Parts Quality Gets Robotic Boost
When manufacturers, such as the world's top car makers and automotive parts suppliers, produce components in their factories, traditional QA testing has been limited to verifying the quality of random parts pulled off the line throughout the day.
It was time consuming to perform the detailed tests required, and defective parts could get through despite randomized tests.
If a defective part caused a recall or accident, manufacturers could face costly litigation or irreparable damage to their reputation.
IntelIntel designs, manufactures, and sells integrated digital technology platforms worldwide. The company's platforms are used in various computing applications comprising notebooks, desktops, servers, tablets, smartphones, wireless and wired connectivity products, wearables, transportation systems, and retail devices. It offers microprocessors that processes system data and controls other devices in the system; chipsets, which send data between the microprocessor and input, display, and storage devices, such as keyboard, mouse, monitor, hard drive or solid-state drive, and optical disc drives; system-on-chip products that integrate its central processing units with other system components onto a single chip; and wired network connectivity products.Featured Subsidiaries/ Business Units:- Intel Inside- Intel Data Center Manager (DCM)- Saffron Technology- Wind River
Manufacturing customers, which include the world's Tier 1 automotive and parts manufacturers.
- CONNECTIVITY PROTOCOLS
Bluewrist installs robots, 3D cameras and sensors on a client's assembly line and deploys comXtream software for analytics and automated controls on an industrial PC powered by Intel® Core™ i7 processors.
After each part is scanned by 3D cameras and sensors, Bluewrist's National Institute of Standards and Technology-certified ScanXtream* creates a point cloud to compare against CAD drawings, and will take automatic action if defects are detected by alerting the manufacturer or shutting down the line.
Clients have the option of using Bluewrist's cloud-based software defined infrastructure, powered by the Intel® Xeon™ processor E5 series, to get the same QA testing, analysis and automated controls without onsite PCs.
- DATA COLLECTED
Fault Detection, Parts Quality, Production Efficiency, Supplier Defect Rate, Traceable Parts
- SOLUTION TYPE
- SOLUTION MATURITY
- OPERATIONAL IMPACT
Impact #1 [Efficiency Improvement - Quality Assurance]
Bluewrist's comXstream software and analytics show miniscule deviations from the approved design specifications so manufactures can modify processes before allowable tolerances are exceeded.
Impact #2 [Efficiency Improvement - Compliance]
By maintaining a record of every scan, manufacturers can prove 100% compliance and traceability with CAD specifications in the event of a recall or accident.
- QUANTITATIVE BENEFIT
Automating analytics and testing lets manufacturers cut QA testing time from an hour or more to under approximately 30 seconds.
Manufacturers check 100% of the parts on the assembly line for 100% inline quality testing, instead of testing less than 1%.
- USE CASES
Predictive MaintenancePredictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.