{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:55:12Z","timestamp":1787691312265,"version":"build-2784847793"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India","award":["VTU SEED (FY 22-23)\/23"],"award-info":[{"award-number":["VTU SEED (FY 22-23)\/23"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Materials"],"abstract":"<jats:p>This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box\u2013Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm\/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595\u00b0, surface roughness of 8.9897 \u00b5m, and material removal rate of 138.13 g\/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates.<\/jats:p>","DOI":"10.3390\/ma18194480","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T06:02:52Z","timestamp":1758866572000},"page":"4480","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Abrasive Waterjet Machining of r-GO Infused Mg Fiber Metal Laminates: ANFIS Modelling and Optimization Through Antlion Optimizer Algorithm"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-0742","authenticated-orcid":false,"given":"Devaraj","family":"Rajamani","sequence":"first","affiliation":[{"name":"Centre for Advanced Materials Processing, Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-8491","authenticated-orcid":false,"given":"Mahalingam","family":"Siva Kumar","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, SRM TRP Engineering College, Tiruchirappalli 621105, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6181-1761","authenticated-orcid":false,"given":"Arulvalavan","family":"Tamilarasan","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 602105, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref_1","first-page":"110","article-title":"A Review on Forming Technologies of Fibre Metal Laminates","volume":"4","author":"Ding","year":"2021","journal-title":"Int. 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