Modeling and multiple optimization in face milling of hardfacing welding applied steel: Force, roughness, power

Abstract

The coating performance for indexable inserts is very important for the main machinability indicators such as energy consumption, cutting force, and surface quality, especially in milling operations. The present study intend to the comprehensive analysis of machining conditions for above mentioned machinability indicators that can be considered most important for sustainability in hard milling of a worn machine part. First, the worn zone of the shearing blade made of X40CrMoV5-1 hot work tool steel was filled with electric arc welding. Then, hard milling experiments were carried out to analyze the performance of different coated carbide inserts in the machining of the hardfacing welded part. Experiments were performed by applying L18 experimental design for invariable cutting depth, three different cutting speed, and feed rate with PVD-AlTiCrN and CVD-TiCN/Al2O3/TiN coated inserts. Machining parameters for the responses namely the resultant force (Fr), surface roughness (Ra), and cutting power (Pc) were simultaneously optimized via applying Taguchi-based grey relationship analysis. Finally, predictive models for the responses were developed by RSM. The results obtained by both coatings shown that the Fr and Ra are mainly influenced by the feed rate, while cutting speed is the parameter that most influenced the Pc. The optimum results with regards to the responses were obtained with the PVD-AlTiCrN coated tool. In the current study, the developed quadratic models and probability graphs indicated that the responses were highly predictable.