Multi-Response Optimization of Abrasive Waterjet Machining of Ti6Al4V Using Integrated Approach of Utilized Heat Transfer Search Algorithm and RSM

• Published in: Materials 2021-12, Vol.14 (24), p.7746
• Main Authors: Fuse, Kishan, Chaudhari, Rakesh, Vora, Jay, Patel, Vivek K, de Lacalle, Luis Norberto Lopez
• Format: Article
• Language: English
• Subjects: Abrasive machining; Accuracy; Aerospace industry; Algorithms; Biomedical engineering; Biomedical materials; Cutting parameters; Data envelopment analysis; Experiments; Heat transfer; Hydraulic jets; Kerf; Mass flow rate; Material removal rate (machining); Multiple objective analysis; Optimization; Pareto optimum; Process parameters; Response surface methodology; Search algorithms; Statistical models; Surface roughness; Titanium alloys; Titanium base alloys; Variables; Variance analysis
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14247746

Machining of Titanium alloys (Ti6Al4V) becomes more vital due to its essential role in biomedical, aerospace, and many other industries owing to the enhanced engineering properties. In the current study, a Box-Behnken design of the response surface methodology (RSM) was used to investigate the performance of the abrasive water jet machining (AWJM) of Ti6Al4V. For process parameter optimization, a systematic strategy combining RSM and a heat-transfer search (HTS) algorithm was investigated. The nozzle traverse speed (T ), abrasive mass flow rate (A ), and stand-off distance (S ) were selected as AWJM variables, whereas the material removal rate (MRR), surface roughness (SR), and kerf taper angle (θ) were considered as output responses. Statistical models were developed for the response, and Analysis of variance (ANOVA) was executed for determining the robustness of responses. The single objective optimization result yielded a maximum MRR of 0.2304 g/min (at T of 250 mm/min, A of 500 g/min, and S of 1.5 mm), a minimum SR of 2.99 µm, and a minimum θ of 1.72 (both responses at T of 150 mm/min, A of 500 g/min, and S of 1.5 mm). A multi-objective HTS algorithm was implemented, and Pareto optimal points were produced. 3D and 2D plots were plotted using Pareto optimal points, which highlighted the non-dominant feasible solutions. The effectiveness of the suggested model was proved in predicting and optimizing the AWJM variables. The surface morphology of the machined surfaces was investigated using the scanning electron microscope. The confirmation test was performed using optimized cutting parameters to validate the results.