{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:51:55Z","timestamp":1777042315076,"version":"3.51.4"},"reference-count":0,"publisher":"International Association of Online Engineering (IAOE)","issue":"08","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Interact. Mob. Technol."],"abstract":"<jats:p>To address the demands of intelligent transformation in the automotive industry under the paradigm of Industry 4.0 and to overcome limitations associated with experience-dependent process design, prolonged iteration cycles, and insufficient mobile interaction capabilities, a deep learning\u2013driven mobile interaction system based on cloud\u2013edge\u2013end collaboration was proposed for automotive process design scenarios. A hierarchical collaborative architecture is established, in which the cloud layer provides knowledge support and computational assurance through a process knowledge graph and data augmentation based on artificial intelligence generated content (AIGC), the edge layer achieves a balance between responsiveness and efficiency via dynamic model scheduling and multi-terminal collaboration, and the mobile layer enhances interaction quality through adaptive rendering and multimodal fusion techniques. To address key technical bottlenecks, a two-stage lightweight model generation framework, an on-device multimodal scene understanding method, and a realtime simulation optimization scheme driven by Physics-informed neural networks (PINNs) were designed, forming a closed-loop mobile design interaction workflow. The effectiveness of the proposed system was validated using the joining process of automotive door outer panels as a representative case, supported by multi-dimensional comparative experiments and ablation studies. Through the deep integration of deep learning and industrial mobile interaction, the proposed approach enables a paradigm shift in automotive process design from tool-assisted operation toward intelligent co-creation, providing both technical support and practical guidance for mobile intelligent design in industrial applications.<\/jats:p>","DOI":"10.3991\/ijim.v20i08.61247","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:02:06Z","timestamp":1777035726000},"source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning-Driven Mobile Interaction System for Industrial Process Design"],"prefix":"10.3991","volume":"20","author":[{"given":"Chutong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2371","published-online":{"date-parts":[[2026,4,24]]},"container-title":["International Journal of Interactive Mobile Technologies (iJIM)"],"original-title":[],"link":[{"URL":"https:\/\/online-journals.org\/index.php\/i-jim\/article\/download\/61247\/17211","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/online-journals.org\/index.php\/i-jim\/article\/download\/61247\/17211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:02:07Z","timestamp":1777035727000},"score":1,"resource":{"primary":{"URL":"https:\/\/online-journals.org\/index.php\/i-jim\/article\/view\/61247"}},"subtitle":["An Automotive Industry Case Study"],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":0,"journal-issue":{"issue":"08","published-online":{"date-parts":[[2026,4,24]]}},"URL":"https:\/\/doi.org\/10.3991\/ijim.v20i08.61247","relation":{},"ISSN":["1865-7923"],"issn-type":[{"value":"1865-7923","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]}}}