Machine Learning Concept with Hexagons

Visual inspection requires precision, especially with small parts requiring tight tolerances, many parts moving along a production line, and a QA process that needs to deliver top-quality products to customers. 

Machine learning is now an integral part of visual inspection processes, something Doss takes to new levels thanks to our deep learning capabilities. Take a look at why machine learning is vital for your visual inspections.

Improved Results Over Time

Deep learning, rather than machine learning, utilizes scalability because of the available data and algorithms. Deep learning can classify over 200 different pieces of metal parts. It can also detect in-line non-conformity for O-rings, including when the top of the ring doesn’t adhere to the bottom.

Because deep learning aggregates layers and layers of data, the algorithm improves over time, mimicking how the human mind works.

Better Sorting of Small Parts

Machine learning in normal visual inspection systems looks at an input layer, a hidden layer, and an output layer. Deep learning taps into a neural network that examines data from several hidden layers.

For example, you have a factory full of production lines that produce small parts. All of the parts are similar or have similar designs. Rather than rely on a database or analysis from one machine on a single line, deep learning relies on a superior database that combines analysis of millions and millions of visual inspections across your entire factory, every line, and every time our machines have inspected and sorted parts on your line. 

That’s because deep learning taps into a neural network compiled of as many data points as possible within your system. The reason? More data points make algorithms better at sorting.

From finding surface defects to sorting variable parts on the same line, deep learning can also look at serial numbers and assess product quality. Our system can autonomously classify data and structure them hierarchically, finding the most relevant and useful ones quickly as it strives to solve problems and improve its performance continuously, just like the human mind works.

Traditional machine learning contains two to three layers of thinking, while deep neural networks can contain over 150 decision-making algorithms that sort data into the most useful ones.

Differentiating Between Tolerances and Defects

Deep learning learns to assess patterns in various ways, including differentiating between tolerances and defects. Deep learning works very well at addressing complex surface and cosmetic defects and determines what is tolerable and what is not. 

For example, visual inspection with deep learning can view scratches and dents on parts turned, brushed, or shiny throughout production. Deep learning adds another layer to the process by teaching itself to differentiate between tolerances and defects by relying on previously classified data stored in the neural network.

Hence, your visual inspection system becomes more accurate. 

Faster and More Accurate Results

Visual inspections are much faster because the human eye cannot see objects moving quickly through the inspection area. Precise calculations make machine inspection possible.

However, deep learning follows instructions, and it learns along the way. It standardizes measurements while understanding the difference between cosmetic anomalies and actual flaws. As the costs of automation go down and labor costs rise, the appeal of deep learning for visual inspections becomes clear.

Test Us! We Offer World-Class Visual Inspection Machines

Reach out to Doss USA for more information on our world-class visual inspection systems that can help your teams process small parts and assemblies. Our deep learning, backed by nearly 30 years of experience and engineering, will help your team become more efficient while delivering superior results.

Contact our U.S. office at (734) 228-5992 today for more information.