The Simulation Study On Hardened Steel Cutting By PCBN Tool In Hard Dry Cutting Conditions

The hard-dry turning is a technique for precision cutting of high hardness materials without using cutting fluids.It has the advantages of high efficiency,environmental protection and high surface quality,which is the most advanced manufacturing technology with the most application prospects.Polycrystalline Cubic Boron Nitride(PCBN)tool is widely used in hard dry machining due to its high hardness,chemical stability and thermal stability.As a typical hard wear-resistant high-hardness material,hardened steel is usually ground by grinding,but the processing is always time-consuming and inefficient.The use of hard dry cutting instead of grinding to complete the final finishing of hardened steel parts has become a trend.Controlling the quality of processed surface for hard dry cutting technology is an important issue to be solved.The indicators for evaluating the quality of the machined surface are mainly focused on cutting forces,cutting temperatures,tool wear and machined surface integrity.In this paper,the PCBN hard dry turning 50 HRC hardened steel is taken as the research object.The cutting force,cutting temperature and surface roughness of the cutting process are simulated by finite element method and artificial intelligence algorithm.The specific contents are as follows:(1)The numerical simulation software AdvantEdge FEM was used to establish the finite element model of the hard dry-steering 50 HRC hardened steel of PCBN tool,and the cutting process was simulated.The single factor variable method was used to analyze the influence of tool geometry parameters and cutting parameters on cutting force and cutting temperature,and the influence curves of various factors on cutting force and cutting temperature were obtained.(2)The orthogonal test scheme is designed with the tool rake angle,tool nose radius,feed rate,feed rate and cutting depth as the design variables.The finite element simulation model was used to simulate the machining.The tool parameters and cutting parameters were optimized for cutting force and cutting temperature.The range analysis method and variance analysis method were used to obtain the influence degree of each factor on cutting force and cutting temperature,and the optimal tool geometry parameters and cutting parameters were determined.(3)The T–S fuzzy neural network is used to establish a surface roughness prediction model based on the tool rake angle,tool nose radius,cutting speed,feed rate and depth of cut to achieve a more accurate prediction of surface roughness.At the same time,the model is used as the objective function to optimize the tool parameters and cutting parameters by genetic algorithm,and the optimal surface roughness and corresponding tool parameters and cutting parameters are obtained.In the case of fewer trials,the established model not only achieves a more accurate prediction of the surface roughness of the machine,but also provides a reference for the reasonable selection of cutting parameters,which is of great significance for improving the actual processing quality and processing efficiency.