In the context of ongoing steel market volatility, maintaining consistent product quality while optimizing material sourcing is becoming increasingly challenging. To navigate this, SKF is exploring advanced technologies that can enhance their production & quality control processes. One key area of interest is the development of an in-channel inclusion detection system to ensure the highest standards of the raw material used in the production of bearings & related products.
Opportunity overview
Ensuring the high standard of SKF’s product portfolio requires stringent control over the quality of materials used in production. Inclusions, or non-metallic particles in steel, present a risk to product performance, affecting durability and precision. Traditional methods of statistical quality control can be restrictive in predicting the presence of defects in raw rings before manufacturing. Instead of relying on statistical averages, assessing each piece for specific defects can lead to higher quality outcomes. Traditional methods of statistical quality control can be restrictive in predicting the presence of defects in raw rings before manufacturing. Instead of relying on statistical averages, assessing each piece for specific defects can lead to higher quality outcomes. SKF is seeking innovative solutions for a real-time, in-line inclusion detection system during the production process. This technology must be capable of identifying defects below the surface of the steel as small as 0.04mm in width with a cycle time of 1.1 seconds, though slight flexibility in this target is acceptable if the system’s accuracy and reliability are exceptional. This would enable the quality control process of steel material to be elevated & while not forming a bottleneck in high-volume production environments, maintaining the commitment to product excellence. By improving our in-line quality assessment capabilities, SKF can maintain the highest standards of product quality, regardless of any variability in the supply chain.
Summary of the Requested Solution
The sought after solution should be able to deliver real-time assessments of steel quality with the focus on identifying inclusions in connection to the manufacturing process. The system should be capable of detecting defects below the surface as small as 0.04mm and operate with a cycle time close to 1.1 seconds. The inspection should focus primarily on black rings before the final manufacturing operations to ensure that potential issues are identified before further processing. The solution should ideally be adaptable to various types of steel, enabling consistent quality assurance across different material sources, particularly GCr15 steel.
Opportunity with us
SKF is the world’s largest bearing manufacturer, with 17,000 distributors in 130 countries. Collaborating with SKF on improving the quality control process of steel material presents an opportunity to contribute to improving the quality & efficiency of industrial manufacturing at a large scale. We want to work together and provide you with valuable learnings that enable your company further opportunities within the industry.
Examples we're looking for
Non-destructive testing
Various innovative NDT methods, such as ultrasonic testing, eddy current testing, or X-ray inspection could also be used to detect and characterize inclusions without causing damage. These systems should be capable of integration into an industrial environment and operate at a cycle time close to 1.1 seconds.
Visual inspection solutions
Advanced AI-driven visual inspection systems capable of detecting and measuring inclusions down to 0.04mm in size, that appear below the surface of the steel. Machine vision or ML driven image analytics could enable the real-time identification of potential defects, ensuring accurate identification of inclusions during high-speed production.
Edge analytics
Infrastructure solutions that can process the data received from the sensors on the inspection line. These solutions should be capable of analyzing high volumes of data at the source, making sense out of the signals and helping to optimize the quality control process.
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