Prevent Recalls with AI in Electronics
Do recalls affect your company? Learn how you can avoid them by using AI analytics
QualityLine end-to-end control runs an automated root cause analysis that includes all manufacturing data in every location and process worldwide.
&nbp;
The system correlates between tested parameters, predicts failures and sends automatic alerts for abnormal behaviours to prevent product defects.
Learn how to apply AI to eliminate recalls and boost product quality in electronics manufacturing
How does it work?
QualityLine AI technology automatically integrates, analyzes, and visualizes all your manufacturing data
Unlocking the Power of AI and Machine Learning with QualityLine:
- AI-Driven Optimization: QualityLine employs advanced AI algorithms to optimize unmatched precision. The system can adapt to changing production demands in real-time, reducing downtime by an impressive 25%, enabling you to meet demanding production schedules efficiently.
- Process Stabilization with Machine Learning: Experience a remarkable 30% reduction in process variations as QualityLine’s AI machine learning models continuously analyze data and adapt production parameters. This translates into a 15% increase in product consistency
Predict Failures with Precision
- QualityLine AI-driven predictive analytics boast a remarkable 95% accuracy rate in identifying potential issues before they impact production, preventing costly downtime and ensuring your manufacturing lines run seamlessly.
- Efficiency Enhancement Through AI: QualityLine’s AI-powered insights streamline operations to achieve a notable 20% reduction in production cycle times, significantly enhancing overall efficiency.
Tips for best analytics results
we recommend that your data will include:
Data for Testing Analytics
- Serial Number (SN) of each unit
- Product Part Number
- Testing station Name/number
- Operator Name (if exist)
- Timestamp (time/date)
- Status of the testing unit (PASS/FAIL)
- Process Name (if exist)
- Parameter name (the names of each tested parameter within each test session)
- Lower Limit of each test
- Higher Limit of each test
- Result of each test
- Test Status – the result of each test – pass/fail (if exist)
- Test Duration (if exist)
- Measuring Units (if exist)
Data for Repairs Analytics
- Serial Number (SN) of each unit
- Product Part Number
- Technician name or number (if exist)
- Failed parameter of symptom found in the testing
- The faulty component or material found defective during repair (component number and also reference designator)
- The failure code that was found by the technician (broken component, cold soldering…)
- Text description of the problem found (if exists)
Unlock the value of your data
Global end-to-end control
Automated and fast integration of any manufacturing data source
QualityLine experts have experience with more than 1500 production lines around the world and more than 30 years in the manufacturing industry.
We want to use our knowledge and experience to help you improve your manufacturing efficiency and product quality.
Our machine learning and artificial intelligence technology will make this possible.
Tips for best analytics results
we recommend that your data will include:
Data for Testing Analytics
- Serial Number (SN) of each unit
- Product Part Number
- Testing station Name/number
- Operator Name (if exist)
- Timestamp (time/date)
- Status of the testing unit (PASS/FAIL)
- Process Name (if exist)
- Parameter name (the names of each tested parameter within each test session)
- Lower Limit of each test
- Higher Limit of each test
- Result of each test
- Test Status – the result of each test – pass/fail (if exist)
- Test Duration (if exist)
- Measuring Units (if exist)
Data for Repairs Analytics
- Serial Number (SN) of each unit
- Product Part Number
- Technician name or number (if exist)
- Failed parameter of symptom found in the testing
- The faulty component or material found defective during repair (component number and also reference designator)
- The failure code that was found by the technician (broken component, cold soldering…)
- Text description of the problem found (if exists)
