Understanding artificial intelligence AI in manufacturing

AI projects improved equipment uptime, increased quality and throughput, and reduced scrap. Rick identified key drivers for successful AI implementation, potential pitfalls and best practices and shared some pro tips. AI manufacturing has revamped every aspect of the industry, from large-scale production lines to the intricate assembly of components. And now, we see increased efficiency, innovation, and remarkable profitability that are helping manufacturers reach new heights. We are living in the Fourth Industrial Revolution (also known as Industry 4.0), marking the rapid transformation of all manufacturing processes. Implementing AI-based technologies has inevitably changed the way goods and services are planned and produced today.

• One of the key benefits of AI in manufacturing for new product development is the ability to analyze vast amounts of data quickly and efficiently.
• Asset planning and maintenance scheduling can also be improved by using computer vision technologies.
• Much of this growth is being fuelled by the ongoing roll-out of internet of things (IoT) and connected devices – a natural precursor to the introduction of AI in industry.

This benefits in the form of data-driven decision-making, accelerated design iterations, and the ability to create products that align with market demands. By embracing AI, manufacturing companies can enhance their competitive edge and introduce innovative and successful products to the market. The new era will be the time of smart connected machines where humans complement their working environment with intelligent cobots. Here, the task of modern manufacturers is to make sure they are well-prepared for the newest digitalization and know how to shape their business process to leverage all these new technologies.

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Intelligent factories can operate more effectively, endure less downtime, and improve customer satisfaction. And now that we are using the internet of things (IoT), it helps us to switch from analog to digital operations. Much of the power of AI comes from the ability of machine learning, neural networks, deep learning, and other self-organizing systems to learn from their own experience, without human intervention. These systems can rapidly discover significant patterns in volumes of data that would be beyond the capacity of human analysts. In manufacturing today, though, human experts are still largely directing AI application development, encoding their expertise from previous systems they’ve engineered. Human experts bring their ideas of what has happened, what has gone wrong, what has gone well.

• In the manufacturing sector, artificial intelligence (AI) is having a profound impact.
• Driven by these needs, the energy firm implemented its Digital Predictive Maintenance Center, according to a case study by AspenTech.
• Manufacturing organizations everywhere use our AI solutions to gain valuable insights from the large amounts of data they generate.
• White-box models generate transparency and empower the developer and Customer to execute complex projects with confidence and certainty.

As we’ve seen in the examples in this article, there are many innovative uses ofAI in manufacturing—all of which can solve critical business challenges. Manufacturing facilities worldwide, such as Suntory PepsiCo, BMW, and Applied Materials use AI to reduce the workload on tedious tasks such as defect detection that were once performed by employees manually. This has the greater benefit of reducing costs, limiting human errors, and freeing humans from repetitive and monotonous work. Driven by these needs, the energy firm implemented its Digital Predictive Maintenance Center, according to a case study by AspenTech. The Digital Predictive Maintenance Center organizes data so reliability engineers can rapidly assess and correct reliability issues proactively.

Humans and machines team up in the factory of the future

So, digital twins are able to simulate any physical object or process, allowing engineers to digitally create and maintain complex products, machines, factories, or even entire supply chains. In 2011, French multinational manufacturer of food products Danone implemented an ML system to create a more accurate demand forecasting platform. For a partner, the manufacturer chose a global provider of supply chain optimization software systems ToolsGroup.

An AI in manufacturing use case that’s still rare but which has some potential is the lights-out factory. Using AI, robots and other next-generation technologies, a lights-out factory operates on an entirely robotic workforce and is run with minimal human interaction. Companies can use digital twins to better understand the inner workings of complicated machinery.

In any case, the future of all businesses will soon be dependent on computer vision systems that are AI. In the production floor, autonomous vehicles, like those Porsche used in the previous example, can automate assembly lines and conveyor belts, and self-driving vehicles and ships can optimize deliveries and operate 24/7. With NVIDIA, designers, engineers, and simulation experts can rapidly accelerate their CAE workflows and achieve efficient, effective results. Learn how top CAE applications running on GPUs are speeding up simulations by 5X or more while lowering costs and energy consumption. We need regular maintenance, fuel, and downtime; even then, we can only operate for about 8 hours daily. Companies have not yet fully realized the advantages of AI-powered manufacturing systems.

However, a nuanced approach reveals that AI can be a powerful ally, fostering collaboration between humans and machines to redefine the factory floor. Logistics are the lifeblood of supply chains, and AI’s role in route optimization is pivotal. AI algorithms analyze factors like traffic conditions, weather, and delivery deadlines to create optimal routes for shipments.

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According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third. Manufacturers should start applying generative AI or other technologies to targeted initiatives to learn, develop skills, and secure early wins that can be used to build organizational momentum and gain buy-in. “It’s about bringing knowledge into the organization about how to use and implement AI,” MIT Sloan professor John Hauser said at the MIMO Symposium.

Still, only 22 percent have adopted it, according to a survey conducted by Market Research Future. Many companies need more expertise to leverage AI technology; domain expertise is essential for successfully implementing AI in the manufacturing industry. In the context of discrete manufacturing, AI has proven itself an extremely valuable asset at nearly every stage of the production process. But what may surprise most (especially younger generations) is that the concept of AI has been around since the 1950s and actually put into practice by the 1970s.

The Impact of AI in Manufacturing: Unleashing Productivity

As we know, traditionally, manufacturing was an area that trained humans to think like machines. However, with the advent of digitalization, the coin flipped and now machines are quite successfully trained to think like humans. In manufacturing, data often appears outdated, biased or fallible, but the overall success of AI adoption directly depends on the quality of the data. For instance, if we speak about plants, here data is frequently built on unconnected systems patented exclusively as a property of a manufacturer. But with machine learning, scientists at General Electric’s research center in New York developed a model to assess a million design variations in only 15 minutes. It analyzes the historical data to check past sales, what’s in stock, and trends to know how much is needed.

Ultimately, AI systems will be able to predict issues and react to them in real time. AI models will soon be tasked with creating proactive ways to head off problems and to improve manufacturing processes. One of the key benefits of AI in manufacturing for new product development is the ability to analyze vast amounts of data quickly and efficiently.

There are many thoughts about this, some coming from the realm of science fiction and others as extensions of technologies that are already being utilized. The most immediate noticeable evolution will be an increased focus on data collection. Artificial intelligence technologies and techniques that are being employed in the manufacturing sector can only do so much on their own. As Industrial Internet of Things devices increase in popularity, use, and effectiveness, more data can be collected that can be used by AI platforms to improve various tasks in manufacturing.

Design engineers in the manufacturing industry can use this method to create a wide selection of design options for new products they want to create and then pick and choose the best ones to put into production. In this way, it accelerates product development processes while enabling innovation in design. Even though these systems have empowered the companies, making space for advanced optimization, they’re far from being perfect. Since their calculations rely on constant parameters and the infinite capacity principle, they do not allow the manufacturers to make realistic predictions.

Operators in factories rely on their knowledge and intuition to manually modify equipment settings while keeping an eye on various indications on several screens. In addition to their regular duties, operators in this system are now responsible for troubleshooting and testing the system. Production losses due to overstocking or understocking are persistent problems.

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