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Related Concept Videos

Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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  9. Knowledge Representation And Reasoning
  11. Constructing A Knowledge Graph-driven Intelligent Data-enabled Design System For Mold Using Deep Semantic Understanding And Intelligent Decision Support.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Knowledge Representation And Reasoning
  11. Constructing A Knowledge Graph-driven Intelligent Data-enabled Design System For Mold Using Deep Semantic Understanding And Intelligent Decision Support.

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Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding
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Constructing a knowledge graph-driven intelligent data-enabled design system for mold using deep semantic understanding and intelligent decision support.

Jiaxing Deng1, Chengcai He1, Jinxiang Chen1

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

Scientific Reports
|March 2, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Data-driven approachDeep semantic understandingIntelligent decision supportKnowledge graph

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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.

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.
Mold design

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.