AI Driven Smart Tightening and Feature Management System for Agile Assembly

The convergence of Industry 4.0, digitization, and the concepts of agile andsmart manufacturing is shaping a future driven by cyber-physical systems (CPS),the industrial internet of things (IIoT), and artificial intelligence (AI). This evo-lution promises unprecedented efficiency, agility, and intelligence in manufac-turing. Correspondingly, these advancements also present new challenges forthe assembly industry. This thesis tackles these challenges in two key areas:trustworthy feature management for agile assembly, and AI-powered tighteningdiagnosis for smart assembly.Agile manufacturing prioritizes flexibility for uncertain markets. To achievethis, assembly device maker are increasingly shifting focus to software thatlargely defines hardware functionality. This shift allows companies to offer plat-forms with customizable features rather than just hardware, which in tern,reduces operational expenses and allows customers to dynamically configuretheir assembly lines via software. This transition also necessitates a trustwor-thy Feature Management System (FMS) to control feature activation throughsoftware licensing. However, existing server-based solutions pose trust issues:sellers fear license abuse, while buyers worry about single points of failure duringserver outages.This thesis contributes a novel permissioned blockchain system designedto address trust concerns in feature management for assembly devices. Theproposed solution combines software licensing for feature control with secureownership transaction records on the blockchain. By leveraging the trust, trans-parency, and security of permissioned blockchain technology, the system ensuressecure and controlled access to license information for authorized parties.Integrating software and physical assembly devices into CPSs enables seam-less data acquisition through communication protocols. Once this data is col-lected, AI becomes a powerful tool for decision-making. In smart tighteningsystems, accurately diagnosing tightening results is critical. Modern assemblylines use advanced electric tightening tools equipped with torque and angletransducers to capture detailed data after each operation, enabling the evalua-tion of tightening performance.Currently, accurate evaluation still requires manual analysis by tighteningexperts, resulting in inefficient quality checks that are limited to small samplesof production units. This thesis introduces innovative deep learning methods toautomate the tightening quality assessment process, achieving expert-level ac-curacy across all manufactured units and reducing the risk of defective productsreaching consumers.Towards AI-powered smart assembly, our initial research contribution fo-cused on developing a sensor fusion approach using convolutional neural networkand transformer-based architectures for diagnosing tightening results throughsupervised learning. However supervised learning requires labeled data, andlabeling tightening results requires significant manual work from tightening ex-perts, leading to a scarcity of labeled datasets. Furthermore, in sensitive assem-bly applications, regulatory constraints may prevent sensor data from leaving theshop floor, where computational resources are often limited. To address thesechallenges, this thesis also contributes novel self-supervised learning, data aug-mentation and data augmentation scheduling methods that reduce reliance onlabeled data and computational resources. These innovations ultimately resultin a robust deep learning solution for diagnosing tightening results in real-worldmanufacturing environments.