Author(s)

DuAng Chen, XinJie Zhou, Fan Xin, ShiWei Xu^*

Affiliation(s)

School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325035, Zhejiang, China.

Corresponding Author

ShiWei Xu

ABSTRACT

A data-driven injection molding production and maintenance decision-making system is designed to address the issues of low efficiency and poor real-time performance in traditional data collection models, meeting the modern industrial needs for high reliability and intelligence. The system adopts a three-layer architecture, including data collection, edge computing, and maintenance decision-making layers. It achieves real-time collection and processing of multi-source heterogeneous data to assess equipment health status dynamically and predict failures. The data collection layer integrates sensor, PLC, and visual device data; the edge computing layer processes key parameters through lightweight models to reduce cloud-side pressure; the maintenance decision-making layer predicts the remaining life of the equipment using the Weibull distribution model and optimizes maintenance strategies. The system proposes a quantitative evaluation index for the health of the injection molding machine and utilizes a weighted fusion algorithm for accurate maintenance decisions, significantly reducing operational costs and improving production efficiency, providing a feasible technical solution for intelligent manufacturing.

KEYWORDS

Injection molding production; Edge computing; Equipment health; Maintenance decision-making

CITE THIS PAPER

DuAng Chen, XinJie Zhou, Fan Xin, ShiWei Xu. Data-driven decision-making system for injection molding production and maintenance. AI and Data Science Journal. 2025, 2(1): 35-39. DOI: https://doi.org/10.61784/adsj3013.

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