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<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>
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<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/20341428 |
| Date | 20 July 2022 |
| Creators | Xingyu Fu (13119960) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY-NC-SA 4.0 |
| Relation | https://figshare.com/articles/thesis/DIGITAL_TWIN_BASED_SELF-LEARNING_FRAMEWORK_FOR_MACHINING_AND_MACHINE_TOOLS/20341428 |
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