AI in Industrial Quality Training in Kuala Lumpur, Malaysia

The AI in Industrial Quality Training focuses on applying Artificial Intelligence and Machine Learning to improve product quality, defect detection, process stability, and continuous improvement in industrial environments. Participants will learn how AI enhances traditional quality systems by enabling predictive quality, automated inspection, root-cause analysis, and real-time decision-making.

Introduction to AI in Industrial Quality

• Evolution of quality management systems

• Limitations of traditional quality methods

• Role of AI in Industry 4.0

• AI-driven quality use cases across industries

Industrial Quality Data Fundamentals

• Types of quality data (structured and unstructured)

• Sensor data, images, and time-series data

• Data quality, labeling, and preprocessing

• Challenges in industrial data environments

AI & Machine Learning Basics for Quality

• Overview of AI, ML, and Deep Learning

• Supervised vs unsupervised learning

• Classification, regression, and clustering

• Model lifecycle in industrial applications

AI for Visual Inspection & Defect Detection

• Machine vision fundamentals

• Image-based defect detection

• Surface inspection and anomaly detection

• Handling variability in lighting, shape, and texture

• Comparing rule-based vision vs AI vision systems

Predictive Quality & Process Monitoring

• Predicting defects before they occur

• AI-based SPC and trend analysis

• Drift detection and early warning systems

• Predictive quality dashboards

Root Cause Analysis Using AI

• Multivariate analysis for quality issues

• Correlation vs causation

• Feature importance and explainability

• Supporting continuous improvement teams

AI for Process Optimization

• Identifying critical process parameters

• Yield improvement using AI models

• Reducing scrap and rework

• Closed-loop quality control concepts

Integrating AI with Industrial Systems

• AI integration

• Edge AI vs cloud AI for quality

• Real-time data pipelines

• Deployment considerations on the shop floo

Model Validation, Monitoring & Governance

• Model accuracy and reliability metrics

• Bias, drift, and performance monitoring

• Validation in regulated industries

• Change management and audit readiness

Ethical, Safety & Compliance Considerations

• AI reliability in safety-critical environments

• Regulatory and compliance requirements

• Explainable AI for quality decisions

• Human-in-the-loop systems

Industry Use Cases & Case Studies

Hands-on Exercises

Summary and Conclusion