Artificial Intelligence Applications in Manufacturing

In the manufacturing market, machine vision has become an important part of many artificial intelligence applications. As artificial intelligence enters the manufacturing shop floor, these standards become especially critical

Across multiple markets driving vision applications, a key trend is ease of use. Sophisticated cameras, sensors and processing technologies have evolved into plug-and-play solutions. We introduce the same approach to the field of artificial intelligence in manufacturing. Our goal is to simplify AI so organizations can start deploying new technology to save time and money. What we provide is a platform of artificial intelligence and vision-based inspection and traceability applications that can easily customize unique workflows to make manufacturing decisions consistent, reliable and traceable

What are some common misconceptions about the use of artificial intelligence in manufacturing?

One of the biggest misconceptions is that it is complicated. This was true a few years ago, but recently there has been an emphasis on making AI tools simpler and easier to use. Our position is that you don’t need to be an expert to develop your own AI algorithms or digital workflows. With user-friendly drag-and-drop development tools and customizable app-based templates, anyone can develop their own AI-based workflows. This is a huge advantage for manufacturers, avoiding vendor lock-in and duplicative integration and development costs

What types of problems can artificial intelligence solve?

Machine vision is good at pass/fail decisions, but it is difficult to manually program acceptable tolerances. In contrast, artificial intelligence can be more easily trained to learn these variable decisions. The example I used was hardwood inspection. It is very difficult to manually program machine vision to discern natural textures and scratches. By comparison, it's much easier to train an AI based on a few good and bad images so that it can recognize the difference. Basically, due to its learning capabilities, AI can help in making subjective decisions.

There are also great opportunities for AI in inspection processes that rely on human decision-making. AI can help us make the right decisions on subjective quality decisions, or catch errors when our attention starts to shift. We're working with an auto parts manufacturer that relies on manual inspection but is adding artificial intelligence assistance to spot defects that may have been missed or determine if defects are within operating tolerances

Visual inspection technology that leverages artificial intelligence can help auto parts manufacturers identify errors and determine whether defects are within acceptable performance tolerances

How today’s manufacturers use labor Intelligent?

#One key area where manufacturers are deploying artificial intelligence is around human decision support. Despite significant investment in manufacturing automation, approximately 70% of processes in the United States still require human decision-making. This is especially true for small-scale, custom or seasonal production, which is too expensive and complex to invest in full automation.

Rewritten content: Visual inspection is an area where artificial intelligence helps humans make correct decisions. As part of a camera-based system, visual inspection applications can highlight product differences or defects to aid operators in inspection. At the same time, this is also an area where we can use the initial decisions of operators when dealing with these defects to train artificial intelligence models to solve the problem of previous misunderstandings. As operators accept or reject these initial differences, they are effectively training the AI ​​model transparently. After multiple inspections, the AI ​​model will begin to provide decision-making recommendations to the operator , including product images and operator instructions to provide full traceability to the manual process. For example, we work with an electronics manufacturer that assembles parts for high-reliability applications, and having complete end-to-end inspection steps and records of operator decisions is critical for traceability.

Dica Electronics uses visual inspection as a “second pair of eyes” to catch potential production errors, while also capturing a complete record of product images and operator notes to ensure traceability. Decca Electronics uses visual inspection as a "second pair of eyes" to catch potential production errors and simultaneously records product images and operator notes to ensure traceability

You're right Any suggestions on how manufacturers can deploy AI?

There is a lot of hype around AI, and manufacturers often build expectations based on perfect use cases. Soon, they ran into a problem. Their application may not be as simple as that perfect use case. A lot of customization is required. A common problem is simply getting the images needed to build and train an AI model—especially if you’re making a unique, low-volume product.

Usually our advice is to digitize the process first and then gradually move towards automation. Visual inspection is a great starting point, where you first use machine vision to detect errors and then add AI-based decision support to extend consistent decision-making across shifts or across different workstations. When you digitize your first error-prone process, you are capturing data that can help guide your next automated decisions. Typically this is adding traceability to visual inspection decisions or incorporating guided work or assembly instructions into the inspection process.

Overall, pick an error-prone process and see how you can use digitization and AI to save you time and money. We are working with many manufacturers who started pilot projects around the first troublesome defect or process, became comfortable with the technology, and are now scaling it across different workstations or production lines.

What is the biggest obstacle to using artificial intelligence in manufacturing?

This is a major issue that is often overlooked, don’t forget it People

Even with automation, many processes still require human decision-making at some point. This could be as simple as explaining to operators why a process is being automated and providing the necessary training so they can apply their expertise in new ways. For example, in robotic welding applications, the goal is to remove humans from repetitive, dirty, and dangerous tasks but still rely on their expert insight and years of training to monitor the process and evaluate the results. Without proper communication and training, humans will quickly abandon technology and resist change. This is who we are

Looking ahead to the next few years, what do you think the application of artificial intelligence in manufacturing will look like? How will Pleora be involved?

Not long ago, people were generally afraid of artificial intelligence. However, this widespread concern is largely disappearing. This is thanks to artificial intelligence technology becoming easier to use and becoming more common in our daily lives. It amazes me that I now leave a lot of decisions to a virtual assistant on my smartphone

We are at the same point in manufacturing. Just a few years ago, AI was expensive and complex, mostly limited to advanced laboratories, but development tools now make it easier for quality managers to design and deploy their own AI-assisted workflows. There is also greater focus on how artificial intelligence technology can help the human workforce, freeing them from boring, dirty and dangerous tasks and assisting them in decision-making

in the manufacturing market The key to widespread adoption of these technologies is making them more accessible to end users. This is our primary focus; providing quality managers with customizable, easy-to-deploy solutions that enable them to reduce manufacturing errors and costs.

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