ArticleMaterials (Basel, Switzerland)2020
Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach.
Article in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Accelerated First-Principles Calculations Based on Machine Learning for Interfacial Modification Element Screening of SiCp/Al Composites.Materials (Basel, Switzerland) · 2024Article
- Fundamental Investigation into Tool Wear and Surface Quality in High-Speed Machining of Ti6Al4V Alloy.Materials (Basel, Switzerland) · 2021Article
- Precision Hard Turning of Ti6Al4V Using Polycrystalline Diamond Inserts: Surface Quality, Cutting Temperature and Productivity in Conventional and High-Speed Machining.Materials (Basel, Switzerland) · 2020Article
- MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks.Materials (Basel, Switzerland) · 2020Article
- The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process.Materials (Basel, Switzerland) · 2020Article
- On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts.Materials (Basel, Switzerland) · 2020Article
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6 authors.
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Abstract
Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.
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