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Updated: May 24, 2025

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

Published on: June 27, 2014

Constructing a knowledge graph-driven intelligent data-enabled design system for mold using deep semantic

Jiaxing Deng¹, Chengcai He¹, Jinxiang Chen¹

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

Scientific Reports

|March 2, 2025

Summary

This summary is machine-generated.

A new Knowledge Graph-driven system enhances mold design by reducing errors and speeding up processes. This intelligent data-enabled approach significantly improves efficiency and lowers labor costs in modern manufacturing.

Keywords:

Data-driven approach Deep semantic understanding Intelligent decision support Knowledge graph Mold design

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Last Updated: May 24, 2025

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Area of Science:

Engineering
Computer Science
Data Science

Background:

Traditional mold design methods suffer from inefficiency and high error rates.
Complex design processes require advanced data handling and intelligent solutions.

Purpose of the Study:

To develop and evaluate a Knowledge Graph-driven Intelligent Data-enabled Design System for molds.
To improve accuracy, efficiency, and reduce costs in mold design.

Main Methods:

Utilized deep semantic understanding and Bidirectional Encoder Representations from Transformers (BERT) for document parsing.
Employed Random Forest (RF) algorithms for accurate information extraction.
Constructed a knowledge graph from preprocessed mold design data.

Main Results:

Reduced error rate in mold design from 0.15 to 0.0975.
Decreased average design task completion time from 20 hours to 12 hours.
Shortened the average design cycle by 0.25 (from 30 days to 22.5 days) and reduced labor costs by 0.2.

Conclusions:

The Knowledge Graph-based system significantly enhances mold design quality and efficiency through intelligence and automation.
The system demonstrates strong theoretical innovation and practical value in intelligent manufacturing.
Future work includes system optimization, domain expansion, and integration with other smart manufacturing technologies.