With growing environmental concerns, the focus on greenhouse gases (GHG) emissions in transportation has increased, and the combination of smart microgrids and electric vehicles (EVs) brings a new opportunity to solve this problem. Electric vehicle routing problem with time windows (EVRPTW) is an extension of the vehicle routing problem (VRP) problem, which can reach the combination of smart microgrids and EVs precisely by scheduling the EVs. However, the current genetic algorithm (GA) for solving this problem can easily fall into the dilemma of local optimization and slow iteration speed. In this paper, we present an integer hybrid planning model that introduces time of use and area price to enhance realism. We propose the GA-A* algorithm, which combines the A* algorithm and GA to improve global search capability and iteration speed. We conducted experiments on 16 benchmark cases, comparing the GA-A* algorithm with traditional GA and other search algorithms, results demonstrate significant enhancements in searchability and optimal solutions. In addition, we measured the grid load, and the model implements the vehicle-to-grid (V2G) mode, which serves as peak shaving and valley filling by integrating EVs into the grid for energy delivery and exchange through battery swapping. This research, ranging from model optimization to algorithm improvement, is an important step towards solving the EVRPTW problem and improving the environment.
随着人们对环境问题的日益关注，交通领域的温室气体（GHG）排放问题日益受到重视，而智能微电网与电动汽车（EV）的结合为解决这一问题带来了新的契机。带时间窗口的电动汽车路由问题（EVRPTW）是车辆路由问题（VRP）的扩展，通过对电动汽车进行调度，可以实现智能微电网与电动汽车的精确结合。然而，目前解决该问题的遗传算法（GA）容易陷入局部优化和迭代速度慢的困境。在本文中，我们提出了一种整数混合规划模型，该模型引入了使用时间和区域价格，以增强现实性。我们提出了 GA-A* 算法，该算法将 A* 算法和 GA 算法相结合，提高了全局搜索能力和迭代速度。我们对 16 个基准案例进行了实验，将 GA-A* 算法与传统 GA 及其他搜索算法进行了比较，结果表明 GA-A* 算法在可搜索性和最优解方面有显著提高。此外，我们还测量了电网负荷，并在模型中实现了车联网（V2G）模式，通过电池交换将电动汽车整合到电网中进行能量输送和交换，从而起到削峰填谷的作用。这项研究从模型优化到算法改进，为解决 EVRPTW 问题和改善环境迈出了重要一步。
{"title":"A combined genetic algorithm and A* search algorithm for the electric vehicle routing problem with time windows","authors":"D.L. Wang, A. Ding, G.L. Chen, L. Zhang","doi":"10.14743/apem2023.4.481","DOIUrl":"https://doi.org/10.14743/apem2023.4.481","url":null,"abstract":"With growing environmental concerns, the focus on greenhouse gases (GHG) emissions in transportation has increased, and the combination of smart microgrids and electric vehicles (EVs) brings a new opportunity to solve this problem. Electric vehicle routing problem with time windows (EVRPTW) is an extension of the vehicle routing problem (VRP) problem, which can reach the combination of smart microgrids and EVs precisely by scheduling the EVs. However, the current genetic algorithm (GA) for solving this problem can easily fall into the dilemma of local optimization and slow iteration speed. In this paper, we present an integer hybrid planning model that introduces time of use and area price to enhance realism. We propose the GA-A* algorithm, which combines the A* algorithm and GA to improve global search capability and iteration speed. We conducted experiments on 16 benchmark cases, comparing the GA-A* algorithm with traditional GA and other search algorithms, results demonstrate significant enhancements in searchability and optimal solutions. In addition, we measured the grid load, and the model implements the vehicle-to-grid (V2G) mode, which serves as peak shaving and valley filling by integrating EVs into the grid for energy delivery and exchange through battery swapping. This research, ranging from model optimization to algorithm improvement, is an important step towards solving the EVRPTW problem and improving the environment.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"10 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150799","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}
Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.
{"title":"Optimizing smart manufacturing systems using digital twin","authors":"R. Ojsteršek, A. Javernik, B. Buchmeister","doi":"10.14743/apem2023.4.486","DOIUrl":"https://doi.org/10.14743/apem2023.4.486","url":null,"abstract":"Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"22 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139148486","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}
Research on the rock-breaking performance of the Polycrystalline Diamond Compact (PDC) cutter has primarily focused on sharp cutters, often overlooking the influence of chamfer. Notably, the design of chamfer parameters has been largely unreported. In this study, we established a theoretical model of cutting force that takes chamfer into account. We analysed the primary and secondary relationships of four factors – back rake angle, depth of cut, chamfer angle, and chamfer length – on the force of the PDC cutter. This was done through a pseudo-level orthogonal level test. A numerical simulation, based on the Smooth Particle Hydrodynamic (SPH) method, was conducted to analyse the rock-breaking force and stress distribution characteristics of PDC cutters with different chamfer angles. Combined with a drop hammer impact test, we provided an optimized design of chamfer parameters. Our findings revealed that while the chamfer had a relatively minor influence on the force of the PDC cutter, it contributed to the optimal distribution of stress on the PDC cutter. This effectively protected the cutting edge and prevented early cracks and spalls of the cutter. When the chamfer angle was less than or equal to the back rake angle, the resultant force of the PDC cutter increased with the increase of the chamfer angle. However, when the chamfer angle was greater than the back rake angle, the resultant force of the PDC cutter first increased and then slightly decreased with the increase of the chamfer angle. Additionally, the resultant force of the PDC cutter increased approximately linearly with the increase of chamfer length. When the chamfer angle of the PDC cutter was between 30° and 45°, the fluctuation of the cutting force was relatively smooth, the rock-breaking process was stable, and the cutter’s impact resistance energy was relatively higher. These findings will provide valuable guidelines for the design of chamfered PDC cutters.
{"title":"Optimizing rock breaking performance: The influence of chamfer on polycrystalline diamond compact (PDC) cutters","authors":"P. Ju","doi":"10.14743/apem2023.4.482","DOIUrl":"https://doi.org/10.14743/apem2023.4.482","url":null,"abstract":"Research on the rock-breaking performance of the Polycrystalline Diamond Compact (PDC) cutter has primarily focused on sharp cutters, often overlooking the influence of chamfer. Notably, the design of chamfer parameters has been largely unreported. In this study, we established a theoretical model of cutting force that takes chamfer into account. We analysed the primary and secondary relationships of four factors – back rake angle, depth of cut, chamfer angle, and chamfer length – on the force of the PDC cutter. This was done through a pseudo-level orthogonal level test. A numerical simulation, based on the Smooth Particle Hydrodynamic (SPH) method, was conducted to analyse the rock-breaking force and stress distribution characteristics of PDC cutters with different chamfer angles. Combined with a drop hammer impact test, we provided an optimized design of chamfer parameters. Our findings revealed that while the chamfer had a relatively minor influence on the force of the PDC cutter, it contributed to the optimal distribution of stress on the PDC cutter. This effectively protected the cutting edge and prevented early cracks and spalls of the cutter. When the chamfer angle was less than or equal to the back rake angle, the resultant force of the PDC cutter increased with the increase of the chamfer angle. However, when the chamfer angle was greater than the back rake angle, the resultant force of the PDC cutter first increased and then slightly decreased with the increase of the chamfer angle. Additionally, the resultant force of the PDC cutter increased approximately linearly with the increase of chamfer length. When the chamfer angle of the PDC cutter was between 30° and 45°, the fluctuation of the cutting force was relatively smooth, the rock-breaking process was stable, and the cutter’s impact resistance energy was relatively higher. These findings will provide valuable guidelines for the design of chamfered PDC cutters.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150332","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}
Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
{"title":"Reduction of surface defects by optimization of casting speed using genetic programming: An industrial case study","authors":"M. Kovacic, U. Zuperl, L. Gusel, M. Brezocnik","doi":"10.14743/apem2023.4.488","DOIUrl":"https://doi.org/10.14743/apem2023.4.488","url":null,"abstract":"Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"359 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139148922","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}
Incentives are quite common to be utilized in engineering applications such as some infrastructure development projects or construction projects. Due to the increasing complexity of construction management and the continuing trend towards outsourcing of component or engineering outsourcing activities, we focus on the issue of incentive design. Time collaboration is one of the main focuses of random project duration time in parallel projects. In this article, we consider a setting where a manufacturer outsources two parallel subtasks to two different suppliers, and the manufacturer is time sensitive. On the premise that the project completion time follows the exponential distribution, some models are established to compare the proposed incentives and we get the comparative analysis of the proposed incentives. This paper puts forward three kinds of time-based incentive mechanisms, namely, deadline incentive mechanism, competition mechanism and mixed incentive mechanism. We do modeling analysis for all incentive mechanisms. We get the optimal work rates determined by suppliers and compare various incentive mechanisms to maximize manufacturers' profits.
{"title":"Incentive modeling analysis in engineering applications and projects with stochastic duration time","authors":"J. Zhao, J.F. Su","doi":"10.14743/apem2023.4.487","DOIUrl":"https://doi.org/10.14743/apem2023.4.487","url":null,"abstract":"Incentives are quite common to be utilized in engineering applications such as some infrastructure development projects or construction projects. Due to the increasing complexity of construction management and the continuing trend towards outsourcing of component or engineering outsourcing activities, we focus on the issue of incentive design. Time collaboration is one of the main focuses of random project duration time in parallel projects. In this article, we consider a setting where a manufacturer outsources two parallel subtasks to two different suppliers, and the manufacturer is time sensitive. On the premise that the project completion time follows the exponential distribution, some models are established to compare the proposed incentives and we get the comparative analysis of the proposed incentives. This paper puts forward three kinds of time-based incentive mechanisms, namely, deadline incentive mechanism, competition mechanism and mixed incentive mechanism. We do modeling analysis for all incentive mechanisms. We get the optimal work rates determined by suppliers and compare various incentive mechanisms to maximize manufacturers' profits.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149398","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 study analyzes the impact of consumer learning behavior and supplier price competition on retailer price competition in a complex adaptive system. Using machine Learning-enhanced agent-based modeling and simulation, the study applies fuzzy logic and genetic algorithms to model price decisions, and reinforcement learning and swarm intelligence to model consumer behavior. Simulations reveal that different learning behaviors result in different retailer competition patterns, and that supplier price competition affects the strength of retailer price competition. Simulation results demonstrate that consumer learning behavior influences retailer competition, with self-learning consumers leading to higher-priced partnerships, and collective-learning consumers leading to a shift in price competition among retailers. In contrast, perfect rationality consumers result in low-price competition and the lowest average margin and profit. Additionally, the competitive price behavior of suppliers impacts retailers' price competition patterns, with supplier price competition reducing retailer price competition in the perfect rationality consumer market and enhancing it in the self-learning and collective-learning consumer markets, leading to lower average prices and profits for retailers. This study presents a simulated market for price competition among suppliers, retailers, and consumers that can be expanded by subsequent scholars to test related hypotheses.
{"title":"Dynamic price competition market for retailers in the context of consumer learning behavior and supplier competition: Machine learning-enhanced agent-based modeling and simulation","authors":"G.F. Deng","doi":"10.14743/apem2023.4.483","DOIUrl":"https://doi.org/10.14743/apem2023.4.483","url":null,"abstract":"This study analyzes the impact of consumer learning behavior and supplier price competition on retailer price competition in a complex adaptive system. Using machine Learning-enhanced agent-based modeling and simulation, the study applies fuzzy logic and genetic algorithms to model price decisions, and reinforcement learning and swarm intelligence to model consumer behavior. Simulations reveal that different learning behaviors result in different retailer competition patterns, and that supplier price competition affects the strength of retailer price competition. Simulation results demonstrate that consumer learning behavior influences retailer competition, with self-learning consumers leading to higher-priced partnerships, and collective-learning consumers leading to a shift in price competition among retailers. In contrast, perfect rationality consumers result in low-price competition and the lowest average margin and profit. Additionally, the competitive price behavior of suppliers impacts retailers' price competition patterns, with supplier price competition reducing retailer price competition in the perfect rationality consumer market and enhancing it in the self-learning and collective-learning consumer markets, leading to lower average prices and profits for retailers. This study presents a simulated market for price competition among suppliers, retailers, and consumers that can be expanded by subsequent scholars to test related hypotheses.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"43 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139151375","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}
Today, Solar Photovoltaic (SPV) energy, an advancing and attractive clean technology with zero carbon emissions, is widely used. It is crucial to pay serious attention to the maintenance and application of Solar Power Generation (SPG) to harness it effectively. The design was more costly, and the automatic monitoring is not precise. The main objective of the work related to designed and built up the Internet of Things (IoT) platform to monitor the SPV Power Plants (SPVPP) to solve the issue. IoT platform designing and Data Analytics (DA) are the two phases of the proposed methodology. For building the IoT device in the IoT platform designing phase, diverse lower-cost sensors with higher end-to-end delivery ratio, higher network lifetime, throughput, residual energy, and better energy consumption are considered. Then, Sigfox communication technology is employed at the Low-Power Wireless Area Network (LPWAN) communication layer for lower-cost communication. Therefore, in the DA phase, the sensor monitored values are evaluated. In the analysis phase, which is the most significant part of the work, the input data are first pre-processed to avoid errors. Next, to monitor the Energy Loss (EL), the fault, and Potential Energy (PE), the solar features are extracted as of the pre-processed data. The significance of utilizing the Transformation Search centered Seagull Optimization (TSSO) algorithm, the significant features are chosen as of the extracted features. Therefore, the computational time of the solar monitoring has been decreased by the Feature Selection (FS). Next, the features are input into the Gaussian Kernelized Deep Learning Neural Network (GKDLNN) algorithm, which predicts the faults, PE, and EL. In the experimental evaluation, solar generation is assessed based on Wind Speed (WS), temperature, time, and Global Solar Radiation (GSR). The systems are satisfactory and produce more power during the time interval from 12:00 PM to 1:00 PM. The performance of the proposed method is evaluated based on performance metrics and compared with existing research techniques. When compared to these techniques, the proposed framework achieves superior results with improved precision, accuracy, F-measure, and recall.
{"title":"IoT-based Deep Learning Neural Network (DLNN) algorithm for voltage stability control and monitoring of solar power generation","authors":"R. Shweta, S. Sivagnanam, K.A. Kumar","doi":"10.14743/apem2023.4.484","DOIUrl":"https://doi.org/10.14743/apem2023.4.484","url":null,"abstract":"Today, Solar Photovoltaic (SPV) energy, an advancing and attractive clean technology with zero carbon emissions, is widely used. It is crucial to pay serious attention to the maintenance and application of Solar Power Generation (SPG) to harness it effectively. The design was more costly, and the automatic monitoring is not precise. The main objective of the work related to designed and built up the Internet of Things (IoT) platform to monitor the SPV Power Plants (SPVPP) to solve the issue. IoT platform designing and Data Analytics (DA) are the two phases of the proposed methodology. For building the IoT device in the IoT platform designing phase, diverse lower-cost sensors with higher end-to-end delivery ratio, higher network lifetime, throughput, residual energy, and better energy consumption are considered. Then, Sigfox communication technology is employed at the Low-Power Wireless Area Network (LPWAN) communication layer for lower-cost communication. Therefore, in the DA phase, the sensor monitored values are evaluated. In the analysis phase, which is the most significant part of the work, the input data are first pre-processed to avoid errors. Next, to monitor the Energy Loss (EL), the fault, and Potential Energy (PE), the solar features are extracted as of the pre-processed data. The significance of utilizing the Transformation Search centered Seagull Optimization (TSSO) algorithm, the significant features are chosen as of the extracted features. Therefore, the computational time of the solar monitoring has been decreased by the Feature Selection (FS). Next, the features are input into the Gaussian Kernelized Deep Learning Neural Network (GKDLNN) algorithm, which predicts the faults, PE, and EL. In the experimental evaluation, solar generation is assessed based on Wind Speed (WS), temperature, time, and Global Solar Radiation (GSR). The systems are satisfactory and produce more power during the time interval from 12:00 PM to 1:00 PM. The performance of the proposed method is evaluated based on performance metrics and compared with existing research techniques. When compared to these techniques, the proposed framework achieves superior results with improved precision, accuracy, F-measure, and recall.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139148602","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 failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity.
{"title":"Comparing Fault Tree Analysis methods combined with Generalized Grey Relation Analysis: A new approach and case study in the automotive industry","authors":"J.L. Shi, Z.C. Lu, H.H. Xu, M.M. Ren, F.L. Shu","doi":"10.14743/apem2023.4.485","DOIUrl":"https://doi.org/10.14743/apem2023.4.485","url":null,"abstract":"The failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"57 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149442","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 realm of precision engineering, particularly in deep hole boring processes, tool vibration emerges as a critical determinant of machining performance. This investigation elucidates the genesis of self-excited vibrations within deep hole boring operations and delineates the underlying mechanisms of cutting tool vibration. A focal point of this study is the optimal alignment of the boring bar to mitigate vibrational impacts, thereby enhancing surface finish quality and extending tool longevity. Central to this analysis is the employment of a Dynamic Vibration Absorber (DVA) aimed at attenuating cutting tool vibration. The deployment of DVA necessitates precise identification of modal parameters, namely the equivalent stiffness (K) and mass (M) of the cutting tool. This research juxtaposes various scholarly methodologies to amalgamate theoretical calculations with simulation approaches, thereby acquiring accurate modal parameters. Utilizing Matlab software, the vibration amplitude of the boring bar under varying spring stiffness scenarios was examined. Results indicate a direct correlation between increased stiffness and reduced amplitude, particularly when the frequency ratio 'g' ranges between 0.5 and 1.1. Consequently, a stiffer DVA configuration is posited as more effective in vibration reduction. Furthermore, the study conducted frequency sweep experiments on a damping boring bar, utilizing a vibration excitation platform. These experiments revealed the existence of an optimal stiffness value for the DVA, thereby underscoring the significance of stiffness matching in vibration mitigation strategies.
在精密工程领域，尤其是在深孔镗加工过程中，刀具振动成为决定加工性能的关键因素。本研究阐明了深孔镗孔操作中自激振动的成因，并描述了切削刀具振动的基本机制。本研究的一个重点是镗杆的最佳校准，以减轻振动影响，从而提高表面加工质量并延长刀具寿命。这项分析的核心是采用动态振动吸收器（DVA）来减弱切削工具的振动。部署 DVA 需要精确识别模态参数，即切削工具的等效刚度 (K) 和质量 (M)。本研究将各种学术方法并列，将理论计算与模拟方法相结合，从而获得精确的模态参数。利用 Matlab 软件，研究了不同弹簧刚度情况下镗杆的振动幅度。结果表明，刚度的增加与振幅的减小直接相关，尤其是当频率比 "g "在 0.5 和 1.1 之间时。因此，较硬的 DVA 配置可更有效地减少振动。此外，研究还利用振动激励平台对阻尼镗杆进行了频率扫描实验。这些实验揭示了 DVA 的最佳刚度值，从而强调了刚度匹配在减振策略中的重要性。
{"title":"Optimization of machining performance in deep hole boring: A study on cutting tool vibration and dynamic vibration absorber design","authors":"L. Li, D.L. Yang, Y.M. Cui","doi":"10.14743/apem2023.3.479","DOIUrl":"https://doi.org/10.14743/apem2023.3.479","url":null,"abstract":"In the realm of precision engineering, particularly in deep hole boring processes, tool vibration emerges as a critical determinant of machining performance. This investigation elucidates the genesis of self-excited vibrations within deep hole boring operations and delineates the underlying mechanisms of cutting tool vibration. A focal point of this study is the optimal alignment of the boring bar to mitigate vibrational impacts, thereby enhancing surface finish quality and extending tool longevity. Central to this analysis is the employment of a Dynamic Vibration Absorber (DVA) aimed at attenuating cutting tool vibration. The deployment of DVA necessitates precise identification of modal parameters, namely the equivalent stiffness (K) and mass (M) of the cutting tool. This research juxtaposes various scholarly methodologies to amalgamate theoretical calculations with simulation approaches, thereby acquiring accurate modal parameters. Utilizing Matlab software, the vibration amplitude of the boring bar under varying spring stiffness scenarios was examined. Results indicate a direct correlation between increased stiffness and reduced amplitude, particularly when the frequency ratio 'g' ranges between 0.5 and 1.1. Consequently, a stiffer DVA configuration is posited as more effective in vibration reduction. Furthermore, the study conducted frequency sweep experiments on a damping boring bar, utilizing a vibration excitation platform. These experiments revealed the existence of an optimal stiffness value for the DVA, thereby underscoring the significance of stiffness matching in vibration mitigation strategies.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139331890","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}
Maintenance plays an increasingly important role in the life of production companies, as professional maintenance is an important prerequisite for the reliable operation of resources. A well-chosen maintenance strategy can make a major contribution to increased efficiency of production processes. The main goal of this research is to propose a novel optimization approach to define optimal maintenance strategy that ensures the efficient operation of the production process while reducing maintenance costs. The developed optimization method is based on Howard’s policy iteration and describes the objective of the planning as a Markov decision process. The novelty and the scientific contribution of the presented study is the application of Howard’s policy iteration methodology in a Markov decision process for agile, condition-based maintenance strategy optimization. As the results of the numerical analysis of the scenarios shows, the implementation of an optimized maintenance strategy based on the proposed approach can significantly increase the maintenance efficiency of the production process. The main reason for this is that the level and type of maintenance is always implemented depending on the current state of the system components, which reduces both the maintenance cost and the losses due to production downtime.
{"title":"Impact of agile, condition-based maintenance strategy on cost efficiency of production systems","authors":"A. Banyai","doi":"10.14743/apem2023.3.475","DOIUrl":"https://doi.org/10.14743/apem2023.3.475","url":null,"abstract":"Maintenance plays an increasingly important role in the life of production companies, as professional maintenance is an important prerequisite for the reliable operation of resources. A well-chosen maintenance strategy can make a major contribution to increased efficiency of production processes. The main goal of this research is to propose a novel optimization approach to define optimal maintenance strategy that ensures the efficient operation of the production process while reducing maintenance costs. The developed optimization method is based on Howard’s policy iteration and describes the objective of the planning as a Markov decision process. The novelty and the scientific contribution of the presented study is the application of Howard’s policy iteration methodology in a Markov decision process for agile, condition-based maintenance strategy optimization. As the results of the numerical analysis of the scenarios shows, the implementation of an optimized maintenance strategy based on the proposed approach can significantly increase the maintenance efficiency of the production process. The main reason for this is that the level and type of maintenance is always implemented depending on the current state of the system components, which reduces both the maintenance cost and the losses due to production downtime.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139333422","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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