Sheng Yang
email: sheng.yang@mail.mcgill.ca

Thomas Page
email: thomas.page@mail.mcgill.ca

Yaoyao Ying Zhang
email: ying.zhang8@mail.mcgill.ca

Fiona Zhao

Towards an Automated Decision Support System for the Identification of Additive Manufacturing Part Candidates

Keywords: additive manufacturing, machine learning, candidate identification, conceptual design

As additive manufacturing (AM) continues to mature, an efficient and effective method to identify parts which are eligible for AM as well as gaining insight on what values it may add to a product is needed. Prior methods are naturally developed and highly experience-dependent, which falls short for its objectiveness and transferability. In this paper, a decision support system (DSS) framework for automatically determining the candidacy of a part or assembly for AM applications is proposed based on machine learning (ML) and carefully selected candidacy criteria. With the goal of supporting efficient candidate screening in the early conceptual design stage, these criteria are further individually decoded to decisive parameters which can be extracted from digital models or resource planning databases. Over 200 existing industrial examples are manually collected and labelled as training data; meanwhile, multiple regression algorithms are tested against each AM potential to find better predictive performance. The proposed DSS framework is implemented as a web application with integrated cloudbased database and ML service, which allows advantages of easy maintenance, upgrade, and retraining of ML models. Two case studies of a hip implant and a throttle pedal are used as demonstrating examples. This preliminary work provides a promising solution for lowering the requirements of non-AM experts to find suitable AM candidates.

Introduction

Additive manufacturing (AM) has the potential to change the way products are designed, produced, and distributed [START_REF] Thompson | Design for additive manufacturing: trends, opportunities, considerations, and constraints[END_REF]. Though many organizations are interested in the idea of incorporating AM into their development process, they are at a natural disadvantage: AM is still in its infancy compared to conventional manufacturing (CM) methods and lacks the centuries of development and knowledge-sharing which leads to an in-depth understanding of how and when it may best be applied. The gap of knowledge vacancy has been identified by the Information and Communications Technology Council of Canada (ICTC) (2017) and other authorities (Wohlers Report 2018). The ICTC also found that only a small amount of companies has used or planned to use AM in the near future. Since AM is not a catch-all solution, educating a workforce on the new technology just to see if it might be of value bears a significant risk. Therefore, as AM continues to push its way into the production manufacturing space, there will inevitably be a lack of talents who are ready and able to deploy it. Those who are in a position to enable this change must first possess the knowledge on how to unlock its unique potentials.

Identifying which parts/assemblies in a repository are suitable for AM is one of such top challenges because there is no clear formula which leads to a successful adoption of the technology. Success varies widely depending on how the needs of the company match up with the unique potentials of AM. Some restrictive rules have been developed which can help guide the decision-making process, such as maximum build volume and batch size [START_REF] Doubrovski | Optimal design for additive manufacturing: opportunities and challenges[END_REF]), but the trade-offs between these AM limitations and the geometric, functionality, economic, and societal benefits are less often discussed. To solve the problem of identifying part candidacy, a few methods including heuristic ones [START_REF] Klahn | Design for additive manufacturing-supporting the substitution of components in series products[END_REF][START_REF] Booth | The design for additive manufacturing worksheet[END_REF][START_REF] Reiher | Holistic approach for industrializing AM technology: from part selection to test and verification[END_REF]) and computational ones (Yang et al. 2018;[START_REF] Yang | A numerical-based part consolidation candidate detection approach with modularization considerations[END_REF][START_REF] Yao | A hybrid machine learning approach for additive manufacturing design feature recommendation[END_REF]) have been reported. These approaches are either highly expertise-dependent or only focused on specific AM potentials (e.g. part consolidation or lightweight). Moreover, as the scale of targeted part repositories (e.g. a complex system with thousands of parts or even more) increases, efficiency and computational cost is of significant concerns. Therefore, an automated method which can quickly identify potential candidates for AM applications and present insights on what values AM may add to a product is needed. Such an approach can serve as a first-level filter for novice AM users (e.g. product managers) to narrow down the scope of potential part candidates, and then AM expertise is sought for further examination.

To achieve the above objectives, comprehensive literatures are first reviewed on the beneficial aspects of AM as well as the existing approaches of part candidacy identification (Section 2). Then a methodological framework to support the automation of identifying part candidacy is presented (Section 3). Implementation of the proposed framework and its validation are illustrated in Sections 4 and 5 respectively. This paper ends up with discussions and future work.

Literature review

This section mainly reviews work on: 1) AM potential analysis, and 2) existing part candidacy identification methods. The first part will provide a comprehensive insight of potential advantages of adopting AM in product development, which helps to yield a set of decision criteria to justify the conditions of "AM suitability". The second part summarizes the ongoing efforts of AM part candidate detection and comparison of its transferability.

Dimensions of AM potentials and its breakdown

AM potentials refer to the opportunistic aspects that encourage the adoptability of AM processes compared to conventional fabrication methods. Although the restrictive aspects of AM, such as material availability, overhang issues, and dimensional accuracy as reviewed in literatures [START_REF] Laverne | Assembly-based methods to support product innovation in design for additive manufacturing: An exploratory case study[END_REF][START_REF] Thompson | Design for additive manufacturing: trends, opportunities, considerations, and constraints[END_REF], are also critical for the decision making of justifying AM's suitability, this paper only focuses on the potential perspective that motivates the consideration of AM. This strategy can help to improve screening efficiency and avoid neglecting parts that fail manufacturing rules but may have big gains if redesigned for AM. Moreover, given the diversity of AM processes and machines, the applicability of AM feasibility investigation at the early design stage is tedious and the feasibility changes from one process to another. Various efforts have been reported to classify the AM potentials. In general, the potentials cover three main perspectives: design + geometric complexity, economic advantages, and social and environmental benefits. The overall AM potential mind map is depicted in Figure 1. Design and geometric complexity mainly focus on functionality and performance improvement as well as expanded manufacturing capability due to AM adoption. The aspect of cost and time savings are distributed in categories of intertwined production and supply chain. Potential influences on organizational management and ecology are also discussed. The breakdown of each AM-potential dimension is represented by the branches connected to the bold box respectively.

Design + geometric complexity

Design, here, refers to the potential to improve in terms of functionality and performance via AM, while geometric complexity refers to the potential to fabricate as is without altering the design. [START_REF] Conner | Making sense of 3-D printing: creating a map of additive manufacturing products and services[END_REF] constructed a three-dimensional space with part complexity, level of customization and production volume being the axes and eight production scenarios were discussed. Numerical metrics were derived for each dimension of a part, and then parts are assessed and assigned to one of the production scenarios, which further indicates the selection of AM or CM. [START_REF] Klahn | Design for additive manufacturing-supporting the substitution of components in series products[END_REF] identified four design potentials of AM as integrated design, individualization, lightweight design, and efficient design. A more expanded classification of design potentials was released by the ISO/ASTM 52910 standards (2015) outlining six items: customization, lightweight, internal channel/structures, function integration, surface structures, and material options (e.g. hybrid materials). These works help to educate engineers the potential enhancement in terms of product functionality and performance to be gained when changing from CM to AM.

Dimensions of AM potentials

Design

Functional integration

Surface structures There is a significant difference between how CM and AM respond to geometric complexity. This disparity makes the complexity of a part a good indicator of which manufacturing method should be used (i.e. CM or AM). In CM context, geometric complexity normally is measured by difficulty-tomanufacture features. Investigation of part features that are designed for CM boosts the appreciation of much expanded manufacturing capabilities of AM. As shown in Figure 1, such features of CM include but not limit to deep holes, large draft angle, cavities and undercuts. A geometric complexity factor was particularly developed for evaluating the shape complexity of castings [START_REF] Joshi | Quantifying the shape complexity of cast parts[END_REF]. The factor was based on geometric parameters such as the number of cored features, volume and surface area of part, core volume, section thickness and draw distance. In contrast, with the help of AM, metrics of geometric complexity should no longer be bounded by these features. Parts with more organic shapes and hierarchical structures created by techniques such as topology optimization [START_REF] Rozvany | A critical review of established methods of structural topology optimization[END_REF]) and cellular structures [START_REF] Tang | Bidirectional Evolutionary Structural Optimization (BESO) based design method for lattice structure to be fabricated by additive manufacturing[END_REF] require more effective metrics for AM applications. Based on the previous work [START_REF] Joshi | Quantifying the shape complexity of cast parts[END_REF], [START_REF] Conner | Making sense of 3-D printing: creating a map of additive manufacturing products and services[END_REF] proposed a weighted complexity factor with aggregation of part volume ratio, surface area ratio, and number of holes or slots. Other computational approaches also used the number of triangles in the STL file [START_REF] Valentan | Basic solutions on shape complexity evaluation of STL data[END_REF]) and convexity ratio [START_REF] Fera | A new perspective for production process analysis using additive manufacturing-complexity vs production volume[END_REF]) as a measure of geometric complexity.

Internal channels or structures

Material options

Lightweighting

Customization

Geometric complexity

Informational

Economic considerations

The potential reduction of production cost and development time is one of major benefits of AM. Economic analysis of whether AM can compete with CM not only needs to consider direct production cost but also the associated cost in the alternation of supply chain [START_REF] Deppe | Development of an economic decision support for the application of Additive Manufacturing in aerospace[END_REF]. A set of metrics for evaluating productivity of conventional manufacturing system was established in [START_REF] Huang | Manufacturing system modeling for productivity improvement[END_REF] as shown in Figure 1. It covers a wide range of parameters including yield, throughput, lot size, etc. In AM field, [START_REF] Deppe | Development of an economic decision support for the application of Additive Manufacturing in aerospace[END_REF] developed a specific cost evaluation model for aerospace components in the sector of Maintenance, Repair, and Overhaul. The established model fully investigated the influencing factors such as machine, material, part, and general costs of labor, maintenance, capacity, and depreciation. Compared to conventional production system, AM depicts small lot-size advantages.

The absence of tooling, individual set-up procedures, and equipment changeover makes AM more favorable, and the lot size of 1 becomes economic [START_REF] Tuck | Rapid manufacturing facilitated customization[END_REF]. The small lot size advantage further enables rapid iteration of new product development and enhances the communication efficiency within the development team. Combined with the design potentials of customization and improved functionality, economic lot size, incremental product launch, reduced production tools, rapid prototyping, and process concentration are concluded as value clusters to support AM decision making [START_REF] Fontana | Value-driven clustering of industrial additive manufacturing applications[END_REF].

Another big change of the production philosophy brought by AM is the shortened supply chain. The possibility of consolidating assemblies into a single part as well as the inventory level significantly reshape the spare parts management and distribution strategy [START_REF] Huang | Additive manufacturing and its societal impact: a literature review[END_REF]. The consequential effects include reduction in the need for warehousing, transportation, and packaging. For spare parts supply chain, the key performance indicators are the response time for an unpredictable demand, and the total costs for production, storage, and logistics of a spare part. Several researchers have investigated the use of AM in spare parts management [START_REF] Hasan | The application of rapid manufacturing technologies in the spare parts industry[END_REF][START_REF] Holmström | Rapid manufacturing in the spare parts supply chain: alternative approaches to capacity deployment[END_REF][START_REF] Thomas | Costs, benefits, and adoption of additive manufacturing: a supply chain perspective[END_REF][START_REF] Knofius | Consolidating spare parts for asset maintenance with additive manufacturing[END_REF][START_REF] Knofius | Selecting parts for additive manufacturing in service logistics[END_REF]. The advantages of increased responsiveness and robustness in a discontinuous supply chain by AM has been outlined by these authors. Repair and remanufacturing can also be highly advantageous from a cost and lead time perspective, as little backup inventory is required and replacement parts can be built at any time [START_REF] Zhang | Modeling and analysis of the on-demand spare parts supply using additive manufacturing[END_REF]

Social and environmental benefits

Sustainability advantages of AM can be concluded as less manufacturing waste, higher material efficiency, reduced energy consumption, and subsequent reduction in the transportation and inventory waste. On the social sustainability aspect, the social issues concerning AM are the work condition and worker's health [START_REF] Huang | Additive manufacturing and its societal impact: a literature review[END_REF]. Potential health benefits may be gained by avoiding long-term exposure to harsh and hazardous work environment. Another social benefit comes from the change of consumption patterns. In contrast with the passive customer behavior, the easy access of CAD software and 3D printers enables a new concept of "prosumer" who consumes and produces a product. [START_REF] Matos | Additive manufacturing: exploring the social changes and impacts[END_REF] further explored the AM social impacts of intellectual properties, work, and education and skills. Particularly, the social impacts on skills and education requirements are discussed because the immaturity and knowledge gaps of this new technology makes AM not well integrated with the education and engineering training. With special interests of analyzing the impacts of advanced manufacturing technology on organizational structure, Ghani and his colleagues ( 2002) enumerated the characteristics of a typical organization and concluded the need for proactive planning to facilitate changes to maximize productivity to take advantages of new technology. On the environmental potency of AM processes and materials, various aspects have been discussed including lightweight benefits (Huang et al. 2015;Tang et al. 2016a), part consolidation [START_REF] Tang | Sustainable design for additive manufacturing through functionality integration and part consolidation[END_REF]Yang et al. 2019a), energy efficiency [START_REF] Baumers | Shape complexity and process energy consumption in electron beam melting: a case of something for nothing in additive manufacturing[END_REF][START_REF] Watson | A decision-support model for selecting additive manufacturing versus subtractive manufacturing based on energy consumption[END_REF], raw material (Kellens et al. 2017), decentralization [START_REF] Bogers | Additive manufacturing for consumer-centric business models: Implications for supply chains in consumer goods manufacturing[END_REF], hybrid manufacturing [START_REF] Caligiana | Integrating QFD and TRIZ for innovative design[END_REF], and process selection [START_REF] Watson | A decision-support model for selecting additive manufacturing versus subtractive manufacturing based on energy consumption[END_REF][START_REF] Paris | Comparative environmental impacts of additive and subtractive manufacturing technologies[END_REF][START_REF] Priarone | Towards criteria for sustainable process selection: On the modelling of pure subtractive versus additive/subtractive integrated manufacturing approaches[END_REF].

Part candidacy identification methods

Identification of suitable parts and applications for AM is one of the challenges for its wider adoption in industry. Some early efforts tried to provide guidence of selecting the pilot study projects and help the practitioners to gain valuable experience. Among which, one research stream follows the path of manfuacturability evaluation. These research works emphasized on using process constraints, such as material availablility, volume size, tolerencing, and geometric complexity, as the filtering criteria of AM eligibility based on the current design. Representative literature includes manufacturing process selection [START_REF] Lovatt | Manufacturing process selection in engineering design. Part 1: the role of process selection[END_REF], geometry-based manfucturability evaluation [START_REF] Tedia | Manufacturability analysis tool for additive manufacturing using voxel-based geometric modeling[END_REF], and DfAM worksheet [START_REF] Booth | The design for additive manufacturing worksheet[END_REF]). However, these filtering criteria are overly rigid and eliminate the opportunities for AM redesign and added value. For instance, AM-enabled new design may drastically differ from the current design; therefore, the constraints of volume limitation become less important.

To encourage more active learning and application of AM, this paper focus mostly on the opportunistic aspects so that potential candidates will not be missed in the first round of screening in the part repository while improving the screening efficiency. Related work in this stream is compared against the rules of candidacy criteria, comprehensiveness (i.e. single potential V.S. multiple potentials), AM expertise requirements, and implementation method as summarized in Table 1. Overall, none of these research has provided an automatic solution for novice AM users in exploring multiple AM potentials. Prior studies with higher AM comprehensivenss generally required good knowledge of AM to interpret the heuristic questions. More detailed discussions follow after the table. Merkt and his colleagues ( 2012) are amongst the first ones to draw attention to the problem of part candidacy identification. They proposed an Integrated Technology Evaluation Model (ITEM) in which the part candidacy via Selective Laser Melting (SLM) is established through a four-stage evaluation. It covers product process analysis, economic analysis, economic potential analysis, and technology potential analysis. However, detailed metrics for AM potentials were not discussed except geometric complexity. [START_REF] Lindemann | Towards a sustainable and economic selection of part candidates for additive manufacturing[END_REF] and Reiher et al. ( 2017) created a Trade-off Methodology (TOM) matrix to compile a shortlist of potential candidates through two stages. The TOM is filled out first by a company employee and then by an AM expert to evaluate the employee's responses and apply weightings to each criterion that covers domain-specific interests (e.g. buy-to-fly ratio). As pointed out by their recent work [START_REF] Kruse | Integrating AM into existing companies-selection of existing parts for increase of acceptance[END_REF], a significant amount subjectivity may occur by the types of questions and weighting scheme of Likert scale. Leutenecker-Twelsiek et al. ( 2017) etablished an Experience Transfer Model (ETM) to transfer AM experience relating to part candidate identification and design through steps of theoretical education, implementation, and reflection. AM eligibility was assessed based on their previous work [START_REF] Klahn | Design for additive manufacturing-supporting the substitution of components in series products[END_REF] which needed inputs of part information and expected AM design benefits. The latter requires pre-acquired AM knowledge through educational workshops, and the heuristic nature introduces bias and uncertainty.

Different from the heuristic methods, some researchers sought for computational ways to identify part candidates concerning reduction of AM expertise requirement and more importantly, efficiency improvement. The first tool was developed by Materialise (i.e. one of the main OEMs in AM field) named as 3D Print Barometer (Materialise 2014). As shown in Figure 2, it takes minimum part information such as size, geometric features, project budget, volume size, and purpose. Then, an analytic score of AM necessity is produced. The limited functions fail to provide what AM potential a part prosseses and the predicted result requires AM expertise for further analysis. A more recent and comprehensive work was reported by [START_REF] Yao | A hybrid machine learning approach for additive manufacturing design feature recommendation[END_REF]. In their work, a non-exclusive list of AM design feature (e.g. honeycomb structure and integrated rotational joint) and a triplet descriptor of each AM design feature were established and numerically coded. Upon which, existing industrial examples were analyzed in terms of its implemented AM design features, and they were used for training. The triplet descriptor was applied for the targeted application, and then a hybrid machine learning algorithm combining classification and clustering was proposed for recommending possible AM design features in a specific part design. This design requirements-rooted approach shows promise in identifying applicable AM-specific design features (e.g. lattice) but lacks considerations of other AM potentials. Moreover, intensive user inputs limit its flexibility in highly complex systems. Beyond the work on miscellaneous AM potentials, some progress on identification of single potential is also reported. Such work is focused on supply chain benefits [START_REF] Senvol | 7 scenarios table to adopt additive manufacturing[END_REF]) and part consolidation [START_REF] Yang | A numerical-based part consolidation candidate detection approach with modularization considerations[END_REF]Yang and Zhao 2018;Yang et al. 2018). Particularly, the literature [START_REF] Yang | A numerical-based part consolidation candidate detection approach with modularization considerations[END_REF]) incorporated the consideration of modularization in finding part consolidation candidates with reasonable computational cost, which made it deployable at the system level rather than simple assemblies.

Overall, prior methods on part candidacy identification may differ in strategies, but mostly show deficiency in comprehensiveness, efficiency, and objectiveness. Therefore, a fast decision support system that requires the least user inputs and minimum AM expertise but computationally produces reliable recommendation of AM eligibility, is demanding in the task planning and conceptual stage of product redesign.

The methodological framework of the proposed DSS

The proposed methodological framework is intended to develop a fast decision support system (DSS) to fill the gap of automated identification of AM part candidacy. The proposed DSS framework is shown in Figure 3. Overall, it is comprised of three main sections -candidacy criteria, data acquisition, and decision model, as marked in sequence. The candidacy criteria are to establish a set of conditions based on which AM eligibility can be justified. As reviewed in Section 2.1, AM potentials cover such a wide range from economic analysis to design potentials that having all criteria to be analyzed in the same time is not cost-efficient. As such, two "filtering principles" are set to down sampling the candidacy criteria.

 Principle of efficiency: how can the data that is used for justifying the AM candidacy be easily accessible?  Principle of objectiveness: can the candidacy criterion be objectively measured with minimum biased information?

Figure 3 The proposed framework of the decision support system for AM part candidacy identification.

With these two principles being the guideline, AM candidacy criteria obtained from CM metrics, AM metrics and publications are down-sampled and parametrized in Section 3.1. The second branch of the DSS framework is to extract useful information (i.e. parameters) from diversified historical data including non-AM applications (e.g. for CNC milling), successful AM applications and failed AM instances. Each instance is evaluated against the candidacy criteria by AM experts and labelled with which AM potentials possess. As the number of labelled instances increases, the database can provide more concrete support for decision models in the next step. Details can be found at Section 3.2. The last part is how to construct decision models referring to existing instances and predict the AM candidacy of a targeted application. In this paper, machine learning algorithms are utilized because it is a promising solution to the problem of subjectivity in part selection, as it can eliminate the need of experts on individual basis and accelerate the decision process in case of massive information intake (see Section 3.3). The output of the DSS will return a list of potentials at a percentage scale. In the end, the targeted instance is fed into the labelled database to further improve the prediction accuracy.

The parameterized candidacy criteria

Comprehensive AM candidacy evaluation of a part requires assessment of both potential design improvement and process limitations. However, this paper mainly aims at establishing a fast-screening tool to narrow down the candidate pool; therefore, only criteria that meet the principles of efficiency and objectiveness are reserved. As such, manufacturability examination of a part is excluded at this early shortlisting stage. These criteria are categorized as geometric analysis, model analysis, economic analysis, and design potential analysis as shown in Table 2. Detailed discussions were reported in our previous work [START_REF] Page | Automated candidate detection for additive manufacturing: a framework proposal[END_REF]). It should be noted that this criteria list is nonexclusive, and other criteria satisfying these two principles can be appended in future work. Further, these criteria are parametrized to determine the decisive factors of "being potential". Taking geometric analysis for example, traditional feature-based complexity metrics either require massive human interpretation or become problematic in case of compound features [START_REF] Babic | A review of automated feature recognition with rule-based pattern recognition[END_REF]. Feature-less computational complexity assessment based on ratios of volume/surface, surface/bounding box (BB) volume, and volume/BB volume is highly favored [START_REF] Valentan | Basic solutions on shape complexity evaluation of STL data[END_REF]). Model analysis is particularly applied for part consolidation potentials by taking the information of the number of components, fasteners, and assembly interfaces. This information reflects the complexity of assembly, and the higher complexity indicates more needs of part consolidation. The sources of the parameters are identified as user, computer-aided design (CAD) model, and Enterprise Resource Planning (ERP) systems from which decisive information can be automatically extracted.

Data acquisition and coding mechanism

Existing applications of both AM parts and CM parts are sought to increase data diversity. A greater variety of instances with differing manufacturing processes, sizes, shapes, complexities, etc., will lead to more success in analyzing future parts. These data are primarily extracted from reported literature and open-source repositories such as GrabCAD. Although data mining methods may be applied to augment the database and accelerate the data acquisition process, some preliminary test showed that publicly accessible data differ in a wide range and lead to poor quality. The extracted poor-quality data led to meaningless results. As such, the exemplified database is manually constructed to validate the proposed DSS framework. The database currently holds approximately 200 instances. Amongst all the decisive parameters, economic analysis is replaced by using an instant quoting plugin -Xometry (2017), offered by an online manufacturing service provider whose business covers manufacturing processes from 3D printing to CNC machining. The plugin is used for estimating lead time and costs with a 5% margin. If the costing tools led to very different estimations, that instance was scrapped.

Expert input regarding the AM potential assessment for each instance is required. To secure data integrity, two AM experts who have more than 3-year experience of product design and AM-related industrial projects work together, and a consensus must be reached with regard to each type of AM potentials. Figure 4 presents a labelled instance with input parameters and identified AM potentials. Each potential is graded by a binary system. It should be admitted that individual bias still exists at its current status; however, real industrial data acquired from its own ERP system should compensate its reliability and form robust decisions.

Decision model

The decision model aims at predicting AM potentials with machine learning (ML) assistance to improve subjectivity as well as efficiency. Different from the traditional way of examining a part whether it meets a special threshold (e.g. lot size of 1000 parts) of an explicit criterion, machine learning helps to identify hidden patterns and produce a probability estimation for further reference. Machine learning, as a promising tool, has been successfully applied in traditional manufacturing processes such as production planning [START_REF] Rodríguez | An intelligent decision support system for production planning based on machine learning[END_REF]), flexible manufacturing system control [START_REF] Chaturved | A synergistic approach to manufacturing systems control using machine learning and simulation[END_REF]) and monitoring [START_REF] Yang | Simultaneous monitoring of mean vector and covariance matrix shifts in bivariate manufacturing processes using hybrid ensemble learning-based model[END_REF], cutting parameters prediction [START_REF] Jurkovic | A comparison of machine learning methods for cutting parameters prediction in high speed turning process[END_REF], and material identification [START_REF] Penumuru | Identification and classification of materials using machine vision and machine learning in the context of industry 4.0[END_REF]) and various AM-related fields, such as manufacturability prediction (Lu 2016), process optimization [START_REF] Aoyagi | Simple method to construct process maps for additive manufacturing using a support vector machine[END_REF], material property estimation [START_REF] Hamel | Machine-learning based design of active composite structures for 4D printing[END_REF], dimensional accuracy analysis [START_REF] Francis | Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data[END_REF]. However, the combination of machine learning with AM candidacy prediction is rarely investigated. In this section, three main research questions are discussed: 1) small sample problem, 2) selection of suitable ML algorithms, and 3) how the ML model is trained.

Small dataset problem

The difficulty of obtaining numerous training samples is often the case in design fields. This situation is because knowledge as an intangible asset is hard to extract and quantify. The most valuable knowledge is often not easy to identify or share as it is stored within the minds of experts through years of experience [START_REF] Dalkir | Knowledge management in theory and practice[END_REF]. This form of knowledge is also referred as tacit knowledge in contrast with explicit knowledge. The other facet of the small sample size comes from the limited understanding of AM and the need of intellectual property protection. As such, feature selection [START_REF] Raudys | Small sample size effects in statistical pattern recognition: Recommendations for practitioners[END_REF] and domain knowledge [START_REF] Hartmann | Model-driven analytics: connecting data, domain knowledge, and learning[END_REF]) have been identified as the most critical points in preparing data for machine learning especially for small sample sizes. The process of applying domain knowledge allows for a deeper understanding of what facets of the input data led to the output decision, and information-rich features will lead to a more successful model as there will be less noise from unnecessary features which causes errors. Corresponding to the AM candidacy identification problem, the domain knowledge and features are equivalent to the candidacy criteria and decisive parameters in this application.

ML model comparison and analysis

Determining the right algorithm is of great importance when applying machine learning, especially in small-sample learning where the computational requirements are typically less demanding. Regression and classification are the main techniques in a supervised learning model. Regression denotes that the output of the model will be continuous values such as print time or component cost, and it has been applied to a broad range of AM process optimization problems like bin packing, nesting, and scheduling [START_REF] Dvorak | Planning and scheduling in additive manufacturing[END_REF]. Choosing regression over classification allows the user to see the potential on a percentage scale rather than just receiving a boolean decision. Since machine learning algorithms are not one-for-all solution when it comes to different data and performance is highly dependent on the unique algorithm/dataset combination [START_REF] Hastie | The elements of statistical learning: data mining, inference and prediction[END_REF], it is necessary to determine the best ML algorithm for each application. Popular ML libraries such as Scikit learn [START_REF] Pedregosa | Scikit-learn: machine learning in Python[END_REF]) and Tensorflow ( 2020) provide good platforms for preliminary suitability test. In this application, Scikit learn is chosen over Tensorflow because the former provides easy ways to build standard ML models while the latter is widely used for deep learning applications with large amount of labeled data such as sound, images, and text [START_REF] Géron | Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent systems[END_REF][START_REF] Patel | What are the main differences between TensorFlow and SciKit Learn? Quora[END_REF].

The initial test of ML algorithms was performed using Python and scikit learn [START_REF] Pedregosa | Scikit-learn: machine learning in Python[END_REF].

Five common regression algorithms are preliminarily tested for each AM potential including linear regression, bayesian linear regression, neural network regression, boosted decision tree regression, and decision forest regression. The root-mean-square error (RMSE) is used as the error evaluation metric for the tested ML algorithms because it represents the average distance from the regression line to each individual training instance. Given the training set of 200, 10% of the samples were set aside for cross validation. The comparison result is summarized in Table 3. From the RMSE of each ML model trained for each AM potential, the Boosted Decision Tree Regression (BDTR) algorithm outperforms all the others for the given training samples. This is because BDTR shows strength to extract complex relationships and operates well on small-sample datasets [START_REF] Roe | Boosted decision trees as an alternative to artificial neural networks for particle identification[END_REF][START_REF] Coadou | Boosted decision trees and applications[END_REF][START_REF] Xia | A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring[END_REF]. Therefore, BDTR is selected as the principle ML algorithm for predicting the AM candidacy. Meanwhile, it is found the RMSEs of other regression algorithms are higher than expected due to the coding mechanism of the labelled data. Current labeling scheme only asks for yes/no with regard to AM potentials to reduce manual inputs and confusion. Therefore, AM potential of each instance will only be put 0 or 1, which makes the model more likely to produce rounded scores instead of the accurate scale. This leads to a higher RMSE when training the models because instances that have a true score of say a 0.67 were labelled as a 1.0; thus, if the training model scored a 0.7, it would have an error of 0.3 as opposed to 0.03. This problem will decrease as the training set is populated with more instances labelled on a percentage scale.

Training the models and prediction

Training of the BDTR model is as follows. First of all, BDTR works in a fashion to create ensemble of regression trees using boosting which means that each tree is dependent on prior trees. The algorithm learns by fitting the residual of the trees that preceded it. Thus, boosting in a decision tree ensemble tends to improve accuracy with small risk of less coverage. BDTR has the advantages of handling different types of predictor variables and accommodating missing data; thus, there is no need for prior data transformation or elimination of outliers. It can also fit complex nonlinear relationships, and automatically handle interaction effects between predictors [START_REF] Elith | A working guide to boosted regression trees[END_REF]. The training and prediction experiment of BDTR model is conducted by using Microsoft Azure studio (2014b), which offers a well-established visual drag-and-drop graphic interface to build and deploy predictive analytic solutions.

The flows of training are depicted in Figures 5. The training flow starts with importing training data from an SQL database as described in Section 3.2. Next, missing data is cleaned using probabilistic primary component analysis, which 'replaces the missing values by using a linear model that analyzes the correlations between the columns and estimates a low-dimensional approximation of the data, from which the full data is reconstructed ' (Azure 2014a). Applying SQL transformation is to single out the decisive parameters by column for its corresponding candidacy criterion to avoid overwhelming the learning model with unnecessary information (e.g. cost for lightweight potential) and improve result accuracy. Each sub-dataset by column is further split into training and validation sets. Then, these datasets are fed into the hyper-parameter tuning module which trains the model from the training set, then tests it on the validation set by calling the BDTR algorithm as shown in Figure 5. Finally, once each model has been optimized, it can be saved and exported as a trained model and be used in the prediction analysis. In Microsoft Azure studio, the new input data is edited to be a format compatible with the type of trained models, and the predicted result will be presented in a numeric form.

Import data

Clean missing data

Apply SQL transformation (Economic)

Split data Split data Split data Split data Split data

Tune model hyperparameters

Tune model hyperparameters

Tune model hyperparameters

Tune model hyperparameters

Tune model hyperparameters

Boosted Decision Tree regression

Apply

Cloud-based tool implementation

The proposed DSS framework is implemented as a cloud-based tool so that it can be easily accessed and allow for instant update regarding training samples and trained models. The developed tool is available at the website ((ADML) 2019b), and the open-source code is also available at GitHub ((ADML) 2019a).

The architecture and user interface of the tool are introduced in the following sections.

Architecture

The tool is designed to be compatible across platforms to facilitate easy access and encourage wider applications. A variety of state-of-the-art technologies were used to support the functionalities as well as easy maintenance. The communication between these technologies are presented in Figure 6

User interfaces

Following the workflow of 1) load CAD model, 2) new data acquisition, and 3) candidacy prediction, the main user interfaces are presented in Figure 7. The developed web application is able to load common CAD files and automatically extract geometric information of volume, surface area, and number of components. Then, users are requested to fill missing data of model information (e.g. number of interfaces), model features (e.g. whether exist internal channels), and economic considerations (e.g. lead time). It should be advised that some of these manual inputs can be replaced by connecting to an ERP system in future iterations. With these inputs, the tool produces candidacy scores for each potential (See Figure 7 c). To learn about the specific AM potential, tutorials can be accessed by hovering above the item (e.g. lightweight bar in Figure 7d). These tutorials will help the novice AM users to develop a better understanding of the potential gains by using AM, as echoed by prior research [START_REF] Yang | Understanding the role of additive manufacturing knowledge in stimulating design innovation for novice designers[END_REF].

With a further goal of improving the accuracy of the tool, it provides a feedback loop for the user to decide whether the new instance is valuable for expanding the training dataset; otherwise, the item will be discarded.

Case study

Two examples of a hip implant [START_REF] Ryan | Fabrication methods of porous metals for use in orthopaedic applications[END_REF]) and a throttle pedal [START_REF] Yang | A numerical-based part consolidation candidate detection approach with modularization considerations[END_REF]) are presented to demonstrate the effectiveness and usefulness of the proposed DSS framework and the developed cloud-based tool. These case studies were chosen because their AM potentials have been already studied in literatures, which cross validates the predicted results of the tool. The details of the examples are summarized in Table 4. The hip implant model is downloaded from GrabCAD [START_REF] Fuentes | Hip replacement prosthesis. GrabCAD[END_REF]) and cost-related data is estimated from publicly available data on the internet. The predicted potentials of the hip implant are shown in Figure 8a. From the result, it is expected to have high potentials of economics (100%), customization (99%) and light-weighting (98%) and low potentials of part consolidation (24%) and having internal channels (0%). The predicted trends of each potential generally agreed with the work reported by Fraunhofer IWU (Schnabel et al. 2017) where MUGETO implant used lattice structures to reduce weight. The economic benefits mainly derived from the advantages of low lots size, while one-of-its-kind characteristic and anatomy compliance highlight the customization potential. As for part consolidation potential, the number of parts and interfaces is not sufficient; therefore, consolidation verification will need further examination by using a specialized tool as developed by Yang et al. (2018).

The throttle pedal model is also available at GrabCAD [START_REF] Miessner | Throttle pedal design challenge[END_REF] and the cost-related data is estimated from publicly available data. The predicted potentials are depicted in Figure 8b. The throttle pedal is claimed to have high potentials of part consolidation (100%) and light weight (76%), and it scores low in terms of economics (28%), customization (0%), and internal channels (0%). The result generally agreed with the work of Yang et al. (2018).

In conclusion, it is interesting to observe how the developed DSS tool helps to predict the AM candidacy potential without AM expertise inputs. The tested examples proved that the tool would be a promising solution to lower the requirements of AM knowledge for finding suitable candidates at early design stage in industrial applications. It should be advised that the predicted scores only indicate the possibility of having a specific potential rather than a definite answer because manufacturing constraints need to be considered in the next step.

Conclusions

Identification of part candidates for AM applications is one of the challenges for its wider industrial deployment. Existing methods are not suitable as they are either too complex and rely too heavily on the user who already has expertise in AM, or they are too simple to provide the user with any real insight. This paper is specially focused on providing an efficient and automated solution for screening of parts for AM potentials at early design stage. A decision support system framework is proposed with carefully selected candidacy criteria and machine learning-assisted workflow which helps to predict AM candidacy of parts. The presented framework is implemented as a cloud-based tool to serve as an accessible platform of training database management, machine learning service, and AM knowledge dissemination. The open architecture makes the maintenance, upgrade, and retraining machine learning models easy to manage. Two case studies of a hip implant and a throttle pedal are presented to demonstrate the workflow and effectiveness of identifying various AM potentials. This preliminary work has proven the potential of employing machine learning methods to identify part candidates for AM applications in the early conceptual re-design stage.

Limitations and future work

It should be acknowledged that current prototype is in its infancy state and helps to establish the pipelines for a ML-assisted decision tool for AM candidacy detection. There are several directions should be further explored before being deployable in industry.

First of all, the number of instances and the diversity of AM/CM parts are still low, but the cost of data acquisition is relatively high. Other factors such as intellectual protection and data security further increase the difficulties. A commonly established data-sharing framework is highly recommended across different industries and labs to enrich data diversity, which in turn could increase the prediction accuracy of trained model. Taking the trained model of "lightweight" as an example, dataset size effect was investigated at the scale of 50%, 60%, 70%, 80%, and 90% of overall raw data (i.e. 200 instances), the Rooted Mean Square Error of each dataset was 0.352, 0.289, 0.253, 0.155, and 0.115 respectively. Similar approach of examining dataset size effect was adopted in the literature (Li et al. 2019). Therefore, the authors hold strong belief that the predictive performance of the trained model will improve as more data feed in. In this experiment, an interesting finding was also observed that the developed tool failed to identify the internal channel potential for the hip implant as the MUGETO implant design (Schnabel et al. 2017) demonstrated. In their project, internal channels were added to the new hip implant for medicine deposition. This deficiency of the current tool highlights the necessity of expanding the diversity of instances and potentially including functions of labelled data as one learning feature as well so that certain new functions can be integrated to the new design. In addition, current data was retrieved from various industries ranging from aerospace, sports, automobile, and medicine. However, the economics model is highly sensitive to the specific field. As such, a more accurate ML model should be customized by taking data from the collaborative company's ERP system in the future.

Second, the AM potential of material options was originally included in the training model. However, preliminary tests with various machine learning algorithms found that this potential could not be accurately determined based on current built-in training samples. These samples did not contain enough information regarding the material properties. To prevent the complication of the criteria through adding material-specific questions which led to little improvement in the results due to the insufficient data, the specific material options potential was removed. It is possible that in the future more complete data will be available and another iteration of the DSS will be able to support material options for AM.

Third, the decisive parameters of part consolidation potential in this paper include the number of components, interfaces, and fasteners. Although it is able to find a chunk of components, it is difficult to more precisely locate the parts to be consolidated. A promising solution is to trace the product hierarchy.

As shown in Figure 9, if the first level of an assembly (e.g. an engine) is found to have minimum part consolidation potential, the searching process continues to the next level. Candidacy evaluation repeats until the nearest child demonstrates part consolidation potential. Repeat the same procedure for other branches until all branches are exhausted. However, tracing of product hierarchy requires access to the ERP system of the company. The accessibility of other data such as lead time and cost also requires permission of the ERP system.

Figure 9 The screening strategy of identification of subassemblies for part consolidation potential.

Fourth, current candidacy criteria are determined to fulfill the principles of efficiency and objectiveness, and they concentrate on the opportunistic aspects of employing AM technology. Although the need of simultaneous consideration of restrictive rules is not encouraged at the very first round of screening, certain manufacturing constraints can be integrated directly thereafter to further gauge the AM candidacy. Other future work may also include changing the labeling system from 0/1 to Likert scales to show more insights of each potential by a percentage scale. Lastly, Although Boosted Decision Tree algorithm works well for current scales of training samples, as more data kicks in, further optimization of machine learning algorithms will be demanded.

Acknowledgement

Financial support from the National Sciences and Engineering Research Council of Canada [Discovery Grant RGPIN 436055-2013]; and McGill Engineering Doctoral Award (MEDA) is acknowledged with gratitude.

Figure 1

 1 Figure 1 the mind map of dimensions of AM potentials.

Figure 2

 2 Figure 2 3D printing Barometer (Materialise 2014).

  Figure4an example of labeled instance with input parameters and identified AM potentials.

Figure 5

 5 Figure 5 the graphic view of training procedure of Boosted Decision Tree regression in Microsoft Azure studio.

  , which contributes to the main architecture of the tool. Since multiple third-party services are utilized in the development of this application, PHP (Hypertext Preprocessor) is used as the liaison for communication between these APIs (Application Programming Interface) as it allows API keys to remain hidden and avoid the same access restrictions as HTTP(HyperText Transfer Protocol). In this tool, three main services are called: Autodesk Forge, Microsoft Azure and AJAX (Asynchronous JavaScript And XML) request. Autodesk Forge is mainly used for CAD format conversion (e.g. SolidWorks file to STL), metadata extraction by using Model Derivative API, and remote storage of CAD models. Microsoft Azure services including training database management and machine learning, are integrated via APIs. Lastly, user interfaces of the web application are developed by using the React JavaScript library (Facebook OpenSouce 2019) to create a multi-state application on a single web page. AJAX calls enable clientserver communication without the need of refreshing the webpage.

Figure 6

 6 Figure 6 The architecture of implementation of the DSS framework.

Figure 7 A

 7 Figure 7 A collection of snapshots of the developed tool.

Figure 8

 8 Figure 8 AM candidacy prediction of the hip implant and the throttle pedal.

Table 1

 1 The first stage requires AM neutral knowledge, while the second stage needs AM experts to evaluate the response.

	summary of part candidacy identification methods
		Candidacy Criteria	Comprehensiveness	AM expertise	Implementation
		Constraint Potential Single	Multiple	Novice Skilled Heuristic Computation
	Merkt et al. (2012)							
	Klahn et al. (2014)							
	Materialise (2014)							
	Lindemann et al. (2015)						*	

Table 2

 2 Selected candidacy criteria and its key parameters.

	Candidacy criteria	Decisive parameters	#	Units	Source
		Ratio of part volume to part surface area	DP1		CAD
	Geometric analysis	Ratio of part surface area to bounding box volume	DP2		CAD
		Ratio of part volume to bounding box volume	DP3		CAD
		Number of components	DP4		CAD
	Model analysis	Number of fasteners	DP5		User
		Number of assembly interfaces	DP6		User
		Manufacturing cost	DP7	$/part	ERP
		Batch size	DP8 # of parts	ERP
	Economic analysis	Lead time	DP9	days	ERP
		Inventory costs	DP10	$	ERP
		Imports/exports cost	DP11	$	ERP
		Does the part contain internal channels/structures? DP12	Y/N	User
	Design potential analysis	Does the part have any surface markings? Are there similar parts with similar modifications?	DP13 DP14	Y/N Y/N	User User
		Does the part require human body compliance?	DP15	Y/N	User

Table 3

 3 Error comparison of ML models. Root

Mean Square Error of Machine Learning Algorithms Economic Lightweight Part consolidation Internal structures Customization Boosted Decision Tree Regression

	Decision Forest Regression	0.367457	0.20334	0.142428	0.241523	0.147326
	Bayesian Linear Regression	0.452348	0.501598	0.160878	0.295722	0.107149
		0.259907	0.115436	0.140231	0.159223	0.099288
	Neural Network Regression	0.568563	0.483725	0.256403	0.470125	0.201107
	Linear Regression	0.361987	0.407623	0.163127	0.342522	0.139623

Table 4

 4 Details of the tested examples.

			Volume (	)	# Components
	Name	Figures	Area (	)	# Fasteners
			BD box (	)	# Interfaces

Cost Quantity Lead time Inventory Import/export Markings Channels Similar parts Size variance Human custom fit

		55910.32
	Hip implant	12367.67
		166.38*106.46*39.97


Sheng Yang
email: sheng.yang@mail.mcgill.ca

Yunlong Tang
email: tang.yunlong@mail.mcgill.ca

Yaoyao Fiona Zhao
email: yaoyao.zhao@mcgill.ca

A new part consolidation method to embrace the design freedom of additive manufacturing

Keywords: Part Consolidation, Design for Manufacture and Assembly, Lattice Structure, Additive Manufacturing, Function Integration

As Additive Manufacturing (AM) evolves from Rapid Prototyping (RP) to the end-of-use product manufacturing process, manufacturing constraints have been largely alleviated and design freedom for part consolidation is extremely broadened. AM enabled part consolidation method promises a more effective way to achieve part count reduction and the ease of assembly compared with traditional Design for Manufacture and Assembly (DFMA) method. However, how to achieve AM enabled part consolidation is not well developed. In this paper, a new part consolidation method comprehensively considering function integration and structure optimization is proposed. This presented method is characterized by two main modules. The first one is to achieve better functionality through surfacelevel function integration and sequential part-level function integration based on design specifications with an initial CAD model which is designed for conventional manufacturing process. The other module is to realize better performance through the introduction and optimization of heterogeneous lattice structures according to performance requirements. The proposed part consolidation method highlights itself from the perspective of functionality achievement and performance improvement. An example of a triple clamp is studied to verify the effectiveness of the proposed model. The optimized results show that the part count has been reduced from 19 to 7 with a less weight by 20% and demonstrates better performance.

Introduction

In order to stay competitive in modern production industry, products should be designed and manufactured within the following two opposite objectives: decreasing time and cost; increasing quality and flexibility. Part count reduction as one of the effective ways to reduce process time and cost has received more and more attention in the past decades. One feasible way to realize part count reduction is part consolidation which is defined as a process of composites fabrication in which multiple discrete parts are designed and fabricated together into a single part, thus reducing the number of fabricated parts and the need to join those parts together (Encyclo 2014). A reduction in the number of assembly operations can have a tremendous impact on production costs and difficulties of products. Firstly, there is no need for dedicated tooling for example fixture, and fasteners and potential assembly difficulties like joining method is avoided. Furthermore, it is often possible to design the consolidated parts to perform better than the assemblies. Ultimately, a reduction in part count means that product complexity is reduced from management and production perspectives since fewer parts need to be tracked, sourced, inspected. Part consolidation is intensively studied in conventional Design Theory and Methodology (DTM) such as Design for Assembly (DFA) [START_REF] Andreasen | Design for assembly[END_REF], Design for Disassembly (DFD) [START_REF] Crowther | Design for disassembly[END_REF], and Design for Manufacture and Assembly (DFMA) [START_REF] Boothroyd | Product design for manufacture and assembly[END_REF]. The problem is that design freedom of part consolidation is heavily stifled by the requirements of Design for Manufacturing (DFM), which leads to the limited reconstruction by only deleting fasteners and merging existing parts together. Moreover, part consolidation stagnates without taking into consideration of structural optimization of the merged design space to achieve better performance; therefore, a global optimal consolidated structure is not achieved. As AM process evolves from RP to the end-of-use product manufacturing method, manufacturing constraints are largely alleviated and the design freedom is extremely expanded. For example, design limitations by conventional manufacturability such as uniform wall thickness, avoiding sharp corners, and minimising weld lines in injection moulding can be overcome by AM [START_REF] Hague | Design opportunities with rapid manufacturing[END_REF].

AM is defined by the American Society for Testing Materials (ASTM) as " a process of joining materials to make object from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies" (ASTM 2012). From manufacturability perspective, the benefit by taking AM process(also well known as Rapid Manufacturing(RM) at the beginning) is the ability to virtually manufacture parts of any geometric complexity without tooling, which used to be one of the typical restrictive factors of today's product development [START_REF] Hopkinson | Rapid manufacturing: an industrial revolution for the digital age[END_REF]. From design perspective, the advantage of AM over conventional subtractive or formative methods is well illustrated by the great design freedom. These design freedoms enabled by AM capabilities are reflected in four categories: shape complexity, hierarchical complexity, material complexity and functional complexity [START_REF] Gibson | Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing[END_REF]. Therefore, the design freedom for part consolidation and performance improvement is largely expanded. In section 2, a literature review of AM enabled design methods for part consolidation and design methods for performance improvement is given.

Literature review

There are numerous literatures on adapting AM to do part consolidation [START_REF] Becker | Rethink assembly design[END_REF][START_REF] Gibson | Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing[END_REF]Hopkinson et al. 2006;[START_REF] Ponche | A new global approach to design for additive manufacturing[END_REF][START_REF] Prakash | New product development by DFMA and rapid prototyping[END_REF]. The first and wellknown reported part consolidation case using AM capabilities was the aircraft duct redesign case [START_REF] Gibson | Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing[END_REF] shown in Figure 1. Since the limitation of conventional manufacturing processes, 16 parts are needed to be assembled to accomplish this aircraft duct. After part consolidation process, only one part is needed to be fabricated by AM process. Realizing the opportunities brought by RM, [START_REF] Becker | Rethink assembly design[END_REF] introduced some major design guidelines for rapid manufacturing and applied these guidelines to a case study of a mix device. The optimized part has advantages in reduced part count, less assembly effort and advanced functionality. A similar case is a fluid control valve with 18 components, which is redesigned to be a new one consisting of 8 parts with better performance based on DFMA and RP [START_REF] Prakash | New product development by DFMA and rapid prototyping[END_REF]). An important application in automobile was reported by Hopkinson et al. (2006) to redesign a door assembly that is composed of 11 pieces for an automotive application subjected to environmental burden and financial profits/costs for end-of-life recycling. It is important to note that although all these reported cases produced new and better design to achieve part consolidation, there is no clear design framework to implement the design process and the extent of success depends much on designers' experience and understanding of the functional requirements.

To overcome the drawback of difficulty in determining the real optimized characteristics for a given AM process from an initial CAD model that is designed for traditional fabrication processes, a global approach (shown in Figure 2) aiming at defining part shapes from the manufacturing process and the functional requirements is presented by [START_REF] Ponche | A new global approach to design for additive manufacturing[END_REF]. This design method is composed of three steps. The first step is a global analysis which allows the delimitation of the design problem in terms of geometrical dimensions in relation to the dimensional characteristics of the AM process. The second step allows the fulfilment of the dimensional and geometrical specifications in relation to the AM process capability and the finishing process characteristics. Finally, the third step allows the fulfilment of the physical and assembly requirements in relation to the capability of the AM process. The proposed design method is applied to a case of a robot hinge made in stainless steel. Based on the building orientation and mechanical behaviour analysis, the final geometry is given (see in Figure 3). However, functional surfaces and functional volumes are not well defined and the gap between these two functional units is not bridged. and other performance requirements are rarely discussed. Actually, the design space derived from part consolidation process can be further partitioned and optimized to achieve better performance. To improve products' performance, lattice structure on a meso-level (0.1~10mm) is widely used [START_REF] Chen | 3d texture mapping for rapid manufacturing[END_REF]Hao and Raymond 2011;[START_REF] Wang | A hybrid geometric modeling method for large scale conformal cellular structures[END_REF]. This structure is defined as a meso-level structure which consists of an interconnected network of solid struts or plates. By carefully designing the topology of a network of lattice and its struts' thickness, some desired structural properties, such as high stiffness weight ratio and high energy absorption rate, can be achieved. Thus, the lattice structure is usually used to replace the solid material for the further performance improvement.

Design methods for lattice structure can be mainly divided into two types. They are design methods for homogenous lattice and design methods for heterogeneous lattice. For homogenous lattice structure, since the homogenous lattice structure on a meso-level can be regarded as homogenous material, traditional material section method can be used to select an appropriate type of homogenous lattice structure [START_REF] Ashby | Materials selection in mechanical design[END_REF]. Compared to design methods for homogenous lattice structure, design methods for heterogeneous lattice are more complex because the struts' thickness, size and orientation of lattice unit cell will unevenly distributed in the design domain. To optimize the struts thickness distribution, some structural optimization methods [START_REF] Rezaie | Topology optimization for fused deposition modeling process[END_REF][START_REF] Rosen | Computer-aided design for additive manufacturing of cellular structures[END_REF][START_REF] Sundararajan | Topology optimization for additive manufacturing of customized meso-structures using homogenization and parametric smoothing functions[END_REF]) are used. Besides optimizing its struts' thickness distribution, the shape and orientation of lattice unit cell can also be optimized to achieve a better performance [START_REF] Chen | 3d texture mapping for rapid manufacturing[END_REF][START_REF] Rosen | Computer-aided design for additive manufacturing of cellular structures[END_REF][START_REF] Wang | A unit cell approach for lightweight structure and compliant mechanism[END_REF]. Generally, the heterogeneous lattice structure can always achieve a better performance than its homogenous counterpart [START_REF] Tang | Design Method for Lattice-Skin Structure Fabricated by Additive Manufacturing[END_REF]. As shown in Figure 4, under the same load condition, compared with homogenous lattice beam in (a), the heterogeneous lattice structure in (c) shows a less maximum displacement since density distribution is in proportion to load condition [START_REF] Tang | Design Method for Lattice-Skin Structure Fabricated by Additive Manufacturing[END_REF]. In order to realize better functionality and better performance with respect to functional requirements and manufacturing constraints, a new part consolidation method which synthesizes function integration and structure optimization is proposed. AM enabled part consolidation is realized from a functional standpoint allowing better functional achievement and performance; meanwhile, it increases the scope in the search for better design solutions.

Proposed methodology

The goal of the presented method is to provide a redesign method that both reduces part count and improves product performance by means of part consolidation at functional level and subsequently through structure optimization. To understand the proposed method, the following terminology must be first defined. Part is defined as a designed object that has no assembly operations in its manufacture. An assembly is a collection of two or more parts. The basic characteristics which the proposed design method should have, and general principles are presented as follows:

 The design method should be suitable for the design with one material type in at least assembly level other than in part level. A design method that starts at part level usually does not have a global view, which is essential to achieve part consolidation.  The design method should be suitable for the design in multiple design domains, i.e. stiffness, heat and dynamics.  The design method should include the considerations of design for the ease of process difficulties and for better performance.  The design method should be a process-based design methodology.  The design method may include the considerations for cost, time and other aspects like energy consumption to achieve a more cost-effective design.

Design framework

A new part consolidation method which satisfies the basic principles and objectives is shown in Figure 5. In this design method, design specifications are given as the initial inputs, which contains the information of Functional Requirements (FRs), Performance Requirements (PRs) and the initial CAD model designed for conventional manufacturing process. According to Dieter's considerations of a good design [START_REF] Bin Maidin | Development of a design feature database to support design for additive manufacturing[END_REF][START_REF] Dieter | Engineering design: a material and processing approach[END_REF]), a function means what it is designed to do, while performance refers to how well the function is achieved. Normally, performance requirements have tolerance while functional requirements do not.

Based on the initial input of this design model, part consolidation process can be divided into two main steps (the highlighted rectangular). The first step is to analyze the initial CAD model and perform part consolidation in functional level according to functional requirements and performance requirements. This step is defined as function integration as shown in Figure 5. In the second step, structure optimization methods are applied to newly generated design space to achieve better performance such as lighter weight, better heat dispatch or dynamic properties under the requirements of performance. The above two steps should also comply with the process constraints of manufacturing, assembly, and standardization. After that, design solutions are verified. If there is no design solution, design flow goes back to function integration and necessary modifications should be done in this step. If design solutions are found, output the design solutions and the original part is redesigned with less part count and better performance.

Function integration

Generally function integration can be further divided into two main sub-steps: functional surfaces extraction and functional volumes generation. Functional Surface (FS) is defined as a set of surfaces which can fulfill certain FRs. Ideally, if FRs can be decomposed into sub-FRs to the lowest level, one FS is designed to fulfill one corresponding FR. For example, Figure 6 (a) shows a surface of airfoil which can provides lift force (FR) for aircraft. Figure 6 (b) shows that the upper face of the chair is a combination of two FSs to provide support and an aesthetic function (2 FRs) respectively. The interesting part is that the mergence of these two FSs helps to achieve function integration in surface level. Based on FS, Functional Volume (FV) is defined as a geometry volume to link functional surfaces to form a solid to undertake desired performance requirements. As shown in Figure 6 (a), FV is formed by the closed FS. Function volume is an effective way to achieve function integration in part level. Hereby, assume that the element number of FRs set is X 0 and the element number of PRs set is X 1 , so FS and FV is represented as follows: { | ( ,,,, )

} i FS fs fs G P A N FR   0 1,2, , ; i n X   L (1) 1 | ( , ) j n jk k FV fv fv fs PR          1 1,2, ; j m X   L (2)
Where G stands for geometric shape and dimensions, P stands for position parameters, and A stands for attributive information such as material prosperities, surfaces specifications or tolerances. N stands for the normal vector of fs to indicate the active face. PR stands for performance requirements and the sum of n j equals n. The detailed procedure to achieve function integration can be seen in Figure 7. Firstly, functional surfaces are extracted from the initial CAD model according to FRs. Other surfaces such as manufacturing surfaces and assembly surfaces are discarded. FSs in this stage has specific geometric information, dimensions, and spatial locations. Secondly, the extracted surfaces are compared and analyzed the possibility of surface-level function integration, otherwise, separated interface pairs are inserted. The last step is to link these FSs to form a solid entity to achieve part-level function integration.

Functional surfaces extraction

Functional surfaces mergence

Functional volumes generation

Lattice Generation and Optimization

In the design stage of lattice generation and optimization, to reduce the weight and cost of products, lattice structures on a meso-level is used to replace the solid material in chosen functional volumes generated in the functional integration stage. Then, a heterogeneous lattice structure designed method is proposed to optimize the strut thickness and lattice distribution to further optimize their functional performance. The detailed design flow of this design stage can be divided into four steps which are shown in Figure 8.

Initial functional analysis

Functional volume categorization

Homogenous lattice generation

Lattice struts thickness optimization In the first step, an initial functional analysis model is firstly built to calculate the structural response or performance of a product. For example, in the structure design field, Finite Element Analysis (FEA) model is usually regarded as functional analysis model to analyze the stress and displacement distribution of product with complex geometry. To reduce the computational load of the analysis model, functional volume is always filled with solid material whose properties are simple to calculate. After initial analysis, functional volumes are further categorized into two categories. The volumes whose stress is relatively high will be filled with solid material, and those functional volumes whose stress is relatively low will filled with lattice structure. Besides these two types of functional volumes, there will be the third type of functional volume whose stress only concentrates in a small region. This type of functional volume will be further divided into two sub-function volumes according to its stress distribution and macro-geometry. And these sub-functional volumes can be further categorized into the first two types. In the third step, the required properties of lattice structure on a meso-level can be calculated. The material chart and traditional material selection method can be used to select appropriate lattice topology and initial struts' thickness. Then based on these parameters, the initial designed product with homogenous lattice structure is generated.

To further improve the functional performance of product, lattice struts' thickness is optimized in the fourth step. In this step, the topology optimization based heterogeneous lattice structure design method is used to reduce the number of design parameters during the optimization process. In this design method, SIMP (Solid Isotropic Material with Penalization) method is firstly used to obtain the optimal relative density distribution in the functional volume of lattice structures. Then the mapping function f is defined as: * ()

tf   * P   , tT  (3)
Where *  is the average relative density of elements in a certain lattice unit cell, t is the struts' thickness of that lattice unit cell. This mapping function must satisfy the following two requirements:

(1) Suppose (2) Mapping function f is bijective from P to T.

Based on this mapping function, the optimal thickness distribution of lattice structure is calculated. In order to further optimize this lattice structure, the lattice structures in the place whose relative density is smaller than a certain area is removed. After this step, an optimized heterogeneous lattice structure is obtained.

Case study and discussion

As shown in Figure 9 (a), an original triple clamp made in stainless steel is composed of 19 parts. Because of the assembly access requirements, part 1 and part 2 need to be fabricated separately and assembled. However, part 2, 3, and 4 are separate because of the consideration of manufacturing difficulties and cost. For example, the top surface of part 4 and that of part 3 are lined up with an angle of 10 degrees. There are two main disadvantages of this original design: excessive assembly operations and being heavy. With AM process, this part can be redesigned with the part consolidation method proposed in this paper. The redesign process is illustrated in Figure 9.

The first step is to define functional surfaces from the original CAD model and design specifications. As shown in step (b), functional surfaces are defined according to the FRS of providing support to external parts. These functional surfaces are colored in yellow. The second step is to extract these FSs that are described by exact information of position and surface boundaries as well as ambiguous information of surface thickness as shown in step (c). The third step is to check if these FSs can be merged together to achieve surface-level function integration. Since all these FSs are restricted by the fixed position for the need of external interfaces, this step cannot be conducted. The fourth step is to link all these FSs to form a solid and the linking volume is calculated as the minimum volume to envelop all these isolated FSs to achieve part-level function integration. The following step is to categorize the essential FVs which are filled with lattice structure according to topology analysis. The last step is to optimize the homogeneous structure's struts' thickness to generate homogeneous structure with skins. After all these steps, a triple clamp is redesigned with lattice structure and skins.

The redesigned part results in a lower mass of the blank part by approximately 20% and the part count is reduced from 19 to 7. It allows for less assembly, less material consumption and better performance. However, an FEA analysis is needed to optimize the obtained functional volume to get a better result in the following research.

Conclusions and future research

In this paper, conventional part consolidation methods and current AM enabled part consolidation methods are reviewed. The limitations of conventional part consolidation methods are classified into three categories. The first drawback is the extreme dependence on designers' experience and understanding of functional requirements. The second one is that there is no clear design framework of how to achieve less part count at design stage. The last one is that performance improvement is rarely involved. Therefore, an innovative part consolidation method focusing on function integration and performance improvement is proposed in this research. This design method consists of two steps. Firstly, achieve surface-level function integration and part-level function integration according to functional requirements. Secondly, functional volumes generated in the first step is categorized and optimized with heterogeneous lattice structures for the purpose of performance improvement. The case study proves that the proposed design method is effective in achieving less part count and better structure performance. To further improve this design method, more research work should be done.

(1) A design method from the upstream design phases should be developed. In this paper, the starting point is to optimize an existing part in order to find a locally optimal design solution with given design specifications.

(2) An analytic model for design process rationalization and multidisciplinary optimization is needed since most of the available design methods are semantic representation which is difficult to be implemented and to keep design flow consistent.

(3) A heterogeneous design method should be established to decide parameters of different struts in single lattice unit. For example, the strut along the direction of maximum normal stress should be thicker than other strut inside the lattice unit.

Acknowledge

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Aircraft duct examples[START_REF] Gibson | Additive Manufacturing Technologies: Rapid Prototyping to Direct Digital Manufacturing[END_REF]

Figure 4 :

 4 Figure 4: Homogenous lattice and heterogeneous lattice

Figure 5 :

 5 Figure 5: Proposed part consolidation method

Figure 6 :

 6 Examples of functional surface and functional volume[START_REF] Bin Maidin | Development of a design feature database to support design for additive manufacturing[END_REF]

Figure 7 :

 7 Figure 7: Procedure to achieve function integration

Figure 8 :

 8 Figure 8: Design flow of lattice generation and optimization



  are the average relative density for two different lattice unit cells,

Figure 9 :

 9 Figure 9: The redesign process of a triple clamp.

Financial

  support from the National Sciences and Engineering Research Council of Canada Discovery Grant RGPIN 436055-2013 and McGill Engineering Doctoral Award (MEDA) is acknowledged with gratitude.