Manufacturers are applying Machine Learning to improve everyday processes and regulatory tasks in their factories. Product Quality, Safety of Workers and Workspaces can be boosted using Artificial Intelligence. As a result, operators and supervisors can prioritize other activities, that helps reduce resource needs and optimize cost.
NATURAL LANGUAGE PROCESSING
Service Reports Interpretation
Quality Engineers periodically analyze service reports of machinery that have broken down to assess the cause of failure. The large volume of reports make this process very tedious and create bottlenecks. AI can be used to optimize this task. The text reports provided by the service team are fed into the ML model, and the AI automatically assigns a failure label to it to analyse the reliability statistics. AI & ML reduces the cost and resources required for the process
Product Quality Inspection
Manual inspection of products, parts, and components is a cumbersome and expensive task. Even a slight variance in material quality can make the entire production run defective. AI techniques that automatically detect early errors can help reduce material waste, repair and rework costs. Automated inspections, assisted by Computer Vision, uses multiple scanners and cameras for inspecting the manufacturing line. This ensures that only the highest quality items move onto the next manufacturing process.
Safety Gear Verification
Personal Protective Equipment ( PPE ) protects workers against safety risks. Not wearing PPE dramatically increases the chances of workplace injuries. Such accidents lead to fines and financial losses. . Round the clock monitoring of workers for PPE compliance is almost impossible with human operators. However, video cameras equipped with the right Artificial Intelligence systems are able to do this automatically. Automation of PPE compliance helps manufacturers reduce the risk of accidents, reduce employer liability, and improves operational efficiency.
Perimeter Monitoring and Protection
Manufacturing plants have various restricted areas that are not suitable for workers to access unintentionally or otherwise. These may be high-risk areas like electrical wiring rooms or crucial areas like server closets. Computer Vision can be used to detect movement in such areas. Line-crossing events are automatically registered by the AI system, which can then notify the appropriate authorities, reducing the risk of damages and injuries.
When an accident or workplace safety incident takes place, it is important to notify the concerned department as soon as possible. In large warehouses, it is difficult to take notice of all such cases through manual monitoring of CCTV footage alone. An AI system can detect such instances automatically and report it to concerned departments quicker than a human can. The ML model can be taught to identify workers fallen on the floor, unexpected breakdown of vehicles, crowding of workers, and blocking of surveillance equipment.
Workers in Manufacturing plants have to follow Standard Operating Procedures ( SOP ). This is done to maintain consistency in the tasks and how the workers interact with the work environment, which is crucial for work efficiency and safety. Any anomalous step/activity that can lead to SOP violations or safety hazards can be identified using Artificial Intelligence. Workers are monitored using cameras, backed by Human Activity Recognition ( HAR ) technology. Any deviation from SOP or suspicious activity is detected by the AI system. Once this information is relayed to the appropriate department, corrective action can be taken, reducing the chances for malpractice.
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