Quality errors have consequences
Welding inspection is still human-dependent: 90% of the inspection is conducted post welding process.
Defects found after the welding process will cost the manufacturer a significant amount of time and resources to rework the machinery.
We automate monitoring and detection systems to help manufacturers cut costs, reduce inspection time, improve efficiency, and ensure 100% of deviations are detected with machine intelligence.
We are the first to ensure 100% consistent weld quality across the enterprise via AI-powered computer vision
Testimonials
What our clients say
“Last month we discovered there were some defects in our weld and because it was not detected during the process, we had to redo almost all the project (rework); it cost us $600K. With Autometrics, our manufacturing line is much more reliable: we can know about defects before the part has to be reworked or worse be scrapped, creating significant cost savings.”
Autometrics prevents rework costs through real-time quality management and automated detection via their AI based solution.
“Integration was very easy; the camera attached to the welding robot arm and the electrical sensor was just a clamp wrapping around the electrical current feeding wire. We didn’t have to shut down the line for renewal; which is perfect because line closure for 1 week can cost us hundreds of thousands of dollars; even a day of interruption is pain.”
Autometrics’ advanced technology works with your existing machinery to enhance quality as an application-independent solution.
“We manufacture for different projects sometimes every week, i.e., changeover rate is high; this solution doesn’t scale because we have to do calibrations every time. Autometrics’ solution has the machine learning brain to look at quality indicators that are common in almost every process, so it scales.”
Autometrics’ advanced AI system is user friendly and easy to integrate through their simple user-interface.
Our Partners & Client
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We would like to acknowledge advisory services and research and development funding from the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP)