Coupling effect of micro-textured tools and cooling conditions on the turning performance of aluminum alloy 6061

Micro-texturing has been widely proven to be an effective technology for achieving sustainable machining.However,the performance of micro-textured tools under different cooling conditions,especially their coupling effect on machined surface integrity,was scarcely reported.In this paper,the non-textured,linear micro-grooved,and curvilinear micro-grooved inserts were used to turn aluminum alloy 6061 under dry,emulsion,and liquid nitrogen cryogenic cooling conditions.The coupling effects of different micro-textures and cooling conditions on cutting force,cutting temperature,and machined surface integrity,including the surface roughness,work hardening,and residual stress,were revealed and discussed in detail.Results indicated that the micro-grooved tools,especially the curvilinear micro-grooved tools,not only reduced the cutting force and cutting temperature,but also improved the machined surface integrity.In addition,the micro-grooved tools can cooperate with the emulsion or liquid nitrogen to reduce the cutting force,cutting temperature,and improve the machined surface integrity generally,although the combination of emulsion cooling condition and micro-grooved tools generated negative coupling effects on cutting forces and surface work hardening.Especially,the combination of curvilinear micro-grooved cutting tools and cryogenic cooling condition resulted in the lowest cutting force and cutting temperature,which generated the surface with low roughness,weak work hardening,and compressive residual stress.

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface quality.However,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments.Herein,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface quality.In the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership inferior.The membership functions are varied based on the machining theory.The inputs of the NANFIS model are cutting parameters,and the outputs are the machining performances.For optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS models.Additionally,the IPSO algorithm as a learning algorithm is used to train the NANFIS models.The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron.The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,respectively.The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models.Based on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency.It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters.The developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and IPSO.Experimental results showed that the IPSO algorithm exhibited the best global convergence performance.The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.