Machining performance analysis for PMEDM of biocompatible material Ti-6Al-7Nb alloy: A machine learning approach

• Published in: Materials letters Vol. 320; p. 132337
• Main Authors: Biswal, Smrutiranjan, Tripathy, S., Tripathy, D.K.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2022; Elsevier BV
• Subjects: Algorithms; Biocompatibility; Biomaterials; Biomedical materials; Corrosive wear; Data analysis; Electric discharge machining; Machine learning; Material removal rate (machining); Materials science; Orthogonal arrays; Parameters; PMEDM; Prediction models; Predictions; Programming languages; Python; Repeated loading; Surface properties; Surface roughness; Surgical implants; Taguchi; Titanium alloy; Titanium alloys; Titanium base alloys; Tool wear; Wear rate; Titanium alloy; PMEDM; Taguchi; Machine learning; Biomaterials; Python
• ISSN: 0167-577X; 1873-4979
• DOI: 10.1016/j.matlet.2022.132337

[Display omitted] •An approach to improve the process performance and efficiency of PMEDM has been proposed.•The machine learning prediction model has been established by linear regression using python programming language.•Experiments were conducted on Ti-6Al-7Nb used in biomedical and aerospace industries.•SiC powder has been mixed to the dielectric in varying concentrations.•The proposed model performs better than other suggested methods. Surface properties of biomedical implants have vital role as they release toxic particles due to corrosion and wear under repeated loading. Powder mixed electric discharge machining (PMEDM) is used to shape difficult-to-machine materials with simultaneous surface modification. In the present work Taguchi’s L27 orthogonal array is used to machine Ti-6Al-7Nb titanium alloy using PMEDM. SiC powder of size < 53 µm is added in varying concentrations to the dielectric. Input parameters chosen for the investigation are powder concentration (Cp), peak current (Ip), pulse on time (Ton), duty cycle (DC) and gap voltage (Vg). The influence of input parameters on material removal rate (MRR), surface roughness (Ra) and tool wear rate (TWR) has been investigated. Machine learning algorithm has been used for data analysis, processing and a prediction model has been established by applying linear regression using python programming language. Numpy, Pandas libraries of python language has been used for data analysis, data pre-processing and prediction has been done using Scikit-Learn library. The error between predicted and confirmatory test results is 2.14%.