{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:37:03Z","timestamp":1768426623731,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T00:00:00Z","timestamp":1685750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US Department of Energy"},{"name":"Office of Nuclear Energy (Advanced Methods for Manufacturing Program)"},{"name":"AI Initiative at Oak Ridge National Laboratory"},{"name":"Arizona State University startup funds"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs\u2019 prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.<\/jats:p>","DOI":"10.3390\/s23115313","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:18:29Z","timestamp":1685931509000},"page":"5313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2159-6128","authenticated-orcid":false,"given":"Shenghan","family":"Guo","sequence":"first","affiliation":[{"name":"The School of Manufacturing Systems and Networks, Arizona State University, Mesa, AZ 85212, USA"}]},{"given":"Dali","family":"Wang","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6573-7933","authenticated-orcid":false,"given":"Zhili","family":"Feng","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6532-1081","authenticated-orcid":false,"given":"Jian","family":"Chen","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-6326","authenticated-orcid":false,"given":"Weihong","family":"Guo","sequence":"additional","affiliation":[{"name":"The Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"041011","DOI":"10.1115\/1.4048957","article-title":"Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing","volume":"143","author":"Tian","year":"2021","journal-title":"J. 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