Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Moving beyond general industrial overviews, this programme focuses on three core shop-floor engineering challenges: cutting scrap and escapes in quality inspection, preventing unplanned downtime through predictive maintenance, and optimising process and scheduling parameters to lift OEE and energy efficiency. Vision-based surface/assembly inspection, vibration/temperature anomaly detection and recipe optimisation are examined through real plant cases.

Participants develop a structured project specification—covering data requirements, model/tool choice, line integration and acceptance criteria—for a quality, maintenance or process problem from their own line.

Who Should Attend?

  • Quality engineers, quality assurance and SPC (Statistical Process Control) specialists
  • Maintenance engineers, reliability and predictive-maintenance teams
  • Process, production, automation (PLC/SCADA) and continuous-improvement (TPM/Lean) engineers
  • Plant digital transformation and manufacturing-engineering leads

Key Takeaways

  • Design vision-based inspection projects balancing lighting, defect labelling and false-reject rates
  • Structure predictive maintenance (PdM) and anomaly detection use cases from vibration, thermal and PLC data
  • Analyse relationships between process parameters (temperature, pressure, speed), scrap and energy use
  • Identify scheduling, changeover (SMED) and batch-sizing optimisation opportunities on bottleneck lines
  • Build a technical copilot workflow that queries historical CMMS work orders and 8D quality records

Curriculum

Visual Quality Inspection and Defect Detection
  • Camera and lighting setups for surface defects, missing components and dimensional checks
  • Training approaches when defect samples are scarce (anomaly detection and few-shot learning)
  • Calibrating false-reject versus defect-escape thresholds on the line
Predictive Maintenance (PdM) and Asset Reliability
  • Cost–criticality matrix across reactive, preventive and predictive maintenance
  • Early fault and remaining-useful-life (RUL) signals from vibration, current and thermal data
  • Combining sensor alerts with CMMS/SAP PM work-order history
Process Parameters, Golden Batch and Energy Optimisation
  • Multivariate golden-batch analysis and root-cause detection for process drift
  • Reducing cycle time, raw-material waste and energy consumption without hurting quality
  • Operator-friendly parameter recommendation systems and soft sensors
Production Scheduling and Bottleneck Optimisation
  • Constraint-based scheduling (APS) and dynamic rescheduling under breakdowns or rush orders
  • Sequencing strategies that minimise changeover (setup) losses
  • WIP and line-balancing scenario simulations
Shop-Floor Deployment, Acceptance Criteria and Engineering Copilots
  • Using LLM engineering copilots across 8D, FMEA and troubleshooting records
  • Moving from PoC to 24/7 production: latency, environmental drift and model upkeep
  • Line-level technical feasibility and site-acceptance (SAT) checklist

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