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1	DIGITAL TWIN BASED SELF-LEARNING FRAMEWORK FOR MACHINING AND MACHINE TOOLS Xingyu Fu (13119960) 20 July 2022 (has links) <p> </p> <p>Smart manufacturing is a broad concept of manufacturing technology that employs the computer aided systems, digital information technology, artificially intelligent algorithms, etc., to realize high-level automation of the production. The rise of the smart manufacturing concept, which has also been treated as the fourth industrial revolution, has been increasingly advocated by the policy makers and investigated by the worldwide researchers. Though machining is one of the key processes in the manufacturing industry, there are only a few researches focusing on automatically scheduling and improving the machining process. The design of the machining parameters and tool path planning still requires engineers with significant knowledge and experience in manufacturing fields to juggle between product quality, machine tool maintenance, and production cost. This design process also requires high level of human intelligence to consider the type of material, machine tool setups, workpiece geometry, and cutting tool property to provide an optimal manufacturing process. The overall machining related processes cannot satisfy the requirement of the ultimate goal of the smart manufacturing – to fully automate the machining process without human’s involvements.</p> <p><br></p> <p>In order to solve this problem, we aim to employ advanced machine learning technologies to enable the machine tool to automatically build up the cutting physics and generate the optimized toolpath. The final optimized result can be conducted automatically and shows a near human level optimization design ability. The generated toolpath beats the result from other commercial software. The overall framework can be fully automated when the machine learning technology is mature. </p> milling process Deep Learning Applications Reinforcement Learning Smart Manufacturing Systems
2	Development and Evaluation of a Machine Vision System for Digital Thread Data Traceability in a Manufacturing Assembly Environment Alexander W Meredith (15305698) 29 April 2023 (has links) <p>A thesis study investigating the development and evaluation of a computer vision (CV) system for a manufacturing assembly task is reported. The CV inference results are compared to a Manufacturing Process Plan and an automation method completes a buyoff in the software, Solumina. Research questions were created and three hypotheses were tested. A literature review was conducted recognizing little consensus of Industry 4.0 technology adoption in manufacturing industries. Furthermore, the literature review uncovered the need for additional research within the topic of CV. Specifically, literature points towards more research regarding the cognitive capabilities of CV in manufacturing. A CV system was developed and evaluated to test for 90% or greater confidence in part detection. A CV dataset was developed and the system was trained and validated with it. Dataset contextualization was leveraged and evaluated, as per literature. A CV system was trained from custom datasets, containing six classes of part. The pre-contextualization dataset and post-contextualization dataset was compared by a Two-Sample T-Test and statistical significance was noted for three classes. A python script was developed to compare as-assembled locations with as-defined positions of components, per the Manufacturing Process Plan. A comparison of yields test for CV-based True Positives (TPs) and human-based TPs was conducted with the system operating at a 2σ level. An automation method utilizing Microsoft Power Automate was developed to complete the cognitive functionality of the CV system testing, by completing a buyoff in the software, Solumina, if CV-based TPs were equal to or greater than human-based TPs.</p> Automation engineering Flexible manufacturing systems Industrial engineering Manufacturing safety and quality Knowledge representation and reasoning Computer vision Object detection and classification object detection and tracking Smart Manufacturing Systems MES system Smart Manufacturing Approach Quality engineering Manufacturing automation systems manufacturing automation Cognitive Systems Engineering cognitive system computer vision machine vision manufacturing execution system robotic process automation Digital Thread (DT) Digital Thread digital twin approach manufacturing assembly assembly quality manufacturing traceability advanced manufacturing advanced quality control Quality 4.0 Industry 4.0 Industry 5.0 Quality 5.0 Human Machine Interactions

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DIGITAL TWIN BASED SELF-LEARNING FRAMEWORK FOR MACHINING AND MACHINE TOOLS

Development and Evaluation of a Machine Vision System for Digital Thread Data Traceability in a Manufacturing Assembly Environment