The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performanc...The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performance of manufacturing systems.However,only a few studies have specifically addressed energy-efficient scheduling for remanufacturing.Considering the uncertain processing time and routes and the operation characteristics of remanufacturing,we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing.An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines,batch machines,and uncertain processing routes and time.The algorithm demonstrated superior performance in terms of optimal value,run time,and convergent generation in comparison with other algorithms.Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size.In addition,the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%.This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.展开更多
Metal powder contributes to the environmental burdens of additive manufacturing(AM)substantially.Current life cycle assessments(LCAs)of metal powders present considerable variations of lifecycle environmental inventor...Metal powder contributes to the environmental burdens of additive manufacturing(AM)substantially.Current life cycle assessments(LCAs)of metal powders present considerable variations of lifecycle environmental inventory due to process divergence,spatial heterogeneity,or temporalfluctuation.Most importantly,the amounts of LCA studies on metal powder are limited and primarily confined to partial material types.To this end,based on the data surveyed from a metal powder supplier,this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization,respectively.Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’physical properties,we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process.This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available.With the predicted energy use information of specific powder,the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages.Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO_(2)equivalency.The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models.This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.展开更多
基金The authors highly appreciate the investigation opportunities provided by SINOTRUK,Jinan Fuqiang Power Co.,Ltd.We are also grateful for the financial support from the National Natural Science Foundation of China(Grant Nos.51775086 and 51605169)Natural Science Foundation of Guangdong Province China(Grant No.2014A030310345).
文摘The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performance of manufacturing systems.However,only a few studies have specifically addressed energy-efficient scheduling for remanufacturing.Considering the uncertain processing time and routes and the operation characteristics of remanufacturing,we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing.An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines,batch machines,and uncertain processing routes and time.The algorithm demonstrated superior performance in terms of optimal value,run time,and convergent generation in comparison with other algorithms.Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size.In addition,the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%.This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.
基金funded by the National Natural Science Foundation of China under Grant No.52305544the Project of Guangdong Science and Technology Innovation Strategy under Grant No.STKJ202209065.
文摘Metal powder contributes to the environmental burdens of additive manufacturing(AM)substantially.Current life cycle assessments(LCAs)of metal powders present considerable variations of lifecycle environmental inventory due to process divergence,spatial heterogeneity,or temporalfluctuation.Most importantly,the amounts of LCA studies on metal powder are limited and primarily confined to partial material types.To this end,based on the data surveyed from a metal powder supplier,this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization,respectively.Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’physical properties,we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process.This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available.With the predicted energy use information of specific powder,the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages.Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO_(2)equivalency.The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models.This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.
