Pub Date : 2023-05-05DOI: 10.20965/ijat.2023.p0206
Fernando Ardilla, Azhar Aulia Saputra, N. Kubota
Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality reduction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depicting objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sampling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly increases the learning speed and adaptability of dynamic data distribution input patterns. This method immediately adds data samples as new nodes to existing networks. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.
{"title":"Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions","authors":"Fernando Ardilla, Azhar Aulia Saputra, N. Kubota","doi":"10.20965/ijat.2023.p0206","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0206","url":null,"abstract":"Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality reduction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depicting objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sampling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly increases the learning speed and adaptability of dynamic data distribution input patterns. This method immediately adds data samples as new nodes to existing networks. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"61 1","pages":"206-216"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90728113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0103
Keigo Takasugi, Naohiko Suzuki, Y. Kaneko, N. Asakawa
As a result of the development of network technologies, diagnosis techniques that can collect machine states continuously and prognostic health management (PHM) are available in the factory. PHM technology is also beginning to be implemented in the machine tool field. However, few studies have described causality between feature values, including vibration and acoustic emission data, collected by machine and physical phenomena of failures under the actual use of machine tools. In the present paper, a PHM system of lubrication failure of bearings in CNC lathe spindles is developed. An acceleration sensor is used to collect machine states, and statistical feature parameters that characterize the lubrication failure are extracted from the obtained vibration data. Moreover, in order to clarify the cause-effect relation between the extracted feature parameters and physical phenomena of lubrication failure, several analyses using surface roughness measurement, residual stress measurement, and grease consistency measurement are conducted.
{"title":"Diagnosis Method of Lubrication Failure by Coolant Immersion for a CNC Lathe Spindle","authors":"Keigo Takasugi, Naohiko Suzuki, Y. Kaneko, N. Asakawa","doi":"10.20965/ijat.2023.p0103","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0103","url":null,"abstract":"As a result of the development of network technologies, diagnosis techniques that can collect machine states continuously and prognostic health management (PHM) are available in the factory. PHM technology is also beginning to be implemented in the machine tool field. However, few studies have described causality between feature values, including vibration and acoustic emission data, collected by machine and physical phenomena of failures under the actual use of machine tools. In the present paper, a PHM system of lubrication failure of bearings in CNC lathe spindles is developed. An acceleration sensor is used to collect machine states, and statistical feature parameters that characterize the lubrication failure are extracted from the obtained vibration data. Moreover, in order to clarify the cause-effect relation between the extracted feature parameters and physical phenomena of lubrication failure, several analyses using surface roughness measurement, residual stress measurement, and grease consistency measurement are conducted.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"20 1","pages":"103-111"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75209390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0183
Yutaka Nomaguchi, Ryotaro Senoo, Shinya Fukutomi, K. Hara, K. Fujita
The Future Design (FD) workshop (FDWS) is a discussion framework based on FD. The aim of FD is to activate a human trait called futurability, considering the preferences of future generations. Previous FD practices with the theme of policy-making in local governments have demonstrated this possibility. However, creating concrete proposals might depend on workshop participants’ abilities and emotions to perceive future society. By comparing two case studies, this study examines the effects of a method for utilizing a causal loop diagram (CLD), a tool for systems thinking, in FDWS to systematically draw the future society and activate discussions among the participants. CLD is a qualitative system model that helps identify the factors that lead to systemic problems and analyze the guidelines for solving them. Its effects on the performance of the FDWS discussion activity are evaluated. They are quantified by text mining analysis using participants’ remark records. Two case studies conducted at policy-making workshops in the local governments of Japan are examined. One is the FDWS in Kyoto City which adopted the proposed CLD utilization method, and the other is the FDWS in Suita City without CLD. The comparative analysis demonstrates that the proposed method makes the discussion livelier, less divergent, and more developed in the FDWS.
{"title":"Utilization Method and Effect Evaluation of Systems Thinking in Future Design: Comparative Analysis of Policy-Making Workshops in Local Governments","authors":"Yutaka Nomaguchi, Ryotaro Senoo, Shinya Fukutomi, K. Hara, K. Fujita","doi":"10.20965/ijat.2023.p0183","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0183","url":null,"abstract":"The Future Design (FD) workshop (FDWS) is a discussion framework based on FD. The aim of FD is to activate a human trait called futurability, considering the preferences of future generations. Previous FD practices with the theme of policy-making in local governments have demonstrated this possibility. However, creating concrete proposals might depend on workshop participants’ abilities and emotions to perceive future society. By comparing two case studies, this study examines the effects of a method for utilizing a causal loop diagram (CLD), a tool for systems thinking, in FDWS to systematically draw the future society and activate discussions among the participants. CLD is a qualitative system model that helps identify the factors that lead to systemic problems and analyze the guidelines for solving them. Its effects on the performance of the FDWS discussion activity are evaluated. They are quantified by text mining analysis using participants’ remark records. Two case studies conducted at policy-making workshops in the local governments of Japan are examined. One is the FDWS in Kyoto City which adopted the proposed CLD utilization method, and the other is the FDWS in Suita City without CLD. The comparative analysis demonstrates that the proposed method makes the discussion livelier, less divergent, and more developed in the FDWS.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"2 1","pages":"183-193"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80363280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0136
K. Nishida, Masatoshi Itoh, K. Nakamoto
For machining operations, preparation work called a “setting operation” is always required in advance. The setting operation, which affects the lead time and machining accuracy, strongly depends on the skill level of the operator. Therefore, to improve the quality of machining operations, skill transfer is necessary by extracting and generalizing the skills related to the setting operation. In addition, a variety of accidents often occur during the setting operation. This can lead to machine tool failure or a serious incident involving the operator. Thus, skill transfer to an unskilled operator is also important for work safety. On the other hand, augmented reality (AR) is a promising human-computer interaction technology to support skill transfer at the manufacturing site. An AR technology generally overlays virtual images on the real-world environment. In this study, an AR-based system is developed to demonstrate a recommended workpiece fixturing method in turning operations for assisting unskilled operators as the first step of skill transfer. In turning operations, two types of fixturing are usually assumed: outer diameter clamping and inner diameter clamping. The dimensions of the targeted product shape are detected, and the workpiece shape is obtained. The removal volume to be machined is calculated as the difference between the targeted product shape and workpiece shape. The fixturing method is determined to avoid contact between the removal volume and the assumed jaw. The results of a case study show that the developed AR-based system is effective for skill transfer of workpiece fixturing by demonstrating the recommended fixturing method using skills acquired from operators.
{"title":"Augmented Reality-Based System for Skill Transfer of Workpiece Fixturing in Turning Operations","authors":"K. Nishida, Masatoshi Itoh, K. Nakamoto","doi":"10.20965/ijat.2023.p0136","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0136","url":null,"abstract":"For machining operations, preparation work called a “setting operation” is always required in advance. The setting operation, which affects the lead time and machining accuracy, strongly depends on the skill level of the operator. Therefore, to improve the quality of machining operations, skill transfer is necessary by extracting and generalizing the skills related to the setting operation. In addition, a variety of accidents often occur during the setting operation. This can lead to machine tool failure or a serious incident involving the operator. Thus, skill transfer to an unskilled operator is also important for work safety. On the other hand, augmented reality (AR) is a promising human-computer interaction technology to support skill transfer at the manufacturing site. An AR technology generally overlays virtual images on the real-world environment. In this study, an AR-based system is developed to demonstrate a recommended workpiece fixturing method in turning operations for assisting unskilled operators as the first step of skill transfer. In turning operations, two types of fixturing are usually assumed: outer diameter clamping and inner diameter clamping. The dimensions of the targeted product shape are detected, and the workpiece shape is obtained. The removal volume to be machined is calculated as the difference between the targeted product shape and workpiece shape. The fixturing method is determined to avoid contact between the removal volume and the assumed jaw. The results of a case study show that the developed AR-based system is effective for skill transfer of workpiece fixturing by demonstrating the recommended fixturing method using skills acquired from operators.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"73 1","pages":"136-143"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85703430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0144
Hidetake Tanaka, K. Yamada, Tatsuki Ikari
A three-dimensional (3D) printer can be used to form various shapes in a single process. However, shell shape formation is difficult because of the low adhesion strength between the layers in 3D printing, and sufficient stiffness cannot be maintained. Therefore, the authors focused on laser-assisted incremental forming, which enables the formation of shell shapes from sheet materials, and used carbon fiber reinforced thermo plastic (CFRTP) for the samples. In the study, a laser-assist incremental forming system based on 3D computer-aided design (CAD) data was developed. The system comprises computer-aided manufacturing (CAM) system, which generates a tool path based on CAD data and evaluates the formability between the CAD data and 3D-scanned data, including alignment compensation. The feasibility of the developed system was demonstrated through a set of forming experiments.
{"title":"Feasibility Study of Laser-Assisted Incremental Forming for Carbon Fiber Reinforced Thermo Plastic Based on 3D-CAD Data","authors":"Hidetake Tanaka, K. Yamada, Tatsuki Ikari","doi":"10.20965/ijat.2023.p0144","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0144","url":null,"abstract":"A three-dimensional (3D) printer can be used to form various shapes in a single process. However, shell shape formation is difficult because of the low adhesion strength between the layers in 3D printing, and sufficient stiffness cannot be maintained. Therefore, the authors focused on laser-assisted incremental forming, which enables the formation of shell shapes from sheet materials, and used carbon fiber reinforced thermo plastic (CFRTP) for the samples. In the study, a laser-assist incremental forming system based on 3D computer-aided design (CAD) data was developed. The system comprises computer-aided manufacturing (CAM) system, which generates a tool path based on CAD data and evaluates the formability between the CAD data and 3D-scanned data, including alignment compensation. The feasibility of the developed system was demonstrated through a set of forming experiments.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"25 1","pages":"144-155"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85317127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0092
Kenta Mizuhara, Daisuke Nakamichi, Wataru Yanagihara, Y. Kakinuma
The demand for the mass production of micro-lens arrays (MLAs) is increasing. An MLA is fabricated through an injection molding process, and its mold is manufactured by a five-axis high-precision machine tool using a small diameter endmill. A visual examination is not available to judge the quality of the mold while machining. Therefore, an effective process monitoring technology must be developed. A promising approach is to apply a servomotor current to in-process monitoring because as long as the servomotor works well, no external sensors, capital investment, or maintenance processes are required. From this perspective, a machine learning-based shape error estimation method using only the servomotor current is proposed. To explore the relationship between the motor current generated during micro-milling and the shape error of the mold, the servomotor current in X-, Y-, and Z-axes was recorded, and the corresponding shape error of the MLA mold was measured after machining. Input data were prepared by converting time-domain servomotor current data to frequency-domain data using short-time Fourier transform and reducing the dimensions of the data via principal component analysis. In terms of a meaningful label for the output data, the average shape error in the machined area corresponding to each window was provided. The input/output relationships were used to train five different machine learning models, and the accuracy of shape error estimation using each model was evaluated. In addition, the estimation accuracies using the X-, Y-, and Z-axes were compared to find the axis that senses the shape error with the highest accuracy. The results show that the non-linear method using the X-axis servomotor current information closest to the machining point achieved the highest shape error estimation accuracy.
{"title":"Machine Learning-Based Shape Error Estimation Using the Servomotor Current Generated During Micro-Milling of a Micro-Lens Mold","authors":"Kenta Mizuhara, Daisuke Nakamichi, Wataru Yanagihara, Y. Kakinuma","doi":"10.20965/ijat.2023.p0092","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0092","url":null,"abstract":"The demand for the mass production of micro-lens arrays (MLAs) is increasing. An MLA is fabricated through an injection molding process, and its mold is manufactured by a five-axis high-precision machine tool using a small diameter endmill. A visual examination is not available to judge the quality of the mold while machining. Therefore, an effective process monitoring technology must be developed. A promising approach is to apply a servomotor current to in-process monitoring because as long as the servomotor works well, no external sensors, capital investment, or maintenance processes are required. From this perspective, a machine learning-based shape error estimation method using only the servomotor current is proposed. To explore the relationship between the motor current generated during micro-milling and the shape error of the mold, the servomotor current in X-, Y-, and Z-axes was recorded, and the corresponding shape error of the MLA mold was measured after machining. Input data were prepared by converting time-domain servomotor current data to frequency-domain data using short-time Fourier transform and reducing the dimensions of the data via principal component analysis. In terms of a meaningful label for the output data, the average shape error in the machined area corresponding to each window was provided. The input/output relationships were used to train five different machine learning models, and the accuracy of shape error estimation using each model was evaluated. In addition, the estimation accuracies using the X-, Y-, and Z-axes were compared to find the axis that senses the shape error with the highest accuracy. The results show that the non-linear method using the X-axis servomotor current information closest to the machining point achieved the highest shape error estimation accuracy.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"30 1","pages":"92-102"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82666333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0176
Hayata Shibuya, Y. Nagai
Transforming assemblies are products that alter their shapes by re-assembling their parts. This idea is applied to a wide range of objects from folding gadgets as outdoor gear aimed at saving space, to robotic characters fighting in Hollywood films which drastically change their appearance. While the former type falls into a folding or packing problems, the latter requires a different viewpoint to be solved since the destination shape is not necessarily aiming at minimizing the occupation space. As a possible solution, this kind of deformation can be decomposed into segmentation of the shape to parts and parts matching. Segmentation is a general problem in shape modeling and numerous algorithms have been proposed for this. On the other hand, matching simultaneously multiple parts (many-to-many matching) has hardly been explored. This study develops a many-to-many matching algorithm for surface meshes of parts from two distinct destination shapes of a single transforming assembly. The proposed algorithm consists of a local geometry analysis and a global optimization of parts combination based on such analysis. For the local geometry analysis, the surface geometric feature is described by a local shape descriptor. Some vertices are detected as feature points by intrinsic shape signature (ISS) and the geometry at the feature points is expressed by the signature of histogram of orientation (SHOT). For all the combination of pairs from each destination shape, the number of feature points with similar descriptor values is counted. In the global optimization, the final matching is determined by the maximum weight matching on a complete bipartite graph whose nodes are the parts, and edges are weighted by the number of the feature points with similar descriptors. We present successful results for several examples to empirically show the effectiveness of the proposed algorithm.
{"title":"Automatic Parts Correspondence Determination for Transforming Assemblies via Local and Global Geometry Processing","authors":"Hayata Shibuya, Y. Nagai","doi":"10.20965/ijat.2023.p0176","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0176","url":null,"abstract":"Transforming assemblies are products that alter their shapes by re-assembling their parts. This idea is applied to a wide range of objects from folding gadgets as outdoor gear aimed at saving space, to robotic characters fighting in Hollywood films which drastically change their appearance. While the former type falls into a folding or packing problems, the latter requires a different viewpoint to be solved since the destination shape is not necessarily aiming at minimizing the occupation space. As a possible solution, this kind of deformation can be decomposed into segmentation of the shape to parts and parts matching. Segmentation is a general problem in shape modeling and numerous algorithms have been proposed for this. On the other hand, matching simultaneously multiple parts (many-to-many matching) has hardly been explored. This study develops a many-to-many matching algorithm for surface meshes of parts from two distinct destination shapes of a single transforming assembly. The proposed algorithm consists of a local geometry analysis and a global optimization of parts combination based on such analysis. For the local geometry analysis, the surface geometric feature is described by a local shape descriptor. Some vertices are detected as feature points by intrinsic shape signature (ISS) and the geometry at the feature points is expressed by the signature of histogram of orientation (SHOT). For all the combination of pairs from each destination shape, the number of feature points with similar descriptor values is counted. In the global optimization, the final matching is determined by the maximum weight matching on a complete bipartite graph whose nodes are the parts, and edges are weighted by the number of the feature points with similar descriptors. We present successful results for several examples to empirically show the effectiveness of the proposed algorithm.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"10 1","pages":"176-182"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74300038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0128
Takumu Yoshikawa, Fumihiro Nakamura, E. Sogabe, K. Nakamoto
In parts machining, process planning is typically conducted by skillful operators. The quality of machining is highly dependent on process planning, which determines the operation parameters, such as the operation sequence and cutting tool. To achieve high-quality machining without depending on the skill level of the operators, standardization of process planning is desired. Therefore, it is necessary to extract and generalize skills related to process planning. Furthermore, eye tracking technology is expected to visualize unconscious human behavior. In this study, eye tracking technology is adopted to detect the movement of the operator’s eyes and gather gaze data when understanding mechanical drawings. Gaze data are analyzed using a heat map and bubble chart to identify differences in eye movement according to skill level. The analyzed heat maps indicate that the gazes of the skillful operator are gathered because the operator focuses on the area that is strongly related to the quality of machining. The analyzed bubble charts also indicate that the skillful operator considers the machining process by checking annotations, then understands the shape, and finally verifies the numerical values of the annotations. From the results of interviews performed based on the analysis, the individual skill could be effectively extracted in detail, particularly the skill regarding the operation sequence. Furthermore, the acquired skills are incorporated into a computer-aided process planning system developed in a previous study. The operation sequence is modified to reflect the acquired skills. Machining experiments confirmed the effectiveness of adopting operators’ skills in process planning.
{"title":"Acquisition of Skills for Process Planning Through Eye Tracking When Understanding Mechanical Drawings","authors":"Takumu Yoshikawa, Fumihiro Nakamura, E. Sogabe, K. Nakamoto","doi":"10.20965/ijat.2023.p0128","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0128","url":null,"abstract":"In parts machining, process planning is typically conducted by skillful operators. The quality of machining is highly dependent on process planning, which determines the operation parameters, such as the operation sequence and cutting tool. To achieve high-quality machining without depending on the skill level of the operators, standardization of process planning is desired. Therefore, it is necessary to extract and generalize skills related to process planning. Furthermore, eye tracking technology is expected to visualize unconscious human behavior. In this study, eye tracking technology is adopted to detect the movement of the operator’s eyes and gather gaze data when understanding mechanical drawings. Gaze data are analyzed using a heat map and bubble chart to identify differences in eye movement according to skill level. The analyzed heat maps indicate that the gazes of the skillful operator are gathered because the operator focuses on the area that is strongly related to the quality of machining. The analyzed bubble charts also indicate that the skillful operator considers the machining process by checking annotations, then understands the shape, and finally verifies the numerical values of the annotations. From the results of interviews performed based on the analysis, the individual skill could be effectively extracted in detail, particularly the skill regarding the operation sequence. Furthermore, the acquired skills are incorporated into a computer-aided process planning system developed in a previous study. The operation sequence is modified to reflect the acquired skills. Machining experiments confirmed the effectiveness of adopting operators’ skills in process planning.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"2012 1","pages":"128-135"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86420102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-05DOI: 10.20965/ijat.2023.p0112
Akio Hayashi, Y. Morimoto
At present, machining with numerically controlled (NC) machine tools is mostly performed by NC programs generated by computer-aided design and computer-aided manufacturing (CAD/CAM) systems. However, even if the machining shape to be machined is the same, there are numerous machining processes involving a series of operations such as determining the machining area, machining order, and machining conditions. These are entrusted to the user, and automation is difficult. In addition, these tasks depend on the experience and know-how of skilled engineers, and it is very difficult to convert them into algorithms and reflect them in the creation of NC programs. Therefore, in this study, artificial intelligence (AI) was used for the process design of multi-tasking machine tools, with the goal of determining and automating the process design using shape examples. We propose a shape recognition method that includes image analysis by AI. This image analysis makes it possible to determine the characteristics of the machining shape, and the machining operator can easily judge the machining process based on the CAD model. Furthermore, because there are shapes that cannot be determined from image data alone, shape features are also extracted from the STEP file of the CAD model. A language analysis of the STEP file can find the characteristic components and their numerical information to determine the coordinates of the shape features. By combining image analysis and language analysis, the method can easily judge the process based on the information in the CAD model. Finally, using the generated learning model and analysis program, we conducted a test to determine whether a multitasking machine tool is necessary for machining.
{"title":"Study on Process Design Based on Language Analysis and Image Discrimination Using CNN Deep Learning","authors":"Akio Hayashi, Y. Morimoto","doi":"10.20965/ijat.2023.p0112","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0112","url":null,"abstract":"At present, machining with numerically controlled (NC) machine tools is mostly performed by NC programs generated by computer-aided design and computer-aided manufacturing (CAD/CAM) systems. However, even if the machining shape to be machined is the same, there are numerous machining processes involving a series of operations such as determining the machining area, machining order, and machining conditions. These are entrusted to the user, and automation is difficult. In addition, these tasks depend on the experience and know-how of skilled engineers, and it is very difficult to convert them into algorithms and reflect them in the creation of NC programs. Therefore, in this study, artificial intelligence (AI) was used for the process design of multi-tasking machine tools, with the goal of determining and automating the process design using shape examples. We propose a shape recognition method that includes image analysis by AI. This image analysis makes it possible to determine the characteristics of the machining shape, and the machining operator can easily judge the machining process based on the CAD model. Furthermore, because there are shapes that cannot be determined from image data alone, shape features are also extracted from the STEP file of the CAD model. A language analysis of the STEP file can find the characteristic components and their numerical information to determine the coordinates of the shape features. By combining image analysis and language analysis, the method can easily judge the process based on the information in the CAD model. Finally, using the generated learning model and analysis program, we conducted a test to determine whether a multitasking machine tool is necessary for machining.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"14 1","pages":"112-119"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88586156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Process planning is well known as the key toward achieving highly efficient machining. However, it is difficult to standardize machining skills for process planning, which depend heavily on skilled operators. Hence, in a previous study, a computer aided process planning (CAPP) system using machine learning is developed to determine the operation parameters for finish machining of dies and molds. On the other hand, in rough machining, it is assumed that some machining operations are conducted sequentially using a respective tool according to the workpiece shape, which induces a much higher complexity in process planning. Therefore, in this study, machine learning is adopted to determine operation parameters for rough machining. The developed CAPP system converts the removal volume into a voxel model and infers a machining operation for each voxel. The inferred machining operation is visualized using different colors and identified corresponding to the voxel. Finally, the removal volume is classified using three different machining operations. However, machine learning is said to have a critical problem in that it is difficult to understand the reasons for the inferred results. Hence, it is necessary for the CAPP system to demonstrate the certainty level of the determined operation parameters. Thus, this study proposes a method for calculating the degree of certainty. If an artificial neural network is trained sufficiently, similar inferred results would always be obtained. Consequently, by using the Monte Carlo dropout to delete weights at random, the certainty level is defined as the variance of the inferred results. To verify the usefulness of the CAPP system, a case study is conducted by assuming rough machining of dies and molds. The results confirm that the machining operations are inferred with high accuracy, and the proposed method is effective for evaluating the certainty of the inferred results.
{"title":"Computer Aided Process Planning for Rough Machining Based on Machine Learning with Certainty Evaluation of Inferred Results","authors":"Naofumi Komura, Kazuma Matsumoto, Shinji Igari, Takashi Ogawa, Sho Fujita, K. Nakamoto","doi":"10.20965/ijat.2023.p0120","DOIUrl":"https://doi.org/10.20965/ijat.2023.p0120","url":null,"abstract":"Process planning is well known as the key toward achieving highly efficient machining. However, it is difficult to standardize machining skills for process planning, which depend heavily on skilled operators. Hence, in a previous study, a computer aided process planning (CAPP) system using machine learning is developed to determine the operation parameters for finish machining of dies and molds. On the other hand, in rough machining, it is assumed that some machining operations are conducted sequentially using a respective tool according to the workpiece shape, which induces a much higher complexity in process planning. Therefore, in this study, machine learning is adopted to determine operation parameters for rough machining. The developed CAPP system converts the removal volume into a voxel model and infers a machining operation for each voxel. The inferred machining operation is visualized using different colors and identified corresponding to the voxel. Finally, the removal volume is classified using three different machining operations. However, machine learning is said to have a critical problem in that it is difficult to understand the reasons for the inferred results. Hence, it is necessary for the CAPP system to demonstrate the certainty level of the determined operation parameters. Thus, this study proposes a method for calculating the degree of certainty. If an artificial neural network is trained sufficiently, similar inferred results would always be obtained. Consequently, by using the Monte Carlo dropout to delete weights at random, the certainty level is defined as the variance of the inferred results. To verify the usefulness of the CAPP system, a case study is conducted by assuming rough machining of dies and molds. The results confirm that the machining operations are inferred with high accuracy, and the proposed method is effective for evaluating the certainty of the inferred results.","PeriodicalId":13583,"journal":{"name":"Int. J. Autom. Technol.","volume":"28 1","pages":"120-127"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80420283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：