A versatile approach, considering tool wear, to simulate undercut error when turning thin-walled workpieces

In-process workpiece elastic deflection is the major source of geometrical error when machining low-stiffness workpieces. It creates an undercut error which needs to be corrected by time-consuming and labour-intensive operations. For this reason, cutting process simulation is growing in interest. To do so, a model representing the workpiece flexibility is coupled with a model to predict the applied cutting forces. For a given tool-material set, these cutting forces depend on the cut section, which therefore depends on current deflection of the part during machining, but also on the level of tool wear. This research work focuses on developing a general coupling approach to tackle this challenge. The case study is the finish turning on thin Inconel 718 discs. The cutting forces are predicted by a mechanistic model taking tool wear into account. The wear effect is expressed using the cumulative removed volume. The workpiece stiffness is determined with a reduced model using a modal basis. When dealing with large workpieces, it results in a remarkable computing time reduction during the time domain simulation. Cutting tests with varying engagements are simulated in a dexel-based versatile framework and undercut errors are compared to experimental observations.

[1]  Philippe Dépincé,et al.  Active integration of tool deflection effects in end milling. Part 1. Prediction of milled surfaces , 2006 .

[2]  Jian-wei Ma,et al.  Instantaneous cutting-amount planning for machining deformation homogenization based on position-dependent rigidity of thin-walled surface parts , 2018, Journal of Manufacturing Processes.

[3]  Svetan Ratchev,et al.  Force and deflection modelling in milling of low-rigidity complex parts , 2003 .

[4]  Seok Won Lee,et al.  Virtual workpiece: workpiece representation for material removal process , 2012 .

[5]  Kai Cheng,et al.  An innovative approach to cutting force modelling in diamond turning and its correlation analysis with tool wear , 2016 .

[6]  Kai Tang,et al.  Multi-axis variable depth-of-cut machining of thin-walled workpieces based on the workpiece deflection constraint , 2018, Comput. Aided Des..

[7]  P. Lorong,et al.  A general method to accurately simulate material removal in virtual machining of flexible workpieces , 2021 .

[8]  H. Karaouni,et al.  Machinability of inconel 718 during turning: Cutting force model considering tool wear, influence on surface integrity , 2020, Journal of Materials Processing Technology.

[9]  Jianbin Xue,et al.  Deformation prediction and error compensation in multilayer milling processes for thin-walled parts , 2009 .

[10]  Han Ding,et al.  Tool Orientation Optimization for Reduction of Vibration and Deformation in Ball-end Milling of Thin-walled Impeller Blades , 2017 .

[11]  Pedro J. Arrazola,et al.  Correlation between tool flank wear, force signals and surface integrity when turning bars of Inconel 718 in finishing conditions , 2014 .

[12]  Feifei Xu,et al.  State of the art in milling process of the flexible workpiece , 2020, The International Journal of Advanced Manufacturing Technology.

[13]  J. Agapiou,et al.  Machining Dynamics , 2018, Metal Cutting Theory and Practice.

[14]  Kai Cheng,et al.  Machining dynamics: Fundamentals, applications and practices , 2008 .

[15]  Soichi Ibaraki,et al.  A cutting sequence optimization algorithm to reduce the workpiece deformation in thin-wall machining , 2017 .

[16]  Philippe Lorong,et al.  Simulation of a finishing operation : milling of a turbine blade and influence of damping , 2012 .

[17]  Tim Van Hook Real-time shaded NC milling display , 1986 .

[18]  K. Bathe Finite Element Procedures , 1995 .

[19]  Philippe Dépincé,et al.  Active integration of tool deflection effects in end milling. Part 2. Compensation of tool deflection , 2006 .