Before01
Company profile
- Uneven furnace heat
- Operator-led variance
- Late cause tracing
AI
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 optimization.
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
WORKFLOW
The workflow behind each capability
For each capability, the order it runs in and the check it passes before moving on.
Combine process values and inspection results by LOT and time
- 01
Collect process data
Sensor · equipment conditions
- 02
Link inspection
Measured value · pass/fail · type
- 03
Align history
LOT · process · time
- 04
Prepare for training
Check missing data · label quality
Recorded alongside
01
LOT · equipment · process
02
Condition history · inspection time
03
Defect type · inspection standard
Operating check
Are process and quality labels linked accurately?
YES
Valid history → analysis data
NO
Missing/mismatched → check source
An example workflow · the detailed links and criteria are set to suit your floor.
SYSTEM ARCHITECTURE
What it connects,and the work it leads to
From the data that goes in to the results on the floor.
01 / INPUT
01Process conditionsTemperature · pressure · speed · time
02Manufacturing contextLOT · raw material · equipment
03Quality resultsMeasurements · pass/fail · defect type
CONNECTED OPERATIONS
AI Quality
Link process and quality history
Predict risk · analyze contributing factors
Recommend condition improvements
03 / OUTPUT
✓Quality riskRisk signals by LOT and process
✓Analysis basisContributing variables · similar history
✓Floor actionCheck · review conditions · re-inspect
01 / INPUT
01Process conditionsTemperature · pressure · speed · time
02Manufacturing contextLOT · raw material · equipment
03Quality resultsMeasurements · pass/fail · defect type
CONNECTED OPERATIONS
AI Quality
Link process and quality history
Predict risk · analyze contributing factors
Recommend condition improvements
CONNECTED OPERATIONS
AI Quality
Link process and quality history
Predict risk · analyze contributing factors
Recommend condition improvements
03 / OUTPUT
✓Quality riskRisk signals by LOT and process
✓Analysis basisContributing variables · similar history
✓Floor actionCheck · review conditions · re-inspect
Linked by common identifiers, master data and historyResults checked → fed into the next run
Who uses itQuality managersProcess engineersProduction operators
METHOD
How it is built
A three-stage pipeline: floor data collected, learned by models, and returned to the operating systems.
Collection01 →
Data Ingestion
PLC
Modbus / OPC-UA
SENSOR
4-20mA / RS-485
IoT gateway
MQTT / HTTP
Vibration / soundTemperature / pressureCurrent / powerEquipment state
Signal processingSTFT / FFT
Filtering · denoising · normalization
Time syncNTP/PTP
Aligns timestamps across sources
Target System
Time-series DBInfluxDB, Timescale
Data lakeRaw waveform
MetadataLOT, work orders, equipment state
Modeling & analysis02 →
AI/ML Modeling
Analysis & prediction
Anomaly detection
XGBoost
Failure classification
CNN, YOLO
RUL prediction
LSTM AutoEncoder
Multimodal fusion
Feedback & refinement
Model evaluation
Action history
MLOps retraining
Service03
Operational visibility
MonitoringLive equipment dashboard
KPI ReportOEE · availability report
System integration
MES
ERP
QMS
Real-time anomaly detection
Early response, as it happens
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
AI Quality
- 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.
- 01
Lower defect rate
Trend analysis corrects small deviations automatically, and machine learning finds what degrades quality so optimal conditions are set quickly.
Defect ratequality variationset-points - 02
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 volumere-runsline stalls - 03
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 lossenergy costwarranty cost - 04
Higher productivity
Time data locates the bottleneck and immediate feedback corrects the conditions, lifting the hourly output the same equipment delivers.
Hourly outputbottlenecksequipment uptime
CASE STUDY
Case study
Quality variation in the carburizing 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