Failure Analysis

Definition

Failure Analysis is a systematic process of collecting and analyzing data to determine the root cause of product or equipment failures. Rather than simply identifying that a failure occurred, the core purpose is to track the chain of cause and effect, identify the root cause, and establish corrective actions to prevent recurrence.

Application in Manufacturing

Practical Use Cases

• Electronics Industry: Identifying defect causes in semiconductors, PCBs, and improving yield rates
• Automotive: Analyzing premature failures in powertrain and engine components for quality improvement
• Equipment Maintenance: Analyzing causes of unexpected production line shutdowns and establishing preventive maintenance strategies
• Product Development: Deriving design improvements through prototype test result analysis

Analysis Methodologies

In the field, analysis typically starts with Non-Destructive Testing (NDT), followed by various methods including microscopy and spectroscopy. This approach preserves failed components, enabling evidence retention and re-analysis.

Manufacturing AI Integration

AI technology is revolutionizing failure analysis:

• Image Recognition AI: Automatic detection of failure patterns like cracks and discoloration in microscopic images
• Predictive Analysis: Forecasting failure precursor symptoms by learning from historical failure data
• Root Cause Inference: Identifying complex failure causes through multivariate data analysis
• Real-time Monitoring: Immediate failure diagnosis through sensor data analysis

Key Takeaways

Proper failure analysis achieves three simultaneous values: cost reduction, safety enhancement, and resource conservation. Particularly when combined with AI, analysis time is significantly reduced and previously undetected patterns can be identified, making it an essential tool for strengthening quality competitiveness in manufacturing operations.