Pub Date : 2024-04-17DOI: 10.3389/fmech.2024.1351922
Xianpeng Ni, Shaohua Xu, Hua Mu
Introduction: Compared with imported welding wire, domestic aluminum alloy welding wire has more internal inclusion defects. To improve the welding quality and reliability of aluminum alloy, the welding performance of aluminum alloy was improved by adding different content of Mn element.Methods: ER5356 aluminum alloy ingot with different Mn content (0.05% and 0.15%) was prepared by semi-continuous casting and gravity casting. After stretching, the mechanical properties and microstructure of ER5356 aluminum alloy were studied. In addition, the microstructure, microhardness and mechanical behavior of ER5356 aluminum alloy welding wire with 6082 and 7005 aluminum alloy joints were studied.Results and Discussion: Compared with gravity casting, the yield strength and tensile strength of ER5356 (0.15% Mn) were increased by 12.8% and 3.17% respectively. The head influence zone of the joint made of metal wire containing 0.15% Mn is slightly wider than that of the joint made of ER535 (0.05% Mn) containing 0.05% Mn. The quality of ER5356 aluminum alloy welding wire blocked by semi-continuous casting is better than that of ER5356 aluminum alloy welding wire blocked by gravity casting method. Mn element can improve the metal deposition process in welding.Conclusion: The research method can improve the welding current control and welding quality, and has important practical significance in improving the mechanical properties of welding seams.
{"title":"Effect of Mn-content of ER5356 welding rods on mechanical properties of Al-alloys joints","authors":"Xianpeng Ni, Shaohua Xu, Hua Mu","doi":"10.3389/fmech.2024.1351922","DOIUrl":"https://doi.org/10.3389/fmech.2024.1351922","url":null,"abstract":"Introduction: Compared with imported welding wire, domestic aluminum alloy welding wire has more internal inclusion defects. To improve the welding quality and reliability of aluminum alloy, the welding performance of aluminum alloy was improved by adding different content of Mn element.Methods: ER5356 aluminum alloy ingot with different Mn content (0.05% and 0.15%) was prepared by semi-continuous casting and gravity casting. After stretching, the mechanical properties and microstructure of ER5356 aluminum alloy were studied. In addition, the microstructure, microhardness and mechanical behavior of ER5356 aluminum alloy welding wire with 6082 and 7005 aluminum alloy joints were studied.Results and Discussion: Compared with gravity casting, the yield strength and tensile strength of ER5356 (0.15% Mn) were increased by 12.8% and 3.17% respectively. The head influence zone of the joint made of metal wire containing 0.15% Mn is slightly wider than that of the joint made of ER535 (0.05% Mn) containing 0.05% Mn. The quality of ER5356 aluminum alloy welding wire blocked by semi-continuous casting is better than that of ER5356 aluminum alloy welding wire blocked by gravity casting method. Mn element can improve the metal deposition process in welding.Conclusion: The research method can improve the welding current control and welding quality, and has important practical significance in improving the mechanical properties of welding seams.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.3389/fmech.2024.1383341
T. Abhishek, Dola Sundeep, C. Chandrasekhara Sastry, K. V. Eswaramoorthy, Gagan Chaitanya Kesireddy, B. V. Siva Reddy, Rakesh Kumar Verma, Sachin Salunkhe, R. Čep, Emad S. Abouel Nasr
The demand for improved small arms ammunition has led to exploring advanced materials and manufacturing techniques. This research investigates the machining characteristics of CM and WNF alloy bullets, aiming to enhance ballistic performance and durability.Bullet profile-making trials were conducted to evaluate the impact of machining parameters such as cutting speed and feed. The study also considered variables including surface roughness, cutting temperature, and hardness, alongside a detailed morphological analysis, The evaluation utilized an orthogonal array and MCDM approach, incorporating the TOPSIS method for decision-making processes.The findings reveal that WNF alloy bullets exhibit 3.01% to 27.95% lower machining temperatures, 24.88%-61.85% reduced surface roughness, and 19.45%-34% higher microhardness compared to CM bullets. Moreover, CM bullets demonstrated higher machining temperatures, resulting in 47.53% increased tool flank wear. WNF bullets showed a 24.89% reduction in crater wear and a 38.23% decrease in compressive residual stress in bullet profiles, indicating superior machining performance.The superior machining performance of WNF alloy bullets suggests their potential to improve the ballistic performance and durability of small arms ammunition. The reduced tool wear and favorable machining parameters highlight WNF alloy's advantages for military and defense applications. A ballistic impact analysis using a finite element method (FEM) model in Abaqus software further supports the potential of WNF alloy bullets, providing a solid foundation for future advancements in bullet manufacturing technologies.
{"title":"Experimental investigation of tungsten–nickel–iron alloy, W95Ni3.5Fe1.5, compared to copper monolithic bullets","authors":"T. Abhishek, Dola Sundeep, C. Chandrasekhara Sastry, K. V. Eswaramoorthy, Gagan Chaitanya Kesireddy, B. V. Siva Reddy, Rakesh Kumar Verma, Sachin Salunkhe, R. Čep, Emad S. Abouel Nasr","doi":"10.3389/fmech.2024.1383341","DOIUrl":"https://doi.org/10.3389/fmech.2024.1383341","url":null,"abstract":"The demand for improved small arms ammunition has led to exploring advanced materials and manufacturing techniques. This research investigates the machining characteristics of CM and WNF alloy bullets, aiming to enhance ballistic performance and durability.Bullet profile-making trials were conducted to evaluate the impact of machining parameters such as cutting speed and feed. The study also considered variables including surface roughness, cutting temperature, and hardness, alongside a detailed morphological analysis, The evaluation utilized an orthogonal array and MCDM approach, incorporating the TOPSIS method for decision-making processes.The findings reveal that WNF alloy bullets exhibit 3.01% to 27.95% lower machining temperatures, 24.88%-61.85% reduced surface roughness, and 19.45%-34% higher microhardness compared to CM bullets. Moreover, CM bullets demonstrated higher machining temperatures, resulting in 47.53% increased tool flank wear. WNF bullets showed a 24.89% reduction in crater wear and a 38.23% decrease in compressive residual stress in bullet profiles, indicating superior machining performance.The superior machining performance of WNF alloy bullets suggests their potential to improve the ballistic performance and durability of small arms ammunition. The reduced tool wear and favorable machining parameters highlight WNF alloy's advantages for military and defense applications. A ballistic impact analysis using a finite element method (FEM) model in Abaqus software further supports the potential of WNF alloy bullets, providing a solid foundation for future advancements in bullet manufacturing technologies.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.3389/fmech.2024.1329345
Viviana Meruane, Ignacio Puiggros, Ruben Fernandez, Rafael O. Ruiz
Recent advancements in additive manufacturing technologies and topology optimization techniques have catalyzed a transformative shift in the design of architected materials, enabling increasingly complex and customized configurations. This study delves into the realm of engineered cellular materials, spotlighting their capacity to modulate the propagation of mechanical waves through the strategic creation of phononic band gaps. Focusing on the design of sandwich panels with cellular truss cores, we aim to harness these band gaps to achieve pronounced wave suppression within specific frequency ranges. Our methodology combines surrogate modeling with a comprehensive global optimization strategy, employing three machine learning algorithms—k-Nearest Neighbors (kNN), Random Forest Regression (RFR), and Artificial Neural Networks (ANN)—to construct predictive models from parameterized finite element (FE) analyses. These models, once trained, are integrated with Particle Swarm Optimization (PSO) to refine the panel designs. This approach not only facilitates the discovery of optimal truss core configurations for targeted phononic band gaps but also showcases a marked increase in computational efficiency over traditional optimization methods, particularly in the context of designing for diverse target frequencies.
{"title":"Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization","authors":"Viviana Meruane, Ignacio Puiggros, Ruben Fernandez, Rafael O. Ruiz","doi":"10.3389/fmech.2024.1329345","DOIUrl":"https://doi.org/10.3389/fmech.2024.1329345","url":null,"abstract":"Recent advancements in additive manufacturing technologies and topology optimization techniques have catalyzed a transformative shift in the design of architected materials, enabling increasingly complex and customized configurations. This study delves into the realm of engineered cellular materials, spotlighting their capacity to modulate the propagation of mechanical waves through the strategic creation of phononic band gaps. Focusing on the design of sandwich panels with cellular truss cores, we aim to harness these band gaps to achieve pronounced wave suppression within specific frequency ranges. Our methodology combines surrogate modeling with a comprehensive global optimization strategy, employing three machine learning algorithms—k-Nearest Neighbors (kNN), Random Forest Regression (RFR), and Artificial Neural Networks (ANN)—to construct predictive models from parameterized finite element (FE) analyses. These models, once trained, are integrated with Particle Swarm Optimization (PSO) to refine the panel designs. This approach not only facilitates the discovery of optimal truss core configurations for targeted phononic band gaps but also showcases a marked increase in computational efficiency over traditional optimization methods, particularly in the context of designing for diverse target frequencies.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3389/fmech.2024.1374491
Dongsheng Ma, Juchen Li
Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability. To meet these stringent requirements, traditional control techniques such as proportional-integral-derivative (PID) control need to be improved.Methods: In this study, radial basis function neural network is introduced to improve the traditional proportional integral derivative control technique. The improved proportional integral derivative technique is applied to the modeling and optimization of eight kinds of bar stamping mechanisms.Results: Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516 s and 1.038 s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture.Discussion: The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and optimization of eight-bar stamping mechanism. It not only provides enhanced motion accuracy and stability, but also brings significant practicality to industrial manufacturing. This integration opens up new possibilities for improving the performance of complex mechanical systems to meet the evolving needs of modern manufacturing.
简介现代工业制造通常要求八杆冲压机构具有较高的运动精度和稳定性。为了满足这些严格的要求，需要改进传统的控制技术，如比例积分导数（PID）控制：本研究引入径向基函数神经网络来改进传统的比例积分导数控制技术。方法：本研究引入径向基函数神经网络对传统的比例积分导数控制技术进行改进，并将改进后的比例积分导数控制技术应用于八种棒材冲压机构的建模和优化：实验表明，改进后的控制技术的峰值时间和调整时间分别为 0.516 s 和 1.038 s，均优于对比控制技术。此外，在八杆冲压机构的对比分析中，所提出的架构在运行效率上得到了 9.3 分，明显高于对比架构：结果表明，PID 控制策略与径向基函数神经网络的结合为八杆冲压机构的动态建模和优化提供了强有力的工具。它不仅提高了运动精度和稳定性，还为工业制造带来了显著的实用性。这种集成为提高复杂机械系统的性能以满足现代制造业不断发展的需求开辟了新的可能性。
{"title":"Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control","authors":"Dongsheng Ma, Juchen Li","doi":"10.3389/fmech.2024.1374491","DOIUrl":"https://doi.org/10.3389/fmech.2024.1374491","url":null,"abstract":"Introduction: Modern industrial manufacturing often requires the eight-bar stamping mechanism to have high motion accuracy and stability. To meet these stringent requirements, traditional control techniques such as proportional-integral-derivative (PID) control need to be improved.Methods: In this study, radial basis function neural network is introduced to improve the traditional proportional integral derivative control technique. The improved proportional integral derivative technique is applied to the modeling and optimization of eight kinds of bar stamping mechanisms.Results: Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516 s and 1.038 s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture.Discussion: The results show that the combination of PID control strategy and radial basis function neural network provides a powerful tool for dynamic modeling and optimization of eight-bar stamping mechanism. It not only provides enhanced motion accuracy and stability, but also brings significant practicality to industrial manufacturing. This integration opens up new possibilities for improving the performance of complex mechanical systems to meet the evolving needs of modern manufacturing.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-11DOI: 10.3389/fmech.2024.1361688
B. S. Nathan, B. V. Siva Reddy, C. C. Sastry, J. Krishnaiah, K. V. Eswaramoorthy
Effective service parts management and demand forecasting are crucial for optimizing operations in the automotive industry. However, existing literature lacks a comprehensive framework tailored to the specific context of the Thai automotive sector. This study addresses this gap by proposing a strategic approach to service parts management and demand forecasting in the Thai automotive industry. Drawing on a diverse set of methodologies, including classical time series models and advanced machine learning techniques, various forecasting models were assessed to identify the most effective approach for predicting service parts demand. Categorization of service parts based on demand criteria was conducted, and decision rules were developed to guide stocking strategies, balancing the need to minimize service disruptions with cost optimization. This analysis reveals substantial cost savings potential through strategic stocking guided by the developed decision rules. Furthermore, evaluation of the performance of different forecasting models recommends the adoption of Support Vector Regressor (SVR) as the most accurate model for forecasting service parts demand in this context. This research contributes to the automotive service industry by providing a nuanced framework for service parts management and demand forecasting, leading to cost-effective operations and enhanced service quality. The findings offer valuable insights for practitioners and policymakers seeking to improve efficiency and sustainability in the Thai automotive sector.
{"title":"Innovative framework for effective service parts management in the automotive industry","authors":"B. S. Nathan, B. V. Siva Reddy, C. C. Sastry, J. Krishnaiah, K. V. Eswaramoorthy","doi":"10.3389/fmech.2024.1361688","DOIUrl":"https://doi.org/10.3389/fmech.2024.1361688","url":null,"abstract":"Effective service parts management and demand forecasting are crucial for optimizing operations in the automotive industry. However, existing literature lacks a comprehensive framework tailored to the specific context of the Thai automotive sector. This study addresses this gap by proposing a strategic approach to service parts management and demand forecasting in the Thai automotive industry. Drawing on a diverse set of methodologies, including classical time series models and advanced machine learning techniques, various forecasting models were assessed to identify the most effective approach for predicting service parts demand. Categorization of service parts based on demand criteria was conducted, and decision rules were developed to guide stocking strategies, balancing the need to minimize service disruptions with cost optimization. This analysis reveals substantial cost savings potential through strategic stocking guided by the developed decision rules. Furthermore, evaluation of the performance of different forecasting models recommends the adoption of Support Vector Regressor (SVR) as the most accurate model for forecasting service parts demand in this context. This research contributes to the automotive service industry by providing a nuanced framework for service parts management and demand forecasting, leading to cost-effective operations and enhanced service quality. The findings offer valuable insights for practitioners and policymakers seeking to improve efficiency and sustainability in the Thai automotive sector.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.3389/fmech.2024.1382664
Meng Du, Hailong Mei
Introduction: With the rapid development of human society and economy, the power generation technology of various new energy vehicles has begun to receive widespread attention.Methods: Due to the lack of inertia and frequency stability in the new energy vehicle power generation system, this paper proposes a power generation control method that combines linear active disturbance rejection control technology and virtual synchronous generator technology. This method first introduces the control strategy and inertial response of the virtual synchronous generator. Then, it uses linear active disturbance rejection control technology to improve the virtual synchronous generator technology to deal with the uncertainty and external interference in the system.Results: The results showed that when the virtual inertia coefficient was 0, and the new energy vehicles would hardly intervene in the regulation of the grid voltage. When the virtual inertia coefficient was 5, the decline rate of the DC bus voltage of new energy vehicles had slowed down. When the virtual inertia coefficient increased, the power output of new energy vehicles can be increased to the grid. When the load suddenly increased, and the corresponding DC bus voltage decreased more slowly. In the VSG output power comparison, under the research method, the frequency fluctuation only increased by 0.09 Hz and returned to the rated frequency of 50 Hz. Additionally, the dynamic process of the system output power was the shortest, lasting only 0.05 s.Discussion: The above results show that the research method has significant superiority and effectiveness in improving the inertial response and overall stability of the new energy vehicle power system.
{"title":"The application of virtual synchronous generator technology in inertial control of new energy vehicle power generation","authors":"Meng Du, Hailong Mei","doi":"10.3389/fmech.2024.1382664","DOIUrl":"https://doi.org/10.3389/fmech.2024.1382664","url":null,"abstract":"Introduction: With the rapid development of human society and economy, the power generation technology of various new energy vehicles has begun to receive widespread attention.Methods: Due to the lack of inertia and frequency stability in the new energy vehicle power generation system, this paper proposes a power generation control method that combines linear active disturbance rejection control technology and virtual synchronous generator technology. This method first introduces the control strategy and inertial response of the virtual synchronous generator. Then, it uses linear active disturbance rejection control technology to improve the virtual synchronous generator technology to deal with the uncertainty and external interference in the system.Results: The results showed that when the virtual inertia coefficient was 0, and the new energy vehicles would hardly intervene in the regulation of the grid voltage. When the virtual inertia coefficient was 5, the decline rate of the DC bus voltage of new energy vehicles had slowed down. When the virtual inertia coefficient increased, the power output of new energy vehicles can be increased to the grid. When the load suddenly increased, and the corresponding DC bus voltage decreased more slowly. In the VSG output power comparison, under the research method, the frequency fluctuation only increased by 0.09 Hz and returned to the rated frequency of 50 Hz. Additionally, the dynamic process of the system output power was the shortest, lasting only 0.05 s.Discussion: The above results show that the research method has significant superiority and effectiveness in improving the inertial response and overall stability of the new energy vehicle power system.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.3389/fmech.2024.1392543
Parveen Sharma, Kashmir Singh Ghatorha, Amardeep Singh Kang, Lenka Cepova, Ajay Kumar, R. K. Phanden
The current study focuses on selecting the most suitable site location for a manufacturing industry using the Factor Rating Method (FRM). The study considers six key factors: Raw Materials Availability, Location, Availability of Labor, Transport, Availability of Utilities, and Environmental Impact. The FRM assign weights to each factor based on their relative importance. The results indicate that Raw Materials Availability holds the highest weight, suggesting its critical influence on site selection decisions. Subsequently, the Analytic Hierarchy Process (AHP) and Best Worst Method (BWM) are utilized to prioritize three available location alternatives through pairwise criteria comparisons. The analysis reveals that Location C emerges as the most favorable option, effectively meeting the manufacturing industry’s requirements. The successful application of these methods demonstrates their value in aiding decision-making processes related to site location selection. By considering multiple factors and utilizing structured methodologies, organizations can make informed choices aligned with their specific needs and goals. This research contributes to the existing body of knowledge by providing insights into effective site selection strategies for the manufacturing industry. Further research opportunities exist in incorporating additional factors, addressing real-world constraints, and conducting sensitivity analyses to enhance the accuracy and applicability of site location decision-making.
本研究的重点是使用因素评级法（FRM）为制造业选择最合适的厂址。研究考虑了六个关键因素：原材料供应、地理位置、劳动力供应、交通、公用设施供应和环境影响。FRM 根据各因素的相对重要性为其分配权重。结果表明，原材料可用性的权重最高，表明其对选址决策具有关键影响。随后，利用层次分析法（AHP）和最佳最差法（BWM），通过成对标准比较对三个备选地点进行优先排序。分析结果表明，地点 C 是最有利的选择，能有效满足制造业的要求。这些方法的成功应用证明了它们在协助与厂址选择相关的决策过程中的价值。通过考虑多种因素并利用结构化方法，企业可以做出符合其特定需求和目标的明智选择。这项研究为制造业的有效选址战略提供了见解，从而为现有知识体系做出了贡献。进一步的研究机会还在于纳入更多因素、解决现实世界中的限制因素以及进行敏感性分析，以提高选址决策的准确性和适用性。
{"title":"Strategic insights in manufacturing site selection: a multi-method approach using factor rating, analytic hierarchy process, and best worst method","authors":"Parveen Sharma, Kashmir Singh Ghatorha, Amardeep Singh Kang, Lenka Cepova, Ajay Kumar, R. K. Phanden","doi":"10.3389/fmech.2024.1392543","DOIUrl":"https://doi.org/10.3389/fmech.2024.1392543","url":null,"abstract":"The current study focuses on selecting the most suitable site location for a manufacturing industry using the Factor Rating Method (FRM). The study considers six key factors: Raw Materials Availability, Location, Availability of Labor, Transport, Availability of Utilities, and Environmental Impact. The FRM assign weights to each factor based on their relative importance. The results indicate that Raw Materials Availability holds the highest weight, suggesting its critical influence on site selection decisions. Subsequently, the Analytic Hierarchy Process (AHP) and Best Worst Method (BWM) are utilized to prioritize three available location alternatives through pairwise criteria comparisons. The analysis reveals that Location C emerges as the most favorable option, effectively meeting the manufacturing industry’s requirements. The successful application of these methods demonstrates their value in aiding decision-making processes related to site location selection. By considering multiple factors and utilizing structured methodologies, organizations can make informed choices aligned with their specific needs and goals. This research contributes to the existing body of knowledge by providing insights into effective site selection strategies for the manufacturing industry. Further research opportunities exist in incorporating additional factors, addressing real-world constraints, and conducting sensitivity analyses to enhance the accuracy and applicability of site location decision-making.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.3389/fmech.2024.1322640
Divyanshu S. Morghode, D. G. Thakur, Sachin Salunkhe, Lenka Cepova, Emad S. Abouel Nasr
This study aimed to select suitable materials and optimize the thickness of these materials so that they could prevent the perforation of 7.62-mm AP bullets at 830 m/s impact velocity. A numerical method is used to analyze the impact on layered configurations of Al2O3 and Al 7075-T651 to fulfill this aim. In order to optimize the thickness of the armor, normal impact and angular impact conditions were considered. Initially, a 20-mm Al2O3 front plate with a 20-mm Al 7075-T651 back plate is analyzed for layered configuration. Back plate thickness is reduced in steps to 10 mm such that no plastic deformation is observed on the rear side of the target. For further optimization of weight, the thickness of the Al2O3 plate is reduced to 18 mm. The weight of this configuration is 1.77 kg, and the areal density is 97.22 kg/m2. This configuration is analyzed for target orientations such as 80°, 70°, and 60°. In this analysis, the projectile deformed in a mushroom shape for 90° and 80° target orientations, while for 70° and 60° target orientations, the projectile experienced more damage on the shank part. The most effective configuration with the highest degree of ballistic performance is a layered combination of the 18-mm Al2O3 front plate and 10-mm Al 7075-T651 back plate at 70° target orientation.
{"title":"Numerical study on the optimized thickness of layer configuration against the 7.62 APM2 projectile","authors":"Divyanshu S. Morghode, D. G. Thakur, Sachin Salunkhe, Lenka Cepova, Emad S. Abouel Nasr","doi":"10.3389/fmech.2024.1322640","DOIUrl":"https://doi.org/10.3389/fmech.2024.1322640","url":null,"abstract":"This study aimed to select suitable materials and optimize the thickness of these materials so that they could prevent the perforation of 7.62-mm AP bullets at 830 m/s impact velocity. A numerical method is used to analyze the impact on layered configurations of Al2O3 and Al 7075-T651 to fulfill this aim. In order to optimize the thickness of the armor, normal impact and angular impact conditions were considered. Initially, a 20-mm Al2O3 front plate with a 20-mm Al 7075-T651 back plate is analyzed for layered configuration. Back plate thickness is reduced in steps to 10 mm such that no plastic deformation is observed on the rear side of the target. For further optimization of weight, the thickness of the Al2O3 plate is reduced to 18 mm. The weight of this configuration is 1.77 kg, and the areal density is 97.22 kg/m2. This configuration is analyzed for target orientations such as 80°, 70°, and 60°. In this analysis, the projectile deformed in a mushroom shape for 90° and 80° target orientations, while for 70° and 60° target orientations, the projectile experienced more damage on the shank part. The most effective configuration with the highest degree of ballistic performance is a layered combination of the 18-mm Al2O3 front plate and 10-mm Al 7075-T651 back plate at 70° target orientation.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.3389/fmech.2024.1373545
Obidah Alawneh, Basaam Rassas, Xianzhi Zhong, Jason Xi, R. Faieghi, Fengfeng Xi
Aircraft seats play a key role in the competition between aircraft companies seeking to differentiate themselves in terms of passengers’ inflight experience. The seat design process relies on computational and experimental methods based on subjective measures, such as comfort rating questionnaires, and objective comfort indicators of seat-occupant interaction, such as contact pressure distribution and muscle activation. Previous studies around muscle activity for seating comfort assessment have primarily focused on more active scenarios or active systems. As such, there are limited studies about the role of muscle force in normal and relaxed sitting conditions, common in aircraft settings. This paper explores the relationship between activities of the neck muscles, sternocleidomastoid, and upper trapezius, measured from human participants seated sedentarily on conventional business aircraft seats and their perceived comfort with different backrest inclinations. The results show, for normal seating without neck pillow, no significant association is found between the backrest inclination and the neck’s comfort or muscle activation. For general seating across different backrest inclinations, a positive medium correlation between muscle activation and comfort is found in upper trapezius (R = 0.5332, p = 0.0187). This work serves as a pilot study of this new approach of comfort evaluation using muscle feedback in seat designing processes and highlights the posterior’s effect to seating experience in the neck region.
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Amidst uncertainty, decision-making in manufacturing becomes a central focus due to its complexity. This study explores complex transportation constraints and uses novel ways to guide manufacturers. The Multi-objective Stochastic Linear Fractional Transportation Problem (MOSLFTP) is a crucial tool for managing supply chains, manufacturing operations, energy distribution, emergency routes, healthcare logistics, and other related areas. It adeptly addresses uncertainty, transforming efficiency and effectiveness in several domains. Stochastic programming is the process of converting theoretical probabilities into concrete certainties. The artistic compromise programming technique acts as a proficient mediator, reconciling opposing objectives and enabling equitable decision-making. This novel approach also addresses the Multi-objective Stochastic Linear plus Linear Fractional Transportation Problem (MOSLPLFTP), which involves two interconnected issues. The effectiveness of these principles is clearly shown with the help of the LINGO® 18 optimization solver. This study uses a ranking method to compare the similar methods to solve the current problems. A meticulously designed example acts as a significant achievement, shedding light on our method in a practical setting. It serves as a distinctive instrument, leading manufacturers through the maze of uncertainty and assisting them in determining the most advantageous course of action. This journey involves subtle interactions between complexity and simplicity, uncertainty is overcome by decisiveness, and invention is predominant.
{"title":"Navigating uncertain distribution problem: a new approach for resolution optimization of transportation with several objectives under uncertainty","authors":"Vishwas Deep Joshi, Medha Sharma, Ajay Kumar, Lenka Cepova, Rakesh Kumar, Namrata Dogra","doi":"10.3389/fmech.2024.1389791","DOIUrl":"https://doi.org/10.3389/fmech.2024.1389791","url":null,"abstract":"Amidst uncertainty, decision-making in manufacturing becomes a central focus due to its complexity. This study explores complex transportation constraints and uses novel ways to guide manufacturers. The Multi-objective Stochastic Linear Fractional Transportation Problem (MOSLFTP) is a crucial tool for managing supply chains, manufacturing operations, energy distribution, emergency routes, healthcare logistics, and other related areas. It adeptly addresses uncertainty, transforming efficiency and effectiveness in several domains. Stochastic programming is the process of converting theoretical probabilities into concrete certainties. The artistic compromise programming technique acts as a proficient mediator, reconciling opposing objectives and enabling equitable decision-making. This novel approach also addresses the Multi-objective Stochastic Linear plus Linear Fractional Transportation Problem (MOSLPLFTP), which involves two interconnected issues. The effectiveness of these principles is clearly shown with the help of the LINGO® 18 optimization solver. This study uses a ranking method to compare the similar methods to solve the current problems. A meticulously designed example acts as a significant achievement, shedding light on our method in a practical setting. It serves as a distinctive instrument, leading manufacturers through the maze of uncertainty and assisting them in determining the most advantageous course of action. This journey involves subtle interactions between complexity and simplicity, uncertainty is overcome by decisiveness, and invention is predominant.","PeriodicalId":53220,"journal":{"name":"Frontiers in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140743454","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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