AI Case Studies
Production
Profet AI uses AutoML to help production departments with parameter optimization, virtual measurements, and anomaly detection. By leveraging data analysis, it enhances production efficiency, ensures product quality, and reduces operational costs.
Application Scenarios
Process Optimization, Quality Prediction, Parameter Recommendation
Machine Equipment Parameter Optimization
In manufacturing, controlling equipment parameters is crucial for ensuring product quality, improving production efficiency, and extending equipment life. Quickly finding the optimal parameter combination is a challenge for production personnel.
Pain Points Analysis:
- Difficulty in Parameter Adjustment: Traditional optimization methods rely on manual experience, leading to inconsistent quality outcomes due to varying operator judgments.
- High Experiment Costs: Extensive trials are needed to find the optimal parameters, increasing the consumption of materials, equipment, and labor.
- Inability to Transfer Experience: Manual experience is hard to summarize and transfer to new employees, making it difficult to apply to new materials and processes.
Outcome Benefits:
- Historical Data Integration: Models help find the optimal parameters for different equipment, reducing process losses and cutting production costs by 10%.
- Time Savings: Reduces the time spent on manual parameter adjustments, speeding up startup times and improving production efficiency by 5%.
Virtual Process Measurements
As manufacturing continues to develop, the demand for high product quality increases, and traditional sampling methods can no longer meet production needs.
Pain Points Analysis:
- Actual Measurement Limitations: Some automated processes are long, and actual measurements require significant costs and time, limited by equipment, materials, and manpower.
- Sampling Inspection: High precision is required, but some processes still rely on sampling due to production capacity, increasing the probability of defective products.
- Difficult Production Line Adjustments: Existing measurement methods cannot provide immediate feedback, making production line adjustments difficult, affecting efficiency and product quality.
Outcome Benefits:
- Predictive Simulation Functions: Combined with production lines, it reduces inspection costs by 10%.
- Immediate Feedback: Helps process personnel adjust production line parameters in real-time, improving quality costs by 12%.
Anomaly Detection AD Model
In modern factories, the stability of equipment operation is crucial for production efficiency and product quality. However, current maintenance processes are usually reactive, with repairs only occurring after problems arise, leading to low maintenance efficiency and production interruptions.
Pain Points Analysis:
- Preventive Maintenance: The industry mainly relies on PM as the primary maintenance method, based on “post-event data,” making it difficult to predict in advance.
- Inability to Monitor in Real-Time: Traditional monitoring methods may have delays, making it hard to reflect the operational status of equipment in real-time and detect anomalies promptly.
Outcome Benefits:
- Rapid Model Establishment: On-site personnel can quickly establish anomaly detection models using the platform, enabling data-driven decision-making.
- Real-Time Monitoring: Implementing models and real-time monitoring reduces downtime and lowers abnormal downtime losses by 12%.