Timo König, Fabian Wagner, Robin Bäßler, M. Kley, M. Liebschner
Abstract Condition monitoring of machines and powertrain components is an essential part of ensuring reliability and product safety in many industries. The monitored machines and components are often divided into different condition classes as well as classified using machine learning methods. In order to enable classification with machine learning algorithms, the acquisition of a sufficient amount of data from each condition class is essential. In reality, the collection of data for faulty system states turns out to be much more difficult, therefore in many use cases balanced data sets are not available. However, when classifying faulty states, an identical number of data per class is of great importance. This problem can be counteracted with synthetic data generation. Generative Adversarial Networks (GAN) are a suitable approach to generate synthetic data based on real measured data. In most cases of synthetic data generation, different damage cases, e.g. from a transmission, are simulated, but a generation of synthetic data is not performed at different operating conditions. However, different speeds and torques are a reality when monitoring, as the drive systems operate under changing operating conditions. Therefore, in the context of this paper, synthetic data generation at different operating states is investigated in order to implement a condition monitoring system for good and bad system conditions which includes different operating states. So, vibration data is acquired at different operating conditions of a transmission on a drive test rig and relevant features are highlighted using a suitable signal pre-processing method. The features, caused by different operating conditions, can also be generated synthetically by GAN. Therefore, it is possible to achieve a similar classification accuracy by integrating synthetically generated data as with real data, which makes the synthetic data generation a viable solution for extending existing data sets.
{"title":"Synthetic data generation of vibration signals at different speed and load conditions of transmissions utilizing generative adversarial networks","authors":"Timo König, Fabian Wagner, Robin Bäßler, M. Kley, M. Liebschner","doi":"10.1515/teme-2023-0001","DOIUrl":"https://doi.org/10.1515/teme-2023-0001","url":null,"abstract":"Abstract Condition monitoring of machines and powertrain components is an essential part of ensuring reliability and product safety in many industries. The monitored machines and components are often divided into different condition classes as well as classified using machine learning methods. In order to enable classification with machine learning algorithms, the acquisition of a sufficient amount of data from each condition class is essential. In reality, the collection of data for faulty system states turns out to be much more difficult, therefore in many use cases balanced data sets are not available. However, when classifying faulty states, an identical number of data per class is of great importance. This problem can be counteracted with synthetic data generation. Generative Adversarial Networks (GAN) are a suitable approach to generate synthetic data based on real measured data. In most cases of synthetic data generation, different damage cases, e.g. from a transmission, are simulated, but a generation of synthetic data is not performed at different operating conditions. However, different speeds and torques are a reality when monitoring, as the drive systems operate under changing operating conditions. Therefore, in the context of this paper, synthetic data generation at different operating states is investigated in order to implement a condition monitoring system for good and bad system conditions which includes different operating states. So, vibration data is acquired at different operating conditions of a transmission on a drive test rig and relevant features are highlighted using a suitable signal pre-processing method. The features, caused by different operating conditions, can also be generated synthetically by GAN. Therefore, it is possible to achieve a similar classification accuracy by integrating synthetically generated data as with real data, which makes the synthetic data generation a viable solution for extending existing data sets.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90291417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The increasing use of automated laser welding processes causes high demands on quality control. 2D or 3D sensor technology can be used for data acquisition to monitor the weld quality after laser welding. Compared to a 2D camera image, the 3D height data, e.g. acquired using optical coherence tomography, contains additional relevant information for quality inspection. However, the disadvantages are system complexity, higher costs, and longer acquisition times. Therefore, we compare image-based methods with the quality assessment based on height data. The first method uses feature vectors from grayscale images taken coaxially with the laser beam. The significant advantage is that a camera is often integrated into the laser system, so no additional hardware is required. In the second approach, we use an AI-based single-view 3D reconstruction method. The height profile is reconstructed from a camera image and used for further quality assessment. Thus, we combine the advantages of 2D data acquisition with higher accuracy in evaluating 3D data. In addition, we consider the usually low data availability in the industrial environment in the development of algorithms. We use a training data set with 95 samples and a test data set with 858 samples. The work uses the contracting process of copper wires to produce formed coil windings to illustrate the method. We analyze a data set with different defect types and compare the quality assessment using the height data acquired with OCT, the feature vectors from the camera images, and the reconstructed height data.
{"title":"Machine learning based geometry reconstruction for quality control of laser welding processes","authors":"J. Hartung, Andreas Jahn, M. Heizmann","doi":"10.1515/teme-2023-0006","DOIUrl":"https://doi.org/10.1515/teme-2023-0006","url":null,"abstract":"Abstract The increasing use of automated laser welding processes causes high demands on quality control. 2D or 3D sensor technology can be used for data acquisition to monitor the weld quality after laser welding. Compared to a 2D camera image, the 3D height data, e.g. acquired using optical coherence tomography, contains additional relevant information for quality inspection. However, the disadvantages are system complexity, higher costs, and longer acquisition times. Therefore, we compare image-based methods with the quality assessment based on height data. The first method uses feature vectors from grayscale images taken coaxially with the laser beam. The significant advantage is that a camera is often integrated into the laser system, so no additional hardware is required. In the second approach, we use an AI-based single-view 3D reconstruction method. The height profile is reconstructed from a camera image and used for further quality assessment. Thus, we combine the advantages of 2D data acquisition with higher accuracy in evaluating 3D data. In addition, we consider the usually low data availability in the industrial environment in the development of algorithms. We use a training data set with 95 samples and a test data set with 858 samples. The work uses the contracting process of copper wires to produce formed coil windings to illustrate the method. We analyze a data set with different defect types and compare the quality assessment using the height data acquired with OCT, the feature vectors from the camera images, and the reconstructed height data.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81852907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Maier, Marcel Reith-Braun, Albert Bauer, R. Gruna, F. Pfaff, H. Kruggel-Emden, T. Längle, U. Hanebeck, J. Beyerer
Abstract Sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.
{"title":"Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting","authors":"G. Maier, Marcel Reith-Braun, Albert Bauer, R. Gruna, F. Pfaff, H. Kruggel-Emden, T. Längle, U. Hanebeck, J. Beyerer","doi":"10.1515/teme-2023-0033","DOIUrl":"https://doi.org/10.1515/teme-2023-0033","url":null,"abstract":"Abstract Sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76946232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Diffraction based multispectral imaging is a method for obtaining snapshot multispectral image information by using a structured diffraction grating or computer-generated hologram in an intermediate image plane of an imaging system. Without the need of complicated filter manufacturing it becomes possible to realize application specific spatio-spectral sampling of an image. The spectral resolution is coupled with the spatial resolution because the central stop of the imaging system serves also as the main spectral filtering component. By using two image sensors and the zeroth order of the computer-generated holograms it becomes possible to obtain high resolution spatial information as well as spectral information on a coarser sampling grid.
{"title":"Diffraction-based dual path multispectral imaging","authors":"T. Haist, R. Hahn, S. Reichelt","doi":"10.1515/teme-2023-0007","DOIUrl":"https://doi.org/10.1515/teme-2023-0007","url":null,"abstract":"Abstract Diffraction based multispectral imaging is a method for obtaining snapshot multispectral image information by using a structured diffraction grating or computer-generated hologram in an intermediate image plane of an imaging system. Without the need of complicated filter manufacturing it becomes possible to realize application specific spatio-spectral sampling of an image. The spectral resolution is coupled with the spatial resolution because the central stop of the imaging system serves also as the main spectral filtering component. By using two image sensors and the zeroth order of the computer-generated holograms it becomes possible to obtain high resolution spatial information as well as spectral information on a coarser sampling grid.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76228269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laura Carrilero, José Ramón Castro, Sandra Pérez, T. Belenguer, F. Salazar
Abstract Imaging through turbid media leads to a great loss of information decreasing the image quality. This loss of quality is due to the light interaction with the medium, since part of the light will pass through it, but another part will be scattered generating a blurred image pattern. In this work we try to decrease this problem by adding an absorbent, eliminating part of the scattered radiation responsible for the turbidity. With this aim, we use graphene nanoplates as an absorbent and we also compare the experimental results with black carbon powder.
{"title":"Image quality improvement through turbid media by using graphene nanoplates","authors":"Laura Carrilero, José Ramón Castro, Sandra Pérez, T. Belenguer, F. Salazar","doi":"10.1515/teme-2023-0014","DOIUrl":"https://doi.org/10.1515/teme-2023-0014","url":null,"abstract":"Abstract Imaging through turbid media leads to a great loss of information decreasing the image quality. This loss of quality is due to the light interaction with the medium, since part of the light will pass through it, but another part will be scattered generating a blurred image pattern. In this work we try to decrease this problem by adding an absorbent, eliminating part of the scattered radiation responsible for the turbidity. With this aim, we use graphene nanoplates as an absorbent and we also compare the experimental results with black carbon powder.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84661036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Geng, A. Moghiseh, C. Redenbach, K. Schladitz
Abstract The size and number of images and the amount of data we process every day have grown rapidly over the last years. Quantum computers promise to process this data more efficiently since classical images can be stored in quantum states. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error-prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on a quantum computer and subsequently retrieved with an error of at most 5 % is 2 × 2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme’s quantum part is an artificial neuron, working well on real quantum computers, too.
{"title":"Quantum image processing on real superconducting and trapped-ion based quantum computers","authors":"Alexander Geng, A. Moghiseh, C. Redenbach, K. Schladitz","doi":"10.1515/teme-2023-0008","DOIUrl":"https://doi.org/10.1515/teme-2023-0008","url":null,"abstract":"Abstract The size and number of images and the amount of data we process every day have grown rapidly over the last years. Quantum computers promise to process this data more efficiently since classical images can be stored in quantum states. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error-prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on a quantum computer and subsequently retrieved with an error of at most 5 % is 2 × 2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme’s quantum part is an artificial neuron, working well on real quantum computers, too.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75327428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In many fields and applications a spatially resolved measurement of the refractive index is desirable. This also applies to the additive manufacturing of optical components and is especially true in the special case of photopolymerization of liquid resins by UV irradiation. The key point here is that the resulting refractive index of the optical component to be printed depends on the degree of cure of the polymer. The latter is directly related to the UV irradiance used for curing. Thus, a local variation of the irradiance can enable a local variation of the refractive index of the additively manufactured optical component. In order to set a defined refractive index distribution, it is desirable to measure the refractive index during the photopolymerization of the resin in a temporally and spatially resolved manner. In this article, a method is discussed with which this can be achieved. The basic principle is that the polymer under investigation is brought into contact with a prism and, based on the principle of total reflection at the interface between prism and polymer, the refractive index of the sample can be inferred.
{"title":"Bildbasierte Bestimmung der räumlichen und zeitlichen Verteilung des Brechungsindex während der Aushärtung von Polymeren in der additiven Fertigung von Optiken","authors":"M. Rank, A. Heinrich","doi":"10.1515/teme-2023-0012","DOIUrl":"https://doi.org/10.1515/teme-2023-0012","url":null,"abstract":"Abstract In many fields and applications a spatially resolved measurement of the refractive index is desirable. This also applies to the additive manufacturing of optical components and is especially true in the special case of photopolymerization of liquid resins by UV irradiation. The key point here is that the resulting refractive index of the optical component to be printed depends on the degree of cure of the polymer. The latter is directly related to the UV irradiance used for curing. Thus, a local variation of the irradiance can enable a local variation of the refractive index of the additively manufactured optical component. In order to set a defined refractive index distribution, it is desirable to measure the refractive index during the photopolymerization of the resin in a temporally and spatially resolved manner. In this article, a method is discussed with which this can be achieved. The basic principle is that the polymer under investigation is brought into contact with a prism and, based on the principle of total reflection at the interface between prism and polymer, the refractive index of the sample can be inferred.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84179734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The present study investigates experimentally the effects of a time-dependent and spatially inhomogeneous magnetic field on liquid metal droplet flow down an inclined substrate. The flow is solely excited by the electromagnetic interactions between the electrically conducting melt and the applied magnetic field. The metal droplet consists of the eutectic alloy GaInSn which is liquid at room temperature. The magnetic field is generated in the gap between two metallic disks that are equipped with a special geometric arrangement of permanent magnets and put into a measured rotation. During the experiments, a droplet of a measured volume is positioned on an electrically non-conducting substrate that is slightly inclined against the horizontal direction. Droplet and substrate are placed in between the two rotating magnetic disks. In our experiments, we record the electromagnetically excited flow of the droplet downwards onto the substrate using a high-speed camera system. Applying standard techniques of digital image processing, we measure both the displacement position and velocity of the droplet as a function of time. We observe that, depending on the rotation rate of the disks and angle of inclination, the magnetic field eventually triggers this spreading process. In more detail, by evaluating the recorded data, we find that the magnetic field excites capillary waves at the free surface of the droplet. These surface waves contribute to a redistribution of volume towards the contact line formed at the downward-facing end tip of the droplet. This mode of transport steepens the contact angle, allowing the droplet to move. Besides the fundamental aspect of this work, the present study may contribute to the electromagnetic control of both the production of metallic microfibers and metallurgic coating processes as well as to the non-contact electromagnetic flow measurement technique of Lorentz force velocimetry applied to liquid metal free-surface flows.
{"title":"Experimental investigation of liquid metal droplet flow affected by a time-dependent magnetic field","authors":"C. Karcher, Z. Lyu","doi":"10.1515/teme-2023-0046","DOIUrl":"https://doi.org/10.1515/teme-2023-0046","url":null,"abstract":"Abstract The present study investigates experimentally the effects of a time-dependent and spatially inhomogeneous magnetic field on liquid metal droplet flow down an inclined substrate. The flow is solely excited by the electromagnetic interactions between the electrically conducting melt and the applied magnetic field. The metal droplet consists of the eutectic alloy GaInSn which is liquid at room temperature. The magnetic field is generated in the gap between two metallic disks that are equipped with a special geometric arrangement of permanent magnets and put into a measured rotation. During the experiments, a droplet of a measured volume is positioned on an electrically non-conducting substrate that is slightly inclined against the horizontal direction. Droplet and substrate are placed in between the two rotating magnetic disks. In our experiments, we record the electromagnetically excited flow of the droplet downwards onto the substrate using a high-speed camera system. Applying standard techniques of digital image processing, we measure both the displacement position and velocity of the droplet as a function of time. We observe that, depending on the rotation rate of the disks and angle of inclination, the magnetic field eventually triggers this spreading process. In more detail, by evaluating the recorded data, we find that the magnetic field excites capillary waves at the free surface of the droplet. These surface waves contribute to a redistribution of volume towards the contact line formed at the downward-facing end tip of the droplet. This mode of transport steepens the contact angle, allowing the droplet to move. Besides the fundamental aspect of this work, the present study may contribute to the electromagnetic control of both the production of metallic microfibers and metallurgic coating processes as well as to the non-contact electromagnetic flow measurement technique of Lorentz force velocimetry applied to liquid metal free-surface flows.","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82237934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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