Thermal Process AI AI SOLUTION
Thermal Process AI
By combining AI with physical simulation,
we improve quality stability, productivity, and energy efficiency in thermal processes.
AI analyzes data from various heat processes such as heat treatment, melting, continuous casting,
and rolling
to minimize defect rates and improve process stability and productivity.
Core AI Technologies
Thermal Analysis and Heat Transfer AI
AI learns complex heat transfer phenomena and process conditions to precisely predict temperature distribution.
Process Prediction AI
It analyzes key process variables such as temperature, time, and atmosphere to predict quality characteristics.
Defect Prediction AI
It proactively predicts the possibility of defects such as cracks, oxidation, deformation, and microstructural nonuniformity.
Process Optimization AI
AI proposes optimal process conditions
to achieve both energy savings and quality improvement.
Key Features
Real-Time Process Monitoring
It collects and analyzes sensor and equipment data in real time
to monitor process conditions and detect abnormal signs at an early stage.
Quality Prediction and Analysis
AI predicts quality characteristics such as microstructure, hardness, and strength, and presents improvement directions through cause analysis.
Anomaly Detection and Alerts
It detects abnormal signs such as temperature deviation, equipment abnormalities, and raw material variations,
and provides response measures along with alerts.
Energy Efficiency Analysis
It analyzes energy usage status and proposes optimal operating conditions to reduce energy costs.
AI-Based Thermal Process Optimization Process
1. Data Collection
· Sensor data collection
· Equipment and environmental data
· Raw material and quality data
· Equipment and environmental data
· Raw material and quality data
2. Physical Modeling
· Construction of process physical models
· Definition of process conditions
· Input condition setting
· Definition of process conditions
· Input condition setting
3. AI Prediction and Analysis
· Temperature distribution prediction
· Quality characteristic prediction
· Abnormal pattern analysis
· Quality characteristic prediction
· Abnormal pattern analysis
4. Process Optimization
· Process variable optimization
· Energy-saving proposal
· Simulation verification
· Energy-saving proposal
· Simulation verification
5. Control and Execution
· Real-time process control
· Application of optimal conditions
· Reflection in process operation
· Application of optimal conditions
· Reflection in process operation
6. Performance Analysis and Improvement
· Performance indicator analysis
· Evaluation of improvement effects
· Continuous optimization
· Evaluation of improvement effects
· Continuous optimization
Expected Benefits
Productivity Improvement
20~40%
Improves productivity through process stabilization and optimization.
Defect Rate Reduction
30~60%
Significantly reduces defect rates through defect prediction and prevention.
Energy Savings
15~30%
Reduces energy costs through optimized energy use.
Operating Cost Reduction
20~40%
Reduces operating costs through improved process efficiency.
Improvement of Quality Uniformity
25~50%
Improves quality uniformity and enhances product reliability.
Application Areas
Heat Treatment
Temperature control of material heat treatment processes
Hardness and strength prediction, deformation minimization
Melting and Smelting
Melting temperature prediction, composition control,
impurity removal, yield improvement
Continuous Casting
Solidification behavior prediction, shrinkage and segregation prediction
Quality stabilization and yield improvement
Rolling
Temperature distribution prediction, microstructure prediction,
surface quality improvement, deformation control
Sintering / Heating
Sintering temperature control, gas atmosphere control,
strength and durability improvement
Coating / Plating
Coating thickness prediction, temperature and time control,
adhesion and uniformity improvement