Machine vision for Manufacturing Industry

от loopr AI Inc.

Leverage AI to revolutionize many slow and costly processes in the manufacturing lifecycle

Artificial Intelligence has made significant inroads in many industries which have vast amount of digital data. Traditional industries like manufacturing have just started realizing the full potential of AI/ML in reducing turnaround time and costs and the significant impact that has on the bottom-line.
Loopr Machine Vision is an automated ML platform, where businesses can build state of the art AI models for different use cases, with minimum prior AI expertise. These models thrive in analyzing images of objects or occurrences and based on that predicting future outcomes. Thus, they can be leveraged to accelerate any existing process by augmenting human decisions with fast, accurate and scalable computer generated decisions. 

Below are some examples of what Machine Vision can do:
Defect Detection 
Traditional defect detection is a labor intensive and manual task which leads to high costs and turnaround time. Loopr Machine Vision's AI model can parse millions of images of machine parts, assembly lines, finished products etc and identify every defect accurately down to a pixel granularity. And since Loopr supports free form tagging, it can even identify defects with completely irregular shapes. Such scale and level of detail is not feasible for human workforce alone. Thus, Loopr Machine Vision can provides orders of magnitude faster and more accurate defect detection at scale, with almost no labor costs. Some common use cases are corrosion detection, crack detection, assembly inspection and  process monitoring.
Predictive Maintenance
Traditional maintenance processes follow a fixed static schedule or depend on human judgement which might not be optimal:  The maintenance could be ahead of time leading to unnecessary down time, or too late resulting in machine breakdown and even longer down time. Loopr Machine Vision's AI model can be trained on past images of machines, having varying level of wear and tear and were prime candidates for maintenance. After this the model can monitor these machines in real time and accurately identify only the machines which require maintenance. This leads to optimal and in-time maintenance resulting in minimum downtime.  
Safety Protocol Monitoring
Loopr Machine Vision AI models can be trained to identify safety protocols which need to be adhered by front-line workers in different scenarios. After this, the model can accurately detect any violation of these protocols in real time without requiring any human intervention or monitoring. Such an AI powered solution is cheaper, less error prone and has a direct impact in improving workplace safety.

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