Leveraging AI-technologies is gaining acceptance across the manufacturing sector as it significantly reduces down time, improves efficiency and provides stringent measures for the delivery of supreme quality end-products. Adoption of new age technologies, such as AI, IIoT, Robotics, among others can play a key role in re-initiating business operations and start production even in adverse market conditions such as that of a pandemic.
However, there are multiple challenges regarding cognitive quality control, machine vision, calibration and cost effectiveness when it comes to implementation of these technologies. In order to provide an overall solution Jidoka Technologies had come into being. Jidoka’s state-of-the-art visual inspection solution offers an optimal mix of artificial intelligence, machine vision, and industrial automation for seamless quality checks in manufacturing.
The key differentiator which Jidoka’s solution presents is that it delivers 98 percent or more defect detection in the QC process with a significant increase in through put by 33 percent decrease in the false positives. 20 percent increase in the speed of inspection with five to eight percent further accurate detections are made possible with the solution.
Sekar Udayamurthy, Co-Founder and CEO, Jidoka Technologies, said, “We at Jidoka, understand the challenges faced by our customers in the manufacturing sector in the QC process and support them with our state-of-the-art automated,
cognitive QC solutions. We achieve this, by combing the power of AI with automation and computer vision”. He added, “Our team is constantly updating on the knowledge-front through research and development to develop futureready solutions”.
Standing Apart
Jidoka’s state-of-the-art automated cognitive QC solution combines the power of AI with automation and machine vision to enhance the quality and efficiency of QC in the manufacturing process. It harnesses and mirrors human reasoning in defect detection delivering 98 percent or higher accuracy in the QC process and a significant increase in throughput. This QC solution primarily consists of two parts – first being the hardware platform that comprises of three models, namely, Huron for large volume complex products, Tigris for flat and light weight products and Miyake for manual load and unload with selective checking. The specific models are chosen and adopted to fit the respective assembly line requirement.
The second part is a software platform called Kompass that connects real-time decision-making to the state-of-the-art AI, in order to create an end-to-end system for visual defect detection. This patented solution is layered over the hardware platform to provide a turnkey solution that fits seamlessly into the production line to deliver up to 200-220 decisions per minute.
While Jidoka is an AI-first company, it sells products including Hardware and automation to provide outcomes for customers. “This is further backed by our Deployment process that is designed for success. The biggest difference between us and our competition is that we are an E2E solution provider with proven implementation experience and not just a software platform”, mentions Sekar.
We at Jidoka, understand the challenges faced by our customers in the manufacturing sector in the QC process and support them with our state-of-the-art automated, cognitive QC solutions
Looking At The Future
To begin with, the company wants to move to a KPI driven triaging of quality for organizations i.e., manage economic, quality and production constraints and drive incremental change to quality, rather than looking at quality as an isolated module.
“Secondly, we are looking at the need for customers to have data to ensure higher levels of accuracy, which is done by various features for data augmentation and experience driven inferencing. For this, we will leverage the cutting-edge work done in AI/Machine vision such as Anomaly detection, 3D image generation, and to this effect we are productionising some of the concepts while patenting them as well”, concludes the CEO.
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