Before01
Company profile
- Uneven furnace heat
- Operator-led variance
- Late cause tracing
AX Solution
Quality prediction
Quality innovation through the intelligence of process data
OVERVIEW
From inspecting for defectsto predicting them away
Quality management moves from after-the-fact inspection to prediction while the process runs.
01Defects that surface only at the end
Quality is judged at final inspection, so a defect shows up in parts that are already made — long after the process condition that caused it has passed.
02Limits of root-cause analysis
When a problem occurs, the root cause is hard to identify, and quality management keeps relying solely on the personal experience of highly skilled operators.
03Lower cost efficiency
Quality issues increase work-in-process and rework volume, so production time grows longer and manufacturing costs rise
KEY FEATURES
Four capabilities that turn quality into prediction
From real-time detection through factor analysis and defect prediction to process optimisation.
Capability Index04
Quality Capability
01 / 04
01
Real-time anomaly detection
Multidimensional quality-factor analysis
Proactive quality-defect prediction
Data-driven process optimization
Data from the various sensors and equipment installed across the process is collected in real time and visualized on a dashboard. The state of the production floor can be grasped at a glance, providing the visibility to respond immediately to unexpected stoppages or errors.
Management Scope
- Sensor data collection
- Real-time monitoring
- Equipment utilization analysis
- Integrated data management
- On-site status board integration
- Anomaly alerts
Real-time monitoring dashboard
An interface that collects process data and equipment status in real time and shows them visually
SYSTEM ARCHITECTURE
How it is built
A three-stage pipeline: floor data collected, learned by models, and returned to the operating systems.
Prevent product defects in advance and maximize yield.
Heat treatment
Metalworking
Coating
Press
CNC machining
Injection molding
Heat treatment
Metalworking
Coating
Press
CNC machining
Injection molding
Defects are predicted before they happen, not found after.
- Equipment conditions
- Work conditions
- Raw material
- Environment
- Quality results
Manufacturing AI
- Defect probability prediction
- Causal variable analysis
- Optimal process condition recommendation
- Defect rate
- Rework
- Cost
- Productivity
EFFECT
Business impact
What it leaves behind — in defect rate, rework, unit cost and productivity.
BUSINESS OUTCOME
Predicted quality,turned into measurable results
- PASS
Lower defect rate
Trend analysis corrects small deviations automatically, and machine learning finds what degrades quality so optimal conditions are set quickly.
Defect rate · quality variation · set-points
- REDO
Less rework
The predicted defect probability is fed back into the process conditions, so less comes back for rework at all and the line no longer stalls waiting to re-run it.
Rework volume · re-runs · line stalls
- COST
Lower unit cost
Scrapping a finished part is avoided, and the energy cost per unit of stable quality plus post-shipment logistics and warranty cost come down.
Scrap loss · energy cost · warranty cost
- UNIT
Higher productivity
Time data locates the bottleneck and immediate feedback corrects the conditions, lifting the hourly output the same equipment delivers.
Hourly output · bottlenecks · equipment uptime
CASE STUDY
Case study
Quality variation in the carburising process quantified as a Q-score and linked to the MES.
Customer industry
Metal heat treatment
After02
What was built
- Time-series data store
- Stage-based prediction
- Q-score linked to MES
Results
AI quality prediction that replaced inspection with foresight
Defects do not fall by inspecting more. They fall by predicting.
Where to start collecting process data and which measure to predict first — designed around your own line.
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