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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized

Chengcai He¹, Jiaxing Deng¹, Jingchun Wu¹

¹Shenzhen Ruipengfei Mold Co., Ltd, Shenzhen City, 518000, China.

Scientific Reports

|July 4, 2025

Summary

This summary is machine-generated.

This study introduces an optimized mold digitalization system using knowledge engineering to enable personalized manufacturing. The system enhances production efficiency and quality control, achieving over 94.7% predictive accuracy.

Keywords:

Knowledge engineering Knowledge graph Mold intelligent data-driven system Personalization Reinforcement learning

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Area of Science:

Manufacturing Engineering
Artificial Intelligence
Knowledge Engineering

Background:

Traditional manufacturing relies on standardized processes, limiting customization.
Personalized production demands flexible and intelligent manufacturing solutions.

Purpose of the Study:

To develop an optimized mold digitalization system for personalized manufacturing.
To integrate knowledge graphs and intelligent algorithms for smart quality control.
To enhance customization capabilities in mold manufacturing.

Main Methods:

Utilized knowledge engineering to convert domain expertise into computable models.
Employed reinforcement learning and graph neural networks for knowledge extraction.
Developed a smart quality control framework using knowledge graphs and graph convolutional networks.

Main Results:

The optimized system achieved qualification rates exceeding 88.1% with strong performance alignment.
The proposed approach demonstrated a predictive accuracy of over 94.7% compared to other models.
The system significantly improved customization, efficiency, and quality in mold manufacturing.

Conclusions:

The optimized mold digitalization system offers a new direction for manufacturing's digital transformation.
The approach enables intelligent, flexible production to meet personalized product demands.
This system enhances manufacturers' ability to deliver customized solutions efficiently and with high quality.