IBM > Case Studies > Industry 4.0 and Cognitive Manufacturing

Industry 4.0 and Cognitive Manufacturing

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 Industry 4.0 and Cognitive Manufacturing - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Automotive
Applicable Functions
  • Discrete Manufacturing
  • Quality Assurance
Use Cases
  • Computer Vision
  • Machine Condition Monitoring
Services
  • System Integration
The Challenge

Manufacturers are facing risky challenges associated with complex visual inspection activities. Many human inspectors, operators and engineers are needed at each manufacturer. They have a full workload of repetitive tasks aimed at identifying hundreds of defects. This results in major plant labour costs, issues with inspection accuracy and consistency, a need for employee training and potential health problems for inspections in hazardous areas.

The Customer
About The Customer

BMW - multinational corporate manufacturer of luxury vehicles and motorcycles headquartered.

KUKA - manufacturer of industrial robots and systems for factory automation.

The Solution

The visual inspection system is based on machine-learning algorithms and leverages many patterns of visual inspection, such as impurity/high-contrast areas, geometry detection and verification, abnormal texture and area detection, and color/brightness feature extraction and verification to determine quality defects. (Examples of what this system can detect include brake caliper defects, body shop and paint shop defects or damage, part deformation, spare part bumps and car dashboard scratches.)

The visual inspection capability of IBM’s suite of solutions is integrated inside the equipment line and robotics. In this showcase, IBM is inspecting several door handles from the new BMW 5 Series for manufacturing defects using the Watson Visual Recognition Service.

Operational Impact
  • [Data Management - Data Collection]

    Gather data from the equipment line and gain real-time insight and scoring.

     

     

     

  • [Process Optimization - Predictive Maintenance]

    Prevent issues before they arise with accurate predictions and early warnings.

  • [Efficiency Improvement

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