Ali Rugbani
email: rugbani@sun.ac.za

Kristiaan Schreve
email: kschreve@sun.ac.za

Modelling and Analysis of the Geometrical Errors of a Parallel Manipulator micro-CMM

Keywords: micro-CMM, parallel manipulator, micro-measurement, covariance matrix, error model, Monte Carlo simulation

A micro coordinate measurement machine (micro-CMM) with high precision and high accuracy is introduced for the measurement of part dimensions in micro scale. This design is intended to achieve submicron resolution for a work envelop of at least (100x100x100) mm. In this study, a mathematical measuring model to explicitly define the coordinate of the probe in x, y and z directions have been represented. An algorithm to find the workspace was implemented. The error model of the machine was created and the effect of structural errors on probe position was studied analytically. The significance of each geometric parameter was studied in order to minimize the measuring error and achieve the best machine design. Finally, the results of the analytical error model were confirmed through a Monte Carlo analysis.

Introduction

The machining, assembly inspection and quality controlling of small objects such as micro-electro-mechanical systems (MEMS) require high positioning accuracy. During the past two decades great attention has been given to micrometrology to fill the gap between the ultrahigh precision measurements of nanometrology and macrometrology [START_REF] Mckeown | Nanotechnology-Special article[END_REF]. In this regard many micro coordinate measuring machines (micro-CMM) were introduced and intensively studied [START_REF] Kuang-Chao | Development of a micro-CMM[END_REF][START_REF] Gao | New kinematic structures for 2-, 3-, 4-, and 5-DOF parallel manipulator designs[END_REF][START_REF] Liu | A new family of spatial 3-DOF fully-parallel manipulators with high rotational capability[END_REF]. The fact that the errors are not cumulative and amplified is one of the major advantages of parallel CMMs over the traditional serial CMMs [START_REF] Gang | Error analysis of three degree-of-freedom changeable parallel measuring mechanism[END_REF]. Nonetheless, the positioning accuracy of parallel mechanisms is usually limited by many other errors, some authors identified the errors affecting the precision of parallel mechanisms as follows [START_REF] Gang | Error analysis of three degree-of-freedom changeable parallel measuring mechanism[END_REF][START_REF] Che | Error model and error compensation of six-freedom-degree parallel mechanism CMM[END_REF][START_REF] Li | Error compensation for a parallel robot using back propagation neural networks[END_REF][START_REF] Meng | The direct-error-compensation method of measuring the error of a sixfreedom-degree parallel mechanism CMM[END_REF]: manufacturing errors, assembly errors, errors resulting from distortion by force and heat, control system errors and actuators errors, calibration, and even mathematical models. However, the main disadvantage of parallel CMMs is the limited workspace [START_REF] Hunt | Structural kinematics of in parallel-actuated robot arms[END_REF][START_REF] Clavel | A fast robot with parallel geometry[END_REF][START_REF] John | Introduction to Robotics: Mechanics and Control[END_REF], and the difficulty of their motion control due to singularity problems [START_REF] Gang | Error analysis of three degree-of-freedom changeable parallel measuring mechanism[END_REF], [START_REF] John | Introduction to Robotics: Mechanics and Control[END_REF]. Many researchers studied the singularity problem and workspace analysis of some planar parallel mechanisms [START_REF]Study of planar three-degree-of-freedom 2-RRR parallel manipulato[END_REF], [START_REF] Liu | Kinematics, singularity and workspace of planar 5R symmetrical parallel mechanisms[END_REF]. Previous studies showed the influence of joint manufacturing and assembly on the positioning error [START_REF] Gang | Error analysis of three degree-of-freedom changeable parallel measuring mechanism[END_REF], [START_REF] Rui | Error Analysis and Distribution of 6-SPS and 6-PSS Reconfigurable[END_REF],. Pierre [START_REF] Pierre | Kinematic calibration of parallel mechanisms: a novel approach using legs observation[END_REF] showed that the operation and the performance of the sensors significantly affect the precision of the manipulator. Hassan analysed the tolerance of the joints [START_REF] Mahir | Design modification of parallel manipulators for optimum fault tolerance to joint jam[END_REF], Tian studied the assembly errors [START_REF] Hunt | Structural kinematics of in parallel-actuated robot arms[END_REF]. Tasi [START_REF] Tsai | Robot Analysis-The Mechanics of Serial and Parallel Manipulators[END_REF], Raghavan [START_REF] Raghavan | The Stewart platform of general geometry has 40 configurations[END_REF], Abderrahim and Whittaker [START_REF] Abderrahim | Kinematic model identification of industrial manipulators[END_REF] have studied the limitations of various modelling methods. The solution of the forward kinematic for various configurations has attracted the attention of many researchers [START_REF] Ak Dhingra | Closed-form displacement analysis of 8, 9 and 10-Link Mechanisms[END_REF][START_REF] Xiaolun | A complete and general solution to the forward kinematics problem of platform-type robotic manipulators[END_REF][START_REF] Didrit | Guaranteed solution of direct kinematic problems for general configurations of parallel manipulators[END_REF][START_REF] Zhang | Forward kinematics of a class of parallel (Stewart) platform with closed-form solutions[END_REF][START_REF] Nanua | Direct kinematic Solution of a Stewart platform[END_REF][START_REF] Sv Sreenivasan | Closed-form direct displacement analysis of a 6-6 Stewart platform[END_REF][START_REF] Griffis | A forward displacement analysis of a class of Stewart platform[END_REF][START_REF] Lin | Forward displacement analyses of the 4-4 Stewart platforms[END_REF]. The performance of micro-CMMs in terms of accuracy and precision is influenced by numerous error parameters that require effective error modelling methods [START_REF] Rui | Error Analysis and Distribution of 6-SPS and 6-PSS Reconfigurable[END_REF], [START_REF] Lin | Precision design of modular parallel kinematic machines[END_REF]. Moreover, the error models are of great importance in order to evaluate the machine and understand the effect of the different parameters. Forward solution for error analysis was also covered [START_REF] Jian | On the accuracy of a stewart platform-Part I: The effect of manufacturing tolerances[END_REF][START_REF] Chunhe | Nongeometric error identification and compensation for robotic system by inverse calibration[END_REF][START_REF] Patel | Calibration of a hexapod machine tool using a redundant leg[END_REF]. This paper presents a micro-CMM based on parallel mechanism [START_REF] Oiwa | New coordinate measuring machine featuring a parallel mechanism[END_REF], [START_REF] Zhuang | A coordinate measuring machine with parallel mechanisms[END_REF]. Its workspace was analysed, and details of the measuring model are reported. An error model of the mechanism given the covariance matrix theory was established, and the effect of geometrical errors on the position were analysed.

Structure and theory

Machine design & Structure

The micro-CMM designed in this research consists of an upper equilateral triangular frame and a lower equilateral triangular moving platform connected to the upper frame with three extendable legs. Each leg contains a prismatic joint, the upper/lower ends of the legs are connected to the frame/platform (at points f i / p i ) with spherical joints. Moreover, laser interferometers are installed on the legs in order to acquire accurate measurement of the length of the legs. The lower platform can only move with respect to the upper frame. The movement of the platform is controlled by three linear motors attached to the upper frame. An isometric view and a top view of the machine are shown in Fig. 1. In Oiwa's design [START_REF] Oiwa | New coordinate measuring machine featuring a parallel mechanism[END_REF], the workspace is very small because of the rotational joints' limitation. Our arrangement provides significant advantage by using spherical joints. The spherical joints represent three rotational degrees of freedom (3-DOF). In this arrangement the use of spherical joints ensures that a very large workspace is achieved. However, the movement of the platform is restricted to always be parallel to the upper frame. The sacrifice of the rotational movement of the moving platform around the axes is beneficial for solving the kinematics model. Fig. 2 shows a schematic drawing of the micro-CMM machine.

Coordinate system

The coordinate system is shown in Fig. 3. The origin O f -(0,0,0) is placed at the centre of the upper frame. The xaxis equally divides the angle at point f 1 and the z-axis is perpendicular to the frame plane. •  i : the angle point f i makes with the x-axis,  1 = 0  ,  2 = 120  ,  3 = 240 

• l min , l max : the maximum and minimum extensions of the legs, l min = 300 mm, l max = 550 mm

Development of the kinematic model

Because of using spherical joints, the movement of the legs can be expressed by the equation of a sphere. Let's assume that the central point of the moving platform (x,y,z) is the point of intersection of three spheres, and thus, point f i must be shifted towards the point of origin O f . by (f ip i ) where:

[ ] (1)
[ ] (2)
Then the equation of movement of the legs can be written as follows:

[ ( ) ] [ ( ) ] [ ] (3)
[ ( ) ] [ ( ) ] [ ] (4)
[ ( ) ] [ ( ) ] [ ] (5)
Where: (x,y,z): the probe location l 1 , l 2 and l 3 : the leg lengths.

The probe location can be found by solving eq's (3), ( 4) and ( 5). This yields explicit expressions for the x, y and z coordinates of the centre point

( )( ) ( )( ) ( ) (6)
( )( ) ( )( ) ( ) (7)
√ [ ( ) ] [ ( ) ] (8)

Analysis of the work space

The reachable workspace of a parallel manipulator is defined as all the positions within the region of the Cartesian space that can be reached by the tip of the probe which is attached to the central point of the platform. The workspace can be determined using the previously described model. The boundary surfaces of the workspace are calculated taking into account all the geometrical limitations and the movement range of the prismatic joints of the legs (maximum extension l max and the minimum extension l min ). Fig. 4 shows a 3D plot of the reachable work space. The processes of constructing the workspace can be summarized as follows:

• Set boundary conditions (y max = (R f -x) tan [START_REF] Jian | On the accuracy of a stewart platform-Part I: The effect of manufacturing tolerances[END_REF],

y min = -(R f -x) tan(30), x max = R f , x min = -R f /2)
• Set one leg l i = l min , increase the other two legs by l from l min to l max .

• Solve the position components (x,y,z) for the outer surfaces S out-li .

• Set one leg l i = l max , increase the other two legs by l from l min to l max .

• Solve the position components (x,y,z) for the inner surfaces S in-li .

• Repeat for each leg.

• Solve for the side surfaces S 1 , S 2 , S 3 .

• S 1 : y = (R f -x) tan [START_REF] Jian | On the accuracy of a stewart platform-Part I: The effect of manufacturing tolerances[END_REF], with x in the range ( [START_REF] Jian | On the accuracy of a stewart platform-Part I: The effect of manufacturing tolerances[END_REF], with x in the range (x min ≤ x ≤ x max )

x min ≤ x ≤ x max ) • S 2 : y = -(R f -x) tan
• S 3 : x = x max and y in the range (y min ≤ y ≤ y max )

Modelling of the error

Analytical error model

Suggesting an error model for the proposed machine is of great importance in order to evaluate the structure and understand the effect of the different parameters on its accuracy. The derivation of the covariance matrix is well known and can be found in in refs. [START_REF] Zhao | The Covariance Matrix of Derived Quantities and their Combination[END_REF], [START_REF] Clarke | Modelling uncertanity: A primer[END_REF]. For more clarification the details of the derivation will be given here:

If F(x) is a vector valued function, the form of the first order Taylor series expansion can be given by:

( ) ( ̅ ) ( ̅ ) ( ̅ ) ( ‖ ̅ ‖ ) (9)
Where: J(x) is the Jacobian matrix of F(x), and  is the error term, is the average of all x samples. Let's assume that the last term is small enough to be neglected. Then the previous equation can be rewritten as:

( ) ( ̅ ) ( ̅ ) ( ̅ ) (10)
The formula for computing the covariance is: In the previous matrix the variance along the diagonal is given for

( ( ) ( ̅ )) ( ( ) ( ̅ )) (11)
l 1 , l 2 , l 3 , R p1 , R p2 , R p3 , R f1 , R f2 , R f3 ,  p1,  p2,  p3,  f1,  f2,  f3 .
Precision error values are mostly considered as three times the standard deviation value ( = 3σ).

Thus, the variance can be estimated by: Where:  p 2 and  p are the variance and the error of the parameters, respectively.

Monte Carlo simulation

In Monte Carlo studies, random parameter values are generated from an allowed error range. A large number of runs are carried out, and the model is estimated for each run. Parameter values and standard errors are averaged over the samples. The Monte Carlo simulations have been used as uncertainty evaluation for CMM's in Refs. [START_REF] Dhanish | Effect of CMM point coordinate uncertainty on uncertainties in determination of circular features[END_REF][START_REF] Van Dorp | A virtual CMM using Monte Carlo Methods based on Frequency Content of the Error Signal[END_REF][START_REF] Brizard | Absolute falling-ball viscometer: evaluation of measurement uncertainty[END_REF][START_REF] Canning | Coordinate metrology uncertainty using parallel kinematic techniques[END_REF]. This method was documented in an ISO document [START_REF]Guide to the expression of uncertainty in measurement (GUM) -Supplement 1: Numerical methods for the propagation of distributions using a Monte Carlo Method[END_REF]. In this study simulations have been performed to examine the validity of analytical error model. Computer software developed in Python was used to overcome the amount of work needed for the simulation. Several combinations of conditions have been chosen (I) at maximum legs extensions, (II) minimum extension of the legs. The number of Monte Carlo iterations are n = 50000.

The following values of the parameters are used: R b1 , R b2 , R b3 = 288 ±0.002 mm; R p1 , R p2 , R p3 = 57 ±0.002 mm;

 p1 ,  b1 = 0  ±0.0005  ,  p2 ,  b2 = 120  ±0.0005  ,  p3 ,  b3 = 240  ±0.0005  ; l 1 , l 2 , l 3 = l ±0.001 mm.

Results of the error model

The geometrical parameters are given as follows: R f1 , R f2 , R f3 = 288 mm, R p1 , R p2 , R p3 = 57.5 mm,  p1 ,  f1 = 0  ,  p2 ,  f2 = 120  ,  p3 ,  f3 = 240  , l min = 288 mm, l max = 433 mm.

The covariance matrix of measurement errors is found by the proposed analytical approach in eq. ( 16). The variance-covariance matrix consists of the variances of x, y and z along the main diagonal and the covariances between each pair of variables in the other matrix positions. The covariance between xy, xz and yz is very small and can be neglected, hence x, y and z can be assumed uncorrelated.

The results of the Monte Carlo simulation are represented in the form of a typical histogram in Fig. 6. The histograms are useful as an aid to understanding the nature of the data distribution. From Fig. 6, the histogram plots for x, y and z indicate that the data are approximated well by a normal distribution, therefore it is reasonable to use the standard deviation as the distribution estimator.

Comparisons of simulated results with the analytical results in terms of standard deviations () are tabulated in Table 1.

Frequency Frequency Frequency

Effect of structural errors on probe position

The following values of the mechanical errors were used in the error model: R fi = ±0.002 mm, R pi = ±0.002 mm, l i = ±0.001 mm,  fi = ±0.0005  and  pi = ±0.0005  . Fig. 7 shows the  x ,  y , and  z estimated using the analytical model. The three continuous lines represent  x ,  y and  z with an error of l i = ±1m, the dashed line represents  z when the error in the legs is ±2 m. From Fig. 7,  z decreases dramatically as the moving platform translates away from the point of origin in the z direction, the  z starts to decrease very slowly when z value is greater than 350 mm, in other words, the probe is 350 mm below the upper frame. The work envelope in the z direction is in the range (350 -450). Moreover, Fig. 8 shows the xy plane at z = 400.

Concluding remarks

A micro-CMM offering very large workspace was proposed, the workspace was analysed, a kinematic model was derived, and the error was modelled. The work envelope with a submicron estimated error was (-50 ≤ x, y ≤ 50) in x and y directions, and (350 ≤ z ≤ 450) in z direction. It can be observed from the results that the analytical error model is effective with an input error of 0.5 to 2 m. This was verified using Monte Carlo simulation results. In this study the error values are chosen arbitrary, more accurate geometrical errors can be estimated using the finite element method. The l i has the most significant contribution to the error is z scale, while in x and y directions angle  i is responsible for the most significant effect on the accuracy. The positioning accuracy of the micro-CMM is also limited by many other error sources. However, geometrical errors can be minimized by improving the precision of manufacture and assembly.

Fig. 1 .

 1 Fig. 1. Micro-CMM design. Isometric (left) and top view (right).

Fig. 2 .

 2 Fig. 2. Schematic drawing of the micro-CMM machine.

Fig. 3 .

 3 Fig. 3. The coordinate system. The geometrical parameters are as follows: • R f : the distance between point f i and the origin O f , R f = 288 mm • R p : the distance between point f i and the origin O f , R p = 57.5 mm

Fig. 4 .Fig. 5 .

 45 Fig. 4. 3D plot of the reachable workspace. (a) (b) (c)

Fig. 6 .

 6 Fig. 6. Results of Monte Carlo simulation, (a) histogram of x, (b) histogram of y, (c) histogram of z.

Fig. 7 .

 7 Fig. 7. Error estimation along z axis.

Fig. 8 .

 8 Fig. 8. The xy plane at z = 400, with  x ,  y magnified 10 4 times. From Fig. 8, it is clear that the maximum error values in x and y directions are in the corners of the xy plane, however, the error decreases as the probe moves closer to the origin, and reaches minimum value at x=y=0. The work envelope in both directions x and y is in the range (-50 ≤ x, y ≤ 50).

Table 1 .

 1 Comparison of the simulation results with the analytical results: l i = ±0.001; R fi , R pi = ±0.002;  fi , pi = ±0.0005 o

		x= 0; y= 0; z= 350	x= 50; y= 50; z= 350
		Theory ()	Simulation ()	Theory ()	Simulation ()
	x(m)	0.811	0. 702	0.760	0.658
	y(m)	0.811	0. 702	0.918	0.796
	z(m)	0.361	0. 313	0.355	0.309

Replacing the dot product by the transpose of two matrices A B = A T B, we get:

Replace eq. [START_REF] Clavel | A fast robot with parallel geometry[END_REF] . in eq. ( 11) to get:

Remember, the dot product is commutative A B = B A, and (AB) T = A T B T , then can be rewritten as:

Since the covariance matrix of x is:

Then the covariance matrix can be written as:

The last equation is very useful to determine the covariance matrix using the input covariance and the Jacobian of the process function.

Assuming that only mechanical errors among these errors have an influence on the positioning accuracy. The geometry of each stage (or leg) can be expressed by five geometrical parameters, which are the leg lengths l i and the position of each spherical joint f i and p i in the Cartesian coordinate system. Therefore in total 15 parameters will be investigated. Thus, eq's ( 6), [START_REF] Li | Error compensation for a parallel robot using back propagation neural networks[END_REF], and ( 8) must be rewritten as:

A full size Jacobian matrix is used in carrying out error analysis, the Jacobian needed will be 3x15 matrix as follows:

The related variances matrix is given by the following 15x15 diagonal matrix:


Giuseppe Zamuner
email: giuseppe.zamuner@epfl.ch

Jacques Jacot
email: jacques.jacot@epfl.ch

A System for the Quality Inspection of Surfaces of Watch Parts

Keywords:

In luxury industry and, in particular in watch making, the quality of a surface is fully associated with its visual appearance and represents a corollary to the technical mastery necessary to manufacture valuable products. Traditionally, the inspection of these surfaces is carried out by human experts. Their judgment is influenced by several factors, which are not easy to control and which introduce variability in quality inspection. Nevertheless, experts have the capacity to handle different situations and they only give access to the specific knowledge related to the inspection of aesthetic surfaces. For these reasons, in the development of systems for automated visual inspection, experts are considered as the reference. The main goal the work presented in this paper is to provide automated tools for the quantitative estimation of the quality of aesthetic surfaces, in order to reduce the variability of the inspection. The different parts of an artificial vision system for surface quality control are investigated and a parallel is drawn with the inspection carried out by human experts. The adopted inspection process is based on three steps: the identification of defects, their quantification and the judgment about their acceptability.

Bevelling Cage

Introduction

This system is being developed for the inspection of the ballbearings manufactured by Micro Precision Systems AG for the watchmaking industry. Figure 1 shows a cutaway drawing of a ballbearing. To give an idea of the size of the ballbearing, the external diameter of the inspected ballbearing is about 6-8 mm. The two sides of the ballbearing need to be inspected and the visual inspection focuses in particular on the surfaces of the core, the cone and the ring (including the cogs). At first, we have decided to focus on the three models of ballbearings shown in figure 2, which, for their characteristics, are representative of the wide variety of ballbearing produced by the manufacturer.

The surfaces are decorated with different types of finishing, such as black polish, sunray brushing, snail finish or diamond finish. The system is required to inspect the surfaces of the ballbearing and to determine their quality in accordance with the evaluations provided by the experts. The proposed methods will be later extended to other models of ballbearing.

Collaboration with human experts

In the development of systems for the inspection of surface quality, human experts are considered as the reference. Their role is fundamental and they only give access to the specific knowledge related to the inspection of aesthetic surfaces. In order to favour the collaboration in the development process, the collaboration framework shown in figure 3 has been defined. It is assumed that the inspection process is based on three steps [START_REF] Guerra | Métrologie sensorielle dans le cadre du contrôle qualité visuel[END_REF]:

DEVELOPMENT OF THE VISION SYSTEM

CONTRIBUTION OF HUMAN EXPERTS

Development

1. Exploration, during which the expert observes the surface in order to detect the anomalies. In the development of the automated vision system, the focus is set on the choice of an appropriate acquisition setup and the development of algorithms for the detection of the anomalies.

2.

Evaluation, which is performed on a specific anomaly that has been detected during the exploration step and which consist in evaluating the seriousness of the anomaly. In the development of the automated system, classification algorithms are developed in order to emulate the evaluations provided by the experts.

3. Decision, which is based on the evaluations of the anomalies but also on other criteria that could depend, for example, on the surface region that has been affected by the anomaly or on the number of occurrences of a specific anomaly. As another example, the evaluations of multiple anomalies could be aggregated together in order to issue a joint decision. This step is related to the judgement issued on the quality of the overall surface or part.

The description of the anomalies and of the defects is supported by a database, which should cover the variability arising from all the different situations that can be encountered during inspection. The use of descriptors helps to structure the database in a coherent way. In the development of the automated system, in order to reproduce the evaluations issued by the experts, it is important to define the gravity of each anomaly and to describe it in terms of its main characteristics, such as size, brightness, regularity, etc.

As an example, figure 4 shows a form indicating the locations and the intensities of anomalies found on the surface of a ballbearing. The ballbearing is rejected as defective. The anomalies are evaluated with three intensities (1, 2 and 3), corresponding to levels of increasing seriousness of the anomaly (negligible, possible defect, serious defect). Figure 5 shows the block diagram of the system, which is essentially composed of two main parts:

Anomaly Intensity Anomaly Intensity Anomaly Intensity

A 3 I 2 Q 2 B 1 J 2 R 2 C 3 K 3 S 3 D 2 L 1 T 3 E 3 M 1 U 1 F 1 N 3 V 1 G 3 O 3 W 1 H 1 P 2

Overview of the system

• The acquisition setup, resulting from an appropriate choice of illumination, optics and camera. In treating this part, we will focus in particular on the use of illumination to highlight the defects. The choice of the appropriate optics and camera is determined by the application and its description is beyond the scope of this paper.

• The software, implementing the following main functional blocks:

A B V C D E F G W H I J K L M N O P Q R S T U V
o The Image Processing block, whose main function is to extract the appropriate features from the acquired images. The analysis of local aspect proceeds in two steps, the anomalies detection and the features extraction. In the first step, the global properties of the surface are estimated and the anomalies that do not comply with these global properties are detected and isolated from the rest of the surface. Then, in the second step, features are computed for the characterization of the detected anomalies.

o The Classification block, which processes the features obtained to characterise the global aspect and the local aspect.

o User Configurable Decision block, which implements an additional degree of flexibility, allowing the system to classify parts on the basis of criteria that cannot be translated in term of features used to describe the global or the local aspects. This is the case, for example, of the more tolerant decision criteria, which intervene when the anomalies appear on the surfaces of watch components that are hidden in the finally assembled movement.

o Controls block, which implements all operations needed for the control of the camera, the illumination and the automation.

o Human Machine Interface block, which allows the users to interact with the system, by providing them with a feed-back about classification and the criteria employed for the classification.

In the following sections, we will describe more in details the acquisition setup, the image processing and the classification blocks. The developed acquisition setup is shown in figure 6. In order to simplify the inspection process, the camera is positioned perpendicular to the plane of inspection and the setup allows to acquire images with the following types of illumination:

• Backlight.

• Ringlight.

• Directional illuminations at different elevation angles.

The images obtained with these illuminations are shown in figure 7. The image obtained with the backlight illumination allows to locate the ballbearing on the plane of inspection and to extract the external outline of the ballbearing. The position and the contour of the ballbearing are automatically combined with some information about the geometry of the ballbearing. This information about the geometry, which is used to define the surfaces that need to be inspected, is manually selected for each model of ballbearing, by using a graphical interface. The advantage of using ringlight is that the surface is illuminated in a uniform way from all azimuthal directions. For example, the low angle ringlight is adapted to highlight surface textures for the characterisation of the global aspect. In the application presented in this paper, the ringlight is used to highlight the defects, which mainly consist of marks of blow appearing on the outer edges of the cogs. The vertical position of the ringlight is a critical parameter that needs to be appropriately chosen in order to make the defects appear on the images. The directional illuminations are used to highlight the local defects that appear on these metallic surfaces. The anomalies should be visible on the acquired images, in order to be detected and segmented. Furthermore, the pixel intensity of the anomalies should be representative of their brightness as it is perceived by the experts. Because of the way the light is reflected from metallic surfaces [START_REF] Nayar | Surface Reflection: Physical and Geometrical Perspectives[END_REF], the illumination and viewing angles determine the appearance of the anomalies in the acquired images. The elevation of the directional illumination influences the contrast between the anomalies and their surrounding regions and the elevation giving the best contrast depends on the surface finishing. This can be easily explained observing that the surface roughness influences the shape of the specular lobe of the scattered light. For block polish, sunray brushing and snail finish, the elevation of 70° yields good contrast in the acquired images, while for diamond finish the elevation of 50° has been chosen. Figure 8 shows the influence of directional illumination on the contrast of an anomaly in the acquired image. Also the azimuthal angle determines how an anomaly is viewed from the camera. For example, a scratch appears with higher contrast when its direction is perpendicular to the illumination direction. Consequently, in order to make all the anomalies of the surface visible, it is necessary to acquire images with several directional sources covering 360°. As it is shown in figure 6, the directional illuminations consist of two rings of 30 LEDs, at 50° and 70°. The number of LEDs depends on the minimum angular spacing (in this case, 12°) necessary to highlight appropriately all possible anomalies present on the surface. In order to reduce the number of acquired images, multiple adjacent LEDs are turned on simultaneously. The limitation is that, increasing the number of LEDs, also the diffuse illumination is increased and, consequently, the contrast in the acquired images is reduced. For the prototype in figure 6, the choice that represents a good compromise consists in turning on 3 LEDs simultaneously at each image acquisition, which results in a total number of 10 images acquired for each ring of LEDs. The realization of the directional illumination can be improved by using more directional light sources in order to limit the diffuse light that reduces the contrast. This would allow to reduce the number of images acquired per each ring of LEDs.

The image processing and classification blocks

As we have already explained the first step of the processing of the acquired images consist in the detection of the anomalies that appear on the surfaces. Since the anomalies are considered as undesired variations of specific surface properties, a representative model for each inspected surface should be determined. We used two types of models, depending on the texture of the surface to be inspected:

• Autoregressive moving average models [START_REF] Zielinski | Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification[END_REF].

• Statistical models [START_REF] Unser | Local linear transforms for texture measurements[END_REF].

These models serve as a reference, in order to isolate the regions of the surface that do not share the same properties with the rest of the surface. Figure 9 shows an example of detection. As it is shown in figure 10, the blobs detected on separate images, which belong to the same anomaly (first two images, on the left and at the center), are processed jointly in order to extract the appropriate features. The blobs from the two images are joined together by calculating the maximum between the pixels of the blob in one image and the pixels at the same locations in the other image. The result is shown in the image on the right of figure 10. The feature extraction process takes place on the anomaly resulting from this combination.

Figure 10. The blobs of the same anomaly, which are detected on images acquired with different directional illumination, are combined together and processed jointly in order to extract features. The blobs from the two images are joined together by calculating the maximum between the pixels of the blob in one image and the pixels at the same locations in the other image. The result is shown in the image on the right.

The following features are calculated:

• The area of the resulting anomaly.

• The sum of the areas of the blobs detected in the different images.

• The length of the resulting blob.

• The mean of the pixels of the resulting blob • The empirical standard deviation of the pixels of the resulting blob. The resulting linear discriminant analysis [START_REF] Johnson | Applied Multivariate Statistical Analysis[END_REF] is shown in figure 11. Table 1 shows the correlation between the features and the first discriminant function, which is the direction along which the distance between the classes, expressed in terms of discriminant ratio, is maximised. The mean pixel intensity of the resulting anomaly is well

Negligible Anomaly Possible Anomaly Serious Anomaly

Discriminant Function 1 Discriminant Function 2 correlated with this axis, as well the mean pixel intensity of all blobs. The correlation of the length of the resulting anomaly and the discriminant function is not significant. It can be seen that this simple list of features allows a rough separation of the classes provided by the experts. The clusters corresponding to the negligible anomalies and the serious defects are clearly separated. The cluster of possible defects is not clearly separated from the other two clusters and this is surely related to the variability of the expert. At this stage of the development, this restrained number of features is sufficient to continue the development further, in order to compare more extensively the obtained results with the classification provided by the experts.

Conclusion

This paper presents an artificial vision system for the inspection of surfaces of watch parts. The proposed approach is based on a tight collaboration with the human experts that the system is designed to emulate. The presented application shows the various improvements that the proposed method can bring to surface inspection:

• The adoption of an approach, based on the separation of the inspection process in distinct phases, which allows a tight collaboration with human experts providing the initial classification.

• The use of directional illumination in order to enhance the local contrast and the acquisition of multiple images with different illumination directions, in order to provide an appropriate description of the aspect of a surface.

• The characterization of anomalies based on the features extracted from the blobs detected in the multiple images, which have been acquired with the different directional illuminations. In particular, we have shown that the processing of features obtained from multiple images brings about an improved classification.

Figure 1 .

 1 Figure 1. Cutaway drawing of a ballbearing.

Figure 2 .

 2 Figure 2. Ballbearings manufactured by Micro Precision Systems AG. The surfaces of the ballbearings present different surface finishing.

Figure 3 .

 3 Figure 3. Development process undertaken in collaboration with human experts.

Figure 4 .

 4 Figure 4. Example of form collecting the labelled anomalies found on a ballbearing. The ballbearing is rejected as defective. The anomalies are evaluated with three intensities (1, 2 and 3), corresponding to levels of increasing seriousness of the anomaly (negligible, possible defect, serious defect).

Figure 5 .

 5 Figure 5. Block diagram of the system.

1. 1 Figure 6 .

 16 Figure 6. Acquisition setup for the inspection of the ballbearings. The axis of the camera is perpendicular to the plane of inspection. The setup allows the use of different illuminations: backlight, ringlight and directional illuminations at elevations of 50° and 70°.

Figure 7 .

 7 Figure 7. Images of the ballbearing obtained using different illuminations: backlight (on the left), ringlight (at the center) and directional illumination (on the right).

Figure 8 .

 8 Figure 8. Polished surfaces of a ballbearing, illuminated by a light source at different elevations: 40° (image 1), 50° (image 2), 60° (image 3) and 70° (image 4).

Figure 9 .

 9 Figure 9. Example of anomalies detected on a surface. The first image, on the left, shows the ballbearing being inspected and the second image, at the center, shows the detail of the surface at the center of the ballbearing. The third image, on the right, shows the result of the segmentation: the detected anomalies are highlighted by the uniform grey pattern, while the surface without anomalies is identified by the chess pattern.

Figure 11 .

 11 Figure 11. Linear discriminant analysis applied to the features extracted from the anomalies detected on the surface of the ballbearing.

Table 1 .

 1 Correlation between the features and the the 1 st discriminant function.

	Feature	Correlation
	Area of the resulting anomaly	0.382
	Sum of the area of all detected blobs	0.575
	Length of the resulting anomaly	0.064
	Pixels mean of the resulting anomaly	0.853
	Pixel mean of all detected blobs	0.792
	Standard deviation of the resulting anomaly	0.897


Rik Lafeber

Gerrit Van Den Bosch

Max Murre

Jitze Bassa

Leo Van Moergestel

Erik Puik
email: erik.puik@hu.nl

Characterisation of High Accuracy, Feedback Controlled, Adhesive Bonding

Keywords: adhesive bonding, force control, bond stress, position accuracy, process parameters, gap size, bond volume, curing time

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

Introduction

Over the last decades, there is an increasing trend in using adhesive bonding for mechanical precision applications. An often-used bonding method for micro applications is to design form fit adhesive bonds [START_REF] Pocius | Adhesion and adhesives technology: an introduction[END_REF]. Advantages of a form fitted adhesive bond are the ease of application and the high strength due to a large contact area [START_REF] Diaz | Stress analysis in a classical double lap, adhesively bonded joint with a layerwise model[END_REF]. Disadvantages are the high stresses in the bond and the unpredictable displacements during the bonding process [START_REF] Vrenken | De invloed van de E-modulus op de sterkte van gelijmde overlapverbindingen[END_REF].

It is generally known that most adhesives decrease in volume during the process of curing [START_REF] Pocius | Adhesion and adhesives technology: an introduction[END_REF] [START_REF] Pocius | The mechanics of adhesion[END_REF]. Holding the components very firmly during the curing process, to maintain the position accuracy, will typically result in highly stressed bonds, caused by adhesive shrinkage (Fig. 1) [START_REF] Neves | Influence of Joint component mechanical properties and adhesive layer thickness on stress distribution in micro-tensile bond strength specimens[END_REF] [START_REF] Vrenken | De invloed van de E-modulus op de sterkte van gelijmde overlapverbindingen-Het berekenen van de sterkte van een lijmverbinding is nog altijd niet goed[END_REF]. Releasing the components during the curing process will however effect unwanted displacement or position inaccuracy, mostly caused by shrinkage of the adhesive (Fig. 2) [START_REF] Van Straalen | Development of Design Rules for Structural Adhesive Bonded Joints-A Systematic Approach[END_REF] [START_REF] Koster | [END_REF]. In this paper, different bonding parameters, which have influence on this position accuracy and stress of the adhesive bond, have been studied. Depending on the geometry, there is no guarantee that the bonds will be more reliable than in the initial situation

The aim of the project is to find an optimised procedure for the application of adhesive bonds, in which:

• The position of the components remains within accuracy specification or better, if possible; • The stress in the joint is minimal;

• An optimal ratio between position accuracy and stresses in the bond is achieved.

The bonding procedure as tested in this project is based on the use of a feedbackcontrolled manipulator, combined with a force sensor. This enables optimal awareness of position, orientation and the applied forces during the curing process of adhesive bonding (Fig. 3). This objective can be applied up to six degrees of freedom, but was initially implemented with just one degree of freedom. A number of different parameters are found to have effect on shrinkage behaviour. These parameters therefore are suspected to worsen position tolerances of the components as well as stress-build-up in the bond. These parameters can be discriminated in process and environmental parameters. In this research, the following most important process parameters were varied, while the other parameters were kept constant:

• Size of the gap between the adherents; • Curing time; • Adhesive volume.

Tests were performed on two extreme bonding situations. In these tests only a single bond was evaluated, being able to control the geometry and the tension of the bond, not suffering from dependencies of neighbouring bonds that might have conflicting behaviour. In the first 'Fixed-Position' test, the adherents were held firmly. This resulted in the highest stress value as marked by the triangular point in Fig. 4. In the second 'Zero-Force' test, the adherents are released, which shows the highest displacement value. This is marked by the round dot in Fig. 4. The stress in the bond is equal to the measured force divided by the effective cross section area of the adhesive bond. The shrinkage is measured by absolute distance, which is proportional to the span of the adhesive bond. The adhesive used for the tests is Dymax Multicure 9-20801, which in these tests was cured by UV light with a wavelength of 365 nm. Applicators or heat curing, which are used in the industry to increase the strength of the bond, are not applied. The reason for this is to prevent introduction of extra parameters in the test setup. This insures a higher reproducibility of the adhesive bond samples.

Test Setup

Test Equipment

The first experiment is to measure and control the force and displacement in both extreme points of the stress-shrinkage curve as shown in Fig. 4. The tests were performed on an industrial 3-axis gantry manipulator that was equipped with tools to hold the bonded parts in place and to continuously measure their positions. The axis' of the manipulator had stroke lengths of 540, 380 and 240 mm in respectively x, y & z direction and were equipped with 1µm resolution encoders. This ensured a position accuracy of <10 µm. The positioning action was applied at the most optimal position of the machine, in the centre and as low as possible on the machine bed, as shown in Fig. 5. This minimised orientation errors due to deformation of the robotic components. A 2-axis force sensor was placed between the bridge of the manipulator and a vacuum gripper as shown in Fig. 6. The sensor is able to measure forces in x and y dimensions. The range of the force sensor was 0-100 N in both positive and negative direction with a non-linearity of 0,15 %.

The high-viscous adhesive was dispensed by a pressure-time dispensing system. The needle tip is pneumatically positioned above the gap, to ensure a repeatable dispensing process.

A camera system equipped with a photographic objective was mounted on a fixed stand and was able to monitor the bonding process accurately. The camera system was used to measure absolute displacement values. Combination of camera and lens provided a resolution of 2 pixels per micron, being able to perform measurements in the micron range.

Calibration

Before start of the tests, the system including camera and force sensor were calibrated. The force sensor was geometrically aligned perpendicular to the gantry system. The output signal of the force sensor was calibrated by use of reference masses.

The stiffness of the robotic manipulator, especially of the gantry construction and the actuators was measured. These parameters were to be used if needed to compensate the displacement due to the deformation of the robotic system when mechanically loaded. This occurs when the forces of the positioning process are transferred through the construction of the manipulator.

Test Samples

Standard test samples are used in all tests. The inner part, attached to the x-and zaxis' was fixed to the vacuum gripper. The outer part is clamped to the base plate, which is mounted to the y-axis. The adhesive was dispensed in the middle of the gap between the inner and outer part. A Teflon sheet was applied to prevent sticking of the adhesive to the base plate (Fig. 6). The height of the inner part was chosen to hover above the Teflon sheet.

Fixed-Position Test

In the Fixed-Position test, the adherents, in this case the inner and outer part of the test sample, are kept in the same place during the entire curing process of the adhesive. To maintain the Fixed-Position, the manipulator was held at a certain encoder value. The correction, needed to compensate for the mechanical deformation of the gantry appeared relatively small. This eliminated the need for the feed forward correction of the gantry stability. The force was measured during the entire curing process in both x-and y-direction.

Zero-Force Test

In the Zero-Force tests, the manipulator was actuated to suppress the accumulation of stress in the bond. The system was programmed to use feedback control based on input of the force sensor. In this way, the stress could be kept close to zero. The displacement of the adherents was measured by the movement of the encoder position and correlated to absolute distance by graphic distance measurement of the camera.

Test Results

The two extreme points of the stress-shrinkage curve, as explained in Fig. 4, are found by performing the Zero-Force and Fixed-Position test repeatedly for the different bonding parameters, gap size, adhesive volume and curing time. A close-up of one of the adhesive joints from test results is shown in Fig. 7. The graph shows that in this situation the accumulation of force takes place mainly within the first 30 seconds. Other adhesives will show a different time constant. The result of a typical Zero-Force test is plotted in Fig. 9. Again, development of force is plotted against time. However, in this case the force is kept under a certain value by controlling the robotic manipulator with a feed back control loop. This enables the manipulator to move along with the moving parts. In the graph of Fig. 9, F y represents the remaining force on the part and as such also on the adhesive bond. As can be seen, though considerable lower than the Fixed-Position test, the force in the Zero-Force test is not completely eliminated. This is a result of the detection limit of the control hardware of the force sensor and discretization pollution of the digital control system.

Value y is the displacement measured by the motor encoder and since the forces in the system during these tests are low, can be considered as a realistic value for the actual displacement of the part. The maximum displacement was verified in to ways; using the control loop and by analysing the result of the camera. These measurements repeatedly showed results within small margins.

The 'step' curve is the cumulative value of all movement commands as registered by the Labview program. It represents the actual adhesive shrinkage of the bond.

From the measurements at this point cannot be determined if the curve shows a linear, exponential or natural behaviour. Fact is that after an average time of 20 seconds, little movement was detected.

Both, Fixed-Position and Zero-Force tests are performed while varying the test parameters. The gap size was varied from 0,5 mm to 1,5 mm in three steps. The maximum force as determined by the Fixed-Position test and the maximum displacement determined by the Zero-Force test, are visualized in Fig. 10. The two points for each parameter-value are connected by a linear relation and are just for indication; they do not represent the real curve. The result of these 'Gap-Size' tests showed that both force and total adhesive shrinkage increase when the gap size increases.

Fig. 11 shows the graph 'Force Versus Shrinkage' for three adhesive volumes, changing from 5 mm 3 to 20 mm 3 . For this graph the same notice applies as to the previous; the lines only connect the end points but do not necessarily represent the points in between.

The effects of varying the adhesive volume on the maximum force and the shrinkage are minimal to none. Taken the deviation into account, adhesive volume has no significant influence on the maximum force and shrinkage.

Conclusions

The study of the parameter gap size shows a proportional relation between the gap sizes of the adherents with the force as well as the adhesive shrinkage. The conclusion can be drawn that the gap size has a high impact on the stress in the bond and the position accuracy of the components. A theoretical explanation for this behaviour could be a proportional shrinkage of the adhesive. A larger gap size will therefore result in a larger span of the adhesive material and thus more absolute shrinkage. However the fact that this leads to a higher force on the bond is not self-evident. The E-modulus of the adhesive is considerably lower than the metal of the parts. Based on this, some degree of suppression of the build-up of tension would be expected. Apparently, the elongation strength of the adhesive is dominant causing the force to increase.

The adhesive volume shows to be of less importance with respect to the force and adhesive shrinkage. Taking the variation into account, the results show no relation between adhesive volume and the measured force. This cannot be fully explained based on the results of these tests. Primarily, the fact that the adhesive shrinkage is comparable can be explained, a bulky adhesive dot could be considered as a number of small dots in parallel. All dots would have the same adhesive shrinkage. However, this would not explain that the total force doesn't increase. The force could expect to be proportional to the cross section area of the bond. A possible cause could be the fact that a larger does not tend to cure homogeneously, see also 'discussion'.

Curing times do not seen to have an effect on the force development and shrinkage during the curing process. With these tests, the intensity of the UV light source were not varied. Increased UV output might show effects on the process.

Destructive tests also show a positive effect on the end strength of the bond at longer curing times. For high filled industrial adhesives, like Dymax Multicure 9-20801, it takes minutes to hours to cure the bond fully. To get the best results it is advised to use a post-cure process.

Discussion

Destructive tests with short curing times have demonstrated that the surface of the adhesive joint cures first and forms a hard and rigid shell. The adhesive material underneath the shell appears to cure more slowly. This can be explained by the decreased intensity of the UV light caused by the 'barrier' formed by the shell itself. 'Deeper' in the adhesive dot, the curing process takes a slower pace leading to an inhomogeneous curing process. When the shell is solidified, the core is still fluid and adapts is shape to the (already deformed shell). Some moments later the core starts to solidify, causing even more shrinkage on the inside of the bond. This process is very dependent on the physics of the illumination. In this project, this was not investigated.

Secondly, performing the Zero-Force tests led to the finding that implied movements of the adherents during the curing process, in some circumstances had negative influence on the bond strength. The effects appeared after complete curing of the adhesive bonds. It is important to understand the effects of movement during the curing process; it could subvert the results of the Zero-Force test.

Further studies must be performed in order to find an optimal ratio between position accuracy and stresses in the bond. Therefore, it is necessary to investigate the entire relation of stress-build-up and adhesive shrinkage as shown in Fig. 4. The data points in between can be found by defining a new test that combines elements from the Fixed-Position and the Zero-Force test. The most optimal ratio between accuracy and stress can be found once the whole stress-shrinkage curve is visualized. Further, this information will lead to a bonding strategy for industrial applications to offer maximum-performance-bonds within the boundaries of accuracy and stress.

For further investigations it is recommended to implement a position sensor for direct feedback to the controller instead of getting position information via motor encoder and camera. This sensor will provide position information with a higher resolution and accuracy so the robotic movements will allow smoother position control, reducing side effects of the manipulative actions.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Fixation of the components during the curing process of the adhesive causes high stress inside the adhesive bonds. Due to the high stress the bonds have a decreased life expectancy

Fig. 3 .

 3 Fig. 3. A robotic manipulator actively controls the position of the part during the curing process. The position can be optimised so 1/most important tolerances are kept but 2/ bond stresses are minimised

Fig. 4 .

 4 Fig. 4. Correlation between stress and shrinkage when applying adhesive bonds. By changing the way the parts are manipulated, any specific situation on the line can be selected. This enables influence on the ratio between position of the adherent and stress in the bond

Fig. 5 .

 5 Fig. 5. The test setup as used for these tests consist of a gantry based robotic manipulator. One part is clamped mechanically, the other part is held using a vacuum gripper

Fig. 6 .

 6 Fig. 6. Test sample clamped to the base plate

Fig. 7 .

 7 Fig. 7. Adhesive bond in one of the test samples. For this test the bond span is about one millimetre Fig. 8 shows the result of the Fixed-Position graphically. The accumulation of force in the bond is plotted against time. The maximum force arises at the end of the test. In this case the figure is plotted with 20 data points. At the end of the graph the bond is fully cured. This was verified by destructive tests on the first samples.The graph shows that in this situation the accumulation of force takes place mainly within the first 30 seconds. Other adhesives will show a different time constant.

Fig. 8 .

 8 Fig. 8. Typical example of the results of the Fixed-Position test. Force accumulation develops in a natural way. In the right-angled direction the force remains low

Fig. 9 .

 9 Fig. 9. Typical example result of the Zero-Force test. In this situation, all major adhesive shrinkage has taken place in the first 20 seconds. After this the control loop idles around a fixed value

Fig. 10 .

 10 Fig. 10. Test result different gap sizes. In all cases the effect, force or shrinkage, increases when a larger span has to be bridged with the adhesive bond. Larger gaps lead to larger Forces and more adhesive shrinkage

Fig. 11 .

 11 Fig. 11. Test results different adhesive volumesInvestigation of the effects of the curing time was done by performing the Fixed-Position (maximum force) test in combination with a destructive test. Fig.12shows the development of force in time, of five measurements. Variation is limited between tests and the maximum force is reached around 250 seconds.

Fig. 12 .

 12 Fig. 12. Test results for curing time. A number of five measurements show good reproducibility. Around 250 seconds the adhesives may be considered fully cured

Acknowledgments. The authors would like to thank the Knowledge Centre at the HU University Utrecht, the companies Adimec, Tegema and MA3Solutions for their support in knowledge and equipment. Much gratitude is afforded for the MicroNed program for funding this research (www.microned.nl).


Konstantin Konrad

Michael Hoffmeister

Matthias Zapp

Alexander Verl

Johannes Busse
email: jbusse@jbusse.de

Enabling fast ramp-up of assembly lines through context-mapping of implicit operator knowledge and machine-derived data

Keywords: Semantics, Ontology, Assembly Line, Knowledge Management, Machine-Derived Data, Operators

Ramp-up of precision assembly lines is a cost-intensive and experiencedriven task. Most of the time the knowledge how to effectively and efficiently setup an assembly line is intrinsic and is therefore neither shared nor reused by production experts. Almost no machine data is recorded until the correct functionality of the line is achieved and human problem solving tasks are not or poorly documented. In this paper a novel approach for structuring operator knowledge and combining it with machine-derived data by the use of semantic technologies is proposed. This enables human operators to express their experience in an easy to understand, machine readable way and makes it therefore accessible to other workers.

Introduction

In the literature different variants for ramp-up are considered, either the start of new products, the introduction of a new or the reorganization of an existing production system as well as all feasible combinations. One possible approach is defining the ramp-up time of products as the "time required from finishing prototyping to full volume production" [START_REF] Sturm | Simulation-based evaluation of the ramp-up behavior of waferfabs[END_REF]. However in this paper we are focusing on the ramp-up of new production processes and the resulting problem areas. Therefore ramp-up is defined as the phase between production development and the full capacity of production with a sufficient level of quality. Depending on the industrial application area ramp-up can take from a couple hours up to several years.

Approximately 82 % of the companies do not reach their targeted goals regarding time, cost and quality [START_REF] Dombrowski | OEE-Kennzahl im Anlagenhochlauf bei KMU: Steigerung der Gesamtanlageneffektivität im Anlagenhochlauf bei KMU mittels der OEE-Kennzahl[END_REF]. This is very critical as volatile market requirements need a rapidly shortened time-tomarket [START_REF] Dombrowski | Lean Ramp-up. Ein Organisationsmodell für den effizienten Serienanlauf in KMU: Lean Ramp-up -An organisation model for efficient series ramp-ups in SME[END_REF]. Main reasons for missing the target goals are continuous changes at product and/or process level. Despite that a lot of companies do not make use of software solutions during their ramp-up procedure besides standard MES or ERP functionalities [START_REF] Dombrowski | OEE-Kennzahl im Anlagenhochlauf bei KMU: Steigerung der Gesamtanlageneffektivität im Anlagenhochlauf bei KMU mittels der OEE-Kennzahl[END_REF]. Successful companies have an overview about present key performance indicators (i.e. Overall Equipment Effectiveness) during the ramp-up or have proper simulation tools installed [START_REF] Fleischer | Ressourcenmanagement für erfolgreiche Produktionsanläufe[END_REF]. As matters of fact, small and medium enterprises (SMEs) do use these tools to an even lower extend due to cost factors and limited availability of human resources. For these SMEs a lack of coordination and communication is a major obstacle for a successful and fast ramp-up of assembly lines [START_REF] Dombrowski | OEE-Kennzahl im Anlagenhochlauf bei KMU: Steigerung der Gesamtanlageneffektivität im Anlagenhochlauf bei KMU mittels der OEE-Kennzahl[END_REF]. However these characteristics are especially important when SMEs concern about the high requirements for precision products.

Key for a successful ramp-up are therefore highly skilled and motivated human workers and the knowledge transfer from experience gained in former projects. Due to time pressure and upcoming deadlines the gathered experience is hardly ever documented in a proper way for reuse. At present, known knowledge management systems are hardly implemented and used during the ramp-up procedure [START_REF] Wiesinger | Schneller Produktionsanlauf von Serienprodukten: Wettbewerbsvorteile durch ein anforderungsgerechtes Anlaufmanagement[END_REF]. Also Berger at al. [START_REF] Berger | Wissensmanagement in der Planungsphase: Research concerning knowledge management for development of complex sub-assemblies in the automotive industry[END_REF] sees the consequent usage of experience gained about comparable assembly machines or lines as key for success. Many important observations and conclusions of production staff are simply lost and not re-implemented as improvements in consecutive projects. Fleischer et al. [START_REF] Fleischer | Proaktive Anlaufsteuerung von Produktionssystemen entlang der Wertschöpfungskette. Proactive controlling of production ramp-ups along the value chain[END_REF] sees the strong dependencies on intrinsic operator experience for troubleshooting as one of the main deficits in production ramp-up.

Success Factors for Ramp-up

All this leads to the conclusion that for a successful ramp-up of assembly lines it is essential to gather the experience from production workers in an effective and efficient way and make it available also to others for re-usage and further refinement. The presented approach will target some major obstacles, which are relevant for many companies in order to perform a successful knowledge management during the ramp-up procedure:

 Easy gathering of operator knowledge. An optimal solution for the ramp-up phase would be to gather knowledge in a causal "if-then" relationship which would enable the further usage and automatic processing of the operator experience [START_REF] Berger | Wissensmanagement in der Planungsphase: Research concerning knowledge management for development of complex sub-assemblies in the automotive industry[END_REF]. However, the presented approach will go beyond that and allow multiple, easy to use, understandable, machine-readable crossrelations between different entities. This facilitates the adequate capturing, transfer and usage of human intrinsic knowledge e.g. about tolerances, assembly issues and material dependencies.

 Flexible context-mapping of static and dynamic data: At present most knowledge capturing systems only provide static capabilities to model the knowledge (i.e. Wiki-systems are widely used in companies). Besides that machine data is mostly stored in predefined databases or data-warehouses. Therefore it is not capable of adequately modeling all possible relations and the system administration is limited to IT experts which can hardly react in a timely manner to the arising problems during ramp-up. We will present a novel approach to combine large quantities of sensor data, external software systems like Statistical Process Control (SPC) software and operator knowledge in a human-centered and flexible way. This is of special importance for precision assembly as due to the high product requirements plenty of sensor data has to be integrated.

 Extensibility & Reusability: Due to the nature of the ramp-up process not all data providers are present from the beginning as e.g. sensors are connected and adjusted in a later stage. Also software systems like SPC or Condition Monitoring are typically installed at a later stage. Also consecutive assembly lines might be equipped with additional or other hardware entities. The already modeled experience should be used in these projects and be further refined. This is of high importance as saving potential through Knowledge Management Systems is particularly high when ramping a similar equipment or line and experiencing alike problems. Due to these reasons the suggested solution should be able to integrate different data sources and apply modifications in an iterative manner. Also the modeled knowledge should be (partially) exportable for re-usage in other projects or for other operators.

Approach & Methology

Semantic Technologies in Manufacturing

In order to cope with the multitude of different data sources in modern production and the constantly changing environment of today's production lines, the application of "Semantic technologies" is intensively discussed in literature. These technologies try to provide an always-upto-date and highly structured access to a certain field of knowledge. According to Breslin et al. [START_REF] Breslin | Semantic Web computing in industry[END_REF], such systems can be well-suited for industrial applications: blogs to effectively share information within organizations, collaborative document editing, support for project management and execution of software development, micro-blogging for fast information transport, semantic wiki systems to pursue knowledge management and semantic web and Web 2.0 approaches to effectively retrieve information from the internet. In addition to this, semantic web services are already addressing the complexity of data integration within industry [START_REF] Breslin | Semantic Web computing in industry[END_REF]. The World Wide Web Consortium (W3C) provides an interesting collection of industrial use-cases within the industry [START_REF]Semantic Web Case Studies and Use Cases[END_REF].

The following sections will provide an overview on the underlying concepts of these technologies, the existing standards and the deployment of these capabilities to industrial environments.

Ontologies

Semantic technologies are building upon "ontologies". According to Gruber [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF], a consensus definition is that an "ontology is a formal, explicit specification of a shared conceptualization". The conceptualization is applied towards a limited "domain", which for instance can be the ramp-up procedures of production lines. The conceptualization tries to break down the different terms and entities of this domain into well-defined and distinctive concepts. The concepts are expressed in a formal way in order to allow technical installations and software to use them. Further on, the concepts shall be explicit in order to avoid inconsistencies and differences in meaning; this can be achieved by using non-ambiguous classifications, relations or metrics. Lastly, the conceptualization shall be shared and agreed among different user groups in order to provide a means of communication and a frame of reference.

Applied to the field of ramping up micro-assembly lines, the use of ontologies opens possibilities to reach a joint understanding about important structural elements of a production line, by which procedures they are affected and how they correlate with different processes and measurement data. This will deepen the understanding about structure and data for all user roles concerned with the ramp-up, as e.g. the engineers designing the line, the software engineers creating the PLC programs as well as the engineers performing the ramp-up itself. All different user roles can use the same source of definition for performing their individual work tasks; boundaries are clearer defined.

Looking at the ramp-up process as a whole, the use of ontologies can make the overall process more explicit and make it decomposable into several well-defined actions, which can be documented and compared with similar ramp-up situations. This breaks up the implicit understanding of a ramp-up process and exposes it to systematic knowledge management, which can be used to gather human knowledge and procedures, to interlink it with further information and to make it re-usable for repeating situations.

Therefore, ideally, the use of ontologies can better organize the effective work of human engineers and, at the same time, they can be used to store and provide important information, process and measurement data.

Semantic Middleware

The creation and maintenance of ontologies can be performed in regular text editor by using an ontology language like Web Ontology Language (OWL). Alternatively, a number of dedicated ontology editors with graphical user interfaces such as Protégé [START_REF] Gennari | The evolution of Protégé: an environment for knowledge-based systems development[END_REF] and OntoStudio [START_REF] Ontoprise | ontoprise: OntoStudio[END_REF] are available. Furthermore, as applied for this work, ontologies can also be defined and maintained via semantic annotated mindmaps, which are automatically transferred into an ontology language [START_REF]Ontoprise, ontoprise: Freemind to F-logic (mm2flo)[END_REF][START_REF] Busse | Semantic Authoring with semAuth[END_REF].

In order to utilize the captured explicit knowledge in ontologies inside the production environment, these ontologies need to be incorporated in software systems. This can be realized in two different ways. One the one hand these functionalities can be directly incorporated into each individual software, e.g. by including libraries such as Jena [START_REF] Mcbride | Jena: a semantic Web toolkit[END_REF], Jess [START_REF]the Rule Engine for the Java Platform[END_REF] and Ontobroker [START_REF] Schmidt | Ontobroker Version 5.1 -Product Documentation[END_REF]. One the other hand software applications can be connected against a central IT service, which provides the reasoning functionalities based on ontologies; such IT services are typically denoted as middleware. The OntoBroker middleware [START_REF] Decker | Ontobroker: Ontology Based Access to Distributed and Semi-Structured Information[END_REF] which available as commercially supported product can be used as an embedded library or operated as a server application; therefore in the later configuration it has been used for this project.

Standards for Semantic Technologies

As production systems typically have a long life-time, the human knowledge and the acquired machine data from the ramp-up phase need to be persisted for an extended period of time. This is especially true, if ramp-up knowledge shall be re-used for subsequent versions of the production system. The reuse of knowledge also calls the need of merging knowledge and data from different sources, as new production systems might incorporate features from more than one prior system. This leads to high requirements for the maintainability, extensibility and scalability of semantic technologies and the need of using a set of well-founded standards, which guarantee accessibility of gained knowledge for a long time.

In the course of the foundation of the Semantic web [START_REF] Berners-Lee | The Semantic Web[END_REF], this need was early recognized by the W3C consortium and a set of standards building on-top of each other, were specified. Semantic technologies, such as semantic middleware and wiki systems, are adhering to these standards. By complying with these, investments in central IT systems, software and personal training can be protected.

Figure 1 Semantic web stack [20]

The relevant standards are organized in the Semantic web stack, to which [START_REF] Breslin | Semantic Web computing in industry[END_REF] gives an overview. For integrating data and merging knowledge, the use of proper URIs (Uniform resource identifiers) within the ontologies and semantic facts is important. Semantic facts can be exchanged with further systems via the RDF and RDFS standard (Resource Description Framework and schemata). For exchanging, merging and integrating ontologies, OWL is a commonly used standard, which offers extended semantic capabilities. The applied semantic middleware for the presented approach (OntoBroker) is able to support these standards both for import and export. In addition, Ontobroker features its own logic language (ObjectLogic), which is proposed to the W3C as upcoming standard. SPARQL (SPARQL protocol and RDF query language) and RIF (Rule interchange format) were not used in this work, but are accepted by the OntoBroker middleware, as well.

Application for Precision Assembly Lines -Implementation and Verification

Consistent with Lohse [START_REF] Lohse | Equipment ontology for modular reconfigurable assembly systems: Methodisches Konzept für die Umgestaltung modularer Fertigungseinrichtungen von Montagesystemen[END_REF] the model of the assembly system is divided into domain concepts suitable for representing the ramp-up of precision assembly systems. The structured model comprises all the information that are not continuously changed during production, representing e.g. the machine model with its sensors, the product definition and corresponding work plan and finally the processes with its capabilities and specifications. However besides modeling the pure static assembly system another layer representing the dynamic changes will be introduced. A mapping functionality correlates the dynamic information with the corresponding structural entities. Besides that also various relationships between structural and/or dynamic entities can be modeled and automatically processed. This is of special importance as in the outlined work this technique was applied for gathering the human experience during ramp-up of precision assembly systems. This all together allows for novel ways of combining information and providing fractions of helpful data to the operators and engineers.

Static Structural Model

For the static structural model we introduced three domain concepts (Product, Process and System) to represent modular micro assembly lines  Product: A hierarchical representation of the products, its variants and its components (atomic parts of a product) as well as the corresponding product and quality features (e.g. product dimensions or targeted surface quality) are modeled here. The final representation of the product specifications results in a working plan describing the sequence of atomic operations and the parameter set-up to achieve the aimed product variant.

 Process: Here the assembly processes are described in detail. Several different process types are created to categorize the diverse processes according to its capabilities. Within these concepts, the relevant parameters for performing successfull and stable processes are adjusted and dependencies on product features are modeled

 System: This is a representation of the actual machine, its modules and its sensors. It is a model of the machine "hardware", hierachically ordering the physical structure of the assembly system.

Cross-Domain Definition of Data and Parameters

Both, ´Parameters´ and ´DataItems´ are considered to be adjustment or value information most of the times with relation to a physical mechanical setting, a measurement or a corresponding PLC variable. As ´Parameter´, all entities are considered, which can be changed by a human operator or a technical installation. As ´DataItem´, all entities are considered, which can be read or asked for, but cannot be changed in a direct way. 1These concepts are modelled in a cross-domain approach which means that they can be related to each of the already presented domains of the static structural model. For explanation a DataItem representing a sensor holds structural information about in which system or module the sensor is installed but also represents an important monitoring variable for a process.

Dynamic Knowledge & Mapping Functionality

Besides the structural static model also data from dynamic sources is considered. The information is stored in the form of events with a timestamp resulting in a time-driven, event-oriented monitoring system. All events have a unique ID and attributes like event classification main-und subtypes, a primary value and context information.

Depending on their origin and available information these events are mapped to the corresponding structural entities. It is not mandatory to model all possible context information as the system is adaptive enough to handle just as much information as provided. As depicted in Figure 2 the concept allows in contrast to databases also multiple connections. This means that an event can be mapped to multiple atomic attributes of single entities and to multiple domain areas at the same time as well as all possible gradations and mixtures in between.

Relationship Model and Reasoning

As mentioned before the static structural model allows representing different types of relationships between heterogeneous entities on all granularity levels of the domain model. Also multiple connections with different types between same entities as well as parametrizable relationships are possible. The usage of semantic technologies enables object driven features like inheritance and inference. This is of special importance for the gathering the operator observations. It is for example possible to model relations like "failureSource" between an inspection and a processing step. This can prevent the case of extremely time-consuming trouble-shooting, which can be in the precision assembly sector a task of several days.

Reasoning

Besides these object-oriented features pre-defined rules support the automatic extension of the manual defined relations. This can be based on very simple rules or more sophisticated type-specific ones. All rules are modeled object-oriented in the format of extended "if-then" -relationships. The presented rule states that if an entity A is a failure source for entity B and B is a failure source for entity C then A is also a failure source for C.

Technical Implementation

The technical implementation of chapters 3.1 to 3.-3 was executed during the course of the European project FRAME [START_REF] Frame | EU FP7 Project[END_REF] with industrial partners.

For the ontology modeling the open source software freemind [START_REF] Freemind | [END_REF] was used for modeling the structure of the machine model (chapter 3.1). With a semantic annotated mindmap [START_REF] Busse | Semantic Authoring with semAuth[END_REF] which can be automatically translated into semantic languages F-Logic and ObjectLogic (which is proposed to the W3C as upcoming standard) we realize on the one hand an intuitive system with a technique (mind mapping) which is very familiar for engineers. On the other hand by using the open-source access we also enable additional applications relevant to fulfill production necessities. In the course of the project we implemented e.g. an operator touch-screen for knowledge gathering directly on the shop-floor and a mindmap validation tool to ensure proper input data. For the presented approach we use the software Ontobroker [START_REF] Schmidt | Ontobroker Version 5.1 -Product Documentation[END_REF] by the company Ontoprise as semantic middleware which stores all modeled and gathered information inside the internal database. The dynamic knowledge which was described in chapter 3.2 is also fed into it. As Ontobroker is currently one of the best performing semantic systems [START_REF]OpenRuleBench -Benchmarks for Semantic Web Rule Engines[END_REF] it is also suitable to store large amount of data which by the nature of micro-assembly production accumulates over the time.

The prototypic implementation is able to process over 100 Events per second. In the presented work the semantic middleware was also integrated with Statistical Process Control and HMI input by the operators.

The relationship model (chapter 3.3) was developed in two separate approaches in F-Logic as well as ObjectLogic to enable multiple relations between the entities. Also querying the database is written within these languages.

Verification

The so implemented system is flexible enough to handle multiple data representation and fulfills the requirements which have been introduced in chapter 1.1.

 Easy gathering of operator knowledge. The workers are able to model their knowledge in a flexible way, providing just the information they think is valuable. Cross-relations between all different entities are possible and not restricted by IT necessities. These connections can also be restricted to certain types or be parameterized. Due to hierarchical structure and rules an automatic creation of connections and therefore an expansion of the assembly model information is realized.

 Flexible context-mapping of static and dynamic data: All gathered dynamic information can be mapped to its structural context in a very human-centered and understandable way. Contrary to databases, features like multiple mappings to different entities with different granularities are possible. There is no need for the involvement of IT experts to model and map new information in the system as this can be done directly by the user.

 Extensibility & Reusability: The system is implemented completely independent from other software and can due to the time-based information in an event form easily be extended to or integrated with corresponding systems. It is possible to extract the modeled information (or part of it), export it and integrate it in other models.

Special potential lies in the combination between "hard", machine-derived data and "soft" human knowlede and observations. The pre-defined rules realize an cost-effective use of of the system. With the presented aproach we are able to combine different data sources in order to provide the shopfloor operators and engineers with helpful data sets to tackle their problems.

Conclusion and Outlook

During the last years the ramp-up phase has become due to market necessities more and more the focus of research activities. Arising problems are mainly solved through human experience and especially for SMEs there is still a lack of software support. In other industrial applications it has been proven that semantic technologies are suitable for gathering and managing knowledge in a machinereadable format. The presented approach applies semantics to the ramp-up process in the assembly sector and tackles some of the most urgent issues in gathering and reusing operator experience in combination with machine-derived data. The presented work is based on existing standards and offers the possibility for a knowledge management system, which is capable of providing required functionalities to support operators during the ramp-up phase.

Further research in connecting the knowledge management system with existing software such as Manufacturing Execution Systems or Quality Control Software is required. Also the system interface to access the knowledge needs to be adapted to the worker requirements in order to optimal support the ramp-up procedure. The presented implementation however provides due to its flexibility and implemented features already a number of benefits for companies.

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement FRAME n°229208.

Figure 2

 2 Figure 2 Three layer design of the implemented system

Figure 3

 3 Figure 3 Example for manual and automated generated relationshipsFigure3demonstrates some relationship features of the implemented system like the automatic creation of new relationships according to specified patterns (Relation 1), the relation between different granularity levels (Relation 2) and multiple relations even between the same entities (Relation 3 and 4). In summary this realizes a hierarchically structured relationship model which is able to represent the operator experience in a flexible way.

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  X [failureSource->?Z] :-?X [failureSource-> ?Y] and ?Y[failureSource-> ?Z] [Example of of a simple Rule for failureSource propagation written in ObjectLogic]

Figure 4

 4 Figure 4 Screenshot of the implemented touch-screen application; displaying parts of the static structural model with a semantic annotated mindmap

E.g. a DataItem can also be used as virtual measurement signal combining other information