Smart decision engine for quality control on production line belt

‍Problem description

• A 360-degree camera inspects the inside of the component for mechanical damage.
• If a scratch is detected, an alert is sent to the engineer for inspection and decision on whether to allow the component to continue rotation based on the scratch's appearance, thickness, depth and length.

Challenge

Is the decision engine able to decide on its own whether the damage is acceptable or not?

Solution

• Trained, tested and deployed dedicated Machine Learning model (Neural Network algorithms) for mechanical damage detections on the production line
• Introduced self-learning algorithms for calibrating and deploying ML model in a real time

Key Results

• Minimized false alarms: through continuous self-learning algorithms, the system became more accurate in distinguishing between acceptable and unacceptable damage, reducing false alarms and unnecessary interruptions in the production process.
• Improved quality control: the primary goal of this solution is to enhance quality control by detecting mechanical damage in real-time. Key results in this regard included a reduction in the number of defective components reaching the end of the production line, leading to higher overall product quality.
• Data-driven decision making: the machine learning model provided data-driven insights into the severity of detected damage. Engineers used this information to make informed decisions about whether a component can continue in the production process or needs further inspection or repair.

Why Choose SLS?

The SLS platform provides organizations with:  

• An intelligent search engine that learns and adapts to deliver better results.  
• Seamless integration of diverse data formats for centralized knowledge sharing.  
• Scalable solutions for both internal and external knowledge management.  

With SLS, organizations can transform how they access, manage, and utilize knowledge, empowering teams and enhancing customer interactions. 

Questions Answered:

How can we improve access to information in our growing knowledge base?  

• Answer: By using the SLS platform's AI-powered search engine, which delivers relevant results quickly and efficiently.  

How can we keep our knowledge base updated with minimal manual effort?  

• Answer: SLS ensures automated updates, consolidating data from various sources to maintain accuracy and relevance.