| Author: | Tsoi, Tsz Shan |
| Title: | Design of a smart injection molding tooling system (SIMTS) |
| Advisors: | Man, H. C. (ISE) |
| Degree: | Eng.D. |
| Year: | 2024 |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | xiv, 219 pages : color illustrations |
| Language: | English |
| Abstract: | The purpose of this research is to develop a smart injection molding tooling system (SIMTS). The proposed system will enable industrial end-users to identify potential components to be manufactured via tooling with a conformal cooling channel (CCC) based on multiple quality characteristics factors for new part selection via an analytic hierarchy process (AHP). Furthermore, a new approach based on the Taguchi experimental design (TED) was developed integrating both CCC development and optimal parameter determination for plastic injection molding (PIM). Accordingly, the molding process is data-driven monitored and smart-quality-control modeled such that key process variables can be evaluated and used in process control by employing in-mold sensor applications in a timely and smart manner. An AHP-TED scheme for multi-criteria decision-making part selection for CCC, in which the optimal setting for CCC fabrication is selected along with its corresponding injection molding process, has not been reported academically to date. To address this gap in knowledge, a tri-stage methodology is proposed. The proposed methodology was applied to experimental case studies to improve the quality of an injection molding product (an automotive seat housing) in terms of shrinkage via CCC design optimization using the TED-based approach followed by mass production with in-mold sensors for real-time process control. The case study results showed that shrinkage can be significantly improved by decreasing the surface temperature via CCC, resulting in a product fabricated according to the TED-suggested parameters. Warpage was reduced by 71% when CCC was employed, with a total cooling cycle time improvement of 44%. Furthermore, the smart in-mold pressure sensor monitoring system used in the case studies can identify defective products with an accuracy of 100% and connects to the injection molding machine to automatically discard the identified defective products; thus avoiding defective product outflow caused by mistakes in the manual inspection process. Performance test results for the proposed SIMTS showed that it achieved its target of addressing a real-world industrial problem, reducing scrap parts, and reducing the process operating costs. The proposed SIMTS is a notable innovation that contributes to the PIM industry by increasing moldability, in turn increasing the output efficiency and obtaining the desired critical quality attributes in the molded products. Finally, several remarks are made to help direct future research in this field, including a larger data collection scale, monitoring factor diversification, and prediction and self-correction by utilizing artificial intelligence and neural networks. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7564.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 6.44 MB | Adobe PDF | View/Open |
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