Quality And Energy Optimization Method And Case Demonstration Of AlSi10Mg Parts Manufactured By Selective Laser Melting

As one of the core technologies,additive manufacturing(AM)has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production.AM is broadly used to fabricate complex and lightweight structures for the aerospace,automobile and other industrial sectors.Among the AM techniques,selective laser melting(SLM)is one important technology which is a hot research spot from both academic and industrial area.During the SLM processes,a large amount of data is created,which has great potential to make decisions about quality management and energy control.However,three issues need to be deeply explored in the existing research.Firstly,significant progress has been made in the fabrication of qualified products from different AM processes,but limited work available on SLM built thin-walled parts of A1 alloys.Secondly,energy consumption is mainly measured for the polymeric materials,and very limited work is done on the metallic parts or metal-based AM systems specifically for the SLM process.Thirdly,big data analytics(BDA)is mainly implemented on the upper-level management system of manufacturing enterprises,but its implementation on the AM processes is limited.In this research work,a novel big datadriven framework for quality and energy control is developed,and its effectiveness is explored through a practical application to the AM process of AlSi10Mg parts.The novelty and work of this dissertation encompass the following four parts:(1)A big data-driven additive manufacturing framework is designed and proposedIn order to meet the energy consumption and product quality requirements of AM industry leaders in the decision making of the product life cycle of AM process,a framework of big data-driven additive manufacturing(BD-AM)is proposed.This framework comprises four components and two key technologies.The four components are perception and acquisition for AM big data,storage and pre-processing of big data,data mining and decision making of big data,and AM big data application services.The two key technologies are the big data perception and acquisition for AM,and big data mining and knowledge sharing.(2)A quality and energy optimization method of SLM fabricated bulk samples of AlSi10Mg alloy is presentedThe effects of three process parameters(laser power,scan speed,and overlap rate)on quality characteristics and specific energy consumption(SEC)were studied for the SLM manufactured bulk samples of AlSi10Mg alloy.Experiment shows that the tensile strength and hardness show increasing trends with an increase of SEC.Besides,a significant percentage(27.8%)of electrical energy could be saved while satisfying the quality requirements via the proper selection of process parameters for the manufacturing of SLMed parts.(3)Deep investigation of wall thickness effect on the mechanical performance of SLM built AlSi10Mg specimensThe effect of heat treatments,i.e.solution heat treatment(SHT)and artificial aging(AA)on the densification,porosity,mechanical properties(such as tensile strength,yield strength,%age breakage elongation),and hardness of the thin-walled specimens are deeply studied.Experiment showed that the densification and mechanical properties increase with the increase in the wall thickness such that maximum densification of 99.21%and higher breakage elongation of 12.04%is achieved for a 5 mm specimen.It is also concluded that densification is enhanced by the A A for small wall thickness specimens such that 99.92%densification achieved for 0.50 mm specimen and higher breakage elongation of 20.66%achieved for 5mm specimen by the AA.(4)A multi-objective optimization method of SLM process for quality and energy of thin-walled specimens is presentedThe effects of various processing parameters such as wall thickness,laser power,scan speed and hatch distance on quality and energy optimization of SLM-built AlSi10Mg thin-walled specimens are deeply investigated.Besides,the process parameters have been optimized for quality and energy by applying Pareto front and statistical regression analysis.Results show that higher densification,tensile strengths,hardness,and breakage elongation values with minimum porosity and energy consumption were achieved by using the best combination of process parameters.The optimized parameters have consumed 57.3%less SEC,which is very beneficial for the green production process.