In 2023, artificial intelligence (AI) will become increasingly integral to the daily operations of manufacturers around the world. Autonomous robots and predictive analytics powered by machine learning are enabling businesses to streamline processes, improve productivity, and reduce environmental damage in a variety of new ways.
Importantly, empowering humans to work more safely and efficiently is a priority for many organizations, rather than replacing human workers.
Today, the concept of AI technology in the factory has gone far beyond the robot-filled workplaces that have characterized the industry since the 1960s, with humans and machines working together, data and analytics enabling better predictions and decision-making. It encompasses the smart, connected manufacturing plant that is possible. -Manufacturing at every stage of the process. Let’s take a look at some of the most interesting use cases for AI in manufacturing in 2023.
cobot
Robots have been used for decades to automate manual tasks in factories and manufacturing plants, but collaborative robots are a relatively new development. What makes them different is that they are designed to work with humans in a safe way while augmenting their abilities with their own.
One of the big advantages of cobots over traditional industrial robots is their lower operating costs, as they do not require a dedicated space to function. This means they can work safely on normal factory floors without the need for protective cages or isolation from humans. You can select components, perform manufacturing operations such as screwing, sanding, and polishing, and operate traditional manufacturing machinery such as injection molding and stamping presses. You can also perform quality control inspections using computer vision-enabled cameras.
Cobots are widely used by automakers such as BMW and Ford to perform tasks such as gluing and welding, greasing camshafts, oiling engines, and quality control inspections.
Consumer goods companies such as Procter & Gamble are also using collaborative robots to streamline manufacturing processes, assembling and packaging products while maintaining the high hygiene standards required. .
AI in additive manufacturing
The term additive manufacturing, better known as 3D printing, is used because it encompasses any manufacturing process that builds a product or object layer by layer. This differs from the more traditional subtractive manufacturing process, where blocks of material are cut to produce products and components.
AI plays a key role in additive manufacturing, not only by optimizing the design of complex products, but also by optimizing how materials are distributed and applied (see Generative Design below). It can also be used to find and fix errors made by 3D printing technology in real time.
Additive manufacturing equipment maker Markforged has created a tool called Blacksmith that uses AI to compare product designs with the physical finished product, automating fine-tuning of the manufacturing process for a more accurate match.
Such technology would benefit footwear giants like Adidas and Reebok. The company is now using 3D printing technology to create complex lattice structures for running shoes that are more comfortable and improve performance.
generative design
Generative design is a bit like the generative AI found in technologies like ChatGPT and Dall-E, except that instead of telling you to create text or images, you’re telling them to design a product.
Designers simply input parameters such as the materials to use, the size and weight of the desired product, the manufacturing method to use, and the cost, and generative design algorithms spit out blueprints and instructions.
Design engineers in manufacturing can use this method to create a wide range of design options for the new product they want to create and choose the best one for production. In this way, we accelerate the product development process while enabling design innovation.
Generative design is particularly powerful when it comes to conceptualizing what new additive manufacturing processes such as 3D printing can do, due to the complexity of the shapes and structures that can be created.
It is used to create new types of parts that are cheaper, lighter, and stronger than existing parts, improving the overall quality of many products, from automobiles and aircraft to prefabricated houses and structures. Improving.
predictive maintenance
Manufacturers are using AI to analyze data from sensors and machines on the factory floor to understand when and how breakdowns are likely to occur. This means you can be sure that the necessary resources and spare parts for repairs are on hand to ensure fast repairs. This also means that you can more accurately predict the amount of downtime expected for a particular process or operation and reflect this in your schedules and logistics plans. Data from vibration, thermal imaging, operating efficiency, oil and liquid analysis within the machine are all processed through machine learning algorithms to provide key insights into the condition of the manufacturing machine.
Practical examples include Pepsi and Colgate. Both companies use technology designed by AI startup Augury to detect problems in manufacturing machines before they fail.
lights out factory
A lights-out factory is a smart factory that can operate fully autonomously without people on site. Mostly theoretical, but some examples already exist. For example, Japanese robot maker Fanuc’s unmanned factory since 2001 can operate without human supervision for up to 30 days.
Electronics manufacturer Philips also operates a factory in the Netherlands that makes electric shavers, requiring a total of nine staff on site at all times. This is a trend we can expect other companies to work towards adopting as the technology becomes more efficient and affordable. Using an all-robot workforce means that factories can potentially run 24/7 without the need for human intervention, potentially offering significant yield and efficiency benefits. Of course, it will be necessary to consider how excluding humans from the manufacturing workforce will affect society as a whole.
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