{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:52:49Z","timestamp":1776131569538,"version":"3.50.1"},"reference-count":0,"publisher":"Scilight Press Pty Ltd","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.sciltp.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:p>Industrial laboratories often remain under-digitized compared to production lines, creating a gap between data acquisition and analytical intelligence, critical for advanced quality control. This study addresses this gap by proposing and validating a novel framework that combines Low-Code digitalisation tools with Machine Learning (ML) and Causal Inference to optimise data collection and analysis in an automotive painting laboratory. A Microsoft Power Apps-based platform was developed in order to digitalise all measurement records, eliminating manual transcription errors (previously \u2248 40.01%) and reducing data-handling time by up to 34% of an operator\u2019s shift, while enabling centralised, traceable storage and Power BI integration. Four datasets were used to assess predictive capacity with Random Forest, XGBoost and Neural Networks; Random Forest consistently provided the most stable results\u2014Mean Absolute Error (MAE) of 0.972, Mean Absolute Percentage Error (MAPE) of 16.45%, and Root Mean Square Error (RMSE) of 1.307. Causal models (Linear Regression, DoWhy, Causal Forest, Double Machine Learning) consistently identified ultrafiltrate I solid content of the electrodeposition process as a dominant causal factor for defects. This study provides a novel framework that bridges digitalisation and ML-based causal reasoning in laboratory settings, offering a scalable approach that can be extended and replicated in other industrial sectors, aiming to develop smart, data-driven quality control systems.<\/jats:p>","DOI":"10.53941\/jmem.2026.100011","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T01:26:04Z","timestamp":1770686764000},"update-policy":"https:\/\/doi.org\/10.53941\/sciltp.crossmark.policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Powered Data Management to Optimize Data Collection and Processing in a Painting Laboratory"],"prefix":"10.53941","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4556-9578","authenticated-orcid":true,"given":"Maria Teresa Ribeiro","family":"Pereira","sequence":"first","affiliation":[{"name":"Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI, ISEP), Instituto Superior de Engenharia do Porto, Polytechnic of Porto, Dr. Ant\u00f3nio Bernardino de Almeida, 431, 4249-015 Porto, Portugal"},{"name":"Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Dr. Ant\u00f3nio Bernardino de Almeida, 431, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-0895","authenticated-orcid":true,"given":"Marisa Jo\u00e3o","family":"Guerra Pereira","sequence":"additional","affiliation":[{"name":"Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI, ISEP), Instituto Superior de Engenharia do Porto, Polytechnic of Porto, Dr. Ant\u00f3nio Bernardino de Almeida, 431, 4249-015 Porto, Portugal"},{"name":"Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Dr. Ant\u00f3nio Bernardino de Almeida, 431, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4623-4916","authenticated-orcid":true,"given":"Miguel Guedes","family":"Tavares","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Dr. Ant\u00f3nio Bernardino de Almeida, 431, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6346-5719","authenticated-orcid":true,"given":"Andr\u00e9 Martins","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, Department of Electromechanical Engineering, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"},{"name":"CISeD\u2014Research Centre for Digital Services, Polytechnic of Viseu, Campus Polit\u00e9cnico Santa Maria, Av. Cor. Jos\u00e9 Maria Vale de Andrade, 3504-510 Viseu, Portugal"}]},{"given":"Hermilio","family":"Vilarinho","sequence":"additional","affiliation":[{"name":"Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI), Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]}],"member":"32106","published-online":{"date-parts":[[2026,2,10]]},"container-title":["Journal of Mechanical Engineering and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/media.sciltp.com\/articles\/2601002921\/2601002921.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/media.sciltp.com\/articles\/2601002921\/2601002921.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:09:45Z","timestamp":1776128985000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciltp.com\/journals\/jmem\/articles\/2601002921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,10]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,10]]}},"URL":"https:\/\/doi.org\/10.53941\/jmem.2026.100011","relation":{},"ISSN":["2982-3544"],"issn-type":[{"value":"2982-3544","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,10]]}}}