000148045 001__ 148045
000148045 005__ 20250923084432.0
000148045 0247_ $$2doi$$a10.3390/app142411840
000148045 0248_ $$2sideral$$a141307
000148045 037__ $$aART-2024-141307
000148045 041__ $$aeng
000148045 100__ $$0(orcid)0000-0002-9140-9367$$aVal, Sonia$$uUniversidad de Zaragoza
000148045 245__ $$aAnalysis and prediction of wear in interchangeable milling insert tools using artificial intelligence techniques
000148045 260__ $$c2024
000148045 5060_ $$aAccess copy available to the general public$$fUnrestricted
000148045 5203_ $$aMilling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction.
000148045 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000148045 590__ $$a2.5$$b2024
000148045 592__ $$a0.521$$b2024
000148045 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b50 / 175 = 0.286$$c2024$$dQ2$$eT1
000148045 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b123 / 239 = 0.515$$c2024$$dQ3$$eT2
000148045 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b283 / 460 = 0.615$$c2024$$dQ3$$eT2
000148045 591__ $$aPHYSICS, APPLIED$$b101 / 187 = 0.54$$c2024$$dQ3$$eT2
000148045 593__ $$aEngineering (miscellaneous)$$c2024$$dQ2
000148045 593__ $$aComputer Science Applications$$c2024$$dQ2
000148045 593__ $$aProcess Chemistry and Technology$$c2024$$dQ2
000148045 593__ $$aInstrumentation$$c2024$$dQ2
000148045 593__ $$aMaterials Science (miscellaneous)$$c2024$$dQ2
000148045 593__ $$aFluid Flow and Transfer Processes$$c2024$$dQ2
000148045 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000148045 700__ $$0(orcid)0000-0003-1401-6495$$aLambán, María Pilar$$uUniversidad de Zaragoza
000148045 700__ $$aLucia, Javier
000148045 700__ $$0(orcid)0000-0002-0692-5982$$aRoyo, Jesús$$uUniversidad de Zaragoza
000148045 7102_ $$15002$$2515$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Ing. Procesos Fabricación
000148045 773__ $$g14, 24 (2024), 11840 [22 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417
000148045 8564_ $$s3744283$$uhttps://zaguan.unizar.es/record/148045/files/texto_completo.pdf$$yVersión publicada
000148045 8564_ $$s2728057$$uhttps://zaguan.unizar.es/record/148045/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000148045 909CO $$ooai:zaguan.unizar.es:148045$$particulos$$pdriver
000148045 951__ $$a2025-09-22-14:44:13
000148045 980__ $$aARTICLE