VS Sewing Machines

RGB image in Sewing Machines

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An image processing pipeline was designed for the thread detection. The complete workflow is visualized in Figure 3. At all times, knowledge about the desired thread pattern model is available. A. Two Partial Luminance Images The composed camera RGB-image is converted to a singlechannel luminance image L(x, y) = 0.3∗R(x, y)+ 0.59∗G(x, y)+ 0.11∗B(x, y). (1) The conversion is based on the assumption, that the thread appears either brighter or darker than the tissue background. However, it is unknown in advance which appearance is given. Therefore, two partial images are generated. The positive image I+(x, y) only contains pixels brighter than the tissue mean, whereas the negative image I−(x, y) only contains pixels darker than the mean, I+(x, y) = max(0, L(x, y) − m) (2) I−(x, y) = max(0, m − L(x, y)) (3) m = mean(L(x, y)). (4) By separation into partial images, the pixels representing the thread will only be visible in one of them. They will always appear as a bright structure. Next to the actual thread pixels, there will also appear spurious pixels from noisy tissue structures. They resemble thread-like structure parts and are stochastically distributed.

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B. Frangi Filtering A Frangi filter [3] is applied on both partial images. The filter operation basically consists of a pixel-wise computation of the Hessian-Matrix and a Gaussian-shaped smoothing kernel. It is also known as a vesselness filter and was originally introduced in the context of medical image analysis to emphasize pixels that are embedded in vessel-like structures. However, an elongated and thin appearance is not only characteristic for vessels inside the human body but also for the considered thread within this work. It is therefore natural to adopt the established methodology for the given task. The result are two images, IF r+ and IF r−, with each pixel value containing the probability that it is embedded in a thread-like structure.

C. Selection of the Thread Image Based on the supplied model prior, an approximate number of expected thread pixels can be estimated. This expectation can be turned into a thresholding operation, that is performed on both images. Since one of the filtered images contains both the thread as well as background noise, while the other image only contains background noise, a robust detection of the thread image is straight forward. The result is a single binary image, wF r(x, y), with a pixel value of 1 indicating a thread pixel.

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D. Classification of Pure Thread Pixels The previous step provides a mask for pixels that are embedded in a thread-like, i.e. elongated and thin, structure. Yet, pixels lying not directly on the thread but nearby may be included. Therefore, the mask is refined using an expectation maximization (EM) algorithm [1]. The refinement is no longer performed on the luminance image, but on the RGB-image. As initialization, the tissue RGB values from the background removal step are taken for the tissue mean and covariance values. The thread mean and covariance values are derived from all pixels masked by wF r. The iterative EM algorithm results in a single binary image, wges(x, y), with a pixel value of 1 denoting apure thread pixel.

Thread Position in High Precision CNC Sewing

Thread Position in High Precision CNC Sewing

CNC sewing technology is applied for assembling layers of textiles. Typical examples are car seats or mattresses. The most important function of the sewing thread is not to fix and connect the different layers, but to appear visually pleasing [4]. Therefore, the final thread pattern must correspond to a model up to acceptable deviations, which are task specific [5]. However, the elasticity of the materials causes pattern deformations due to geometric shifts of older stitches, if newer stitches are set within striking distance. In order to achieve an optimal appearance, it is necessary to account for these shifts and anticipate the deformation of the thread pattern within the CNC sewing program. Up to now, this is addressed by human experts in an iterative way. Using the current state of the CNC program, a specimen is created. Subsequently, the human expert inspects the specimen and modifies the program accordingly. The program modification is performed more or less intuitively and requires a lot of experience. This procedure is repeated as long as necessary and may take up to two days. Next to the time commitment, this trial and error approach comes along with a high material cost, since a sufficient amount of test samples is needed to optimize the CNC program.

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The goal of this work is to automate the process of inspection and subsequent adaptation of the CNC program, with the final workflow being as follows: Given a new pattern, an initial sewing is performed using the desired model. This will result in a distorted thread pattern, as it is visualized in Figure 1. The specimen created this way is placed in a camera based inspection system. Based on the single specimen and given knowledge about the desired pattern, the inspection Fig. 1: An exemplary visualization of the distortions. The black line shows the desired thread pattern. However, sewing in such a pattern might actually lead to the distorted pattern shown in red. system automatically detects the real thread pattern using image processing techniques. Subsequently, a registration and adaptation of the desired model is performed, such that it optimally fits the detected thread pattern. Based on the calculated deformation, the CNC program required to achieve the desired pattern is calculated.

This paper focuses on the image processing part, including the image acquisition and model-based registration/adaptation. On the most abstract level, it can be separated into a hardware part, consisting of the construction of the camera based inspection system, and a software part, consisting of the image processing and model adaptation. Section II describes the inspection system and the image acquisition process. Image processing techniques to automatically detect the thread are described in Section III. A pipeline for the thread pattern registration and adaptation is proposed in Section IV. The presented inspection system does not only lay the foundation for the automated computation of the CNC program, but can be used as a standalone system for quality assurance.

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The textile specimen we consider have a size of 1m x 1m. Simultaneously, the spatial accuracy of detected stitch positions needs to be in the order of 50µm to account for very thin threads. In a real world scenario, both requirements can only be combined by using a commercially available camera sequentially scanning individual parts of the specimen. Afterwards, all of the acquired tiles are composed to obtain a unified, large RGB image, C(x, y) = (R(x, y), G(x, y), B(x, y))T , where (x, y) denotes the pixel position. Figure 2 shows the real world system for image acquisition and quality inspection. A camera is mounted on a gantry robot, allowing to automatically translate the camera in 3D space. The specimen to inspect is placed on the floor below. The camera has a sensor size of 2332 x 1752 pixels. Considering the employed lens, this translates to a resolution of 8 pix mm . However, the pixel resolution can be dynamically changed by decreasing or increasing the camera height over the conveyor plate using the gantry robot. Once the measurement has been started, the specimen is scanned in equidistant intervals resulting in a set of tile images which need to be composed. The image composition is performed using standard image registration techniques [8].

Manova analysis for sewing machines

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Preliminary assumption testing was conducted to check for normality, linearity, univariate and multivariate outliers, homogeneity of variance-covariance matrices, and multicollinearity, with no serious violations noted. The results of the MANOVA analysis include the F-statistic value, average (M), standard deviation (SD), Wilks’ Lambda, significance level p and partial eta squared. Wilks’ Lambda is one of the most reported statistics. If the associated significance level p is less than 0.05, then it can be concluded that there is a significant difference between groups. Partial eta squared, also known as effect size, shows the proportion of the variance in the dependent variable than can be explained by the independent variable. The guidelines proposed by Cohen [17] have been used in this work: 0.01=small effect, 0.06=moderate effect, 0.14=large effect. When the results for the dependent variables were considered separately, a Bonferroni adjusted alpha level of 0.017 was used. In this case, a significance level p smaller than 0.017 represents a significant difference.

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It was found that the number of fabric layers/material thickness produces statistically significant variations when analyzing the dependent variables, Peak1, Peak2 and Peak 3, in combination, and for all three types of fabrics. As expected, and according to the stitch formation process, in all materials Peak 3 exhibits the highest values. This value has been observed to be lower in the experiments with 4 fabric layers than with two fabric layers. When analyzing the dependent variables separately, the high partial eta squared values indicate a large effect produced by the number of layers on the three force peaks. The measurement is thus able to distinguish between the number of layers presented to the machine for all fabrics and almost all thread force peaks. Surprisingly, the highest thread force peak decreases with more layers of fabric. The expectation of the team, based on the empirical knowledge on the sewing process, would be different. This shows that the quantification of sewing variables can in fact provide more detailed knowledge about the process variables and their relations, allowing development of finer control and monitoring of the machines.

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A sewing test rig based on a lockstitch machine has been set up using software previously developed and used in the assessment of the operation of overlock machines. A comprehensive experiment involving materials, adjustments and needle choice is being carried out, of which some results have been shown. The relations between sewing variables are complex and experimentation is extensive due to the number of parameters involved. However, some insights are being gained that promise methods for automatic adjustment and defect detection. These are very desirable functionality for today’s textile industry, dealing with small production orders with varying materials or for technical textiles, where a more quantitative process monitoring can assure more reliable and safe products. It has been shown that the number of plies / material thickness produces statistically significant variations in thread force peaks, as expected. However, the variations are not always as expected. Further experimentation relating the static thread tension adjustment, the materials and the resulting thread forces, related to the evaluation of the seams by experienced sewing technicians and objective tests such as seam strength will shed some light on the principles of correct adjustment. This will allow the development of systems to automatically adapt the machine to new materials and to monitor the process avoiding defects and low quality seams. Other process variables such as needle penetration force and thread consumption will be included in this analysis in future work.

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An industrial PFAFF 1183 lockstitch

An industrial PFAFF 1183 lockstitch (stitch 301 according to ISO 4915) machine (Fig.1) has been instrumented with a thread tension sensor (Fig.2) connected to a signal conditioning circuit which in turn plugs to a National Instruments PCI-MIO16E-1 data acquisition board (although often called thread tension, the parameter measured is actually a thread pulling force). The machine’s “synchronizer” (a rotary optical encoder) provides 512 pulses per rotation of the machine, which is used as sample clock for signal acquisition. It is thus possible to determine the exact angle at which each signal sample is acquired, allowing relating the signal directly with the events during the stitch cycle. Signals are thus represented on a continuous angle rather than a time scale, in which the rotation N of the machine corresponds to the angles between 360º·(N-1) and 360º·N. The sensor (custom-designed by Petr Skop) is a cantilever beam with semiconductor strain gauges at the base, configured as a complete Wheatstone bridge. A glass sphere with a rounded slot allows a low-friction interface with the sewing thread. A thread guide with two ceramic O-rings has been designed to guide the thread around the thread sensor. The thread pulling force produces deformation on the cantilever sensor that is picked up by the strain gauges. Thread tension is imposed to sewing threads by a device called a tensioner (partially visible in Fig.2). This device consists of two disks between which the thread passes. A spring holds the two disks together. The pre-tension of this spring can be adjusted and is called in this context static thread tension.

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A software application has been developed in Labview allowing the acquisition and processing of the resulting signals. The signal processing functions of this software have been reported elsewhere [3]. The most important one is splitting the thread tension signals into stitch cycles (each cycle corresponding to one rotation of the machine’s main shaft) and in turn dividing each stitch cycle into phases, which are associated to specific events of stitch formation. For each one of these phases, that will be described later, features such as peak values, power, energy or average of the signal is computed. In the current experimental work, thread force waveforms throughout the stitch cycle are being analysed when varying parameters such as static thread tension adjustment, number of fabric layers, mass per unit area and thickness of fabric, needle size and sewing speed. Both the effect of the machine settings and process variables on the thread tensions, as well as the effect of the material properties are investigated. In this paper, the effect of static thread tension and the influence of the fabric on the dynamic tension signals are analysed.

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The first step was to observe the resulting thread tension signals and interpret their relation to the stitch formation process. Some trials with the adjustment of the needle thread pre-tensions were made.

Afterwards, a more comprehensive experiment was set up to investigate on the influence of the material being sewn.

Three similar shirt fabrics with different mass per unit area were used, namely

• Fabric 1 : 1×1 plain weave ; 100% cotton; 102 g/m2; thickness 0,22 mm

• Fabric 2 : 2×1 twill fabric; 100% cotton; 127 g/m2; thickness 0,23 mm

• Fabric 3 : Mixed structure; 100% cotton; 118 g/m2; thickness 0,23 mm

The machine was set-up as following:

• Groz-Beckert 134 needle with round point and size 8;

• 100% corespun polyester thread with ticket number 120;

• Constant sewing speed of 2000 stitches per minute;

• Stitch length 3,5 mm

• Static thread tensions were adjusted empirically for the fabric with average weight; no difference in stitch alance and tightness could be observed sewing the three fabrics with this adjustment. Adjustment was maintained unchanged throughout the experiment.

For each fabric, strips of fabric of 10 cm width and 30 cm length were cut. Specimens with two and four layers of these strips were prepared. On each one of them, 10 seams with 20 stitches each were performed.

Peak values for each of the three defined stitch cycle phases (see next section) were extracted by the developed software. Results were compared between specimens of two and four layers. For this purpose, the Statistical Package for the Social Sciences (SPSS 20.0) was used. For each experiment a MANOVA (one-way between-groups multivariate analysis of variance) was performed. Three dependent values were used: Peak values of thread force in phase 1, 2 and 3. The independent variable was the number of layers: 2 or 4. The analysis was carried out following the recommendations of Pallant [16].

Monitoring and Control of Industrial Sewing Machines

Monitoring and Control of Industrial Sewing Machines

The processing of textile products by sewing them together is a very complicated process. This may not be apparent at first glance, but a closer look at the process reveals that, due to the flexible, often extensible nature of the materials, their handling is a procedure that in almost all cases requires human hand. Another important aspect is setting the machines for the great variety of materials used currently. This can only be accomplished by experienced sewing technicians. Machine configuration and adjustment is an empirical, time-consuming process that is more and more significant considering that textile industry has been constantly moving away from massproduction to small orders with varying materials and styles.

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Machines should be able to set themselves up when the data regarding material properties and desired process parameters is known. During the process, it would be ideal if they could adapt themselves and detect defects or malfunction automatically. This would reduce set-up times, increase flexibility of the machines and increase product quality and process reliability, avoiding defects and rejected products. Research in this direction has been carried out by several investigators, such as Clapp [1], who studied the interface between the machine and the material feeding system, Stylios [2] who proposed the principles of intelligent sewing machines, amongst others. Within our team, previous work has been carried out on thread tensions, material feeding and needle penetration forces in overlock machines [3-5]. Other studies targeted needle and bobbin thread tension measurement on lockstitch machines [8-10].

The sewing process is a cyclic process in which several occurrences take place. The objective is to interlace thread(s) with each other and through a fabric, for the purpose of joining, finishing, protecting or decorating. Three main “sub”-processes can be identified that ideally should be monitored and/or controlled automatically: -Material feeding. Seams are produced on the fabric with a certain pattern, which is, in the simplest case, a straight line, but may also be a complicated form such as the ones used in embroidery operations. To form these patterns, the material has to be transported-“fed” by a distance that is called the stitch length. Given that industrial machines operate at very high speeds (some of them attaining 10 000 stitches per minute), the dynamics involved is complex and there are very often problems with material deformation and irregular stitch length. Some of these aspects have been addressed in [1-3, 5];

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-Needle penetration. Considering again the high sewing speeds that occur, problems with needle penetration can arise due to the mechanical and thermal interaction between needle and fabric. Fabric yarns may be torn by the forces acting during needle penetration or they may fuse due to the high needle penetration produced by friction. Systems to monitor needle penetration forces during the process to detect defects and offline systems to support the choice of needles and fine-tune fabric structures and finishing to avoid these problems, would be of high value to the industry. This kind of approach has been studies by several authors, such as in [4-8].

-Stitch formation/Thread tensions. The interlacing of the threads itself, which constitutes the actual stitch formation, cannot be dissociated from the processes of material feeding and needle penetration. However, there are two variables directly linked to the thread that most intimately represent it: Thread tensions and thread consumption. The relationships between fabrics, machine set-up and stitch formation in lockstitch machines have already been studied in [9-15]. Methods for defect detection have been developed for overlock machines and presented in [3]. However, an automatic system for setting thread tensions online is still missing. Wang and Ma [15] describe thread tension control in embroidery machines, but the work only tackles the issues associated to the control of the actuator. Setting of the correct references for the controllers to produce a high-quality product in varying conditions is the key issue, and this has to be further tackled.

This paper describes current work on the behavior of thread tensions in an industrial lockstitch sewing machine using a new measurement set-up. Methods previously investigated for monitoring of thread tensions and establishing the correct variable references are being ported and/or re-evaluated. The first step is the study of the relations between material properties and thread tensions. Some aspects are still not clear in this regard. In [13], for instance, the authors state that the thickness of fabric plies does not affect the needle thread tension. This is one of the aspects to be studied in this work.

Textile Stream Smart Manufacturing Innovation

He smart factory is a futuristic production paradigm that transforms ICT(Information and communication technology) into a new smart/green/urban production system by integrating the existing traditional industrial production system.[1-2] Industry 4.0 proposed by DFKI, is defined as the 4th industrial revolution based on Internet-of-Things(IoT), cyber-physical systems(CPS), and Internet-of-Services(IoS). [3-6] In the textile industry, the smart factory is a factory based on the CPS that incorporates ICT and IoT technology into the existing production system.[7-8] In order to build a smart factory between textile and apparel streams, the connectivity of the CPS should be strengthened.

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This study focuses on the construction of a CPS system to realize a smart factory by deriving three representative processes (fabric, dyeing, sewing) among textile streams. The data flow of CPS based inter-stream smart manufacturing system. The rectangle marked with read lines represents the part for detecting and controlling the sewer data for the smart of the sewing process which is the core of this research.

Textile stream smart factory CPS implementation can only be done by linking together the ordering system, design automation system, product information management system, production information integration system and production equipment automation.[6] The interlinkage of high-throughput, high-productivity production systems that minimize plant-to-plant collaboration and prototype production to accommodate small-volume and multi-stream requirements between streams, and can be instantly produced on demand.

II.SEWING MACHINE SENSING DEVICE DEVELOPMENT FOR SMART FACTORY A. Device for checking and indicating the rest of underthread sewing yarn of sewing machine The sewing work can work in a situation where there is no under-thread by mistake. This leads to defective products and economic losses. To solve this problem, there is a need for a device for detecting the remaining amount of under-thread and transmitting it to an operator. The sensing signal configuration for system design to detect the residual under-thread amount and the system configuration diagram to control it by linking it. The orange block shows the status of the warning lights, the PLC, the touch screen, and the main brake, while the blue block indicates each sensor and control signal for control.

Software algorithms were designed to implement the logic sequence so that if the under-thread is insufficient, the operation stops immediately.

The configuration of under-thread residual sensing and display system. Each component of the test apparatus for the detection of the residual thread volume consists of the lower part of the sewing machine and the display part. Under-thread residual sensing device was designed and implemented as primary and secondary sensing parts. The primary sensing uses a cylinder (CXSM630) for the bobbin and a SMAT-8M sensor for the FESTO position transmitter.[9] The system is implemented so that the remaining amount of the bottom thread can be calculated by the data that the cylinder pin advances and senses the distance gap.

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Secondary detection shows the sensing principle to detect and warn the bobbin rotation state while reducing the defect caused by the bobbin not rotating when the bobbin is tangled or defective. To check the bobbin rotation status, the Omron NPN type photo sensor (E3Z-LL61) checks the rotation of the bobbin with four pairs of black and white stickers at 45 degrees on the bobbin.

B. Stitch control device and sewing thread information detection system concept configuration In order to make smart factory of sewing factory, it is necessary to prevent worker’s mistakes and to record and confirm the current work. The position where the residual under-thread detection device and the stitch automatic control device are to be attached in the sewing machine being used in the sewing factory. In order to automatically adjust the stitches, the information about the fabric currently being worked on is entered in advance, so that the number of stitches can be automatically adjusted.

Detailed data and method of monitoring system at the upper left part of the figure are explained below. C. Information flow of the sewing machine detected from sensors for smart sewing process the flow of information obtained from the parts(under-thread residual detection device, automatic stitch control device, monitoring system) developed for the sewing process smartization. The collected information is displayed in the monitoring system, and it is transmitted to the POP system, the PDM system and the final customer-linked system, so that the sewing process can be made smart.[10-11]

It is a study to apply smart factory to the textile industry in this research and development. A study on the smartization of sewing process among several textile streams was conducted.[12-13] In order to make the sewing machine smart, we applied the same sewing machine which is used in the present industrial field and modified the sewing machine. First, the residual amount of the under-thread was detected to reduce the worker’s mistake and product defect. Secondly, in the sewing industry where workers are aging, it is possible to control the automatic stitch number according to the product type. Next, monitoring of the overall sewing process requires further work on the presser foot pressure control, tension control, POP(point of production) system and all monitoring data interlocks.

Thread Position in CNC Sewing

Be yourself; Everyone else is already taken.
— Oscar Wilde.

The adaptation process corresponds to finding a unique deformation vector for each model stitch point.

A. Thread Representation Positions of the thread appearing in the image are extracted using a blob detection on the binary image wges(x, y). Every position found this way will in the following be called a thread representative. The representatives are preferably distributed in equidistant steps over the length of the thread. Furthermore, they can be ordered to form a sequence of points, representing the entirety of the thread. The result is displayed in Figure 5c, with the circles visualizing the found representatives. However, it can be seen that outliers are possible (visualized in red). Additionally, individual thread positions might be missed. The model fitting and adaptation is not performed using each individual stitch, since such an approach would be highly susceptible to interferences like outliers. Instead, more abstract features are considered, that can be robustly recognized within both the set of representatives and the pattern model. • Characteristic points may be thread endings, points where the thread pattern changes abruptly its direction, or the intersection of lines. • Polygons may be formed by the linear connection of neighboring representatives. The corresponding polygons within the pattern model are formed by the linear connection of neighboring model stitch positions. Next to the improved robustness, the computation complexity is heavily reduced.

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B. Initial Model-Based Registration The specimen placed inside the inspection system may be arbitrarily rotated and shifted. For this purpose, an initial estimation of the rough placement of the thread pattern relative to the prior model is estimated. The estimation utilizes the generalized Hough transformation (GHT) [2] [6], which has been proven to fulfill this task. The goal is to compute a global shift vector minimizing the distance between model and real world thread features.

C. Iterative registration Based on the initial model-based registration, the model adaptation is performed in an iterative procedure. Each iteration consists of multiple steps. First, an assignment between features within the model and the representatives is established. The assignment needs to be considered separately for both feature types. The target of a characteristic model point is a characteristic thread representative having the same type and being at the minimum Euclidean distance. The search for corresponding target polygons is based on the polygon center and its direction. Once the assignments are established, a transformation of the current model features is performed to approximate the thread pattern. Within the first iterations, the assignments are rather unreliable. Therefore, only a rigid registration having few transformation parameters but many assigned feature points is determined. Over the course of the iterations, the assignments become more reliable and the number of free transformation parameters is increased. In the end, the assignments become extremely reliable. Thus, individual model points are only then allowed to be shifted towards individual thread points, resulting in a controlled deformation of the model to adapt to the real thread pattern. Every transformation is estimated using an energy minimization approach, independent of the degree of freedom. There exist two types of energies, internal and external. The internal energy is a measure of the deformation of the model. The more a model polygon vector deviates from its original vector in size and direction, the higher the cost. The contribution of the external energy depends on the type of feature. If an assignment between a characteristic point in the model and its counterpart on the thread is possible, the model point can be directly attracted to its target. The strength of attraction is independent of the direction and depends only on the Euclidean distance between both. For most cases, an exact assignment from model polygons to individual thread polygons is not useful due to the high number of them that causes confusability. However, a direction of attraction can be determined. Therefore the external energy for polygons and the points spanning those polygons is computed in dependance on the direction of the model polygon normal at its center point and the projection of that normal onto a thread polygon vector.

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1) Rigid registration: A rigid registration consists of a rotation and translation. Scaling is determined and applied as part of the deformation registration later on. The transformation is the result of an optimization over all model features and their associated targets, such that a non-linear equation system needs to be solved. An optimal solution is found using the conjugate gradient method [7]. 2) Deformable registration: New positions for the model points are the result of an optimization procedure that minimizes the total energy depending on the new positions. A linear equation system with the coordinates of all model points needs to be optimized. The relative weighting between external and internal energy determines how near a model point is attracted to its target with the model steepness as the constraint. A large weight for the internal energy means a very steep model that is robust but fails to model each local detail of the thread deformation. A small weight means a very flexible but not very robust model. Hence, the improvement of the quality of the found targets with each iteration allows a dynamic adaptation of the weights, resulting in a model adapting better and better to local deformations recorded in the thread image. The finally obtained deformation vectors between thread pattern and model prior are shown in Figure 5d. V. DISCUSSION Given arbitrary complex thread patterns, the system automatically detects the real world thread pattern and compares it with the pattern intended by the manufacturer. A deformation vector can be calculated for every individual stitch with an accuracy of up to 50µm. The system may serve as a stand-alone for quality assurance. However, it also lays the foundation for the automated correction of stitch positions in textiles distorted by the elasticity of the material. Based on the computed deformation vector field, an automated correction of the CNC program to create the desired pattern is possible. An obvious limitation of the proposed system is the requirement for the thread to appear visually different than the background tissue. However, this is not the case for a variety of products, for which the thread color is wanted to be identical to the background color. A typical example is a black car seat sewed using a black thread. We are currently working on exchanging the RGB-camera for a multi-spectral image acquisition in order to distinguish originally metamer, i.e. identical appearing, objects.

Simulation Modeling of Sewing Process

Industrie 4.0, proposed by DFKI [1], is defined as the 4th industrial revolution based on Internet-of-Things (IoT) [2], cyber-physical systems (CPS) [3], and Internet-of-Services (IoS) [4]. One of the characteristics of Industrie 4.0 is that it includes smart factories capable of generating customized products for customers. One of the important issues to implement a smart factory is to complete and deliver customized products to customers within specified time. For this, we need an efficient scheduling algorithm [5]. It becomes more and more sophisticated work to validate a production schedule in factories. Simulation is a tool for validating a production schedule and changing it if needed. For using simulation, we need appropriate simulation models. In this paper, we propose a sewing machine model for simulating sewing process. The proposed sewing machine model includes sensing, sewing, forwarding and control functions as submodels. Also, we propose a modeling tool that includes the proposed model. The proposed modeling tool manages a model library that can be continuously extended for sewing process simulation. Further, it can automatically generate and build source codes for simulation models. Therefore, users can easily develop their own models and simulate them.

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SIMULATION MODEL FOR SEWING PROCESS A. Motivation One of the distinguishing features of smart factories compared to existing factories is that the smart factories generate customized products. Other characteristics of a smart factory are as follows [6-8]. 1) Each product has a unique ID. 2) Each product passes a different sequence of processes until all required processes are completed. 3) Products and facilities communicate with each other to determine each product’s production schedule. Therefore, facilities in the smart factories should be modeled differently than facilities in existing factories.

B. Sewing machine model (Structural model) We defined a sewing machine model for simulating sewing machine processes. The sewing machine model was defined as a structural model consisting of four component models (Sensor / Work / Forward / Control). Sensor model detects raw material or semifinished products arrived at the sewing machine. Work model performs sewing process for the arrived raw material or semifinished products. Forward model chooses the next forwarding facility and passes the processed semi-finished product on the selected next facility. Finally, Control model governs the whole operations of the sewing machine.

C. Sensor model (Behavioral model) Sensor model abstracts a sensor module that detects raw material or semi-finished products arrived at the sewing machine. Sensor model has 4 phases and moves from one phase to another whenever state transition occurs. The Sensor model in Init phase stores its current location and moves to the Sensing phase. In Sensing phase the Sensor model periodically checks whether semi-finished products has been arrived at the sewing machine. If there is one, it goes to Detected phase. Otherwise, it goes to Non-detected phase. In Detected phase the Sensor model outputs the information of arrived product through the port Out_Sensor_Detection and then returns to the Sensing phase. In Non-detected phase it returns to the Sensing phase after a predefined time.

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D. Work model (Behavioral model) Work model represents the sewing operation and changes the properties of an arrived product. Work model has 3 possible phases (Idle, Working, Reporting). In Idle phase, it waits for a product to arrive at the sewing machine. When an input is arrived through the port In_Work_Command, the Work model moves to the Working phase. It changes the properties of the arrived product in Working phase and outputs the work result through the port Out_Work_Report.

E. Forward model (Behavioral model) Forward model implements a variable process of a smart factory by choosing the next forwarding facility for a semifinished product and delivers the product to the selected facility. It waits until the sewing operation ends in Idle phase. When an input is arrived through the port In_Forward_Command, the Forward model goes to the SelectNext phase. In the SelectNext phase it chooses the next forwarding facility based on the workload of candidate facilities and then goes to the Forwarding phase. The Forwarding model pass the semi-finished product to the selected facility and moves to the Report phase. Finally, it generates the output through the port Out_Forward_Report.

F. Control model (Behavioral model) Control model manages the other submodels of the Sewing machine model. Control model has 4 possible phases (Sensing, Working, Forwarding, Logging) that correspond to an operation cycle (detection, sewing, sending, and recording) of a sewing machine in a smart factory environment. Control model in Sensing phase waits for an input from Sensor model and goes to the Working phase when it receives arrived product information through the port In_Control_Detection. In Working phase it sends a work command to the Work model through the port Out_Control_WorkCommand. When the Control model receives the forwarding result from the Forward model, it goes to the Logging phase. In Logging phase, the Control model records the processing result and returns to the Sensing phase.

MODELING TOOL FOR DEFINING SIMULATION MODELS We have implemented a modeling tool that manages a model library including the described Sewing machine model. A user can easily add models such as sewing machine to a simulation scenario by using the proposed modeling tool. Further, the modeling tool automatically generate and build source codes for the models to be executed by the simulator. Therefore, the modeling tool support addition, deletion, modification and reuse of simulation models in the model library.

We should complete and deliver personalized products to customers within specified time to implement smart factories. Whether we can complete the customized products in specified time depends on the production schedule used. Simulation can work as a tool for validating production schedule and changing it if needed. In the manuscript we define a Sewing machine model organizing a sewing process and a modeling tool. The proposed model and modeling tool can be continuously improved and extended.