Towards Zero-Defect Manufacturing Based on Artificial Intelligence through the Correlation of Forces in 5-Axis Milling Process

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dc.contributor.authorCascón-Morán, Itxaso
dc.contributor.authorGómez, Meritxell
dc.contributor.authorFernández, David
dc.contributor.authorGil Del Val, Alain
dc.contributor.authorAlberdi, Nerea
dc.contributor.authorGonzález, Haizea
dc.contributor.institutionROBOTICA_FLEX
dc.date.accessioned2024-07-24T12:16:09Z
dc.date.available2024-07-24T12:16:09Z
dc.date.issued2024-04
dc.descriptionPublisher Copyright: © 2024 by the authors.
dc.description.abstractZero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces.en
dc.description.statusPeer reviewed
dc.identifier.citationCascón-Morán , I , Gómez , M , Fernández , D , Gil Del Val , A , Alberdi , N & González , H 2024 , ' Towards Zero-Defect Manufacturing Based on Artificial Intelligence through the Correlation of Forces in 5-Axis Milling Process ' , Machines , vol. 12 , no. 4 , 226 . https://doi.org/10.3390/machines12040226
dc.identifier.doi10.3390/machines12040226
dc.identifier.issn2075-1702
dc.identifier.urihttps://hdl.handle.net/11556/4624
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85191391136&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofMachines
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywords3D scanning
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsmechanistic model
dc.subject.keywordsmonitoring
dc.subject.keywordsZero-Defect Manufacturing
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsComputer Science (miscellaneous)
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsControl and Optimization
dc.subject.keywordsIndustrial and Manufacturing Engineering
dc.subject.keywordsElectrical and Electronic Engineering
dc.titleTowards Zero-Defect Manufacturing Based on Artificial Intelligence through the Correlation of Forces in 5-Axis Milling Processen
dc.typejournal article
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