TEKNOPAR Industrial Automation > Case Studies > Real-Time Welding Optimization with Welding Robot

Real-Time Welding Optimization with Welding Robot

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 Real-Time Welding Optimization with Welding Robot - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Digital Twin / Simulation
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Equipment & Machinery
  • Metals
Applicable Functions
  • Discrete Manufacturing
Use Cases
  • Condition Monitoring
  • Digital Twin
  • Predictive Maintenance
  • Predictive Quality Analytics
Services
  • Data Science Services
  • Software Design & Engineering Services
  • System Integration
About The Customer

Manufacturing of pressed sheet metal parts in the automotive industry.

The Challenge

Automating welding processes with robots enhances productivity and environmental friendliness. However, the quality of robotic welding can deteriorate due to various factory conditions. Continuous monitoring and adjustments are necessary to maintain high standards.

The Solution

TEKNOPAR integrated data from multiple sensors, including microphone and vibration sensors, environmental sensors, fiberoptic laser sensors, and Magmaweld devices, to create a comprehensive real-time monitoring system. Advanced regression models analyzed these data streams to detect potential quality issues, allowing immediate adjustments to the robot's parameters.

Data Collected
Acceleration, Humidity, Microphone, Pressure, Temperature
Operational Impact
  • [Cost Reduction - Waste]

    50% less scrap and near-zero defects in automotive parts manufacturing.

  • [Efficiency Improvement - Production Uptime]

    Increased process efficiency by reducing downtime by 15%.

  • [Process Optimization - Real Time Monitoring]

    Real-time data collection from welding machines and robots was achieved.

Quantitative Benefit
  • In automotive parts manufacturing processes, quality control has been improved by applying advanced AI-based models for optimization and restructuring, producing 50% less scrap, waste and near-zero defect parts.

  • Increased process efficiency by reducing downtime by 15%.

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