Finding Why Solder Joints Failed, Not Just Which Ones

Finding Why Solder Joints Failed, Not Just Which Ones

AI-based X-ray image analysis identified improper soldering on PCBs and traced the process conditions behind recurring quality problems.

~2%X-ray defect detection and root-cause analysis cut bad-quality losses

ITChamps implemented an AI-enabled X-ray image analysis and quality intelligence solution for an ESDM manufacturer, identifying improper soldering and other PCB defects and tracing their probable causes.

The Challenge

Detecting bad PCBs was easy; explaining why they were bad was not

Conventional inspection methods could identify poor-quality products, but identifying the underlying reasons for recurring quality problems was more challenging, limiting the ability to improve the process itself.

  • Improper soldering and other PCB quality issues recurred during manufacturing.
  • Conventional inspection could flag defective units but not explain recurring causes.
  • The team needed insight into causes of poor quality, not just detection.
  • The goal was better process control, not just more inspection.

The Solution

X-ray defect detection linked back to process conditions

The solution analyzed X-ray images of manufactured PCBs with AI to detect defects, then combined quality-pattern analysis with process data to identify probable root causes and feed insights back into the process.

01X-Ray Image Acquisition

Collected X-ray images of manufactured PCBs for analysis.

02AI-Based Defect Identification

Analyzed X-ray images with AI to identify improper soldering and other quality deviations.

03Quality Classification

Differentiated good and poor-quality products based on identified characteristics.

04Quality-Pattern Analysis

Analyzed historical quality and process data to identify recurring patterns.

05Root-Cause Identification

Identified probable reasons contributing to poor-quality output.

06Process-Control Feedback

Fed insights back into the manufacturing process to support corrective and preventive action.

Results

Measured business impact

~2%Bad-quality loss reduction

Reduction in bad-quality-related losses.

AIX-ray defect detection

Automated identification of improper soldering and other deviations.

↑Process control

Insights fed back into the process to support corrective action.

→Detection to prevention

Shift from defect detection toward defect prevention.

Highlights

  • Went beyond identifying poor-quality PCBs to explaining why they occurred.
  • Enabled corrective action at the process level, not just the inspection line.
  • Shifted the organization from defect detection toward defect prevention.
  • Improved manufacturing control through process-level feedback.

Services and technology

AI Quality InspectionRoot-Cause AnalysisProcess ControlAI Image AnalysisX-Ray Defect DetectionQuality Pattern Analysis

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