Digital Twins for Quality & Process Control Training Course

Course Overview

Learning Objectives

• Explain the principles, architecture, and applications of digital twins in quality management and process control.
• Differentiate digital twins from digital models, simulations, and digital shadows.
• Identify suitable quality and operational processes for digital twin implementation.
• Map physical processes, assets, sensors, data sources, and digital representations.
• Integrate process parameters, inspection results, and operational data into digital twin models.
• Apply real-time monitoring techniques to detect process deviations and quality risks.
• Use simulation scenarios to evaluate process changes and their potential quality impact.
• Interpret predictive analytics and process performance indicators generated through digital twin environments.
• Establish methods for validating model accuracy and maintaining alignment with physical operations.
• Develop an implementation roadmap for digital twin-enabled quality improvement.

Who Should Attend

Learning Outcomes

• Describe the main components and data flows of a digital twin architecture.
• Select appropriate digital twin applications for quality and process control challenges.
• Define the physical assets, process variables, and quality attributes required for a use case.
• Evaluate data quality, availability, connectivity, and integration requirements.
• Interpret real-time process information and identify deviations from expected operating conditions.
• Use digital simulations to assess process changes and potential defect risks.
• Interpret predictive indicators and evaluate their relevance to quality decisions.
• Assess digital twin model performance against actual process results.
• Define monitoring, validation, and model-update requirements.
• Prepare a practical roadmap for deploying digital twins within quality and operational improvement programmes.

Course Outline