Digital Geometric Profiling & Dimensional Root Cause Analytics
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Company Background
Siam Yamato Steel Co., Ltd. (SYS) manufactures structural H-beams and heavy sections used in major infrastructure and building projects. In structural steel production, dimensional accuracy is mission-critical — even small geometric deviations can result in non-compliance, rework, or rejected shipments.
Unlike unit weight, dimensional defects such as tapering, waviness, or width drift are often detected too late, after full-length production is complete.
Challenges
SYS relied on manual point-based inspection methods for dimensional verification. This created several operational risks:
- Limited visibility across the full longitudinal beam profile
- Delayed detection of tapering or shape drift
- Reactive quality control after production completion
- Inconsistent cross-shift benchmarking
- Root cause investigations based on averaged data rather than defect-specific conditions
- Undetected gradual drift
- Increased variability between batches
- Material waste when defects were discovered post-production
- Inconsistent operational standards
Solution
SYS implemented a Digital Geometric Analytics Engine using Seeq to transform dimensional quality control from reactive inspection to real-time visual intelligence.
1. Historian Integration Data streams integrated into Seeq included:
2. Full-Length Digital Geometric Profiling
1. Historian Integration Data streams integrated into Seeq included:
- Leg A and Leg B width measurements
- Stand torque and tension values
- Rolling temperature zones
- Hydraulic pressures
- Cooling water flow parameters
2. Full-Length Digital Geometric Profiling
Using Seeq Workbench:
- The system generated full longitudinal dimensional trajectories for LegA and LegB
- Actual width profiles were rendered against tolerance bands
- Operators could instantly detect tapering, waviness, and progressive drift
3. Golden Batch Benchmarking
A quantitative “Precision Score” aggregated deviation across all measurement points. The system identified a statistical Golden Batch (Rank 1) for geometric precision. Current batches were ranked against this baseline, enabling:
A quantitative “Precision Score” aggregated deviation across all measurement points. The system identified a statistical Golden Batch (Rank 1) for geometric precision. Current batches were ranked against this baseline, enabling:
- Objective process comparison
- Standardization of best-in-class operating parameters
- Continuous improvement benchmarking
4. Automated Dimensional Root Cause Discovery
Using multivariate and correlation analysis:
- The system assigned impact scores to upstream process variables
- For Example, Identified Torque Stand X and Rolling Temp Zone X as primary dimensional drivers
- Quantified correlation strength for engineering validation
5. Self-Service Analytics
All models, visualizations, and benchmarking logic were developed within Seeq, enabling:
- Rapid iteration by process engineers
- Independent model refinement
- Elimination of spreadsheet-based dimensional analysis
- Repeatable forensic workflows
Benefits
Operational Efficiency
- Detection of geometric drift
- Reduced post-production rejection risk
- Faster root cause identification
- Continuous visualization against tolerance bands
- Standardized benchmark operating conditions
- Reduced variability between batches
Asset & Process Stability
- Clear identification of high impact stands and temperature zones
- Reduced unnecessary broad machine adjustments
- Targeted mechanical calibration
- Transition from manual point inspection to full digital profiling
- Empowered engineers with self-service multivariate analysis
- Established a standardized precision ranking system
Cost Saving
Dimensional defects in structural steel production directly translate into:
- Rework cost
- Scrap cost
- Delivery delays
- Customer dissatisfaction risk
