摘要：*** Given the emergence of intelligent manufacturing capabilities,we may wonder whether design for manufacturing(DFM)practices should be reconceptualized to take advantage of these ***,if so,what should design for intelligent manufacturing(DFIM)be?The integration of cloud computing,data analytics,artificial intelligence(AI),and the Internet of Things(IoT)with advanced manufacturing technologies has enabled the emergence of what has been called new-generation intelligent manufacturing[1].Such new-generation intelligent manufacturing capabilities will enable transformational new products and services with unprecedented levels of quality,responsiveness,and efficiency.

摘要：This work investigates the machining temperatures of ultra-fine-grained titanium(UFG Ti),prepared by equal channel angular extrusion,through analytical *** Ti has great usefulness in biomedical applications because of its high mechanical strength,sufficient manufacturability,and high *** temperatures were predicted using a physics-based predictive model based on material constitutive relation and mechanics of the orthogonal cutting *** minimization between the stress calculated using Johnson–Cook constitutive model and the same stress calculated using mechanics model yields the estimation of machining temperatures at two deformation *** agreements are observed upon validation to the values reported in the *** machinability of UFG Ti is investigated by comparing its machining temperature to that of Ti–6Al–4V alloy under the same cutting *** lower temperatures are observed in machining UFG *** computational efficiency of the presented model is investigated by comparing its average computational time(~0.5 s)to that of a widely used modified chip formation model(8900 s)with comparable prediction *** work extends the applicability of the presented temperature model to a broader class of materials,specifically ultra-fine-grained *** high computational efficiency allows the in situ temperature prediction and optimization of temperature condition with process parameters planning.