENG, ESM1, MCAM (CD146), CD93, MMRN2, CAV1, NOSTRIN, and ICAM2. Specific LSECs markers such as LYVE1, STAB1, and CD36 were also found in these top-100 proteins over-expressed in hiPSCs-derived LSECs. When the analysis was performed on the top-250 proteins over-expressed in hiPSCs-derived LSECs, ACE, NRP2, PLVAP, ERG, MRC1 (a mannose receptor), CLEC14A (a C-type lectin), and AQP1 were additionally identified. The complete signature extracted from this study is presented in Table 1.

To extract pathways specific to the hiPSCs-derived LSECs, proteins with a fold change above 1.2 / below 0.8 and with the P-value < 0.05 (579 proteins) and all proteins with P-value < 0.05 (2537 respectively) were input in the KEGG pathway database. The complete list of proteins used, and pathways extracted is given in Supp.

File 2. Important LSECs and endothelial cells related pathways were expressed in hiPSCs-derived LSECs (Table 2). Among those were found C-type lectin receptor signal (4 and 15 hits respectively with the 579, and 2537 proteins lists), endocytosis (17 and 76 hits), NOD-like receptor signaling (12 and 33 hits), TOLL like receptor (2 and 12 hits), Apelin signaling (8 and 31 hits), VEGF signaling (2 and 11 hits), vascular smooth muscle contraction (5 and 24 hits), Wnt signaling (1 and 22 hits), cGMP-PCK signaling (7 and 24 hits), Vasopressin regulated water reabsorption (2 and 14 hits), PPAR signaling (7 and 21 hits), fluid shear stress (9 and 34 hits), relaxin signaling (8 and 25 hits), and renin secretion (6 and 11 hits). Targets in the HIF (5 and 28 hits) and FOXO (5 and 19 hits) signaling pathways were also found (gene names in pathways are given in Supp. File 2).

The extracted pathways were further arranged using iDEP.91. In that regard, a heatmap of the most discriminating biological processes extracted by the Gene Set Enrichment Analysis which characterized the profiles of hiPSCs and the hiPSCsderived LSECs was created (Fig. 3A). The hierarchical clustering of the important biological processes enriched by the proteins, extracted by Desq2 analysis was also created (Fig. 3B, Supp. File 2 for the list of genes in each pathway). It was found that the profile of hiPSCs was characterized by biological processes such as RNA process, DNA process, and cell cycle while the hiPSCs-derived LSECs' profile was characterized by vesicle transport, sprouting angiogenesis, IL12 production, calciumrelated signaling, and endo/exocytosis processes.

Characterization of the metabolome profile of hiPSCs-derived LSECs

Further characterization of hiPSCs-derived LSECs was done by screening for secreted metabolites. In that regard, the metabolomic analysis was performed and allowed to identify 86 metabolites (Supp. File 3). The multivariate analysis contributed to separate the basal medium and the used in culture with hiPSCs-derived LSECs, culture medium (Fig. 4A-B). The top 30 metabolites differentially present in both culture media included elements related to the consumption of amino acids (leucine, valine), TCA substrates (citramalic acid, succinic acid, pyruvic acid), carbohydrate (mannitol, glucose), and lipid-related compounds (glyceric acid, azelaic acid). In parallel, the production of several amino acids (glycine, threonine, phenylalanine, proline, trans-4 hydroxy-proline), carbohydrates (mannose, fructose, tagatose, xylulose, lyxose, sorbitol, threitol, oxalic acid (TCA-derived metabolite), glycolic acid (TCA-derived metabolite, from xylulose metabolism), picolinic acid (metabolites of tryptophan metabolism), benzoic acid, ethanolamine, putrescine, and glycerol was also confirmed.

Among those metabolites, 26 were found to be differentially present in both media with a P-value below 0.05 (Fig. 4C). Pathway analysis on those revealed phenomena such as enrichment of the galactose metabolism, glucose-alanine cycle, the fructosemannose degradation, Warburg effect, gluconeogenesis. Various amino acid metabolisms, glycolysis, and pathways related to the metabolism of TCA were also highlighted (Fig. 5A-B, Supp File 3). Enrichment pathway analysis, using KEGG as a database, highlighted the valine-leucine isoleucine biosynthesis, Aminoacyl-tRNA biosynthesis, and Galactose metabolism as the top 3 pathways (Supp File 3).

Discussion

In the present study, the proteomic and metabolic profiles of hiPSCs-derived LSECs were extracted to evaluate the quality of the differentiated cellular model. Those results complete the histologic and transcriptomics analysis previously performed [START_REF] Danoy | Transcriptome profiling of hiPSC-derived LSECs with nanoCAGE[END_REF]. In this previous study, hiPSCs-derived LSECs were shown to exhibit typical patterns of LSECs as compared to their primary counterpart. In addition, the cells were also found to be Stabilin-2 + , Stabilin-1 + , CD144 + , and CD31 + . In Table 2, the signature observed for the proteomic and metabolomic analysis of hiPSCs- et al., 2010;[START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF] or CD31 + , LYVE-1 + , L-SIGN + , Stabilin-1 + , CD34 -, PROX-1 -cells [START_REF] Lalor | Human hepatic sinusoidal endothelial cells can be distinguished by expression of phenotypic markers related to their specialised functions in vivo[END_REF][START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF]. In hiPSCs-derived LSECs, the proteome signature was characterized as CD34 -, CD45 , 2004). Thus, the fact that hiPSCs-derived LSECs co-express PECAM and ICAM1 illustrates that fenestration is probably not achieved in the current cellular model. This is in agreement with previously published results [START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF], and is also widely observed in models of LSECs cultured as a 2D monolayer [START_REF] Lalor | Human hepatic sinusoidal endothelial cells can be distinguished by expression of phenotypic markers related to their specialised functions in vivo[END_REF]. Finally, a brief comparison between the proteomic signatures from the literature (HUVECs, as a positive control, and fibroblasts, as a negative control) was performed to further confirm the quality of the cellular model. In HUVECs, LSECs markers such as CD36, LYVE1, and STAB2 were not expressed while NRCAM, F11R, CEACAM21, CEACAM19, CADM4, CADM3, CADM1, JAM2, CDON, SELE were found to be expressed, but not in hiPSCs-derived LSECs [START_REF] Madugundu | Integrated transcriptomic and proteomic analysis of primary human umbilical vein endothelial cells[END_REF].

The metabolic signature of LSECs is usually found to be highly variable depending on their state. In their quiescent state, endothelial cells are characterized by intense fatty acid oxidation and moderate glycolysis [START_REF] Dumas | Metabolic signatures of distinct endothelial phenotypes[END_REF]. As migratory phenomena are induced, a switch to the production of lipids for membrane fluidity is usually observed [START_REF] Dumas | Metabolic signatures of distinct endothelial phenotypes[END_REF]. As proliferation is induced, endothelial cells were characterized by intense glycolysis and glutamine breakdown, moderate fatty acid oxidation, oxidative phosphorylation, and serine biosynthesis [START_REF] Dumas | Metabolic signatures of distinct endothelial phenotypes[END_REF]. In Table 3, the metabolomic profile observed in the presented analysis of hiPSCs-derived LSECs was synthesized and compared to the metabolomic profile of quiescent, migratory, and proliferative endothelial cells found in the literature. In the presented cellular model, the profile was found to be typical of beta-oxidation as shown by the low levels of azelaic acid. Besides, consumption of several TCA substrates, which reflects glycolytic activity was observed while no change in the levels of glutamine and serine was found. High levels of PPP intermediates such as sorbitol, xylulose could also be observed. As the analysis was performed on confluent hiPSCs-derived LSECs, the profile observed would illustrate a quiescent-like phenotype rather than a proliferative behavior. Endocytosis is also known to be involved in the regulation of the transport of lipids which is also one of the major functions of LSECs [START_REF] Hammoutene | Role of liver sinusoidal endothelial cells in non-alcoholic fatty liver disease[END_REF].

Furthermore, lipids have also been found to promote LSECs proliferation as well as the maintenance of their differentiation [START_REF] Hang | Lipids promote survival, proliferation, and maintenance of differentiation of rat liver sinusoidal endothelial cells in vitro[END_REF]. The present dataset suggests a switch in the lipid metabolism in the differentiation of hiPSCs into LSECs.

At the proteome level, this could be characterized by the overexpression of FASN in hiPSCS (fatty acid synthetase), and of CPT1A and HADHA (fatty acid beta-oxidation) in hiPSCs-derived LSECs which implies a degradation of fatty acid as this would be the case in mature LSECs. Besides, lipid homeostasis was highlighted via PPAR signaling in the proteome dataset while the fatty acid degradation was illustrated by the consumption of azaelic acid in the metabolome dataset. hiPSCs-derived LSECs also exhibited an activation of the pentose phosphate pathway (PPP) via the measured concentrations of xylulose and sorbitol which is consistent with the phenotype observed in healthy endothelial cells' phenotype [START_REF] Dumas | Metabolic signatures of distinct endothelial phenotypes[END_REF]. Activation of the PPP could further be confirmed via its function toward nucleotide synthesis for angiogenic activity (18 hits and 15 hits in nucleotide sugar metabolism and nucleotide excision repair respectively at the proteome level and production of putrescine, a substrate of polyamine at the metabolome level), antioxidant defense (overexpression of GPX1, GPX2, GSTO1, MGST2, MGST3, GSTK1, GSTM3 in the proteome dataset), and nitric oxide synthesis (overexpression of NOS3 in the proteome dataset) [START_REF] Bierhansl | Central role of metabolism in endothelial cell function and vascular disease[END_REF] which further confirms the viability of the presented cellular model.

Nonetheless, this model still presents important limitations toward the complete reproduction of the in vivo phenotype of LSECs. Indeed, fenestration could not be observed in hiPSCs-derived LSECs [START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF] which is a strong impairment toward the function of the cells in fluid exchange and transport of molecules.

Fenestration is known to be nitric oxide-dependent via the endogenous nitric oxide synthase (eNOS)-soluble guanylate cyclase (sGC)-cGMP pathway (DeLeve et al., 2017) for which the overexpression was found in the proteome dataset. However, one of the main sources of NO in the normal liver is known to be mediated via endothelial nitric oxide synthase (eNOS) activation by shear stress [START_REF] Shah | Liver sinusoidal endothelial cells are responsible for nitric oxide modulation of resistance in the hepatic sinusoids[END_REF][START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF], which is not reproduced in the current experimental setup. Subjection to shear stress is known to induce the KLF2 transcription factor (Gracia-Sancho et al., 2019[START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF] which stimulates the endothelial upregulation of vasodilating agents such as nitric oxide (NO) [START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF][START_REF] Parmar | Integration flow-dependent endothelial phenotypes by Kruppel-like factor 2[END_REF].

Although partial activation of shear stress-related markers (in the KEGG pathway) and vasoconstriction/vasodilation markers (via THSB1) could be confirmed, the lack of direct exposition to shear stress remains a major weakness in the current approach.

Finally, further confirmation of the differentiation protocol is required with different hiPSCs lines, as biological variability has been observed (Volpato et al., 2020;Ortmann et al., 2020).

Conclusions

In this study, the analysis of the proteome and metabolome dataset of hiPSCsderived LSECs was presented to assess the quality of this differentiated cellular model.

The cell signature could be confirmed as characteristic of LSECs, expressing typical vascular and endothelial markers in addition to LSEC specific proteins while important liver and LSEC patterns were also observed. Specifically, vital functions such as lipid metabolism, the pentose phosphate pathway, and the production of polyamine could be confirmed. In terms of future perspectives, the study of the hiPSCs-derived LSECs in conditions reproducing their in vivo microenvironment could help to further characterize the cells via exposition to shear stress and to other liver cell types in coculture.

Figures captions

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  The multivariate analysis allowed to separate hiPSCs-derived LSECs and hiPSCs as shown in Fig.2A-B. The top 50 proteins differentially expressed in the PLS analysis are shown in the heatmap of Fig. 2C. Among the proteins overexpressed in hiPSCs, FASN (fatty acid synthetase), HSP90AB1, FKBP4 (protein interaction at the estrogen receptor), PSAT1, PHGDH (Amino Acid metabolism), and CEBPZ were identified as discriminant proteins. hiPSCs-derived LSECs were characterized as expressing high levels of CPT1A, HADHA (involved in the fatty acid beta-oxidation), OGDH (TCA), ITGB1, MYH9, ACTN4, FLNB, VIM, THBS1 (tight junction, focal adhesion, regulation of actin), CD93 (receptor for mannose-binding leptin) which confirms the changes in metabolism occurring during the differentiation.

derived

  LSECs was compared to other LSECs signatures found in the literature. In details, LSECs are typically characterized by a specific signature including markers such as VEGFR3 +

  By integrating both the proteomic and metabolomic profiles of hiPSCs-derived LSECs, characterization of the cellular model was further performed. Pattern recognition receptors and components of inflammatory response usually expressed in LSECs include endocytosis receptors, Toll-like receptors, and Nod-like receptors[START_REF] Deleve | Liver sinusoidal endothelial cell: an update[END_REF]. The enrichment of both Toll-like receptors and Nod-like receptors signaling (12 and 33 hits with P-value < 0.05 resp.) was confirmed in the present analysis. Regarding endocytosis, known to be one of the major functions of LSECs in the normal liver, it could be highlighted as illustrated by the KEGG endocytosis pathway (76 hits with P-value <0.05), and the C-type lectin receptor signaling (15 hits, P-value <0.05). Also, the LSECs endocytose is known to involve the scavenger receptor (SR), the mannose receptor, and the FC gamma receptor Ilb2.Notably, the main known SRs are SR-H/Stabilin 1 and SR-H/Stabilin 2[START_REF] Poisson | Liver sinusoidal endothelial cells: Physiology and role in liver diseases[END_REF][START_REF] Deleve | Liver sinusoidal endothelial cell: an update[END_REF]. The presence of STAB2 was also confirmed by western blot in the current cellular model[START_REF] Danoy | Transcriptome profiling of hiPSC-derived LSECs with nanoCAGE[END_REF][START_REF] Koui | An in vitro human liver model by iPSC-derived parenchymal and non-parenchymal cells[END_REF] while mannose was detected in the metabolome as a significantly modulated molecule which further confirms the endocytosis function of the presented cellular model.

Figure 1 :

 1 Figure 1: Morphology of hiPSCs-derived LSECs on Day 1 (A) and upon reaching confluence on Day 7 (B). Morphology of primary human LSECs upon reaching confluence on Day 5 (C).

Figure 2 :

 2 Figure 2: Multivariate statistical analysis on proteomic profiles. PLS-DA scores plot discriminating hiPSCs-derived LSECs and hiPSCs (A). Volcano plot (log2 fold change (hiPSCs-derived LSECs/hiPSCs) plotted against -log10 P-value) highlighting proteins differentially expressed in hiPSCs-derived LSECs and hiPSCs (P < 0.05). Proteins upregulated and downregulated in hiPSCs-derived LSECs (compared to hiPSCs) are labeled in red and blue respectively (B). Heatmap of top 50 proteins differentially expressed in both conditions (C).

Figure 3 :

 3 Figure 3: Important biological processes extracted with iDEP.91 in the comparison between hiPSCs-derived LSECs and hiPSCs proteome profile. Corresponding heatmap of the parametric gene set enrichment analysis in which red denotes

Figure 4 :

 4 Figure 4: Multivariate statistical analysis of metabolomic profile. PLS-DA scores plot discriminating medium in culture with hiPSCs-derived LSECs and basal medium (A).Volcano plot (log2 fold change (medium in culture with hiPSCs-derived LSECs/basal medium) plotted against -log10 P-value) highlighting metabolites differentially expressed in culture with hiPSCs-derived LSECs and basal medium (P < 0.05).Metabolites upregulated and downregulated in the medium in culture with hiPSCsderived LSECs (compared to basal medium) are labeled in red and blue respectively (1: citramalic ac, 2: pyruvic ac, 3: azelaic ac, 4: lactic ac, 5: ethanolamine, 6: xylulose, 7: mannose, 8: lyxose) (B). Heatmap of metabolites differentially expressed in both conditions (C).

Figure 5 :

 5 Figure 5: Metabolic pathways analysis based on the metabolomic profile (in the comparison between medium in culture with hiPSCs-derived LSECs and basal medium). Pathway impact enrichment extracted from the analysis. 1: valine-leucine biosynthesis, 2: aminoacyl-tRNA biosynthesis, 3: galactose metabolism, 4: arginine and proline metabolism, 5: pyruvate metabolism, 6: phenylalanine, tyrosine, and tryptophan Biosynthesis, 7: TCA cycle (A). Top 20 pathways extracted by the metabolite set enrichment analysis (B).

  CD93, MRC1 (both mannose receptors), and NOS3 (nitric oxide endothelial ROS) as shown in Table1. Notably, and VEGFR3 and L-SIGN were not detected in the proteome signature of hiPSCs-derived LSECs and major mature LSECs markers including CD4 and CD32, and immature markers such as CD34, 1F10(Poisson et al.,

	-, PROX1 -, CD31 + , LYVE1 + , either. Concerning CD31, it can be noted that this adhesion molecule, which is expressed in several endothelial cells, including in LSECs, is known to disappear in STAB1 2017), CD4, CD32, CD34, and 1F10 proteins were not expressed in this cellular model the latter with the establishment of fenestration (De Leve et a.

+ , VE Cadherin + (CDH5), VWF + , VEGFR2 + , and completed by the expression of CD146 (marker common in several types of endothelial cells), CLEC14A (C type lectin),

Table 1 :

 1 Signature of proteins overexpressed in hiPSCs-derived LSECs in the comparison with hiPSCs. Comparison with data found in the literature.

Table 2 :

 2 Selected pathways related to the function of LSECs, and associated protein extracted with a p_value below 0,05 in the present analysis.

Table 3 :

 3 Comparison of the metabolic profile of the hiPSCs-derived LSECs analyzed in the present analysis with profiles defined in the literature. Data reproduced from the review of[START_REF] Dumas | Metabolic signatures of distinct endothelial phenotypes[END_REF]

Table 1 :

 1 Signature of proteins overexpressed in hiPSCs-derived LSECs in the comparison with hiPSCs. Comparison with data found in the literature.

	Proteomic signature oh hiPSCs-derived LSECs extracted from the present analysis	Data from the literature
	Endothelial Markers FC (LSECs/hiPSCs)	Raw.pval	LSECs Markers
	PECAM1 (CD31)	1,8946	4,95E-05
	vWF	1,7691	6,62E-08
	CDH13	1,5819	8,76E-08
	MMRN2	1,555	7,77E-07
	MCAM (CD146)	1,538	9,02E-05
	THBS1	1,5274	1,42E-08
	CD93	1,5114	1,42E-08
	ICAM1	1,4641	7,89E-07
	ANGPT2	1,4566	7,89E-06	Lalor et al., 2006; Poisson et
	CDH5 CAV1	1,4537 1,451	2,88E-05 1,63E-06		al., 2017
	ESAM	1,4421	4,73E-07
	CLDN5	1,4226	3,03E-05	CD31 + , LYVE-1 + , L-SIGN + ,
	ITGA5	1,3886	2,59E-06	Stabilin-1 + , CD34 -, PROX-1 -
	ITGA3	1,3859	1,23E-05
	ENG	1,3813	9,11E-06
	NOSTRIN	1,3795	3,25E-05	Ding et al., 2010; Poisson et al
	ESM1	1,3701	3,91E-05		2017
	ICAM2	1,3661	0,0011909
	ACE	1,3586	1,71E-05
	NRP2	1,3533	2,24E-06	VEGFR3 + , VEGFR2 + , VE-
	PLVAP	1,3523	0,00013547	Cadherin	+ , FactorVIII + (VWF),
	ERG LSECs Specific Markers	1,2822 FC (LSEC/hiPSCs)	0,00084371 raw.pval	CD34 -, CD45 -
	LYVE1	1,6043	1,23E-06
	STAB1	1,5184	3,86E-07
	CD36	1,4815	2,94E-05
	MRC1	1,3299	0,00058979
	CLEC14A	1,3136	1,06E-05
	AQP1	1,2736	0,00010051

Table 3 :

 3 Comparison of the metabolic profile of the hiPSCs-derived LSECs analyzed in the present analysis with profiles defined in the literature. Data reproduced from the review of[START_REF] Dumas | Metabolic signatures of distinct endothelial phenotypes[END_REF]

	Profile extracted from the present analysis	Quiescent Endothelial Cells	Migrating Endothelial Cells	Proliferative Endothelial Cells
	Low azelaic acid High glucose, fructose Low pyruvic acid Low succinic, citramalic acids (TCA) High sorbitol, xylulose No glutamine variation	High Fatty acid oxidation Moderate Glycolysis Oxidative pentose phosphate pathway	High Fatty acid synthesis High Glycolysis High Glutamine breakdown Proline biosynthesis Cholesterol transport	Moderate Fatty acid oxidation Fatty acid synthesis High Glycolysis High Glutamine breakdown Serine biosynthesis High OXPHOS
				respiration

Acknowledgments

This project was supported by the iLite ANR-16-RHUS-0005 and by a LIMMS internal funding. We acknowledge the 3P5 proteomic platform (Université de Paris, Institut Cochin, INSERM, U1016, CNRS, UMR8104, F-75014 PARIS, France) and François Guillonneau for their precious advice on the proteomic analysis and Cédric Broussard for monitoring the project. The Orbitrap Fusion mass spectrometer was acquired with funds from the FEDER through the « Operational Programme for Competitiveness Factors and employment 2007-2013 », and from the « Canceropole Ile de France. F.S. is an INSERM director of research. The authors also thank the JSPS Core-to-Core Program.

Authors contribution

M.D. designed, performed the experiments & the analysis, and participated in the writing of the manuscript. R. J. performed the analysis of the metabolome, the integration of the omics dataset, and participated in the writing of the manuscript. Y.T.

designed & performed the protein extraction. J.B, M.L., and M.L.G. performed the analysis of the proteome. F.G. and B.G. performed the analysis of the metabolome. B.S. participated in the hiPSCs differentiation and human LSECs primary cultures. T.K. and A.M. participated in the design of the experiment and developed the LSECs differentiation protocol. F.S. participated in the data analysis and the writing of the manuscript. Y. S. participated in the design of the experiment. E. L. participated in the design of the experiment, its analysis, and the writing of the manuscript.

Conflict of interest statement

The authors declare no conflict of interest.

Ethical issue

No ethical approval was required for the work presented in this study.


Dr Jennifer Morvan

Tom Mcbride

Dr Idriss Curbet

Dr Sophie Colombel-Rouen

Dr Thierry Roisnel

Dr Christophe Crévisy

Dr Duncan L Browne
email: duncan.browne@ucl.ac.uk

Marc Mauduit
email: marc.mauduit@ensc-rennes.fr

Continuous Flow Z-stereoselective Olefin Metathesis: Development and Applications in the Synthesis of Pheromones and Macrocyclic Odorant Molecules

Keywords: Olefin metathesis, Z-selectivity, Continuous Flow, Pheromones, (Z)-Civetone

The first continuous flow Z-selective olefin metathesis process is reported. Key to realizing this process was the adequate choice of stereoselective catalysts combined with the design of an appropriate continuous reactor setup. The designed continuous process permits various self-, cross-and macro-ring-closingmetathesis reactions, delivering products in high selectivity and short residence times. This technique is exemplified by direct application to the preparation of a range of pheromones and macrocyclic odorant molecules and culminates in a telescoped Z-selective crossmetathesis/Dieckmann cyclisation sequence to access (Z)-Civetone, incorporating a serial array of continually stirred tank reactors.

10.

Olefin metathesis 1 has emerged as a powerful synthetic tool to construct carbon-carbon double bonds. The versatility of olefin metathesis is evident from its successful application to natural product synthesis, 2 the valorisation of renewable feedstocks 3 or the preparation of new materials such as polymers. [START_REF] Soulé | Slugovc in Olefin Metathesis: Theory and Practice[END_REF] The gamut of applications is largely due to the development of efficient, welldefined, air stable and easy to handle catalysts, such as the ruthenium-arylidene complexes which demonstrate high tolerance towards various organic functionalities. 1 A significant challenge for catalyst design has been the selective formation of Z-alkenes. As numerous highly valuable molecules feature a Zalkene moiety, special attention has recently been focused on the development of a new class of Ru-based complexes to enable high selectively towards Z-olefins (Figure 1). [START_REF] Hoveyda | For selected reviews on stereoselective and stereoretentive metathesis[END_REF] Cyclometalated Rucatalyst Ru-1 (Grubbs) 6 and monothiolate Ru-catalyst Ru-2 (Jensen) 7 have proved to be highly Z-stereoselective in crossmetathesis (CM) of terminal olefins (up to >99:1) while stereoretentive dithiolate catalyst Ru-3 (Hoveyda) 8 efficiently promoted the transformation of Z-olefins into corresponding Zproducts by retaining the stereochemical information (up to >99:1). Recently, we described the synthesis of a cost-effective Z-selective cyclometalated Ru-catalyst Ru-4 9 featuring an unsymmetrical unsaturated NHC (U2-NHC) ligand accessible through a multicomponent process. 10 Apart from its high versatility and excellent Z-selectivity demonstrated in self-, cross-and ring-opening-polymerization metathesis, the novel cyclometalated catalyst Ru-4 showed impressive robustness in reactive media affording good fidelity of high Z-selectivity over time, surpassing previously described Ru-catalysts. This sought-after feature led us to focus our attention on the development of a continuous flow Z-selective process. Over the last decade, continuous flow olefin metathesis has been well studied; with specific emphasis on the Ru-catalysts employed (both hetero-and homogeneous), and a variety of reactor designs, with varying degrees of success. 11 A key criteria in reactor design is to enable efficient mass transfer thus allowing the removal of ethylene gas, this is especially key at larger scales. Typically, gas-to-liquid-phase mass transfer in batch vessels become less efficient as scale increases owing to diminishing returns on surface area to volume ratios. Indeed, in 2014 Skowerski and co-workers demonstrated efficient mass transfer removal of ethylene gas with the application of a vacuum across a tube-in-tube permeable membrane reactor. 11k Recent studies from Jamison, Bio and coworkers elegantly demonstrated an alternative perevaporation approach ('blowing' a stream of N2 gas across a permeable membrane to carry the ethylene gas out of the liquid phase) as an effective strategy for the mass transfer removal of ethylene gas. 11o Despite these key developments in reactor design, a general approach to continuous flow Z-selective metathesis remains elusive. In order to realise an effective continuous process, careful attention must be given to both the catalyst choice and the continuous reactor design (Figure 1,b) but success in this area paves the way to the continuous preparation of a variety of pheromone and odorant molecules.

We initiated our study by investigating the application of a Teflon AF-2400 tube-in-tube semi-permeable membrane reactor [START_REF] O'brien | For previous developments with tube-in-tube reactors, see for instance[END_REF] to the Z-selective process, preliminary results highlighted that application of a 'vacuum-on' across the membrane for ethylene removal, versus 'vacuum-off' delivered a clear benefit in terms of yield (See Supplementary Information (SI); table S4 entry 5). Taking this further, the self-metathesis of ethyl 9-decenoate under continuous flow condition using a 1 mL Teflon AF-2400 tube-intube reactor was explored in more detail against a focused collection of catalysts Ru-1, Ru-4 and Ru-5 (Scheme 1. For the gram-scale synthesis of Ru-4,-5 and X-ray characterisation of Ru-5, [START_REF]Cambridge Crystallographic Data Center[END_REF] see SI, Scheme S1 and Figure S5). Despite an excellent 95/5 Z/E ratio observed with Ru-1 within 0.5 h, the selectivity dropped gradually as the conversion increased reaching 78% after 3 h of residence time (Scheme 1,a). [START_REF] Herbert | We suspect catalyst decomposition over time. For decomposition pathways for Z-selective catalysts[END_REF] [a] Catalyst loading: 1 mol% (14-23 mM in THF). [b] Determined by 1 H NMR with mesitylene as internal standard. [c] Isolated Yield. [d] Determined by GC. [e] Residence time: 4 h. [f] Determined by quantitative 13 C NMR. [g] Catalyst loading: 2 mol% (40 mM in THF). [h] 13% of SM-product were detected by 1 H NMR. [i] Catalyst loading: 0.5 mol% (10-17 mM in THF). [j] 5% of isomerized byproduct from allylbenzene were detected by 1 H NMR.

The novel Ru-5 catalyst, which is a structural link between Ru-1 and Ru-4, deserved to be examined. Unfortunately, a lower range of Z-selectivity over time (87 to 69%) was observed, although the resulting diester 6 was produced in a higher yield (84%, Scheme 1,c). [START_REF] Herbert | We suspect catalyst decomposition over time. For decomposition pathways for Z-selective catalysts[END_REF]15 Consequently, the DIPP group appears to be the key structural moiety to deliver the highest selectivity. It is worth underlining that continuous flow metathesis can be conducted outside a glovebox while batch conditions require an open vessel inside the glovebox to efficiently remove the ethylene and reach high conversions. 16 Having identified the combination of Ru-4 and a Teflon AF-2400 vacuum-on tube-in-tube design as the most efficient combination to achieve continuous Z-selective catalysis, a range of several cross-and self-metathesis transformations were explored in a larger 3 mL reactor (Scheme 2). Initially running a range of substrates through the reactor with a two-hour residence time and 1 mol% catalyst loading led to moderate to good conversions and yields. Notably, all CM products were formed in excellent Zselectivity, ranging from 94 to 98.5%, with the exception of allylic alcohol 11 which afforded a Z/E ratio of 80/20 17 Furthermore, a 0.5 mol%, Ru-4 loading was sufficient to promote the selfmetathesis of allyl-benzene and other unfunctionalized linear terminal alkenes furnishing, after 2 hours, the corresponding internal Z-olefins 12-14 in excellent selectivity (up to 98%) and moderate to good isolated yields. Using the designed flow reactor rig, highly valuable semiochemicals 15-20, [START_REF] Turczel | For an interesting review dealing with the synthesis of semiochemicals via olefin metathesis[END_REF] acting as potential bio-pesticides against Lepidoptera (moth) and Tenebrio Molitor 15 [a] [Lepidoptera, Moth] 0.5 mmol, 58% conv. (52%) [b] 98.5/1.5 Z/E ratio [d] Me 4 8 [a] Me 5 Me 7

AcO 17 [a] [Tenebrio Molitor, Bettle] 0.5 mmol, 75% Conv. [b] (59%) [c] 98/2 Z/E ratio 4 Me AcO 3

16 [a] [Lepidoptera, Moth] 0.5 mmol, 55% conv. (43%) [d] 98/2 Z/E ratio [d] 6

Me HO 9

10 [e] 3 AcO AcO 9 [a] Me HO 7

11 [e][g] 7 [a] 6 mmol, 75% Conv. [b] (60%) [c] 97/3 Z/E ratio 67% conv. [b] (37%) [c] 96/4 Z/E ratio [b] 75% conv. [b] (62%) [c] 98/2 Z/E ratio [d] 59% conv. [b] (39%) [c] 98.5/1.5 Z/E ratio [d] 70% conv. [b] (41%) [c] 92/8 Z/E ratio [f] Ph Ph Me Me 82% conv. [b] (47%) [c], [h] 80/20 Z/E ratio [b] 13 [i] 14 [i] 12 [i] 58% conv. [b] (48%) [c], [j] 95/5 Z/E ratio [d] 80% conv. [b] (63%) [c] 95/5 Z/E ratio [d] 57% conv. [b] (51%) [c] 98/2 Z/E ratio [d] Ru-4 (0. insect pheromones n 2.5 mmol, 64% Conv. [d] (51%) [c] 97/3 Z/E ratio 6 mmol, 80% Conv. [b] (59%) [c] 96/4 Z/E ratio 18 [a] [Lepidoptera, Moth] 0.5 mmol, 62% conv. [d] (44%) [c] 98/2 Z/E ratio [d] 20 [a] [Lepidoptera, Moth] 0.5 mmol, 72% Conv. [d] (57%) [c] 96/4 Z/E ratio 19 [a] [Lepidoptera, Moth] 0.5 mmol, 69% conv. [d] (49%) [c] 96/4 Z/E ratio [d] 10.1002/anie.202106410

Accepted Manuscript

(beetle), were efficiently produced with excellent Z-selectivity (96-98.5%). Noticeable, a similar efficiency was observed at 6 mmol scale (12 times the standard substrate scope scale) with a slight alteration of Z-selectivity (96-97%).

We next turned our attention to the macro-RCM reaction of terminal olefins. Typically, macro-RCM requires higher dilution than CM so as to minimise the competitive oligomerization reaction; reaction concentration therefore becomes a variable. [START_REF] Herbert | We suspect catalyst decomposition over time. For decomposition pathways for Z-selective catalysts[END_REF] As depicted in Table 1 entries 1 and2, similar catalytic performances were observed with Ru-1 and Ru-5 in the formation of the 16-membered macrocycle 22 when the reaction was run at 20 mM 19 in 1,2-dichloroethane (70 °C, 3 hours residence time, 70% and 75% isolated yield respectively). Nevertheless, the Zselectivity still remained moderate reaching 86% and 82% respectively. To our delight, Ru-4 showed an impressive 97/3 Z/E ratio although a significantly lower yield was observed (24% isolated yield, Table 1, entry 3). [START_REF]This lower productivity was also observed in batch conditions[END_REF] By increasing the residence time to 4.5 h (entry 4), the yield could be slightly improved without any alteration of Z-selectivity demonstrating again the excellent stability of Ru-4. At a higher concentration (50 mM), similar isolated yield and Z-selectivity were observed but a competitive oligomerization occurred (entry 5). Applying these conditions for Ru-4 and Ru-5 (Table 1 entries 2 and3) in the context of highly desirable macrocyclic odorant molecules or pheromones, cyclometalated Ru-4 surpassed Ru-5 in the mRCM (at 20 mM) providing (Z)-civetone 23 [START_REF] Fürstner | For previous stereoselective synthesis of (Z)-Civetone[END_REF] and Cryptoletes pusillus (rac)-(Z)-24, [START_REF]For previous synthesis of (rac)-24 by (non Z-selective) olefin metathesis[END_REF] which were isolated in 44% yield and excellent 95-98% Zselectivity, respectively. Surprisingly, Ru-4 was inefficient toward (Z)-yuzu lactone 25, 23 where a higher yield was reached with the parent Ru-5 (32% isolated yield) but the Z/E ratio remained moderate (82/18).

Table 1. Catalytic performances of cyclometalated Ru-complexes Ru-1,4,5 in continuous flow macro-RCM.

[a] Determined by 1 H NMR spectroscopy using mesitylene as internal standard.

[b] Isolated yield. [c] Z/E molar ratio were monitored by GC analysis. [d] Oligomers were also detected. [e] 1 H NMR yield. Nd: not determined Scheme 3. Catalytic performances of stereoretentive Ru-3 in continuous flow CM and mRCM. [a] 2 equiv of cis-butenediol were used (1.9 M in THF). [b] 4 equiv of cis-1,3-diacetoxy-2-butene were used (neat). [c] Determined by 1 H NMR with mesitylene as internal standard. [d] Isolated yield. [e] Determined by GC. [f] Catalyst loading: 6 mol% (2.4 mM in THF); diene (40 mM in THF), 70 °C, 3 h. [g] Some amounts of dimer by-product were also detected.

Given the deficiencies found in our efforts towards continuous flow Z-selective metathesis, namely moderate Z/E ratios observed in CM involving allylic alcohols (product 11, Scheme 2) and moderate performance in mRCM reactions, we investigated the catalytic performance of the stereoretentive catecholdithiolate catalyst Ru-3 24 (Scheme 3). To our delight, the rapid reaction (within 5 min.) between cis-butenediol and 1-undecene led to the desired internal olefin 11 with a remarkable 99% Z-selectivity and a moderate 56% yield. It is worth noting that without vacuum, a slightly lower productivity and selectivity were observed despite the limited production of ethylene observed here. Interestingly, the in situ generated Ru-3, which avoids the requirement of a glove-box led to 11 with the same efficiency (see SI for details). [START_REF] Müller | The in situ formation of dithiolated catalysts is advantageous as it avoids the use of a glove-box for weighing the air-sensitive catalyst Ru-3[END_REF] Furthermore, the stereoretentive Ru-3 catalyst was also able to produce internal olefins 7, 26 and 27 with moderate to good yields and excellent Z/E ratios (up to >99/1). Unfortunately, Ru-3 was inefficient towards the mRCM leading to (Z)-yuzu lactone 25, delivering a low 7% yield despite a prolonged residence time and higher reaction temperature. As some amounts of dimer byproduct were also detected, we suspect that the semi-permeable membrane reactor is unable to efficiently remove the 1-butene coproduct.

At last, due to the dilution condition (20 mM) required to produce Z-civetone 23 via macro-RCM (Table 1), we envisaged an alternative synthetic route that involves a macrocyclisation via a Dieckmann reaction of the Z-diester 6. 21b,c Such a proposed route sets a challenge for continuous flow reactor design. Similarly to mRCM, macro-Dieckmann cyclisations also requires careful control over the reactive intermediate concentration, so as to favor cyclisation over oligomerization. Reactions of this type are controlled in batch by dropwise addition of one of the components to keep the concentration of intermediate low, favoring cyclisation (Scheme 4, b). [START_REF] Fürstner | For previous stereoselective synthesis of (Z)-Civetone[END_REF] Design of a reactor to achieve this in flow requires multiple injection points along the length of the reactor, where, at each point a portion of one component is introduced via a mixer to the flowing stream. Depending on the rate of reaction there may need to be a maturing period for the reaction prior to the next 'injection' or 'drop' of material.

O O O

with Ru-4 62% conv. [a] (44%) [b] 95/5 Z/E ratio [a] with Ru-5 74% conv. [a] (55%) [b] 70/30 Z/E ratio [a] with Ru-4 25% conv. [a] (6%) [e] nd Z/E ratio with Ru-5 61% conv. [a] (32%) [b] 82/18 Z/E ratio [a] 1 2 3 4 5 70 75 24 29 30 [d] Yield [b] Entry Catalyst Time (h) conv. (%) [a] conc. @ tee (mM) Z:E ratio [c] 23; [Civetone, Civet Cat] 17 13

Ru

25; [Yuzu Lactone, Citrus Fruit]

with Ru-4 59% conv. [a] (44%) [b] 98/2 Z/E ratio [c] with Ru-5 79% conv. [a] (46%) [b] 93/7 Z/E ratio [c] 24; [Cryptoletes pusillus, Beetle]

14 O O Me HO 4
26 [a] 57% conv. [c] (48%) [d] 99/1 Z/E ratio [c] Me HO 5 27 [a] 66% conv. [c] (60%) [d] 98/2 Z/E ratio [c] Me HO 7 11 [a] 67% conv. [c] (56%) [d] 99/1 Z/E ratio [c] Me AcO 7 7 [b] 63% conv. [c] (49%) [c] >99/1 Z/E ratio [e] O O 13 25 [f] [Yuzu Lactone] 45% conv. [c] 7% yield. [c,g] Ru-3 (5 mol%) 56 mM in THF Teflon AF-2400 tube-in-tube 25 °C 5 min

3 mL R 1 R 2 X R 1 R 2 R 1 or n m
vacuum pump ethylene/1-butene without vaccum: 61% conv. [c] (49%) [d] 96/4 Z/E ratio [c] with in situ Ru-3: 67% conv. [c] (49%) [d] 99/1 Z/E ratio [c] R 1 = CH 2 OH, CH 2 OAc or ethyl 10.1002/anie.202106410

Accepted Manuscript

Angewandte Chemie International Edition This can be achieved using a continuous stirred-tank reactor design (CSTR), which can also accommodate active stirring (Scheme 4, c). The use of multiple injection points and CSTRs is also a good reactor design if there is the risk of fouling, bridging or precipitation in the reactor. Deprotonations, or reactions incorporating organometallic reagents can encounter fouling or precipitation issues. Preliminary experimentation with a flow system which simply combined LiHMDS with a full equivalent of diester 6 at a tee-piece, highlighted the propensity to form a precipitate and block the reactor. With this in mind we opted for a reactor design consisting of a serial array of CSTRs. 26 Incorporating this into a semi-continuous process targeting Civetone, we began with a larger scale self-metathesis of the biosourced ethyl 9-decenoate 5 catalyzed by cyclometalated Ru-4. Pleasingly, after 3 hours inside the 3 mL tube-in-tube reactor, 6 was isolated in good yield (71%; 1.27 g) and excellent 96% Zselectivity (Scheme 5, a). Diester 6 was then diluted in THF and split across 5 input feeds. The base, lithium bis(trimethylsilyl)amide (LiHMDS), was used in large excess; such pseudo-first order conditions in base favour macrocyclization. LiHMDS was introduced into the serial CSTR reactor through input port 1 where it met the first portion of diester in tank 1 of the reactor and progressed through the reactor cascade to meet a total of 5 portions of diester before exiting to a batch collection flask (Scheme 5, b). The flowing output was collected into a stirring RBF and a solution of aqueous sodium hydroxide was added and heating commenced to saponify the cyclized ester intermediate (Scheme 5, c). Treatment of the b-keto acid with sulfuric acid induced decarboxylation and furnished (Z)-Civetone in 62% yield, which is a comparable yield for the batch preparation of this material via a Dieckmann cyclisation approach.

In summary, we have developed the first continuous flow Zstereoselective olefin metathesis. Key to achieving this was finding the right combination of catalyst and reactor design. Among a selection of Z-stereoselective Ru-complexes, cyclometalated Ru-4 as well as dithiolated Ru-3 catalysts have proven to be the most efficient toward the formation of Z-internal olefins. Moderate to good yields and remarkable Z-selectivity (up to >99%) were obtained in various CM and mRCM allowing for the production of highly valuable pheromones and macrocyclic odorant molecules. Additionally, the continuous flow synthesis of (Z)-Civetone was successfully achieved in >98% Z-selectivity and 48% yield over 3 steps from a biosourced raw material via a Zselective CM followed by a Dieckmann cyclisation involving serial array of CSTRs.

Accepted Manuscript

Entry for the Table of Contents

The first continuous flow Z-stereoselective olefin metathesis was described with a set of Z-stereoselective Ru-catalysts. Good productivity and excellent Z-selectivity (up to >99%) were obtained in the formation of various internal olefins including highly desirable pheromones and macrocyclic odorant molecules. A telescoped Z-selective cross-metathesis/Dieckmann cyclisation sequence to access (Z)-Civetone, incorporating a serial array of continually stirred tank reactors was also reported.

Institute and/or researcher Twitter usernames: ((optional)) Accepted Manuscript

Figure 1

 1 Figure 1 a) Previously described Z-stereoselective catalysts (Ru-1,-2 and -4) and stereoretentive catalysts (Ru-3). b) Z-stereoselective continuous flow olefin metathesis (this work).

Scheme 2 .

 2 Scheme 1. Catalytic performances of cyclometalated Ru-complexes Ru-1, -4, and -5 in continuous flow self-metathesis of Ethyl 9-decenoate 5.

Scheme 4 .

 4 Scheme 4. Reactor design considerations for the continuous macro-Dieckmann cyclisation reaction.

Scheme 5 .

 5 Scheme 5.Reactor design for the telescoped continuous Z-selective cross-metathesis/Dieckmann cyclisation approach to (Z)-Civetone 23.

Acknowledgements

We are grateful to the CNRS and the Ecole Nationale Supérieure de Chimie de Rennes. This work was supported by the region Bretagne (ARED 2018 N° 601 -BIOMETA; grant to JM) and the FASO (grant to IC). TM thanks Cardiff University and EPSRC for a studentship (EP/N509449/1). Umicore AG & Co is acknowledged for a generous gift of Ru-complexes. We are grateful to Elsa Caytan and the PRISM core facility (Biogenouest©, UMS, Biosit, Université de Rennes 1) for NMR experiences.


B Jousselin

Y Quinsat

C Tournier

Tool axis adjustment for 5-axis roughing operations

Keywords: 5-axis machining, Roughing, Optimization, Tool axis orientation

The increased accessibility of the tool achieved by using 5-axis roughing reduces the overall machining time of complex parts by reducing or eliminating the re-roughing and semi-finishing operations thereby decreasing the volume of remaining material before finishing operations. However, 5-axis milling generates significant variations in tool orientation which, combined with the high tool engagement in the material required by the roughing conditions, can be penalizing. Controlling these variations is therefore mandatory to guarantee the tool life, the productivity and the quality of the roughing operation. Thus, a multi-objective optimization is proposed to define the successive orientations of the tool axis along the path that minimize the feedrate slowdowns and thus the machining time, balance the pushing or pulling machining configurations and respect the programmed scallops heights of the machined surface. In this work, the tool path is defined by two curves, one constraining the position of the tool and the other its orientation allowing for a parametric synchronization to conduct the optimization. From the definition of the weights of each of the minimization objective functions, it is possible to find a tool axis orientation solution that satisfies the given constraints all along the path. The application on a test part by simulation and machining highlights the effectiveness of the proposed approach and the advantages of controlling the evolution of the tool axis orientation in 5-axis roughing.

Introduction

5-axis milling operations offer many advantages when machining complex cavities, including improved productivity due to increased tool accessibility compared to 3-axis operations. This makes it possible to reduce the path lengths and required tool lengths [START_REF] Davim | Machining of complex sculptured surfaces[END_REF]. Mainly used for finishing operations, 5-axis milling can also prove beneficial in roughing by reducing the overall machining time and minimizing the volume of material remaining at the end of roughing [START_REF] Krimpenis | The uniformity of remaining volume on rough-machined sculptured surface parts[END_REF].

Most of the strategies offered for 5-axis roughing use 5-axis simultaneous milling only in areas that cannot be machined in 3-axis milling. The local addition of 5-axis rework paths can, however, lead to abrupt changes in the radial engagement of the tool in the material: one part of the tool may be moving into an area where the material has already been removed while another is under high local load due to heavy engagement (Figure 1a).

Anticipating the tool orientation required during the last path on the flank helps to smooth out these engagement variations between successive radial paths and thus distribute the load applied to the tool more evenly along the cutting edge (Figure 1b). It is then necessary to generate a continuous 5-axis trajectory for the entire roughing operation.

In contrast to the finishing operation, which concentrates on the sweeping of surfaces, roughing is applied to the removal of volumes. The engagement values of the tool with the workpiece are then much greater and might present risks for the part Email address: christophe.tournier@ens-paris-saclay.fr (C.Tournier) and the tool if they are not controlled [START_REF] Prat | Experimental Analysis and Geometrical Modelling of Cutting Conditions Effect in 5 Axis Milling with Ti6Al4V Alloy[END_REF]. This control is all the more crucial during multiaxial roughing, during which tool orientation variations can be significant.

These variations in the orientation of the tool axis affect the machining process in several ways. The first one concerns the smoothness of the tool path and its consequences on the manufacturing time and the kinematic behavior of the machine tool. The major works on this subject deal with the smoothing of tool paths in a context of finishing. Several techniques for global smoothing of the successive orientations of the tool axis have been proposed in order to improve the kinematic performances of the machine-tool axes and especially rotary axes. In the part space by including kinematic constraints of the machine tool such as axes jerk or tangential jerk in [START_REF] Sun | A cutter orientation modification method for five-axis ball-end machining with kinematic constraints[END_REF][START_REF] Lavernhe | Optimization of 5-axis high-speed machining using a surface based approach[END_REF], by minimizing the shortest path between angular configurations [START_REF] Pessoles | Optimisation of workpiece setup for continuous five-axis milling: application to a five-axis BC type machining centre[END_REF][START_REF] Plakhotnik | Graph-Based Optimization of Five-Axis Machine Tool Movements by Varying Tool Orientation[END_REF], or by minimizing the variations in the acceleration of the machine's rotary axes [START_REF] Castagnetti | The domain of admissible orientation concept: A new method for five-axis tool path optimisation[END_REF][START_REF] Lu | Smooth Tool Path Optimization for Flank Milling Based on the Gradient-Based Differential Evolution Method[END_REF]. However, the smoothing of the trajectory in the joint space makes it difficult to control the orientation and positioning of the tool with respect to the workpiece. The deviations of the path must be controlled, especially in the case of flank milling, to minimize geometrical deviation of the machined surface as proposed in [START_REF] Beudaert | 5-Axis tool path smoothing based on drive constraints[END_REF] for global smoothing and in [START_REF] Beudaert | 5-axis local corner rounding of linear tool path discontinuities[END_REF] for corner smoothing. However, the dependency of these methods on the kinematic configuration of the machine tool is a disadvantage for the development of a CAM solution that is sufficiently cross-functional and independent of the machine tool and CNC control used. The optimization of the orientation of the tool axis in the coordinate system associated to the part is therefore preferable from this point of view [START_REF] Ho | Five-axis tool orientation smoothing using quaternion interpolation algorithm[END_REF][START_REF] Pechard | Geometrical deviations versus smoothness in 5-axis high-speed flank milling[END_REF].

Global smoothing strategies in the part space have been developed to limit abrupt changes in the orientation of the tool axis along a path [START_REF] Chen | Collision-free tool orientation optimisation in five-axis machining of bladed disk[END_REF]. These methods improve the kinematic performance of the machine axes, but do not optimize the orientation of the tool axis with respect to the workpiece. When roughing a cavity, tilt optimization must be conducted by considering both the orientation of the tool with respect to the flank and bottom surfaces to ensure that the part geometry is respected.

Variations in the orientation of the tool axis also have a direct effect on the quality of the bottom surface and the volume of material remaining [START_REF] López De Lacalle | Toolpath selection based on the minimum deflection cutting forces in the programming of complex surfaces milling[END_REF]. Indeed, combined with the geometries of the cylindrical or toric tools commonly used in roughing, these variations will generate scallops of varying heights on the bottom surfaces during roughing. It is therefore necessary to control these variations in order to guarantee the most regular excess thickness possible to avoid or minimize the semifinishing operations. In the same way, controlling the orientation of the tool axis in relation to the part allows the management of local and global collisions [START_REF] Jun | Optimizing tool orientations for 5axis machining by configuration-space search method[END_REF].

Finally, the orientation of the tool axis also has a direct influence on the cutting forces experienced by the tool [START_REF] Gilles | Optimum orientation of a torus milling cutter: Method to balance the transversal cutting force[END_REF] which are strongly linked to the cutter workpiece engagement. However, determining the continuously changing cutter workpiece engagement remains a challenge, especially for toroidal and flat-end cutters during 5-axis roughing and semi-finish milling of sculptured parts [START_REF] Kiswanto | An analytical method for obtaining cutter workpiece engagement during a semi-finish in five-axis milling[END_REF], and is out of the scope of this paper. Thus, depending on whether the tool is working with a positive (pulling) or negative (pushing) tilt angle, the distribution of forces on the tool will be different, as will its dynamic behavior in terms of the generation of vibrations. The tool life, especially in hard materials, is closely related to these cutting conditions.

The strategy developed in this article therefore aims at controlling the tool posture with respect to the part during a 5-axis roughing operation in order to ensure productivity, surface quality and operating conditions the tool. The evolution of the tool axis orientations during a pocket roughing operation on a 5-axis machining center is optimized in order to respect these criteria through a compromise between the smoothness of the tool path axis behavior along the tool path and the programmed tilt angle between the tool axis and the surface being machined. Lastly, the kinematic characteristics of the CNC machine such as max. velocity, max. acceleration and max. jerk of the axes are not directly taken into account in order to remain independent from the machine used.

The rest of the paper is organized as follows: the calculation of the guide curves used for the minimization of the objective function and the determination of the adjusted tool orientations is explained in section 2. The determination of tool axis orientations is presented in section 3, then the minimization of the objective function is described in section 4. The application of the proposed method to the roughing of a pocket is presented in part 5 before concluding remarks on the benefits of this method in the last section.

Orientation of the tool axis by synchronization of the guide curves

In order to control the orientation of the tool axis and to ensure that it evolves continuously (Figure 1b), the tool is considered to be guided by 2 curves (Figure 2 a): the path curve C P (s P ) with s P ∈ [0, 1], is the intersection of the tool axis with the end machined surface, and the tilting curve C T (s T ) with s T ∈ [0, 1], is the location of another point belonging to the tool axis to orient it. These two curves are constructed from the geometry of the pocket (bottom and flanks) so as to follow as closely as possible the geometry of the flank surfaces during the last radial pass, and thus remove as much material as possible during the roughing phase with a continuous evolution of the radial engagement of the tool [START_REF] Jousselin | A 5-axis pocket roughing strategy reducing the remaining material volume[END_REF].

The guide curves defined in this way allow the orientation of the tool axis to be constrained in the plane orthogonal to the feed direction (Figure 2b). However, a degree of freedom still has to be defined in the feed plane to fully constrain the tool orientation. The tool is then moved along the resulting axis until it contacts the end-machined surface to determine the position of the tool center (Figure 2c). The whole part of the calculation of the tool axis orientation is therefore performed without the exact knowledge of the position of the contact point of the tool with the end machined surface. Both the path and the tilt curves are oriented with a starting point set to s P0 and s T 0 (Figure 3). The orientation of the tool axis u with respect to the endmachined surface is set by the yaw (θ n ), roll (θ f ), and tilt θ t ) angles, defined in the (C C , f, n, t) frame. C C is the contact point between the tool and the end-machined surface, f is the feed direction of the tool with respect to the surface, n is the surface normal to the point C C , and t is the vector product between f and n. An approximation on the evaluation of the contact normal is then performed in order to simplify the calculations. As shown in Figure 3, the (f, n, t) basis is computed at the P P(s P ) point of the path curve and not at the real contact point C C , whose position is still unknown. This approximation is equivalent to considering that the orientation of the surface normal varies little in a defined area to within one tool radius. Since θ f is already constrained by the fact that the tool axis has a point on C P and a point on C T , simply setting θ t will constrain the tool orientation. The problem is therefore to find the values of curvilinear abscissa s P and s T to obtain the desired orientation (Equation 1).

θ t = u (s P ,s T ) • f |u (s P ,s T ) • f| arccos        u (s P ,s T ) -u (s P ,s T ) • t • t • n u (s P ,s T ) -u (s P ,s T ) • t • t        (1)
This is achieved by synchronizing the curves, i.e. correlating the curvilinear abscissa values of C P and C T to generate the successive tool axes over the entire trajectory. The uniform parametric synchronization consists in making s P and s T evolve according to the same and unique parameter s ∈ [0, 1].

Optimization of the guide curves synchronization

Definition of the objective function

The linear evolution of the curvilinear abscissa provided by the uniform parametric synchronization is not efficient enough to control the evolution of the tool orientation. A finer model must therefore be used to control more precisely the evolution of the curvilinear parameters s P and s T which are characterized by the distribution functions g P and g T (Equation 2).

∀s ∈ [0, 1] ,                    s P = g P (s) = n T =3 i=0 c (P,i) • s i s T = g T (s) = n D =3 i=0 c (T,i) • s i (2)
Cubic functions were chosen in order to provide an inflection point which allows limiting the evolution of the parameter on one curve compared to the other and thus to adjust the orientation of the tool. Also, all the values (s P , s T ) which define the orientation of the tool can be obtained by the sole determination of 8 parameters: 4 coefficients c (P,i) and 4 coefficients c (T,i) (i ∈ [0, 3]).

The first considered objective in the search for these 8 coefficients is to maintain the orientation of the tool within an angular range [θ tmin , θ tmax ] defined according to the desired machining conditions (Figure 4a) and so that there are no collisions between the tool and the workpiece. For example, machining at θ t strictly negative or strictly positive avoids transitions between machining by pushing and pulling along the tool path, which allows a better control of the dynamic behavior of the tool and ensures its lifetime. Thus, the length L HL traveled by the tool outside the angular range has to be minimized and is expressed as (Equation 3):

L HL = s |θ t (s) - θ tmax + θ tmin 2 | • H(s) • ds (3)
Where H takes the value 1 when the orientation of the tool is outside the limits, or 0 otherwise (Equation 4).

H(s) =          1 i f |θ t (s) -θ tmax +θ tmin 2 | ≥ θ tmax -θ tmin 2 0 otherwise (4)
Compared to a strict constraint of working within the angular range, the use of this function allows keeping solutions that may locally exceed the limits but offer degrees of freedom to the other objectives presented below.

The observance of an operating angular range can also lead to abrupt angular variations that can affect the kinematic performance of the machine axes during path traversal. Indeed, significant variations in the orientation of the tool axis induce large amplitude movements of the machine tool's rotary axes that may affect the actual feedrate. Thus, the quadratic sum of the angular variations along the curvilinear abscissa (Figure 4b) is used to quantify the smoothness criterion E s (Equation 5).

E s = s          ∂θ t ∂s (s)          2 • ds (5)
Although an angular range is specified, more precise control of the tilt angle along the trajectory may be desired (Figure 4c), in particular to control the variations of scallop height on the last axial level which realize the bottom surface. The quadratic sum of the deviations to an objective angle θ tob j is defined as the tilting criterion E t (Equation 6).

E t = s θ tob j -θ t (s) 2 • ds (6)
Finally, the global objective function F ob j of the optimization problem is the sum of the introduced targets, balanced with the weight β of the limits criterion, the weight ϕ of the smoothness criterion, that could be adapted to the kinematic performance of a given machine, and the weight τ of the tilting cri-terion. The setting of these weights allows balancing the importance to give to each criterion in function of the case to face (Equation 7). As each tool path can be different, it is difficult to propose a standardization of the three weights given that the orders of magnitude of the objective functions can be very different. The choice of weights must be made by the operator in order to define the particular setting that is appropriate for his application.

F ob j = β • L HL + ϕ • E s + τ • E t (7)
The synchronization is thus defined by the 8 coefficients c P,i and c T,i (i ∈ [0, 3]) which minimize the objective function under technological constraints which ensure the travel of the total trajectory. Indeed the evolution of the curvilinear abscissa on each curve must be increasing, and never be constant simultaneously on both curves, to ensure that the tool always progresses on the path (Equation 8). It is also important that the range of computed abscissa values allows at least the totality of the guide curves to be covered from the starting point set by the user (Equation 9).

∀s ∈ [0, 1] ,          ġP (s) ≥ 0 ġT (s) ≥ 0 ġP (s) + ġT (s) > 0 (8)          g P (1) -g P (0) ≥ 1 g T (1) -g T (0) ≥ 1 g P (0) = 0 (9)
All the objective functions are defined from the positioning of the tool in the part coordinate system in order to be independent of the machine tool used. However, this means that the kinematic modeling of the machine is not taken into account and that effects linked to singular positions may occur and must be verified a posteriori.

Optimization method

The optimization problem tackled previously reveals solving difficulties due to its definition as a multi objective, non linear, and with non linear constraints problem. The Sequential Quadratic Programming (SQP) algorithm is able to solve this kind of problem. This algorithm is applied to solve the optimization issue defined in Equation 10, which aims to minimize a non linear differentiable function f under constraints represented by the g function. [START_REF] Davim | Machining of complex sculptured surfaces[END_REF][START_REF] Sun | A cutter orientation modification method for five-axis ball-end machining with kinematic constraints[END_REF] -→ F ob j g(x) ≤ 0, g : R 8 -→ R 6 c [P,T ], [START_REF] Davim | Machining of complex sculptured surfaces[END_REF][START_REF] Sun | A cutter orientation modification method for five-axis ball-end machining with kinematic constraints[END_REF] -→ g(x) [START_REF] Beudaert | 5-Axis tool path smoothing based on drive constraints[END_REF] with g(x) defined from Equation 8 and Equation 9.

               min f (x), f : R 8 -→ R c [P,T ],
This algorithm allows solving non-linear constrained optimizations and is particularly efficient in trajectory optimization calculations [START_REF] Gill | SNOPT: An SQP Algorithm for Large-Scale Constrained optimisation[END_REF]. Iterating by local descent, the result however shows a strong dependence on the choice of the initial guess. Consequently, the initial guess should give acceptable tool axis orientation results. It is then chosen as the non-optimized path, namely the uniform parametric synchronization. In this initial configuration, all the reparameterization polynom coefficients are set to 0, excepted the first order ones which are equal to 1, which means that g P (s) = g T (s) = s.

Trajectory computation

The inputs required to compute the entire optimized trajectory are the optimization weights (β, ϕ, τ) to define the machining case, the guide curves C P and C T , the machining tolerance to fix the maximum distance between two successive C L points, the target tilt angle and associated boundaries. The coordinates of the entrance point may be declared but remain optional since the latter point is automatically chosen in the most accessible area.

The SQP optimization gives the optimized reparameterization polynomials of the guide curves considering the weighting strategy. These polynomials are then used to compute the adjusted curvilinear abscissas for each guide curve, s P and s T (Equation 2).

Tool axis orientation is evaluated for each couple of points (P P(s P ) , P T (s T ) ) and interpolated along the guide curves in order to respect the machining tolerance required. As there could be an interference between the tool and the bottom surface when changing the orientation of the tool, the positioning of the tool is recalculated after the parameterization of the curves, similar to the one described in figure Figure 2c. The C C points are then evaluated to compute the final list of C L points which, associated with the tool axis orientations at each point, define the complete trajectory (Figure 5).

Due to the SQP solving, the results are particularly dependent on the initial guess fixed for the optimization. If the entrance point is chosen in an area which is too hardly accessible by the tool, the optimization will converge but might result in tool axis orientations that are senseless considering a physical tool with a finite cutting length, or regarding the evolution of the tool engagement.

Experimental investigations

The proposed tool axis orientations adjustment approach has been applied to the trajectories of a complete axial roughing level of a closed pocket with large undercut areas (Figure 7) considering several weighting scenarios in the optimization problem:

• case 1: uniform parametric synchronization (β = 0, ϕ = 0, τ = 0),

• case 2: machine axes smoothness optimization (β = 0, ϕ = 1, τ = 0),

• case 3: limits optimization in [-30

• , 0 • ] (β = 1, ϕ = 0, τ = 0),
• case 4: smoothness optimization in [-45

• , 0 • ] (β = 5, ϕ = 10, τ = 0).
The angular ranges of the two latest optimization tests ensure negative values for the tilt angle of the tool in order to reduce the bending stresses on the tool [START_REF] Gilles | Optimum orientation of a torus milling cutter: Method to balance the transversal cutting force[END_REF], and then the vibrations of the subsystem composed of the tool, the tool-holder, and the spindle. It can be noted that the tool length is quite long (L c = 41 mm) to guarantee the roughing of the whole cavity. Combined with the high ratio between the tool-holder's length and its diameter required to increase accessibility (L = 192 mm, D = 27 mm), this does not favor the subsystem stiffness. The tilt angle optimization is not introduced yet because the optimal tilt angle is unknown for the tool and the material used. Therefore the tilt angle at the entrance point is set to 0.

Based on the orientations obtained for each different values of β, ϕ and τ, the paths have been simulated with the CAD/CAM software TopSolid'Cam, then tested in real machining conditions on a 5-axis machine tool Mikron UCP 710 whose structure according to the ISO10791 standard is: The G-Codes are spawned thanks to a post-processor developed by TopSolid and result in about 1800 to 3400 lines (tool location point) for an entire axial level, depending on the weighting strategy selected for the optimization. Given a computation time of approximately 10ms per tool location point on the tool path, the total computation time varies between 20s and 30s on a laptop computer.

V[w C A b X m Y m Z m (C1) t]
The closed pocket is machined in a 2017A aluminium alloy stock with a FRAISA AluSpeed C5275.450 toroidal tool (diameter D = 10 mm, corner radius r c = 1.5 mm, cutting length L c = 11 mm, number of Z = 2) with the cutting conditions recommended by FRAISA for grooving. The maximum axial and radial engagements have been set to 4 mm and 4.5 mm respectively. The feed per tooth is set to f z = 0.08 mm, the spindle speed to N = 15000 rpm and the feedrate to F = 2400 mm/min.

The spindle is instrumented with two Bruël & Kjaer accelerometers (sensibility of 10.1 mV/g), positioned respectively along the X and Y directions (Figure 8a), in order to observe the evolution of the vibratory phenomena (Figure 8b). The acquired data are treated with the National Instruments system NI 9233.

They are supplemented with the servo traces analysis of the Siemens 840D controller, which gives access to articular positions and speeds of the machine axes during the machining experimentation. The velocities of the 5 axes (X,Y,Z,A,C) of the machine-tool during the machining experimentation have been recorded and the effective tangential velocity of the tool with respect to the workpiece F (feedrate) has been computed by using the forward kinematics. Since the maximal acquisition time of the CNC is about 20 s, the measurement of machining times and vibrations is only performed during the most constraining path, i.e. all along the last path on the pocket flank. The results are listed in Table 1. Figures 9 to 12 show the evolution of θ t , effective feedrate F, and amplitude of the spindle vibrations on X and Y as a function of time.

The synchronization of the different data is done by identifying the first point of entry in the material of the trajectory on each recording and by correlating all the results of this identification. For the vibration analysis, a vibration peak is noted when the tool enters the material and allows to locate the entry point in the material.

In order to locate the measured phenomena on the CAD part, the four corners of the cavity are numbered in the order of tool traveling (Figure 7) and identified on the results graphs (Figures 9 to 12).

Numerous vibratory areas can be noted on the results of the uniform parametric synchronization strategy (case 1), especially in the corners where the fluctuations of θ t are significant (Figure 9) and where the immersion of the tool increases and causes chatter. While no issue was encountered during the machining of the aluminium alloy stock, it could be risky for the tool to apply such a strategy in harder material like in titanium or nickel alloys. Indeed, the multiple inversions in the sign of θ t would generate a rear gouging of the tool.

The smoothness optimization (case 2) provides a decrease in the machining time of more than 20 % compared to the non optimized strategy, despite having the same path length which is in line with what one would expect. Improved smoothness of the motion also reduces the excitation of machine modes hence reduces the inertial vibrations at tool path locations where the drives have sharp motion changes. Thus, even if the maximum amplitude of the vibrations is greater, the portions of the trajectories subjected to these vibrations are much less numerous. At last the results for this strategy highlight that vibratory phenomena does not occur systematically in the corners but also along the side of the pocket.

The limits optimization (case 3), aiming to keep the values of the tool axis orientation in a preferred range chosen here among negative values, results in a mitigation of the vibration waves all along the path (Figure 11) compared to the uniform parametric synchronization strategy (Figure 9). The explanation and the modeling of this observed dynamic behavior are part of the perspectives. Though, this strategy increases the machining time by more than 20 % compared to the uniform parametric synchronization. Its effects are then literally opposed to the smoothness optimization, especially in corner 3 where the machining time is almost three times longer.

The balance found with the combined optimization (case 4) induces a loss of productivity of 11 % compared to the uniform parametric synchronization strategy. However, this strategy remains more efficient than the limits optimization (case 3), whose the speed up percentage is more than twice as low. Moreover, the vibratory phenomena are removed all along the path, excepted around the corner 2 where the maximal amplitude of vibrations is still much lower than that of the smoothness opti- A combination between the weights β and ϕ intends thus to find an ideal balance between the dynamic behavior and the machining time reduction.

Regarding the results in terms of remaining volume homogeneity, Figure 13 details the distribution of remaining material along the CAD surfaces. The strategies which use an optimization of the tool axis orientation angle limits (case 3 and 4) lead to a lower scallop height thanks to the limiting of the effective cutting radius induced. Nevertheless, these values are very high and are all identified to be in the traveling of the corner 2 which is the more constraining considering the undercut areas involved. The high radial engagement of the tool worsen the scallop heights, which could then be reduced by reducing the radial engagement. The remaining material percentage values are sensibly equal for each strategy, which is normal given the fact that the tool travels along the same guide curves for all the tested strategies.

Finally, the strategy to apply depends on the machining application required, as a compromise cannot be found considering each optimization criterion. For example, when roughing in aluminium alloys, the productivity can be highlighted by a smoothness optimization. But in harder materials, such as titanium or nickel alloys, the guarantee of the tool lifespan prevails. In this case, the tool axis orientation limits optimization is preferable compared to the reducing of the machining time not to break the tool prematurely.

It can be noted that one of the strength of this optimization method lies in the possibility of optimizing each path with different optimization weights and then optimize productivity or remaining material homogeneity in function of the localization of the path in relation to the finished surfaces. By this way, the productivity would be promoted along the paths which does not machine a finish surface, and the homogeneity of remaining material would prevail along the paths which machine a finished surface.

Conclusion

Controlling the orientation of the tool axis is critical in a pocket roughing operation in 5-axis milling, where tool workpiece engagement is important. A strategy is therefore proposed to constrain the tool axis orientation along the entire trajectory. First, guide curves are used to constrain the tool orientation in the plane orthogonal to the feed direction, with maximum respect of the geometry of the flank surfaces. Then, a Sequential Quadratic Programming algorithm is used to carry out a threeobjective optimization to constrain the orientation of the tool with respect to the end milled surface. It is thus possible to reduce machining times as well as the vibrations during roughing and depending on the weights assigned to each of the optimization objectives. In terms of perspectives, further studies could be carried out on the dynamic behavior of the tool, tool holder and spindle assembly and tool life according to the evolution of the tool axis orientation in rough machining.

a) 3 -Figure 1 :

 31 Figure 1: Evolution of the radial engagement of the tool between successive tool paths

  a) Guide curves b) Tool axis orientation computation c) Cutter location point computation

Figure 2 :

 2 Figure 2: Tool axis guiding along 2 curves

Figure 3 :

 3 Figure 3: Tool axis orientation setting

Figure 4 :

 4 Figure 4: Optimisation targets

Figure 5 :

 5 Figure 5: Optimized trajectory computation steps

Figure 6 :

 6 Figure 6: Example of a multi-objective optimization

  a) Representation of the test pocket b) Computed guide curves

Figure 7 :

 7 Figure 7: Test pocket and guide curves along one axial level of roughing

Figure 8 :

 8 Figure 8: Spindle instrumentation and consequences of vibrations in corners

Figure 9 :Figure 10 :Figure 11 :Figure 12 :

 9101112 Figure 9: Case 1: uniform parametric synchronization (β = 0, ϕ = 0, τ = 0)

Table 1 :

 1 Evolution of the criteria according to the optimization parameters mization.

	Objective	Weighting Amplitude Time SpeedUp Max. scallop Remaining mat. β ϕ τ [m.s -2 ] [s] [%] [mm] [%]
	case 1: Uniform	0 0 0	1426	14.5	0	1.95	1.42
	case 2: Smoothness 0 1 0	1672	11.4	21.4	2.39	1.20
	case 3: Limits	1 0 0	569	17.9	-23.4	1.81	1.15
	case 4: Combined	5 10 0	1181	16.1	-11	2.2	0.6