CLEAN
ENVIRONMENT

CONTINUOUS EFFORTS
FOR A CLEANER ENVIRONMENT

Smart Factory

  • Clean Environment
  • Smart Factory

An eco-friendly smart factory where machines and systems collaborate harmoniously, led by people

It is an intelligent factory where production activities-related all resources (*4M1E) are connected to IT technology, ensuring connectivity and visibility, and the operational efficiency is optimized through data-based system control. 4M1E : Man, Machine, Material, Method, Environment

Selected as K-Smart Lighthouse Factory in 2021 by the Ministry of SMEs and Startups!
Selected as the 'Representative Smart Factory' in 2018 by the Ministry of Trade, Industry and Energy!
Intelligent Automation Process
The past before the construction of the intelligent factory
    • Fastening rivets with air hand tools
      (decreased productivity)
    • Polluted workplace environment due to the missing fastener and rivet's byproducts
    • Repetitive manual work with heavy goods of 30 to 60kg by workers
    • Accident risk and reduced productivity due to the burden of a musculoskeletal condition
    • Repetitive manual work with heavy goods of 100 to 700kg
    • Increased return manpower, reduced productivity, and potential accident risks
System-based facility and process control
Implementation of PLC and PC-based automation
  • 01
    Automatic
    AL casing bending
  • 02
    Automatic
    AL casing assembly
  • 03
    Collaborative robot
    (COBOT)
  • 04
    Automatic
    packaging robot
  • 05
    Autonomous
    mobile robot (AMR*)
    *AMR : Autonomous Mobile Robots

Eco-friendly Smart Factory: Shinsung E&G's Innovative Future

Operating System
Machine Vision COBOT System
  • Machine vision-based precision position control (Data correction by offsetting (X, Y, and T) pre-align and precision-align coordinates)
  • Automatic bolt fastening
    (Fastening torque value management: Work quality assurance)
Big Data Performance
Prediction System: BIG DATA
  • Development of AI model for predicting quality performance through quality performance data analysis
  • Application of the deep learning method (DNN) and repeated group k-fold learning method
Manufacturing Execution System: MES
  • Real-time synchronization and monitoring of on-site equipment status and production performance
  • Tracking management, status monitoring, and control of work details
  • Quality data analysis and defect management
Virtual Physical System : Human CPS
  • Productivity analysis and prediction and production time prediction
  • Real-time analysis of comprehensive task difficulty and body part-specific difficulty with motion capture equipment and RULA indicators
  • Real-time verification of work hours based on standard work hours
Micro Grid Operation System: EPOS
(Energy Production & Operation System)
  • Active operation of solar power plants and ESS according to production plans, load on production facilities, and solar power generation
  • Advanced AI-based solar power generation prediction system
  • Development and operation of an operating model optimized for a time-of-use variable rate
  • Optimization of energy consumption throughout the business site, including energy cost reduction, etc.
  • Presentation of the optimal operation model for the RE100 stage
Best Solution for Energy Independence