PCB Surface Character Detection

Project Background

The customer is an automated detection software service provider in the SMT (Surface Mount Technology) manufacturing industry. The end customer requires automated hardware to detect components on PCB surfaces on their production line.

Project Challenges

The customer's original solution involved a combination of manual inspection and automated detection. However, the automated detection system lacked accuracy, and manual inspection often led to errors and missed defects. Thus, a new solution was needed to improve accuracy and reduce manual intervention.

Solution

Overall Objective

Collaborate with the customer to create an optimal lighting setup, camera selection, and complete vision hardware configuration to improve detection accuracy and reduce manual errors.

Customer Requirements

1. Field of View (FOV): 78mm x 48mm
   • The FOV must cover the product with a margin to ensure complete detection.
2. Working Distance: 150mm
   • The production environment restricts the working distance, requiring hardware selection within this range.
3. Comprehensive Hardware Solution
   • Provide a complete hardware solution that integrates with the software to capture images for analysis.
   • Achieve clear, high-contrast images that emphasize product contours and feature details to support software-based recognition.

Configuration Parameters

1. Project Configuration Table
   • This table outlines the necessary hardware components, including cameras, lenses, and light sources, based on the required field of view and working distance.
2. Customer-Provided Samples
   • Analyze the samples to determine appropriate hardware selection, focusing on achieving clear and high-contrast images for accurate character detection.

Solution Overview

Given the customer requirements, the proposed solution included:

• Working Distance and FOV: Achieved a field of view of 82mm x 55mm, providing adequate coverage for PCB inspection.
• Lighting Setup: Designed to create high-contrast images, emphasizing product contours and ensuring accurate detection.

Results

• Improved Accuracy: The new solution significantly improved automated detection accuracy while reducing manual intervention.
• High-Contrast Images: The lighting setup provided clear and distinct product contours, enabling accurate software analysis.
• Reduced Human Error: The reduction in manual inspection led to fewer missed defects and increased overall reliability.

Effectiveness

Based on the proposed lighting setup and hardware configuration, the system effectively detects and analyzes PCB surface characters. The solution meets the customer's requirements for automated inspection, leading to improved product quality and production efficiency.

Next Steps

• Implement the designed solution in the production environment.
• Test and calibrate the system to ensure reliable performance.
• Monitor and adjust as needed to maintain consistent inspection results.

Additional Considerations

• Software Integration: Ensure the hardware solution integrates seamlessly with the existing software to support automated detection.
• Calibration and Maintenance: Develop a calibration and maintenance plan to ensure consistent system performance over time.