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A hybridization of machine learning and NSGA-II for multi-objective optimization of surface roughness and cutting force in AISI 4340 alloy steel turning

Nguyen Anh-Tu, Nguyen Van-Hai, Le Tien-Thinh, Nguyen Nhu-Tung

Journal of Machine Engineering

2023

Vol. 23, No. 1

133--153

This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average surface roughness (Ra) and cutting force under the cutting parameter constraints of cutting speed, feed rate, depth of cut, and tool nose radius in a range of 50–375 m/min, 0.02–0.25 mm/rev, 0.1–1.5 mm, and 0.4–0.8 mm, respectively. The present study uses five ML models – namely SVR, CAT, RFR, GBR, and ANN – to predict Ra and cutting force. Results indicate that ANN offers the best predictive performance in respect of all accuracy metrics: root-mean-squared-error (RMSE), mean-absolute-error (MAE), and coefficient of determination (R2). In addition, a hybridization of NSGA-II and ANN is implemented to find the optimal solutions for machining parameters, which lie on the Pareto front. The results of this multi-objective optimization indicate that Ra lies in a range between 1.032 and 1.048 μm, and cutting force was found to range between 7.981 and 8.277 kgf for the five selected Pareto solutions. In the set of non-dominated keys, none of the individual solutions is superior to any of the others, so it is the manufacturer's decision which dataset to select. Results summarize the value range in the Pareto solutions generated by NSGA-II: cutting speeds between 72.92 and 75.11 m/min, a feed rate of 0.02 mm/rev, a depth of cut between 0.62 and 0.79 mm, and a tool nose radius of 0.4 mm, are recommended. Following that, experimental validations were finally conducted to verify the optimization procedure.

Turning operation of AISI 4340 steel in flooded, near-dry and dry conditions: a comparative study on tool-work interface temperature

Dennison Milon Selvam, Sivaram N. M., Barik Debabrata, Ponnusamy Senthil

Mechanics and Mechanical Engineering

2019

Vol. 23, nr 1

172--182

The objective of this study is to analyse the effect of tool-work interface temperature observed during the turning of AISI 4340 cylindrical steel components in three machining conditions, namely flooded, near-dry and dry conditions with three separate CNMG-PEF 800 diamond finish Titanium Nitride (TiN) coated carbide cutting tool. The machining parameters considered in this study are cutting velocity, feed rate and depth of cut. The experimentswere planned based on full factorial design (33) and executed in an All Geared Conventional Lathe. The toolwork interface temperature was observed using a K-type tool-work thermocouple,while the machining of steel, and subsequently, a mathematical model was developed for the tool-work interface temperature values through regression analysis. The significance of the selected machining parameters and their levels on tool-work interface temperature was found using analysis of variance (ANOVA) and F-test. The results revealed that machining under near-dry condition exhibited lesser temperature at the tool-work interface, which is the sign of producing better quality products in equivalence with the machining under flooded condition.