Abstract
Industry 5.0 (I5.0) is increasingly governed by Operation Technology and Information Technology. It hosts Deep Learning (DL) solutions as a premier tool for the informatization of Quality Inspection (QI) of Mass Produced Products (MPP) and Mass Customized Products (MCP) at the on-premise edge devices. Besides, these ever-evolving MCPs in I5.0 require newer DL models to informatize customized quality parameters, mandating significant storage and computing resources, that is challenging to achieve at resource-constraint edge platform. This work proposes PackMASNet, a continual learning-based information integration scheme that sustainably incorporates the QI information of multiple MCPs in a single Deep Neural Network (DNN) under the following constraints: (i) resource-limited on-premise edge devices, (ii) no access to prior product data, and (iii) negligible deterioration of quality information of frequently manufactured MPPs. A lightweight Cosine-similarity threshold mechanism is incorporated to leverage the benefits of knowledge transfer (cloning) while avoiding its adverse impact in multi-varied MCPs. The scheme proposes DNN performance, storage, computation, data, and time efficient solutions for industries implementing the edge computing paradigm on I5.0. The efficacy of the proposed scheme is validated for the predictive QI of real-world use case in injection molding. The scheme preserves the frequently used information of MPP by 99.07%, saves computation cost by 132 times, reduces the DNN training time by 20.45%, and improves overall performance by 43.60% over the state-of-the-art schemes.
Published Version
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