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Traditional methods relying on machining parameters and simplified surface finish indicators often fail to capture the full complexity of surface topography. This study introduces a novel approach that leverages cutting power data, an accessible proxy for discharge parameters, to predict and reconstruct surface topography in WEDM. The method decomposes surface topography into roughness, waviness, and form components using complete ensemble empirical mode decomposition with adaptive noise and sample entropy. Each component is modeled separately: roughness as a random process following a normal distribution, waviness as a uniform sinusoidal function, and form as a cubic B-spline. A multi-task convolutional neural network is developed to simultaneously predict these components based on features extracted from cutting power data. Experimental validation was conducted on a 6061 aluminum (AL 6061) workpiece machined under various cutting conditions. The proposed model outperformed benchmark models in both frequency and spatial domain metrics, accurately capturing key surface characteristics such as roughness variations, waviness patterns, and shape deviations. These results demonstrate an enhanced reliability in surface topography predictions. By integrating cutting power data with an advanced decomposition technique and artificial intelligence model, this study provides a framework for real-time surface topography prediction in WEDM, contributing to improved process optimization and control.<\/jats:p>","DOI":"10.1115\/1.4069964","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T16:20:38Z","timestamp":1758817238000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Modeling of Surface Topography Using Power Data in Wire Electrical Discharge Machining"],"prefix":"10.1115","volume":"25","author":[{"given":"Bochi","family":"Liu","sequence":"first","affiliation":[{"name":"Rutgers University\u2013New Brunswick Department of Industrial and Systems Engineering, , 96 Frelinghuysen Road, CoRE Building Room 738, , \u00a0 ;","place":["Piscataway, NJ, 08854"]},{"name":"Dalian University of Technology State Key Laboratory of Coastal and Offshore Engineering, , No. 2 Linggong Road, \u00a0 ,","place":["Dalian, China, 116024"]}]},{"given":"Yifei","family":"Guo","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05vt9qd57","id-type":"ROR","asserted-by":"publisher"}],"name":"Rutgers University\u2013New Brunswick Department of Mechanical and Aerospace Engineering, , 500 Bartholomew Road, Richard Weeks Hall of Engineering Room 218, , \u00a0","place":["Piscataway, NJ, 08854"]}]},{"given":"Y. B.","family":"Guo","sequence":"additional","affiliation":[{"name":"Rutgers University\u2013New Brunswick Department of Mechanical and Aerospace Engineering, , 500 Bartholomew Road, Richard Weeks Hall of Engineering Room 218, , \u00a0","place":["Piscataway, NJ, 08854"]}]},{"given":"Weihong (Grace)","family":"Guo","sequence":"additional","affiliation":[{"name":"Rutgers University\u2013New Brunswick Department of Industrial and Systems Engineering, , 96 Frelinghuysen Road, CoRE Building Room 220, , \u00a0","place":["Piscataway, NJ, 08854"]}]}],"member":"33","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"volume-title":"Fundamentals of Modern Manufacturing: Materials, Processes, and Systems","year":"2010","author":"Groover","key":"2025102712000169900_CIT0001"},{"issue":"12","key":"2025102712000169900_CIT0002","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1016\/j.ijmachtools.2004.04.017","article-title":"State of the Art in Wire Electrical Discharge Machining (WEDM)","volume":"44","author":"Ho","year":"2004","journal-title":"Int. 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