At present, great changes are taken place in the internal production management and resource allocation model of manufacturers. Under the premise of rational resource allocation, the completion period of products largely depends on the timeliness of resource allocation. The related studies mostly tackle the allocation of a single type of production resources in a single workshop, without considering much about the mutual influence between workshops. Through in-depth research on workshop manufacturing practices, this paper chooses to explore the planning, allocation, and demand prediction of manufacturing resources, which has long been a difficulty in workshop production. The research has great scientific research significance and practical value. The authors designed an algorithm based on the difference of the mean stagnation time of different production processes in the execution process, and used the algorithm to predict the number of production resources required in each period, before formulating the optimal configuration plan. This method is highly reasonable and applicable. After presenting a prediction method for the allocation demand of workshop manufacturing resources, the authors discussed whether the manufacturing resource allocation between different workshops is balanced in a fixed period. Then, a new idea was proposed for collaborative production between machines of different workshops in a specific environment, and an optimization algorithm was put forward to optimize the manufacturing resource allocation to machines facing the operation execution process. Through experiments, the authors compared the utilization rate of material, technological or human production resources in each period, and thereby verified the effectiveness of the proposed algorithm.
{"title":"Demand prediction and optimization of workshop manufacturing resources allocation: A new method and a case study","authors":"J. Wan","doi":"10.14743/apem2022.4.445","DOIUrl":"https://doi.org/10.14743/apem2022.4.445","url":null,"abstract":"At present, great changes are taken place in the internal production management and resource allocation model of manufacturers. Under the premise of rational resource allocation, the completion period of products largely depends on the timeliness of resource allocation. The related studies mostly tackle the allocation of a single type of production resources in a single workshop, without considering much about the mutual influence between workshops. Through in-depth research on workshop manufacturing practices, this paper chooses to explore the planning, allocation, and demand prediction of manufacturing resources, which has long been a difficulty in workshop production. The research has great scientific research significance and practical value. The authors designed an algorithm based on the difference of the mean stagnation time of different production processes in the execution process, and used the algorithm to predict the number of production resources required in each period, before formulating the optimal configuration plan. This method is highly reasonable and applicable. After presenting a prediction method for the allocation demand of workshop manufacturing resources, the authors discussed whether the manufacturing resource allocation between different workshops is balanced in a fixed period. Then, a new idea was proposed for collaborative production between machines of different workshops in a specific environment, and an optimization algorithm was put forward to optimize the manufacturing resource allocation to machines facing the operation execution process. Through experiments, the authors compared the utilization rate of material, technological or human production resources in each period, and thereby verified the effectiveness of the proposed algorithm.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124537164","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}
With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm.
{"title":"Monte Carlo Tree Search improved Genetic Algorithm for unmanned vehicle routing problem with path flexibility","authors":"Y.D. Wang, X. Lu, Y. Song, Y. Feng, J. Shen","doi":"10.14743/apem2022.4.446","DOIUrl":"https://doi.org/10.14743/apem2022.4.446","url":null,"abstract":"With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128196830","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}
Logistics is an important guarantee for economic and social development. Among the various aspects of logistics, the urban logistics end distribution link, which involves the direct connection between distribution personnel and customers, has a direct impact on customers' sense of experience and satisfaction with logistics services. At present, there are unscientific and unreasonable selection methods for logistics end distribution paths, often based on the subjective experience of distribution personnel, which often results in a mismatch between distribution paths and distribution needs, affecting market demand while further increasing the distribution costs of enterprises. Therefore, based on the characteristics of customer-consumers, this paper considers that consumers can select multiple receiving addresses, and each address has a corresponding time window limit. This paper finds that it needs to spend a lot of costs for the enterprise to improve the service level of distribution, and the enterprise can save the cost from time window, as well as obtain the better distribution time by using alternative addresses through the verification and analysis of an example. Based on the above analysis, this paper proposes the urban logistics terminal distribution path optimization path based on large-scale neighbourhood search algorithm, which can promote the further matching between logistics distribution enterprises and customer needs, so as to improve the probability of consumers receiving goods in time as well as reduce the cost of enterprises.
{"title":"End-of-line delivery vehicle routing optimization based on large-scale neighbourhood search algorithms considering customer-consumer delivery location preferences","authors":"Xiang Niu, S.F. Liu, Q. Huang","doi":"10.14743/apem2022.4.447","DOIUrl":"https://doi.org/10.14743/apem2022.4.447","url":null,"abstract":"Logistics is an important guarantee for economic and social development. Among the various aspects of logistics, the urban logistics end distribution link, which involves the direct connection between distribution personnel and customers, has a direct impact on customers' sense of experience and satisfaction with logistics services. At present, there are unscientific and unreasonable selection methods for logistics end distribution paths, often based on the subjective experience of distribution personnel, which often results in a mismatch between distribution paths and distribution needs, affecting market demand while further increasing the distribution costs of enterprises. Therefore, based on the characteristics of customer-consumers, this paper considers that consumers can select multiple receiving addresses, and each address has a corresponding time window limit. This paper finds that it needs to spend a lot of costs for the enterprise to improve the service level of distribution, and the enterprise can save the cost from time window, as well as obtain the better distribution time by using alternative addresses through the verification and analysis of an example. Based on the above analysis, this paper proposes the urban logistics terminal distribution path optimization path based on large-scale neighbourhood search algorithm, which can promote the further matching between logistics distribution enterprises and customer needs, so as to improve the probability of consumers receiving goods in time as well as reduce the cost of enterprises.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131208801","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}
Sustainable development (SD) activities within a manufacturing should be integrated with the Industry 4.0 (I4.0) technologies implementation due to ensure the continuous evaluation and even prediction the SD level. Such integration should be provided cross all company areas but must be strictly defined for each core process realised within a company. Therefore, the main purpose of the study is to build the new approach to assess the maintenance sustainability (MS) level in a manufacturing company, as a good example of integrating I4.0 technologies and SD activities within a company, using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). The major contributions of the work are as follows: 1) to the existing literature by identification the key objectives of MS, in the context of Industry 4.0 2) using the F-TOPSIS method and based on the empirical data received from 125 Polish manufacturing enterprises, 3) the establishment of the integrated approach, which allow continuous monitor the level of the MS within a manufacturing, 4) demonstrating the usefulness of the fresh framework in managerial practice through its verification in the five Polish manufacturing companies. Managers of manufacturing enterprises, thanks to the use of the proposed approach, may assess and constant monitor the MS level, while application of the I4.0 technologies.
{"title":"An approach to maintenance sustainability level assessment integrated with Industry 4.0 technologies using Fuzzy-TOPSIS: A real case study","authors":"J. Patalas-Maliszewska, H. Losyk","doi":"10.14743/apem2022.4.448","DOIUrl":"https://doi.org/10.14743/apem2022.4.448","url":null,"abstract":"Sustainable development (SD) activities within a manufacturing should be integrated with the Industry 4.0 (I4.0) technologies implementation due to ensure the continuous evaluation and even prediction the SD level. Such integration should be provided cross all company areas but must be strictly defined for each core process realised within a company. Therefore, the main purpose of the study is to build the new approach to assess the maintenance sustainability (MS) level in a manufacturing company, as a good example of integrating I4.0 technologies and SD activities within a company, using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). The major contributions of the work are as follows: 1) to the existing literature by identification the key objectives of MS, in the context of Industry 4.0 2) using the F-TOPSIS method and based on the empirical data received from 125 Polish manufacturing enterprises, 3) the establishment of the integrated approach, which allow continuous monitor the level of the MS within a manufacturing, 4) demonstrating the usefulness of the fresh framework in managerial practice through its verification in the five Polish manufacturing companies. Managers of manufacturing enterprises, thanks to the use of the proposed approach, may assess and constant monitor the MS level, while application of the I4.0 technologies.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126133440","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 collaboration between manufacturing companies and demand companies is the focus of effective operation of cloud manufacturing platform. The evolutionary game model of manufacturing company, demand company and cloud platform was established, and the strategy stability of the three parties was analyzed in this paper. Based on Lyapunov discrimination method, the equilibrium points of the system were explored, and the simulation was applied to analyze the influence of key factors in the evolution process by MATLAB2021a. The results show that: (1) The evolution of company collaborative cooperation strategies in the cloud platform environment is staged; (2) The collaborative subsidy to the manufacturing company and the demand company by the cloud platform, the collaborative effort degree of the manufacturing company and the demand company, the value-added profits of the manufacturing company, the penalties and profits of the manufacturing company's speculation behavior, the loss of information leakage of demand company, and the government's subsidy for cloud platform supervision are important factors that affect the strategies of each subject; (3) The establishment of the cloud platform supervision mechanism can promote collaboration between the manufacturing company and the demand company. The results of the study can provide a beneficial strategic decision guidance for the development of the cloud manufacturing platform.
{"title":"Evolutionary game analysis of company collaborative strategy in cloud manufacturing platform environment","authors":"M. Xiao, Z. Tian","doi":"10.14743/apem2022.3.437","DOIUrl":"https://doi.org/10.14743/apem2022.3.437","url":null,"abstract":"The collaboration between manufacturing companies and demand companies is the focus of effective operation of cloud manufacturing platform. The evolutionary game model of manufacturing company, demand company and cloud platform was established, and the strategy stability of the three parties was analyzed in this paper. Based on Lyapunov discrimination method, the equilibrium points of the system were explored, and the simulation was applied to analyze the influence of key factors in the evolution process by MATLAB2021a. The results show that: (1) The evolution of company collaborative cooperation strategies in the cloud platform environment is staged; (2) The collaborative subsidy to the manufacturing company and the demand company by the cloud platform, the collaborative effort degree of the manufacturing company and the demand company, the value-added profits of the manufacturing company, the penalties and profits of the manufacturing company's speculation behavior, the loss of information leakage of demand company, and the government's subsidy for cloud platform supervision are important factors that affect the strategies of each subject; (3) The establishment of the cloud platform supervision mechanism can promote collaboration between the manufacturing company and the demand company. The results of the study can provide a beneficial strategic decision guidance for the development of the cloud manufacturing platform.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114331080","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}
Gauge blocks are an important basis for maintaining traceability in dimensional metrology, used for calibrating length measuring instruments and for adjustments in all branches of manufacturing. Their important feature is that they can be wrung with small dimensional uncertainty. An overview of the factors influencing the accuracy of a stack length, such as the quality of the gauge blocks (grade, wear), surface preparation (cleaning and usage of a lubricant), wringing (way and time, temperature of hands and gloves) is given in the paper. Experiments for determining these influences were performed with a highly precise gauge block comparator. Proper selection of gauge blocks, preparation of their surfaces and oiling improve the accuracy of a stack length. Application of a lubricant, wiped with a dry cloth or paper towel, helps to wring them more easily, but its contribution to the stack length in the experiment was 0.1 µm for oil and 0.2 µm for grease. Temperature changes of gauge blocks were estimated by holding them, and, during wringing in well controlled air conditions, monitoring them to yield the empirical coefficients of their warming up. The results showed that usage of gloves reduces the warming up by approximately half, but still the stack must be stabilised in well controlled conditions for at least one hour if it is used for micrometre-level precise measurements.
{"title":"Experimental determination of influences on a gauge block’s stack length","authors":"L. C. Lipus, B. Ačko, J. Tompa","doi":"10.14743/apem2022.3.440","DOIUrl":"https://doi.org/10.14743/apem2022.3.440","url":null,"abstract":"Gauge blocks are an important basis for maintaining traceability in dimensional metrology, used for calibrating length measuring instruments and for adjustments in all branches of manufacturing. Their important feature is that they can be wrung with small dimensional uncertainty. An overview of the factors influencing the accuracy of a stack length, such as the quality of the gauge blocks (grade, wear), surface preparation (cleaning and usage of a lubricant), wringing (way and time, temperature of hands and gloves) is given in the paper. Experiments for determining these influences were performed with a highly precise gauge block comparator. Proper selection of gauge blocks, preparation of their surfaces and oiling improve the accuracy of a stack length. Application of a lubricant, wiped with a dry cloth or paper towel, helps to wring them more easily, but its contribution to the stack length in the experiment was 0.1 µm for oil and 0.2 µm for grease. Temperature changes of gauge blocks were estimated by holding them, and, during wringing in well controlled air conditions, monitoring them to yield the empirical coefficients of their warming up. The results showed that usage of gloves reduces the warming up by approximately half, but still the stack must be stabilised in well controlled conditions for at least one hour if it is used for micrometre-level precise measurements.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127663587","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}
Fused deposition modelling (FDM) is an additive-based manufacturing technique used by various industries due to its effectiveness & ability to make complicated geometries possible. This technique requires sufficient knowledge about the process and its parameters including their effect on the component’s mechanical characteristics. Thus, it is crucial to review the available articles on this topic not only to identify the practical and useful aspects, limitations, and process variables but also to understand how the results of the literature are relevant to be used for real applications and further studies. A systematic literature review is carried out based on the type of 3D printing materials. The printing parameters which influence the mechanical characteristics of the FDM specimens are discussed based on the results presented in the literature. From the present study, it has been found that the process variables such as orientation, raster angle, raster width, layer height, and contours directly affect the quality of the 3D-printed parts. It has also been found that the effect of these process variables also varies with the type of thermoplastic materials. The present article will help researchers to select FDM processed material and appropriate process variables for further research.
{"title":"Effect of printing parameters on the mechanical behaviour of the thermoplastic polymer processed by FDM technique: A research review","authors":"C. Tripathy, R.K. Sharma, V. K. Rattan","doi":"10.14743/apem2022.3.436","DOIUrl":"https://doi.org/10.14743/apem2022.3.436","url":null,"abstract":"Fused deposition modelling (FDM) is an additive-based manufacturing technique used by various industries due to its effectiveness & ability to make complicated geometries possible. This technique requires sufficient knowledge about the process and its parameters including their effect on the component’s mechanical characteristics. Thus, it is crucial to review the available articles on this topic not only to identify the practical and useful aspects, limitations, and process variables but also to understand how the results of the literature are relevant to be used for real applications and further studies. A systematic literature review is carried out based on the type of 3D printing materials. The printing parameters which influence the mechanical characteristics of the FDM specimens are discussed based on the results presented in the literature. From the present study, it has been found that the process variables such as orientation, raster angle, raster width, layer height, and contours directly affect the quality of the 3D-printed parts. It has also been found that the effect of these process variables also varies with the type of thermoplastic materials. The present article will help researchers to select FDM processed material and appropriate process variables for further research.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"347 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263380","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 focuses on financing choices and information-sharing strategies in the capital-constrained supply chain. We model four scenarios with the capital constraints of the respective manufacturer and retailer using bank credit financing (BCF) and trade credit financing (TCF) approaches to address financing problems, and investigate the retailer’s willingness to share demand forecasting information. We find that TCF is an equilibrium financing choice for a capital-constrained supply chain. However, when a capital-constrained member chooses TCF, sharing demand information over the supply chain becomes more difficult. The interactions between the choices of financing approach and information sharing based on the game equilibriums, as well as the conditions that encourage the well-funded member to offer TCF in the capital-constrained supply chains, have also been analytically explored and numerically verified. Additional managerial insights are provided for discussions.
{"title":"Financing and information sharing in capital-constrained supply chain","authors":"H. Duan, M.T. Wang, Y. Ye","doi":"10.14743/apem2022.3.435","DOIUrl":"https://doi.org/10.14743/apem2022.3.435","url":null,"abstract":"This paper focuses on financing choices and information-sharing strategies in the capital-constrained supply chain. We model four scenarios with the capital constraints of the respective manufacturer and retailer using bank credit financing (BCF) and trade credit financing (TCF) approaches to address financing problems, and investigate the retailer’s willingness to share demand forecasting information. We find that TCF is an equilibrium financing choice for a capital-constrained supply chain. However, when a capital-constrained member chooses TCF, sharing demand information over the supply chain becomes more difficult. The interactions between the choices of financing approach and information sharing based on the game equilibriums, as well as the conditions that encourage the well-funded member to offer TCF in the capital-constrained supply chains, have also been analytically explored and numerically verified. Additional managerial insights are provided for discussions.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132315481","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 article presents the design and development of a flexible tooling system for sheet metal bending. The flexible tooling system aims to reduce manufacturing disturbances and increase the efficiency of the forming process. First and foremost, the structural behaviour of the sheet metal is investigated using the finite element method for the numerical simulation of the three-point bending process. The analysis’ findings enabled the prediction of component reaction to loads, which are essential for the further optimization and enhancement of the tooling system’s flexibility. At the initial stage of the development phase, SolidWorks, the computer-aided design software, is utilized to visualise the flexible tooling system and improve the tooling connectivity design. Furthermore, the prototype is developed by integrating mechanical and electrical components, such as the Arduino Mega microcontroller, stepper motors, and digital stepper drivers. Automation is achieved by programming the Arduino microcontroller board and controlling the stepper motors’ movement to ensure precise displacement and speed control of the forming tools. The tooling system’s major qualities are its high flexibility, achieved through the implementation of two moveable support cylinders and the possibility of being further upgraded to a closed-loop forming system. The higher level of automation and optimization of the sheet metal bending process can lead to improved processing efficiency and help achieve the desired formed products with higher quality and the required geometric tolerance. It is expected that the development of a flexible tooling system will find widespread application in sheet metal bending processes, resulting in reduced material costs, rapid equipment set-up and higher processing repeatability.
{"title":"Development of a flexible tooling system for sheet metal bending","authors":"E. Stefanovska, T. Pepelnjak","doi":"10.14743/apem2022.3.438","DOIUrl":"https://doi.org/10.14743/apem2022.3.438","url":null,"abstract":"This article presents the design and development of a flexible tooling system for sheet metal bending. The flexible tooling system aims to reduce manufacturing disturbances and increase the efficiency of the forming process. First and foremost, the structural behaviour of the sheet metal is investigated using the finite element method for the numerical simulation of the three-point bending process. The analysis’ findings enabled the prediction of component reaction to loads, which are essential for the further optimization and enhancement of the tooling system’s flexibility. At the initial stage of the development phase, SolidWorks, the computer-aided design software, is utilized to visualise the flexible tooling system and improve the tooling connectivity design. Furthermore, the prototype is developed by integrating mechanical and electrical components, such as the Arduino Mega microcontroller, stepper motors, and digital stepper drivers. Automation is achieved by programming the Arduino microcontroller board and controlling the stepper motors’ movement to ensure precise displacement and speed control of the forming tools. The tooling system’s major qualities are its high flexibility, achieved through the implementation of two moveable support cylinders and the possibility of being further upgraded to a closed-loop forming system. The higher level of automation and optimization of the sheet metal bending process can lead to improved processing efficiency and help achieve the desired formed products with higher quality and the required geometric tolerance. It is expected that the development of a flexible tooling system will find widespread application in sheet metal bending processes, resulting in reduced material costs, rapid equipment set-up and higher processing repeatability.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123268791","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}
D. Vukelić, K. Simunovic, Ž. Kanović, T. Šarić, K. Doroslovacki, M. Prica, G. Simunovic
In this study, the modelling of arithmetical mean roughness after turning of C45 steel was performed. Four parameters of cutting tool geometry were varied, i.e.: corner radius r, approach angle κ, rake angle γ and inclination angle λ. After turning, the arithmetical mean roughness Ra was measured. The obtained values of Ra ranged from 0.13 μm to 4.39 μm. The results of the experiments showed that surface roughness improves with increasing corner radius, increasing approach angle, increasing rake angle, and decreasing inclination angle. Based on the experimental results, models were developed to predict the distribution of the arithmetical mean roughness using the response surface method (RSM), Gaussian process regression with two kernel functions, the sequential exponential function (GPR-SE) and Mattern (GPR-Mat), and decision tree regression (DTR). The maximum percentage errors of the developed models were 3.898 %, 1.192 %, 1.364 %, and 0.960 % for DTR, GPR-SE, GPR-Mat, and RSM, respectively. In the worst case, the maximum absolute errors were 0.106 μm, 0.017 μm, 0.019 μm, and 0.011 μm for DTR, GPR-SE, GPR-Mat, and RSM, respectively. The results and the obtained errors show that the developed models can be successfully used for surface roughness prediction.
{"title":"Modelling surface roughness in finish turning as a function of cutting tool geometry using the response surface method, Gaussian process regression and decision tree regression","authors":"D. Vukelić, K. Simunovic, Ž. Kanović, T. Šarić, K. Doroslovacki, M. Prica, G. Simunovic","doi":"10.14743/apem2022.3.442","DOIUrl":"https://doi.org/10.14743/apem2022.3.442","url":null,"abstract":"In this study, the modelling of arithmetical mean roughness after turning of C45 steel was performed. Four parameters of cutting tool geometry were varied, i.e.: corner radius r, approach angle κ, rake angle γ and inclination angle λ. After turning, the arithmetical mean roughness Ra was measured. The obtained values of Ra ranged from 0.13 μm to 4.39 μm. The results of the experiments showed that surface roughness improves with increasing corner radius, increasing approach angle, increasing rake angle, and decreasing inclination angle. Based on the experimental results, models were developed to predict the distribution of the arithmetical mean roughness using the response surface method (RSM), Gaussian process regression with two kernel functions, the sequential exponential function (GPR-SE) and Mattern (GPR-Mat), and decision tree regression (DTR). The maximum percentage errors of the developed models were 3.898 %, 1.192 %, 1.364 %, and 0.960 % for DTR, GPR-SE, GPR-Mat, and RSM, respectively. In the worst case, the maximum absolute errors were 0.106 μm, 0.017 μm, 0.019 μm, and 0.011 μm for DTR, GPR-SE, GPR-Mat, and RSM, respectively. The results and the obtained errors show that the developed models can be successfully used for surface roughness prediction.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128351769","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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