Getting products out the door quicker, and with less waste, can have significant impacts on both customer service and the bottom line. However, with businesses facing numerous challenges lately – chief among them being a prolonged labor shortage – achieving that goal has become more difficult.
Automated quality control systems improve the manufacturing process efficiency and effectiveness while simultaneously reducing the potential for human error and boosting the final product’s overall quality — and robotics and sensors play a critical role.
Since the beginning of modern industrial robots in the early 1980s, robots have been guided by machine vision. Originally there were only a few robots with vision, but today it is over 5,000 robots annually in the North American market and significantly more globally.
By applying DL with a Data-Centric Approach, Users Can Streamline Even the Most Challenging Manufacturing Steps with Fast, Accurate Automated Inspection.
A sub-discipline of artificial intelligence (AI), deep learning (DL) has become a breakout technology in high-profile market sectors such as retail and high-tech.
Some people might be nervous about completely switching careers, but not Jared Curtis. For his dedication to quality, strong communication skills, and interest in always learning, Jared Curtis is our 2023 Quality Rookie of the Year.
My informal observations of published white papers and interviews with colleagues support that quality is moving in the direction of Quality 4.0, but very slowly.