AI Case Studies
Quality Management
Automate quality inspection processes with AutoML to detect and correct issues early, enhancing product quality. Analyze quality data from the production process to identify key factors affecting quality for continuous improvement.
Application Scenarios
Root Cause Analysis, Quality Prediction
Process Defect Cause Analysis
Quality control personnel often need to analyze the impact of process parameters on product quality, identify and control key variables, and ensure the product meets the required quality standards.
Pain Points Analysis:
- Numerous Process Parameters: The manufacturing process generates large and complex data covering multiple process parameters and quality indicators, making it difficult for manual analysis.
- Multiple Influencing Factors: There may be complex interactions between various process parameters that traditional methods cannot effectively capture, leading to inaccurate analysis results.
- High Trial and Error Costs: Finding the cause of anomalies through continuous trial and error is time-consuming and makes it difficult to summarize the relationship between parameters and outcomes.
Outcome Benefits:
- Reduced Analysis Time: Machine learning shortens analysis time, allowing quality control personnel to address process issues more quickly.
- Objective Data-Driven Analysis: Helps factories move away from relying heavily on the experience-based analysis of seasoned workers, achieving objective, data-driven analysis and experience transfer.
Process Quality Result Prediction
Traditional manufacturing often uses sampling inspection for product quality testing due to cost and time considerations. However, sampling inspection only covers a portion of the products, posing a risk of undetected quality issues.
Pain Points Analysis:
- Inability to Fully Inspect: Sampling can miss defective products, leading to overall quality decline and additional costs for rework or remake.
- Time-Consuming Manual Inspection: Inspection methods are often manual, requiring significant human resources and time.
Outcome Benefits:
- Predict Quality Issues in Advance: Predict quality inspection items for each batch in advance, allowing quality control personnel to proactively inspect products that do not meet predicted results, reducing the risk of defective products by 8.3%.
- Reduced Human Inspection Costs: Knowing production results in advance lowers human inspection costs and increases overall inspection efficiency by 9.8%.