Smart Manufacturing (SM) emphasizes autonomous self-adoption and decision making, which is possible by the aid of information technology such as big data, sensors, and machine learning techniques. Picking objects autonomously by industrial robots from cluttered bins (Bin picking) is one of topics that the technologies could be applied to manufacturing processes, especially in flexible input and output logistics. One of the methods is to analyze 3D point clouds from depth sensors, and are matched to the geometry model to calculate possible robot posture, which required heavy calculation and complex algorithm to handle the point clouds. Another method is to train neural networks from reinforced learning, however it requires huge amount of trials and trainings to establish the model, starting with failures. In this paper, a convolutional neural network (CNN) model was initially trained from human skills, and it was trained by itself to improve the job accuracy. In the initial stage, an operator selected a block with a depth image from a Lidar sensor by their intuition that a block can be picked up by a robot. The robot tried to pick up the block, and the image of block with the result of the trial by the robot was recorded. CNN was trained after collecting 500 datasets by the operator. Next, in the self-learning stage, the system automatically tried to pick up candidate blocks from the CNN’s prediction. Collected data during the trial was utilized to gradually train the CNN model. The result shows that the job accuracy was 39% with initial CNN, and improved by 71% after 2,000 trials by self-learning step. The collaboration between human and autonomy would enable to apply the system in shop floors by reduced time, simple development, and improved pick-up accuracy.
{"title":"Development of Autonomous Robotic Bin Picking System Using Convolutional Neural Network (CNN) Initially Trained by Human Skills","authors":"Huitaek Yun, Jin-Soo Park, M. Jun","doi":"10.1115/msec2022-84712","DOIUrl":"https://doi.org/10.1115/msec2022-84712","url":null,"abstract":"\u0000 Smart Manufacturing (SM) emphasizes autonomous self-adoption and decision making, which is possible by the aid of information technology such as big data, sensors, and machine learning techniques. Picking objects autonomously by industrial robots from cluttered bins (Bin picking) is one of topics that the technologies could be applied to manufacturing processes, especially in flexible input and output logistics. One of the methods is to analyze 3D point clouds from depth sensors, and are matched to the geometry model to calculate possible robot posture, which required heavy calculation and complex algorithm to handle the point clouds. Another method is to train neural networks from reinforced learning, however it requires huge amount of trials and trainings to establish the model, starting with failures. In this paper, a convolutional neural network (CNN) model was initially trained from human skills, and it was trained by itself to improve the job accuracy. In the initial stage, an operator selected a block with a depth image from a Lidar sensor by their intuition that a block can be picked up by a robot. The robot tried to pick up the block, and the image of block with the result of the trial by the robot was recorded. CNN was trained after collecting 500 datasets by the operator. Next, in the self-learning stage, the system automatically tried to pick up candidate blocks from the CNN’s prediction. Collected data during the trial was utilized to gradually train the CNN model. The result shows that the job accuracy was 39% with initial CNN, and improved by 71% after 2,000 trials by self-learning step. The collaboration between human and autonomy would enable to apply the system in shop floors by reduced time, simple development, and improved pick-up accuracy.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"29 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85821449","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}
A predictive cutting force model is essential for power requirement estimation, cutting tool design, surface error estimation and stability analysis during the end milling operation. Mechanistic model estimates cutting forces by correlating analytically computed chip geometry with lumped coefficients combining tool-work material properties through empirical relationships. Establishing reliable relationships through the statistical curve-fitting is demanding due to the need for several experiments, anomaly or noise in the experimental data, and process disturbances that deteriorate the goodness of fit. Machine learning models can effectively deal with such inherent uncertainties and serve as an alternative to the statistical curve-fitting. The present work proposes to improve the empirical relationship between instantaneous uncut chip thickness and cutting coefficients by employing a deep learning algorithm, namely Adaptive Moment Estimation (ADAM). The ADAM algorithm is augmented with decoupled weight decay and warm restart features for the improved performance. The decoupled weight decay assigns dynamic sensitivity values to the data points for outlier removal resulting in better model generalization, while warm restart allows better guesses through adaptive learning rates. The proposed approach has been implemented as a computational tool to determine improved coefficients values and empirical relationships. The cutting forces predicted using coefficient values determined using statistical curve fitting and ADAM-based machine learning are compared with experimentally measured data over an extensive range of cutting conditions. It is concluded that the augmentation of the ADAM approach enables the Mechanistic force model to effectively capture end milling process physics by estimating better coefficients values resulting in enhanced prediction abilities.
{"title":"Machine Learning-Based Cutting Constant Estimation for Mechanistic Force Models of End Milling Operation","authors":"Shubham Vaishnav, K. A. Desai","doi":"10.1115/msec2022-85587","DOIUrl":"https://doi.org/10.1115/msec2022-85587","url":null,"abstract":"\u0000 A predictive cutting force model is essential for power requirement estimation, cutting tool design, surface error estimation and stability analysis during the end milling operation. Mechanistic model estimates cutting forces by correlating analytically computed chip geometry with lumped coefficients combining tool-work material properties through empirical relationships. Establishing reliable relationships through the statistical curve-fitting is demanding due to the need for several experiments, anomaly or noise in the experimental data, and process disturbances that deteriorate the goodness of fit. Machine learning models can effectively deal with such inherent uncertainties and serve as an alternative to the statistical curve-fitting. The present work proposes to improve the empirical relationship between instantaneous uncut chip thickness and cutting coefficients by employing a deep learning algorithm, namely Adaptive Moment Estimation (ADAM). The ADAM algorithm is augmented with decoupled weight decay and warm restart features for the improved performance. The decoupled weight decay assigns dynamic sensitivity values to the data points for outlier removal resulting in better model generalization, while warm restart allows better guesses through adaptive learning rates. The proposed approach has been implemented as a computational tool to determine improved coefficients values and empirical relationships. The cutting forces predicted using coefficient values determined using statistical curve fitting and ADAM-based machine learning are compared with experimentally measured data over an extensive range of cutting conditions. It is concluded that the augmentation of the ADAM approach enables the Mechanistic force model to effectively capture end milling process physics by estimating better coefficients values resulting in enhanced prediction abilities.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"273 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79596729","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}
M. Hashemitaheri, R. Mittal, H. Cherukuri, R. Singh
Stability properties of micro-milling operations are characterized by the Stability Lobe Diagram (SLD). The material removal rates during micro-milling operations depend on the optimal values chosen for the depth of cut and also spindle speed. Theoretically, the stability boundary is calculated having the structural dynamics and the cutting parameters. However, some discrepancies are usually observed between the empirical results and the expected results that the theory supports. The driver of such a gap is that the dynamics is affected during machining operation by parameters such as the spindle speed, cutting loads, thermal changes, feed rate, etc whereas the theory is based on the structural dynamics parameters in the idle state of the machine (zero speed). Consequently, the selection of chatter-free values for cutting depth and spindle speed based on SLD in the idle state of the machine is not reliable. In addition, measuring structural dynamics parameters under cutting conditions is difficult. In this study, a novel approach is introduced to determine in-process structural dynamics parameters based on a multivariate Newton-Raphson method. Having the empirical SLD characterized by experimental data, our method tries to find the structural parameters under which the theory can support the given empirical SLD. Note that the theoretical SLD is usually characterized as a function of the cutting and structural dynamics parameters. Here our method follows the inverse flow and utilizes the empirical SLD to return the underlying parameters. The parameters returned by our method are those supported by the physics-based theories. Therefore, our approach is a hybrid method where the physics-based model is combined with the experimental results. For any given empirical SLD, with the cutting parameters fixed, the in-process structural dynamics parameters are determined using the proposed inverse approach. We use a multivariate Newton-Raphson method approach where through the iterations, an initial guess selected for the set of the parameters is adjusted step-by-step until the final set of the parameters can justify the empirical SLD based upon physics-based models.
{"title":"Extracting the In-Process Structural Dynamics Parameters in Micro-Milling Operations","authors":"M. Hashemitaheri, R. Mittal, H. Cherukuri, R. Singh","doi":"10.1115/msec2022-85621","DOIUrl":"https://doi.org/10.1115/msec2022-85621","url":null,"abstract":"\u0000 Stability properties of micro-milling operations are characterized by the Stability Lobe Diagram (SLD). The material removal rates during micro-milling operations depend on the optimal values chosen for the depth of cut and also spindle speed. Theoretically, the stability boundary is calculated having the structural dynamics and the cutting parameters. However, some discrepancies are usually observed between the empirical results and the expected results that the theory supports. The driver of such a gap is that the dynamics is affected during machining operation by parameters such as the spindle speed, cutting loads, thermal changes, feed rate, etc whereas the theory is based on the structural dynamics parameters in the idle state of the machine (zero speed). Consequently, the selection of chatter-free values for cutting depth and spindle speed based on SLD in the idle state of the machine is not reliable. In addition, measuring structural dynamics parameters under cutting conditions is difficult. In this study, a novel approach is introduced to determine in-process structural dynamics parameters based on a multivariate Newton-Raphson method. Having the empirical SLD characterized by experimental data, our method tries to find the structural parameters under which the theory can support the given empirical SLD. Note that the theoretical SLD is usually characterized as a function of the cutting and structural dynamics parameters. Here our method follows the inverse flow and utilizes the empirical SLD to return the underlying parameters. The parameters returned by our method are those supported by the physics-based theories. Therefore, our approach is a hybrid method where the physics-based model is combined with the experimental results. For any given empirical SLD, with the cutting parameters fixed, the in-process structural dynamics parameters are determined using the proposed inverse approach. We use a multivariate Newton-Raphson method approach where through the iterations, an initial guess selected for the set of the parameters is adjusted step-by-step until the final set of the parameters can justify the empirical SLD based upon physics-based models.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82474859","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}
Laser-induced forward transfer (LIFT) presents promising perspectives towards high precision three-dimensional metal microstructure fabrication. However, the positional deviation of deposits produced in LIFT reduces the printing resolution. In this study, we experimentally investigate the effects of flight distance on the depositing behaviors of the metal droplets in LIFT. A series of droplet deposition experiments with different flight distances were performed, printing a large number of particles in each set of parameters to avoid random errors. Morphology of deposited particles under different conditions was compared. Positional information was extracted by the image matching algorithm. The flight distance was optimized by analyzing the positional deviation and the morphology of particles. The results demonstrate that the positional deviation of particles increases linearly with the flight distance, while the average size of particles is constant. Excessive flight distance increases the oxidation of copper droplets. For the distance less than 20 μm, a portion of particles disappears in the array. There is a flat surface on the top of particles, indicating that droplets have been squashed by the carrier substrate. This conjecture is confirmed by the observation of residual metal particles on the carrier substrate. This study will advance the understanding of droplet generation and the application of LIFT in the industry.
{"title":"Effects of Flight Distance on Metal Microdroplet Depositing Behaviors in Laser-Induced Forward Transfer","authors":"Di Wu, Zi-Xing Lu, Guohu Luo, Yongxiang Hu","doi":"10.1115/msec2022-85508","DOIUrl":"https://doi.org/10.1115/msec2022-85508","url":null,"abstract":"\u0000 Laser-induced forward transfer (LIFT) presents promising perspectives towards high precision three-dimensional metal microstructure fabrication. However, the positional deviation of deposits produced in LIFT reduces the printing resolution. In this study, we experimentally investigate the effects of flight distance on the depositing behaviors of the metal droplets in LIFT. A series of droplet deposition experiments with different flight distances were performed, printing a large number of particles in each set of parameters to avoid random errors. Morphology of deposited particles under different conditions was compared. Positional information was extracted by the image matching algorithm. The flight distance was optimized by analyzing the positional deviation and the morphology of particles.\u0000 The results demonstrate that the positional deviation of particles increases linearly with the flight distance, while the average size of particles is constant. Excessive flight distance increases the oxidation of copper droplets. For the distance less than 20 μm, a portion of particles disappears in the array. There is a flat surface on the top of particles, indicating that droplets have been squashed by the carrier substrate. This conjecture is confirmed by the observation of residual metal particles on the carrier substrate. This study will advance the understanding of droplet generation and the application of LIFT in the industry.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83315929","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}
Matt Ryan, Yiwen Wang, Qinqin Xiao, R. Liu, Yunbo Zhang
In order to address the demand for skilled machinists and limitations with current training programs, we introduce an immersive Virtual Reality (VR) CNC machining training environment for CNC machine setup processes with a novel error management based training curriculum. Current machinist training programs are several years long requiring active mentorship from a skilled individual and are very costly due to the materials and tools required. Mistakes and errors made during the set up process can create safety risks, waste material and break equipment requiring additional time to reset. Existing VR CNC milling training environments fail to address mistakes that can occur during the setup process. In order to address these operational challenges, a novel error-management based training in VR is proposed which allows trainees to learn the set up procedure,learn the common errors & mistakes and practice identifying errors in addition to practicing activities for the setup. The training first introduces students to the setup procedure, followed by demonstrations of error cases and identification and management strategies culminating in practice opportunities. Trainees witness a spatial demonstration of the procedure, guided by auditory and text instructions. Users are able to actively explore the spatial teaching environment while controlling a virtual CNC milling machine. A preliminary user training test is performed comparing the VR method to a video training and a video training with error management curriculum.
{"title":"Immersive Virtual Reality Training With Error Management for CNC Milling Set-Up","authors":"Matt Ryan, Yiwen Wang, Qinqin Xiao, R. Liu, Yunbo Zhang","doi":"10.1115/msec2022-85770","DOIUrl":"https://doi.org/10.1115/msec2022-85770","url":null,"abstract":"\u0000 In order to address the demand for skilled machinists and limitations with current training programs, we introduce an immersive Virtual Reality (VR) CNC machining training environment for CNC machine setup processes with a novel error management based training curriculum. Current machinist training programs are several years long requiring active mentorship from a skilled individual and are very costly due to the materials and tools required. Mistakes and errors made during the set up process can create safety risks, waste material and break equipment requiring additional time to reset. Existing VR CNC milling training environments fail to address mistakes that can occur during the setup process. In order to address these operational challenges, a novel error-management based training in VR is proposed which allows trainees to learn the set up procedure,learn the common errors & mistakes and practice identifying errors in addition to practicing activities for the setup. The training first introduces students to the setup procedure, followed by demonstrations of error cases and identification and management strategies culminating in practice opportunities. Trainees witness a spatial demonstration of the procedure, guided by auditory and text instructions. Users are able to actively explore the spatial teaching environment while controlling a virtual CNC milling machine. A preliminary user training test is performed comparing the VR method to a video training and a video training with error management curriculum.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73697848","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}
Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of CNC and PLCs along with hardware innovations (quick-change tooling, for example) and various operator assist technologies enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. Customized for manufacturers by system integrators, these systems are often large monolithic systems assembled around an ERP/MRP framework or a precariously integrated set of decision-support software tools with a patchwork of communications enabling information flow between them. On the other hand, cloud-based information service platforms such as those encountered in social networks and service brokers have seen rapid and multiple cycles of evolution resulting in a meteoric rise in their ability to handle increasingly large data scales and rates, while still maintaining their elasticity and flexibility. This rapid evolution of cloud-based information services has ignited a new era in the manufacturing industry as evidenced by emerging manufacturing cyberphysical system technologies such as the Industrial Internet of Things (IIoT), and Cloud Manufacturing (CM). These technologies are part of the broader context of what is thought to be the unfolding fourth industrial revolution (Industry 4.0 or Digital Manufacturing). This revolution places at its core, connectivity, information, and machine-based intelligence to create a new paradigm for manufacturing that is highly flexible, scalable, responsive, and intelligent. This paper describes how we leveraged the newest advances in CPS, IIoT, CM, and distributed systems to create a flexible manufacturing information system infrastructure that separates information collection and distribution for decision-making functions. The first part of the paper introduces the architecture for a novel full-stack manufacturing infrastructure that is envisioned to facilitate and track the interaction between a manufacturing job, physical resources, and the software services (or apps) around them. We call this platform the Operating System for Cyber-physical Manufacturing (OSCM). In the second part of the paper, we introduce an event-based architecture for OSCM so that resource or transaction related events/data can be flexibly distributed to different decision-making/manufacturing software tools through an event/message exchange/bus. Further, such an architecture allows modularization and incremental development of different manufacturing software tools and services as new needs are identified.
{"title":"Operating System for Cyber-Physical Manufacturing (OSCM): A Flexible Event-Driven Shopfloor Information Platform for Advanced Manufacturing","authors":"Ricardo Toro Santamaria, P. Ferreira","doi":"10.1115/msec2022-85576","DOIUrl":"https://doi.org/10.1115/msec2022-85576","url":null,"abstract":"\u0000 Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of CNC and PLCs along with hardware innovations (quick-change tooling, for example) and various operator assist technologies enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. Customized for manufacturers by system integrators, these systems are often large monolithic systems assembled around an ERP/MRP framework or a precariously integrated set of decision-support software tools with a patchwork of communications enabling information flow between them.\u0000 On the other hand, cloud-based information service platforms such as those encountered in social networks and service brokers have seen rapid and multiple cycles of evolution resulting in a meteoric rise in their ability to handle increasingly large data scales and rates, while still maintaining their elasticity and flexibility. This rapid evolution of cloud-based information services has ignited a new era in the manufacturing industry as evidenced by emerging manufacturing cyberphysical system technologies such as the Industrial Internet of Things (IIoT), and Cloud Manufacturing (CM). These technologies are part of the broader context of what is thought to be the unfolding fourth industrial revolution (Industry 4.0 or Digital Manufacturing). This revolution places at its core, connectivity, information, and machine-based intelligence to create a new paradigm for manufacturing that is highly flexible, scalable, responsive, and intelligent.\u0000 This paper describes how we leveraged the newest advances in CPS, IIoT, CM, and distributed systems to create a flexible manufacturing information system infrastructure that separates information collection and distribution for decision-making functions. The first part of the paper introduces the architecture for a novel full-stack manufacturing infrastructure that is envisioned to facilitate and track the interaction between a manufacturing job, physical resources, and the software services (or apps) around them. We call this platform the Operating System for Cyber-physical Manufacturing (OSCM). In the second part of the paper, we introduce an event-based architecture for OSCM so that resource or transaction related events/data can be flexibly distributed to different decision-making/manufacturing software tools through an event/message exchange/bus. Further, such an architecture allows modularization and incremental development of different manufacturing software tools and services as new needs are identified.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76848303","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}
Sustainability has become an important topic in society, industry, and academia. It influences decisions on many levels and there are already many approaches to achieving sustainability in everyday life. One of the areas with the greatest impact on sustainability is production. Maintenance is often neglected when considering sustainable development, although it can play an important role in achieving sustainability within the three dimensions economy, ecology and social. This paper is a scoping literature review that identifies and classifies publications on sustainable maintenance in the manufacturing industry for the period 2017 to November 2021 and presents the results organized into categories. The categories include the contribution of maintenance to sustainability, influence of sustainability on maintenance, methods, and tools for sustainable maintenance, and enabling technologies for sustainable maintenance 4.0. A trend can be identified that sustainable maintenance has received increasing attention in recent years. The research takes place mainly in theory. Numerous methods, tools and frameworks have been identified that can increase sustainability in maintenance. Not all outcomes however can be classified as covering all three pillars of sustainability. While many focus on ecology, only few are committed to social sustainability as well. For the future, it is important to consider the three areas of sustainability as a unit rather than separately, and to apply the research conducted in sustainable maintenance in practice.
{"title":"What Role Does Maintenance Play in Achieving Sustainability in Manufacturing? - A Scoping Literature Review","authors":"Christina Bredebach","doi":"10.1115/msec2022-84064","DOIUrl":"https://doi.org/10.1115/msec2022-84064","url":null,"abstract":"\u0000 Sustainability has become an important topic in society, industry, and academia. It influences decisions on many levels and there are already many approaches to achieving sustainability in everyday life.\u0000 One of the areas with the greatest impact on sustainability is production. Maintenance is often neglected when considering sustainable development, although it can play an important role in achieving sustainability within the three dimensions economy, ecology and social.\u0000 This paper is a scoping literature review that identifies and classifies publications on sustainable maintenance in the manufacturing industry for the period 2017 to November 2021 and presents the results organized into categories. The categories include the contribution of maintenance to sustainability, influence of sustainability on maintenance, methods, and tools for sustainable maintenance, and enabling technologies for sustainable maintenance 4.0.\u0000 A trend can be identified that sustainable maintenance has received increasing attention in recent years. The research takes place mainly in theory. Numerous methods, tools and frameworks have been identified that can increase sustainability in maintenance. Not all outcomes however can be classified as covering all three pillars of sustainability. While many focus on ecology, only few are committed to social sustainability as well.\u0000 For the future, it is important to consider the three areas of sustainability as a unit rather than separately, and to apply the research conducted in sustainable maintenance in practice.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77731412","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}
This paper presents an analysis of primary chatter under velocity-induced negative process damping in the peripheral outer diameter turning of medium carbon steel. A first-order approximation model of the instant specific cutting force with respect to dynamic cutting speed was established and the slope was defined as the specific process damping coefficient (SPDC) to investigate the negative process damping with respect to cutting speed, depth of cut, and chip thickness. The process damping coefficient was defined as the product of the specific process damping coefficient and chip load. The total system damping coefficient as the sum of the process damping coefficient and structural damping coefficient determines the system stability and predict primary chatter. The SPDCs were obtained through experiments under various speeds, feeds, and depths of cut by using a tool system with force sensors and accelerometers. The SPDCs were insensitive to cutting speeds of 2.5 to 5.5 m/sec and ranged from −1514 and −716 MPa·s/m for feeds per revolution of 0.058 to 0.118 mm, respectively. The higher negative SPDC at smaller chip thickness reduces the limiting stable chip load. Equations for the limiting chip load and limiting depth of cut were derived and validated by experiments. Stability diagrams of limiting chip load and limiting depth with respect to feed per revolution were created to provide guidance on preventing primary chatter.
{"title":"Primary Chatter and Limiting Chip Load in Turning Under Negative Process Damping","authors":"Ming-Jen Hsu, Jiunn-Jyh Wang","doi":"10.1115/msec2022-85293","DOIUrl":"https://doi.org/10.1115/msec2022-85293","url":null,"abstract":"\u0000 This paper presents an analysis of primary chatter under velocity-induced negative process damping in the peripheral outer diameter turning of medium carbon steel. A first-order approximation model of the instant specific cutting force with respect to dynamic cutting speed was established and the slope was defined as the specific process damping coefficient (SPDC) to investigate the negative process damping with respect to cutting speed, depth of cut, and chip thickness. The process damping coefficient was defined as the product of the specific process damping coefficient and chip load. The total system damping coefficient as the sum of the process damping coefficient and structural damping coefficient determines the system stability and predict primary chatter. The SPDCs were obtained through experiments under various speeds, feeds, and depths of cut by using a tool system with force sensors and accelerometers. The SPDCs were insensitive to cutting speeds of 2.5 to 5.5 m/sec and ranged from −1514 and −716 MPa·s/m for feeds per revolution of 0.058 to 0.118 mm, respectively. The higher negative SPDC at smaller chip thickness reduces the limiting stable chip load. Equations for the limiting chip load and limiting depth of cut were derived and validated by experiments. Stability diagrams of limiting chip load and limiting depth with respect to feed per revolution were created to provide guidance on preventing primary chatter.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81688495","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}
The soft materials used in the infrastructure of hydrogen storage and distribution systems are vulnerable because exposure to high-pressure hydrogen can lead to mechanical damage and property degradation. Polymers are one of the widely used classes of soft materials within hydrogen infrastructure. Many small cavities exist within the polymer material due to their long molecular chains. When exposed to high-pressure hydrogen gas, the gas diffuses through the polymer material and occupies these cavities. When outside hydrogen pressure reduces suddenly, the hydrogen gas inside the cavities does not get enough time to diffuse out as diffusion is a much slower process. Instead, this trapped gas causes blistering or in extreme cases rapture of polymer material. This phenomenon is also known as rapid decompression failure. In this study, a continuum mechanics-based fully coupled diffusion-deformation model with damage is developed to predict the stress distribution and damage propagation while the polymer undergoes rapid decompression failure. The hyperelastic material model, along with the maximum principal strain failure theory, was chosen for this study as it represents the nonlinear material response with sudden failure observed in uniaxial tensile tests perfectly. EPDM polymer was chosen for this study because of its commercial availability and common use in hydrogen storage and distribution system. It has superior mechanical properties, high and low-temperature resistance, and certain compounds work well in hydrogen gas. Stress concentration was observed on the periphery of the cavity at the point closest to the outside surface which lead to damage initiation at the same location. Also, this work showed that the coefficient of diffusion plays an important role in damage initiation. As the value of the coefficient of diffusion increases, the amount of damage decreases due to the higher coefficient of diffusion ensures a safe passage for trapped hydrogen to escape to the atmosphere. This work is useful for design engineers to alter the parameters while manufacturing polymer composites to increase their performance in a high-pressure hydrogen environment.
{"title":"Coupled Diffusion-Deformation-Damage Model for Polymers Used in Hydrogen Infrastructure","authors":"Shank S. Kulkarni, K. S. Choi, K. Simmons","doi":"10.1115/msec2022-80231","DOIUrl":"https://doi.org/10.1115/msec2022-80231","url":null,"abstract":"\u0000 The soft materials used in the infrastructure of hydrogen storage and distribution systems are vulnerable because exposure to high-pressure hydrogen can lead to mechanical damage and property degradation. Polymers are one of the widely used classes of soft materials within hydrogen infrastructure. Many small cavities exist within the polymer material due to their long molecular chains. When exposed to high-pressure hydrogen gas, the gas diffuses through the polymer material and occupies these cavities. When outside hydrogen pressure reduces suddenly, the hydrogen gas inside the cavities does not get enough time to diffuse out as diffusion is a much slower process. Instead, this trapped gas causes blistering or in extreme cases rapture of polymer material. This phenomenon is also known as rapid decompression failure.\u0000 In this study, a continuum mechanics-based fully coupled diffusion-deformation model with damage is developed to predict the stress distribution and damage propagation while the polymer undergoes rapid decompression failure. The hyperelastic material model, along with the maximum principal strain failure theory, was chosen for this study as it represents the nonlinear material response with sudden failure observed in uniaxial tensile tests perfectly. EPDM polymer was chosen for this study because of its commercial availability and common use in hydrogen storage and distribution system. It has superior mechanical properties, high and low-temperature resistance, and certain compounds work well in hydrogen gas. Stress concentration was observed on the periphery of the cavity at the point closest to the outside surface which lead to damage initiation at the same location. Also, this work showed that the coefficient of diffusion plays an important role in damage initiation. As the value of the coefficient of diffusion increases, the amount of damage decreases due to the higher coefficient of diffusion ensures a safe passage for trapped hydrogen to escape to the atmosphere. This work is useful for design engineers to alter the parameters while manufacturing polymer composites to increase their performance in a high-pressure hydrogen environment.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82408856","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}
In the era of Industry 4.0, the machining sound has been extensively adopted in tool condition monitoring systems, virtual machining environment, and remote machining solutions. However, only limited attention has been paid to understand how experienced machinists detect tool wear and improper cutting conditions based on their hearing in the real machining environment. This paper aims to experimentally investigate and analyze the auditory perception of CNC operators during the cutting process and their capabilities of detecting unfavorable cutting conditions and faults using their sense of hearing and expertise. The sound in the machining environment was analyzed in the aspect of sound pressure levels (SPL). Optimal positions for sound sample acquisition were determined and audio data was recorded for future analysis. Experimental cutting tests with simulated process faults were conducted, where machinists with varying degrees of experience observed the process, listened to the machining sound and tried to determine whether cutting conditions were normal or if faults occurred. The primary research goal was to analyze how well operators can monitor the process using their various senses and to investigate the role of sound and auditory perceptions of trained professionals in cutting process supervision and monitoring. SPL measurements have shown that the sound pressure varies substantially in the machining environment, which is expected to affect the quality and volume of recorded machining sound depending on microphone positioning. Cutting tests have shown that the machinists use various senses to determine faults in the process, relying most significantly on auditory stimuli, with other factors, such as vibrations or visual examination of the workpiece having a secondary effect in the assessment of cutting process conditions and outcomes.
{"title":"Investigating the Role of Auditory Perception of Cutting Process Conditions in CNC Machining","authors":"K. Jarosz, Yunbo Zhang, R. Liu","doi":"10.1115/msec2022-85804","DOIUrl":"https://doi.org/10.1115/msec2022-85804","url":null,"abstract":"\u0000 In the era of Industry 4.0, the machining sound has been extensively adopted in tool condition monitoring systems, virtual machining environment, and remote machining solutions. However, only limited attention has been paid to understand how experienced machinists detect tool wear and improper cutting conditions based on their hearing in the real machining environment. This paper aims to experimentally investigate and analyze the auditory perception of CNC operators during the cutting process and their capabilities of detecting unfavorable cutting conditions and faults using their sense of hearing and expertise. The sound in the machining environment was analyzed in the aspect of sound pressure levels (SPL). Optimal positions for sound sample acquisition were determined and audio data was recorded for future analysis. Experimental cutting tests with simulated process faults were conducted, where machinists with varying degrees of experience observed the process, listened to the machining sound and tried to determine whether cutting conditions were normal or if faults occurred. The primary research goal was to analyze how well operators can monitor the process using their various senses and to investigate the role of sound and auditory perceptions of trained professionals in cutting process supervision and monitoring. SPL measurements have shown that the sound pressure varies substantially in the machining environment, which is expected to affect the quality and volume of recorded machining sound depending on microphone positioning. Cutting tests have shown that the machinists use various senses to determine faults in the process, relying most significantly on auditory stimuli, with other factors, such as vibrations or visual examination of the workpiece having a secondary effect in the assessment of cutting process conditions and outcomes.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88612773","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}
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