Quality professionals use deep learning AI to quickly identify and prevent significant product defects, bringing a substantial leap forward in quality assurance and process improvement. Unlike traditional automation controllers, deep learning allows computers to proactively grow their knowledge base and adapt to evolving circumstances.
In manufacturing, operators, inspectors, engineers, and systems measure characteristics to ensure product quality. This data helps monitor real-time processes, tracking performance indicators like cycle time, throughput, and efficiency.
Rapid growth in electric vehicle sales along with an increasing interest in fuel-cell-electric powertrain systems presents the auto industry with serious and largely unanticipated quality-control issues.
Automated inspection systems help to improve the quality of parts and products. With few experienced CMM operators in the field, manufacturers are progressively turning to automated quality control solutions to not only stop production bottlenecks but also to boost the quality of the parts being inspected.
In our 21st Annual Spending Survey, we’ve looked into who will be buying what and when. Despite the economic upset resulting from COVID-19, equipment budgets look to be steady, if not growing, for the next fiscal year.
Quality and job satisfaction seem to go hand in hand. Only 7% of respondents were not at all satisfied with their job, according to our 18th Annual State of the Profession Survey.
On Demand Join Glen Putnam of NuSkin and Glen Fraser of ETQ on May 8, 2024, at 2:00 PM ET for a 30-minute webinar where you will get practical advice about transitioning to an enterprise-grade electronic QMS.
On Demand Join Hexagon and Quality Magazine for our next webinar and discover how quality automation can revolutionize your quality control process, increase reliability, and ultimately reduce costs.
On Demand This webinar is designed to empower participants with the knowledge and tools necessary to conduct thorough failure investigations, ensuring that root causes are not just identified but effectively addressed to prevent future occurrences.
Drawing from 30 years of experience using neural networks in various machine vision applications, Ned will share anecdotes and examples to help you predict which kinds of applications are going to be AI wins and which ones are going to give you fits.