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Revolutionizing Titanium Alloy Machining: ANFIS Model for Advanced Machining Performance Optimization Mohan Babu University

SAE Technical Papers (1906-current) Available online

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Format:
Book
Conference/Event
Author/Creator:
Pasupuleti, Thejasree, author.
Contributor:
Katta, Lakshmi Narasimhamu
Kiruthika, Jothi
Natarajan, Manikandan
Raju, Dhanasekar
Silambarasan, R.
Conference Name:
Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility (ADMMS'25) (2025-02-07 : Chennai, India)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2025
Summary:
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 19 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 19 provides more flexibility, efficiency, and accuracy in comparison to conventional approaches, allowing for greater monitoring and control in ECM operations. Moreover, the study investigates the potential uses of Titanium Grade 19 in the automotive industry, emphasizing its crucial function in sectors that demand resilient materials in corrosive environments. The experimental validation demonstrates a strong correlation between the projected results and the actual performance, confirming the effectiveness of the ANFIS-based strategy
Notes:
Vendor supplied data
Publisher Number:
2025-28-0046
Access Restriction:
Restricted for use by site license

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