Manufacturing is one of the main industries that use Artificial Intelligence and Machine Learning technologies to its fullest potential. Smart Factories, also known as Smart Factories 4.0 have major cuts in unexpected downtime, better design of the products, improved efficiency and transition times, the overall quality of the product and safety of the workers. Artificial Intelligence is the heart of Industry 4.0, delivering more productivity while staying environmental-friendly.
Siemens, GE, Fanuc, Kuka, Bosch, Microsoft and NVIDIA among other industry giants are already heavily investing in manufacturing AI with machine learning approaches to boost every part of manufacturing. TrendForce estimates that Smart Manufacturing (the blend of industrial AI and IoT) will expand massively in the period from three to five years, by 2020 the global smart manufacturing market will be valued over $320 billion, with a compound annual rate of growth at 12.5%. In 2015 the number of functioning industrial robots in factories was 1.6 million, in 2019 the number is expected to grow to 2.6 million, according to the International Federation of Robotics.
Being a very important part of every asset-reliant production operation, maintenance of equipment is one of the biggest expenses in the manufacturing — unplanned downtime cost nearly $50 billion to plants and factories worldwide, 42% of it is because of asset failure.
That’s why predictive maintenance became a vital solution that will help to save an enormous amount of money, complex AI algorithms like neural networks and machine learning are generating trustworthy predictions regarding the status of assets and machinery. Remaining Useful Life (RUL) of equipment becomes significantly longer. If something needs to be repaired or replaced, technicians will know beforehand and even will know which methods to use to fix the issue.
Generative design is the method that allows putting detailed brief created by humans into an AI algorithm. The information in the brief can contain different parameters like available production resources, budget and time. The algorithm examines all possible variations and generates a few optimal solutions. This set of solutions can be evaluated by pre-trained deep learning models adding more insights and picking certain options. You can go through this process as many times as you want to settle on a perfect one. Artificial Intelligence is completely objective without any unproven assumptions unlike humans could have.
In the modern world of short TTM deadlines and increased level of complexity of the products, it becomes even harder to meet the highest standards and regulations in terms of quality. Customers expect impeccable products and defects that cause recalls, which massively damages the reputation of the company and its brand. AI can alert about the problems at the production line that can result in quality issues. These faults could be major or subtle, but they all influence the overall level of production and could be eliminated in the early stages.
Machine vision, for example, is an AI solution that uses high-resolution cameras to monitor defects way better than a human can. It could be combined with a cloud-based data processing framework which generates an automatic response. Also, manufacturers can obtain data on the performance of their products when they hit the market to make better strategic decisions in the future.
AI and ML are already an essential element of Factory 4.0, but they also can improve supply chains, making them interactive to changes on the market beforehand, managers can improve their strategic vision relying on AI suggestions. Estimates are generated by AI based on linking together a number of factors like political situation, weather, consumer behavior, the status of the economy, e.t.c. Staff, inventory, the supply of materials could be calculated according to predictions.
The biggest companies around the globe are already utilizing Artificial Intelligence and Machine Learning in manufacturing and investing millions in its development. Here are some of the most prominent examples of companies using it.
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