Improve first pass yield in electronics with artificial intelligence in manufacturing
QualityLine increases first pass yield by quickly identifying any errors in the manufacturing process. The patented technology automatically integrates, collects and analyses any type of manufacturing data collected during your manufacturing process:
- SMT machines
- AOI, Flying probe, SPI, X-Ray
- Automated test equipment
- Repair/rework
- ERP/EMS
- Machines
&nbp;
Ready to optimize your first pass yield?
Learn how to apply AI to maximize first pass yield in electronics manufacturing
How does it work?
QualityLine AI technology automatically integrates, analyzes, and visualizes all your manufacturing data
With QualityLine’s AI advanced analytics, your products quality and manufacturing yield will increase by 30% in the first year.
- KPI’s analytics (First Pass Yield, Rolled Throughput Yield, Total Yield, DPMO, Failure rate.
- Products quality – CP/Cpk, SPC and detailed analytics to single unit
- Cross correlation between product parameters
Predictive failures rate and machine downtime & Automatic Alerts for abnormal issues and Manufacturing Analytics Reports
- Testing Stations stations performance and availability
- Repairs Analytics
- Retests Analytics
- SMT Analytic
- Scheduling of automated reports and SMS/email alerts to prevent degradation in the manufacturing process.
.
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)
