Development of a feed rate adaption control system for high-speed rough and finish end-milling of hardened EN24 steel

Abstract

High-speed machining centers are used for end-milling operations of a variety of parts, dies, and molds needed in power and transport industries. Different approaches are used for rough and finish endmilling, since desired productivity and quality are important in the respective cases. In the present work, a feed rate adaption control system is proposed by integrating different requirements of high-speed endmilling. Hardened EN 24 steel which is being widely used in the production of dies, molds, and other parts is taken as a candidate work material for implementation of the proposed control system. Based on extensive experimentation, investigations have been carried out on highspeed rough and finish end-milling operations, and the details are reported by the authors (Saikumar and Shunmugam, Int J Adv Manuf Technol, under review). In this paper, relevant response surface and artificial neural network models have been used, and suitable reference parameters are obtained for the proposed control system. In the case of rough end-milling, material removal volume is taken as the objective, and the reference values for cutting force and cutting time are used. Only a reference cutting force is used for finish end-milling in which surface roughness is considered as the objective. Implementation details of the proposed PC-based control system are presented. The results obtained for a newly devised H-A-S-H test (short run) along with those for long-run tests are presented and discussed.

A generalised geometrical model of turning operations for cutting force modelling using edge discretisation

“…For example, the feed can be modified along the tool path in order to limit the cutting forces, while minimising the cycle time [14,15]; the machining allowance may also be variable. The feed can be adapted in real-time by measuring the forces and modifying the numerical command (NC) instructions [16]. Nevertheless, predictive methods should be preferred because of the cost of the monitoring equipment and the difficulties in modifying the NC command data or the set-point value in the speed control loop.…”

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

“…In Saikumar & Shunmugam [25] a feedrate adaption control system for high speed milling was proposed. In the research work, different control strategies were used for rough and finishing operations.…”

“…For micro and macro milling operations different modeling techniques have been applied for tool wear or part quality estimation and they include response surfaces [8,[22][23][24][25], and Artificial Intelligence (AI) models such as Artificial Neural Networks (ANN) [8, 11-13, 15, 23-27], Adaptive Neuro Fuzzy Inference Systems (ANFIS) [28,29], Fuzzy Systems [12,30,31], Hidden Markov Models (HMM) [24], Bayesian Networks (BN) [32,33] and Least Squares Support Vector Machines (LS-SVM) [34][35][36]. Comprehensive reviews about modeling techniques applied in machining can be found in [19,37,38].…”