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Abstract: This study presents a multi-objective optimization of the Photochemical Machining (PCM) process for copper, a critical manufacturing technique for producing stress-free, intricate micro-components. The research systematically investigates the influence of key process parameters—ferric chloride (FeCl3) concentration, etchant temperature, and etching time—on the material removal rate (MRR), surface roughness, and undercut. An L9 Taguchi Design of Experiments was employed, and Grey Relational Analysis (GRA) was utilized to resolve the conflicting objectives of maximizing MRR while minimizing surface roughness and undercut. Experimental results identified a distinct parameter interdependence, with FeCl₃ concentration being the most statistically significant factor, contributing 65.03% to the overall performance as per ANOVA. The comparative analysis of grey relational grades revealed an optimal parameter set: 500 gm/lit concentration, 45°C temperature, and a 3-minute etching time. This optimal configuration was validated to simultaneously enhance machining efficiency and component precision. The findings provide a robust, data-driven framework for optimizing copper PCM in industrial applications such as microelectronics and precision medical device manufacturing, ensuring superior surface integrity and dimensional accuracy while improving resource utilization.
"A Comparative Analysis and Experimental Review for the Optimization of Photochemical Machining Characteristics on Copper Substrates", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.c19-c27, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603204.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator