Reliability-Based Design Optimization of Bearing Hub Preform for Minimizing Defects Considering Manufacturing Tolerance in Hot Forging Process
A study on the optimal design of preforms has previously been actively conducted as a method to solve defects such as voids and flash in forged products. However, previous research has generally been performed through deterministic optimization for ideal cases that do not take manufacturing toleranc...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/23/11316 |
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| Summary: | A study on the optimal design of preforms has previously been actively conducted as a method to solve defects such as voids and flash in forged products. However, previous research has generally been performed through deterministic optimization for ideal cases that do not take manufacturing tolerances into account. As a result, the application of such optimal designs in actual processes may be limited due to various factors such as material manufacturing tolerances and the machining environment of preforms. Therefore, this study conducted reliability-based optimization considering tolerances in billets and preforms. The objective of the study was to optimize the design of a bearing hub and minimize defects in the final product. When comparing deterministic optimization and reliability-based optimization, the former showed relatively superior results in terms of defect indicators but had a higher occurrence of voids and lower forming loads, increasing the probability of void occurrence. On the other hand, the reliability-based optimization showed relatively lower performance in quality improvement indicators, but it successfully met the target reliability of 99% by reducing the probability of defect occurrence. These results were derived using an approximate model based on the Kriging method, providing an optimal design that is practical and effective in actual manufacturing processes. |
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| ISSN: | 2076-3417 |