Manufacturing is a very data-intensive industry. Most of that data has so far been managed on a different floor than processes like assembly. AI can now process enough information fast enough to be of immediate use to engineers on the line. PTC has developed multiple tools to assist users in utilizing AI technology.
This blog post was co-written with Matthias Bastian. The original version was first published on ptc.com. We have adapted the content to fit our audience.
Artificial Intelligence is one of the most engaging emerging technologies today. AI has the potential to impact just about every aspect of human society.
In addition to consumer AI, the technology also holds promise for manufacturing. There are many use cases for AI technologies, such as computer vision, that are useful in everyday environments. This article looks at several specific use cases and applications of AI in manufacturing.
Why Does AI in Manufacturing Matter?
AI has several potential use cases in manufacturing, including automating some work processes completely. Right now, most roles that AI takes on involve helping human workers access more information more efficiently.
This use of AI helps experienced workers work better. It also helps to transition workers into new roles or help new workers learn the ropes faster. Far from taking human jobs, AI is empowering human workers. It is making sure that they can enter positions that companies are actively trying to fill.
What Is AI’s Impact on the Manufacturing Industry?
Improving efficiency and productivity has always been a major incentive for collecting and analyzing data. This used to mean taking data from the floor, analyzing it in offices, and communicating findings back to the workers on the floor.
Industrial artificial intelligence – with sufficient human supervision – can carry out this whole process on the floor. It can provide insights to engineers in real-time when and where they need it. This compresses the information flow from a day or more to fractions of a second. Some processes, like ordering more parts and materials before they run out, have already been automated by comparatively basic AI systems.
Artificial intelligence is already being used across the manufacturing industry to recognize potential workplace hazards, automate component ordering, and guide workers. Industrial artificial intelligence is also used to help analyze information and convey it in actionable documents and communications.
These existing use cases aren’t going anywhere. However, advances in AI are introducing emerging use cases and making existing ones even more efficient.
What Are the Advantages of AI in Manufacturing?
1/ AI-powered Visual Inspection
Quality control and visual inspection have already seen massive improvements from AI. PTC’s Vuforia Step Check walks supervisors through the process of training an AI on digital and physical models to create a program that helps inspection engineers identify and even troubleshoot potential product issues. Step Check then automates the documentation process, increasing worker efficiency.
2/ AI in Equipment Maintenance and Facilities Management
AI in manufacturing can also use information from Industrial Internet of Things devices to generate predictive maintenance strategies. These strategies can optimize output by keeping machines in peak working order. This also prevents costly downtime by scheduling maintenance instead of waiting for repair. PTC’s Kepware allows human operators to connect smart devices and see their real-time diagnostics at a glance.
Manufacturers are also patching industrial artificial intelligence systems into inventories to automate ordering essential parts and supplies before they run out. As supply chains are already sensitive, AI can prevent delays from the simple mistake of not ordering components in time.
3/ AI-assisted CAD
Generative AI is increasingly proving itself capable of creating usable content from prompts, including in the age-old field of CAD. Tools like PTC’s Creo are likely to find themselves increasingly augmented by inputs from artificial intelligence specializing in product design.
4/ Enabling a New Kind of Workforce
Further, the prevalence and usefulness of completely automated processes are skyrocketing, as are the number of “cobots” in manufacturing.
The expansion of their use in manufacturing at the same time as explosive growth in the fields of LLMs and natural language processing help to drive dreams of fully intelligent and interactive robots that communicate organically with human coworkers. However, this remains a thing of the future for the time being.
AI and AR
1/ A Powerful Duo in Manufacturing
Augmented reality is another emerging technology that already has several established use cases in manufacturing. AR models are increasingly replacing physical mockups in early design phases where it saves material cost and iteration time. These models can also be used in remote collaboration programs to save travel costs, as well as for training modules. These models can even be generated from CAD programs that companies already use in the conventional design workflow.
We’ve already mentioned the interplay between industrial artificial intelligence and AR in manufacturing. When artificial intelligence powers augmented reality applications, the benefits of each technology multiply.
For example, Magna International subsidiary Nascote Industries leveraged Vuforia Step Check both for new-hire training and to enhance the visual inspection process. The software was even able to identify a “soft connection” that often got past inspection engineers but would eventually become loose.
AI is initially trained from images of a physical product or existing models. However, powerful AIs can then generate their own images and models simulating different situations and conditions. The process of synthetic data generation can help to optimize product design. It can also prepare an industrial artificial intelligence system for situations that haven’t yet been encountered. This is similar to how a human might imagine what they might do in a future situation.
Augmented reality, by presenting spatial information in an intuitive medium, is also a great way to convey complex information efficiently. It could make workers effectively harness the volume of information that AI makes available. In this way, AR becomes an interface through which humans can interact and AI becomes a practical work aid.
2/ AI for AR: The Future of Work Instructions and Documentation
Step Check can do more than scan for defects. It incorporates work instructions that guide the inspection engineer through an entire visual inspection workflow. The program helps them navigate around the object in space and even troubleshoot common problems if possible. It also generates a report on each inspection, including any issues that might have been found with each unit.
These last processes – AI-powered work instructions and documentation solutions – have futures as their own projects. Inspection engineers aren’t the only kinds of workers currently following physical work instructions and managing their own documentation. Workers in all kinds of roles are currently jostling paperwork instructions and stepping away from their real jobs to file reports. These hassles are inefficient.
Further, AR solutions can be engaging in a way that standard processes typically aren’t. While not explicitly gamified, these solutions offer mental stimulation and provide a refreshing break from repetitive tasks during long shifts. This can go a long way toward improving job satisfaction.
Industrial AI will also help new workers catch up and transfer knowledge. Training the AI from human experts turns the AI into an expert in its own right. Also like a human engineer, the AI continues to learn from each use. That knowledge is then passed on to newer human engineers who see the accumulated knowledge of the AI through intuitive AR displays. With the advancement of natural language models, we may also soon see AIs that upskill workers more efficiently than conventional training methods.
3/ AI and AR in Service
AI in manufacturing isn’t the end of the story. Applications similar to those that we’ve explored on the assembly room floor can also be implemented after a product ships so that service personnel can maintain products without sending them back to the manufacturer. One day, these tools may be expanded to the average customer.
While the potential for AI in service after sale is huge, there are still some questions before the practice becomes commonplace. For example, will an AI trained on potentially proprietary product information be a security risk for companies adapting that AI for public use? Will there be a language barrier to overcome when a program meant for engineers begins communicating with non-specialists? Who might be responsible for any consequences of that?
We’re still at a moment defined by companies and individuals alike becoming comfortable with using AI for more and more tasks. Questions shouldn’t stop us from exploring these possibilities. They should guide us as we move forward with new AI implementations.
What Are the Challenges in AI Adoption?
Some of the concerns mentioned above deal with corporate privacy and security. This is a real concern for companies. In many cases, companies have strict security regulations for services involving the use of a camera. These can often be solved with on-premise solutions that don’t always lend themselves well to AI. However, these concerns are increasingly being solved by private cloud infrastructure or edge computing that maintains information on the device.
One myth of AI adoption has to do with replacing human workers. The fact is that the growing skills gap in manufacturing promises to leave millions of critical jobs unfilled over the next decade. Implementing AI in roles that support human workers gives people the resources necessary to step into roles that are already available.
In many situations, AI changes the jobs that humans perform. Rather than performing a dangerous or exhausting physical task, they now need to supervise a machine performing that task. AI in manufacturing doesn’t put humans out of work, though it might put them out of harm’s way.
However, AI is a powerful transitional technology. Making the most of it means building trust in AI systems and ensuring regulatory compliance. Mindful regulation can keep humans and companies safe while using AI.
For example, regulation might require that critical decisions be made by humans rather than machines. Or that money spent on AI infrastructure comes with a budget for training humans to work with AI-powered devices and systems (upskilling) or to transition into jobs that can’t be filled by emerging technology (reskilling).
What is the Future of AI in Manufacturing?
We’re in an explosive moment for AI. However, AI will only become more practical in the manufacturing industry through the adoption of companion technologies like AR and advanced data systems. These technologies allow AI to work through existing infrastructure in ways that are accessible to existing workers, driving workforce efficiency.
The future of industrial AI isn’t just the future of technology. It’s also the future of the people who use it and benefit from it. Companies using AI will see production and environmental costs go down as they save on material, travel, downtime, and rework.
Workers working alongside AI will see job satisfaction increase as AI automates both the most mundane and the most dangerous elements of their jobs. Customers will benefit from more affordable and more reliable products. They will have an increased ability to maintain and repair those products when necessary.
AI has already been involved in the industry in terms of data management and interpretation. We’re just now starting to see the seismic shift that occurs as AI finds its way to the production floor through robots, cobots, generative AI, and AR.
This is an exciting time for forward-looking companies to start integrating AI into their existing workflows and connecting their existing infrastructure.
