Curriculum
- 6 Sections
- 35 Lessons
- 1 Day
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- The Quality Imperative in Tyre Manufacturing6
- 1.1Tyre performance parameters: uniformity, balance, rolling resistance, and endurance
- 1.2QA Framework: Incoming → In-process → Final inspection → Market feedback loop
- 1.3Cp, Cpk, Ppk, and Process Sigma levels
- 1.4Quality cost pyramid: Prevention, Appraisal, Internal & External failure costs
- 1.5“Zero Defect” manufacturing philosophy and quality goals
- 1.6Exercise: Map QA checkpoints to identify value-adding vs. redundant inspection stages
- Advanced Tyre Testing Techniques7
- 2.1Mechanical Testing: Endurance, High-speed, Bead unseating, and Plunger energy tests
- 2.2Dynamic Testing: Rolling resistance, cornering stiffness, heat build-up, ride comfort
- 2.3Chemical & Physical Analysis: Mooney viscosity, rebound resilience, cure curve, abrasion index
- 2.4Indoor vs. Outdoor Testing Correlation: Vehicle simulation vs. real-road validation
- 2.5High-speed uniformity (HSU) testing & radial run-out monitoring
- 2.6Industry Benchmark: CEAT’s dynamic test rigs & Michelin’s indoor simulators for virtual validation.
- 2.7Case analysis — compare two compound formulations and predict how differences affect rolling resistance and endurance test results
- Digital QA Systems & Inline Inspection Automation7
- 3.1Automated X-ray inspection and laser shearography for non-destructive testing (NDT)
- 3.2Computer vision and AI-based surface defect detection systems
- 3.3Machine Learning for anomaly classification (bulges, blisters, splices)
- 3.4Data fusion: merging vision + process data for real-time control
- 3.5Smart inspection cells & integrated QA dashboards (OEE–defect correlation)
- 3.6Case Study: Automation of curing-to-dispatch QA workflow — vision-based uniformity checks
- 3.7Exercise: Review sample defect images and classify failure type using given inspection parameters
- Quality by Design (QbD) and Statistical Process Control (SPC)7
- 4.1Quality by Design (QbD) methodology in tyre production
- 4.2Control charts (X-bar, R, P, NP) and interpretation
- 4.3Process capability indices (Cp, Cpk) and their improvement strategies
- 4.4DOE (Design of Experiments) for identifying critical process parameters
- 4.5Six Sigma and DMAIC application in tyre uniformity improvement
- 4.6Industry Examples: Uniformity enhancement program using SPC & DOE analysis
- 4.7Exercise: Analyze sample SPC data to identify variation trends and propose process adjustments
- AI, Predictive QA & Digital Twin Validation6
- 5.1Predictive defect analytics using AI/ML models
- 5.2Generative AI for synthetic defect data creation & training inspection algorithms
- 5.3Digital twin models for predicting curing uniformity and structural integrity
- 5.4Closed-loop feedback systems for real-time QA adjustments
- 5.5Case Insight: R&D initiative in AI-based early defect prediction using historical inspection data
- 5.6Interactive Exercise: Design a predictive QA dashboard — define key defect signals, thresholds, and corrective action triggers
- Quality Culture & Continuous Improvement Framework2
Zero Defect Challenge Exercise – Design a mini quality improvement project targeting a key pain area (e.g., curing defects, bulge rate, bead failure)
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