Industry 4.0 or connected factory or smart factory concept is being adopted by wide range of manufacturing industries to increase productivity and thus competitiveness. Be it OEE & predictive maintenance or asset tracking or inventory management, Industry 4.0 has been adding a great value by merging operation technology with information technology. Manufacturer now wants to track the usage of their product directly from consumer and improve future variants. And to achieve this the complete value chain to be connected – supplier, manufacturer, logistics, warehouse, retailer and end customer.
Manufacturing operations are very cautious to deliver the highest quality during every stage of the production or assembly process. Over half of these quality checks involve visual confirmation to ensure the parts are in the correct locations, have the right shape or colour or texture, and are free from any blemishes such as scratches, pinholes, foreign particles, etc. Automating these types of visual quality checks is very difficult because of the volume of inspections, product variety, and the possibility that defects may occur anywhere on the product and could be of any size. This is where a new cognitive visual inspection can deliver its highest value.
Images (captured through high end camera) of normal and abnormal products from different stages of production can be submitted to the centralized ‘learning service’ that will build analytical models to discern OK vs Not OK characteristics of parts, components and products that meet quality specifications and those that don’t. This kind of system can be trained to perform such tasks with a high level of confidence.
Based on advanced neural networks, the models trained by software system can be deployed on pre-configured hardware on the factory floor so that there can be very little decision latency during production.
Train the model by uploading and analysing defect image -> Distribute the trained model to edge systems->Analyse image & provide result -> Review result
The solution can learn continuously by taking feedback from manual inspectors who can review the automated classification and override them based on human judgment. The corrective information along with the image from the production floor is then included in the next training cycle for that analytical model, thereby improving its ability to discern in the future. Such a unique Cognitive approach is highly recommended especially electronics, automotive, and industrial products manufacturing units.
- Inspection time reduction
- Quality inspection cost reduction
- Inspection process consistency