Optimization of machining parameters when machining beyond recommended cutting speed

Author:

Kaladhar M.

Abstract

Purpose Even though austenitic stainless steels have been extensively used in industries, owing to some of the characteristics of the material, its performance in machining is difficult to understand, in particular at high cutting speeds. There is no availability of dependable and in-depth studies pertinent to this matter. In this work, performance of AISI 304 austenitic stainless steel was studied in terms of surface roughness (Ra) and material removal rate (MRR) at high cutting speeds. Subsequently, parametric optimization and prediction for responses were carried out. Design/methodology/approach Turning operations were conducted using L9 orthogonal array and the outcomes were analyzed to attain optimal set of machining parameters for the responses using signal-to-noise (S/N) ratio and Pareto analysis of variance (ANOVA). In the present work, the cutting speed values were considered beyond the recommended range as designated by tool manufacturers. Finally, multiple regression models were developed to predict responses. Findings From the results, 350 m/min was found to be a significant speed. The investigation reveals that even though the speeds are taken beyond the recommended values, the results are favorable. The optimal machining parameters values for surface quality obtained were cutting speed of 350 m/min, feed of 0.15 mm/rev and depth of cut of 2.0 mm. In case of MRR, the optimal values were: cutting speed of 400 m/min, feed of 0.25 mm/rev and depth of cut of 2.0 mm. It was found out that there was an improvement in Ra and MRR (around 15 and 4%) due to optimization. The results indicate that Pareto ANOVA is easier than S/N ratio. This revealed that the feed rate and depth of cut were mostly affected parameters for Ra and MRR. The developed models are capable of predicting the responses accurately. Practical implications The outcome of the work reveals that even though the speeds were taken beyond the recommended value, the results are favorable for manufacturing industries when the tool cost is considered insignificant. Originality/value No work was reported on machining of the chosen material beyond the recommended cutting speed. Moreover, it was observed from the past works that cutting speeds were limited to 100–300 m/min.

Publisher

Emerald

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Reference45 articles.

1. Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method;The International Journal of Advanced Manufacturing Technology,2017

2. Hard turning multi-performance optimization for improving the surface integrity of 300M ultra-high strength steel;The International Journal of Advanced Manufacturing Technology,2019

3. Effects of free cutting additives on the machinability of austenitic stainless steels;Journal of Materials Processing Technology,2003

4. Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel;Journal of Materials Processing Technology,2009

5. Multi-responses optimization in dry turning of a stainless steel as a key factor in minimum energy;The International Journal of Advanced Manufacturing Technology,2018

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