Tool life prediction by RSM for cryogenic milling of inconel 718

Purpose The purpose of this study is to present the tool life optimization of carbide-coated ball nose milling inserts when high-speed milling of Inconel 718 under cryogenic CO2 condition. The main aims are to analyze the influence level of each cutting parameter on the tool life and to identify the optimum parameters that can lengthen the tool life to the maximum. Design/methodology/approach The experimental layout was designed using Box–Behnken RSM where all parameters were arranged without combining their highest and lowest values of each factor at the same time. A total of 29 milling experiments were conducted. Then, a statistical analysis using ANOVA was conducted to identify the relationship between the controlled factors on tool life. After that, a predictive model was developed to predict the variation of tool life within the predetermined parameters. Findings Results from the experimental found that the longest tool life of 22.77 min was achieved at Vc: 120 m/min, fz: 0.2 mm/tooth, ap: 0.5 mm and ae: 0.2 mm. ANOVA suggests the tool life of 23.4 min can be reached at Vc: 120.06 m/min, fz: 0.15 mm/tooth, ap: 0.66 mm and ae: 0.53 mm. All four controlled factors have influenced the tool life with the feed rate and radial depth of cut (DOC) as the major contributors. The developed mathematical model accurately represented the tool life at an average error of 8.2 per cent when compared to the actual and predicted tool life. Originality/value These experimental and statistical studies were conducted using Box–Behnken RSM method under cryogenic CO2 condition. It is a proven well-known method. However, the cooling method used in this study is a new technique and its effects on metal cutting, especially in the milling process of Inconel 718, has not yet been explored.

FUZZY REGRESSION MODELING FOR TOOL PERFORMANCE PREDICTION AND DEGRADATION DETECTION

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study — namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52–54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.