IBM Case Studies Industry 4.0 and Cognitive Manufacturing

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	Industry 4.0 and Cognitive Manufacturing IBM

Industry 4.0 and Cognitive Manufacturing

IBM

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
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. Read More
About Customer	BMW - multinational corporate manufacturer of luxury vehicles and motorcycles headquartered. KUKA - manufacturer of industrial robots and systems for factory automation. Read More
Customer Name	BMW KUKA Read More
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. Read More Log in to view content
Contents

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

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.

About Customer

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

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

Customer Name

BMW

KUKA

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.

Impact #1	[Data Management - Data Collection] Gather data from the equipment line and gain real-time insight and scoring.
Impact #2	[Process Optimization - Predictive Maintenance] Prevent issues before they arise with accurate predictions and early warnings.
Impact #3	[Efficiency Improvement

Impact #1

[Data Management - Data Collection]

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

Impact #2

[Process Optimization - Predictive Maintenance]

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

Impact #3

[Efficiency Improvement

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Overview

Industry 4.0 and Cognitive Manufacturing

Operational Impact