Industrial AI: How Artificial Intelligence is Revolutionizing the Manufacturing Industry?

Implementing AI in manufacturing facilities is getting popular among manufacturers. 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. Advertise with TechnologyAdvice on Datamation and our other data and technology-focused platforms. The Website is secured by the SSL protocol, which provides secure data transmission on the Internet. First, it can serve research purposes, allowing the companies to come up with new materials that carry desirable properties while being biodegradable or fully recyclable. In addition, it can help them optimize the usage of resources to minimize waste.

Since the industrial era, manufacturers have been aiming at optimizing their production according to the infinite growth principle. The fundamental imperative is to produce more, faster, and at lower costs. Artificial intelligence can identify inefficient processes in terms of production volume or energy use in order to minimize waste and reduce costs. In addition, robotic assembly lines fuelled by AI can bring productivity to the next level, reducing the number of human errors and speeding up the manufacturing processes. Likewise, the manufacturing, FinTech, and cybersecurity industries leverage AI-based predictive analytics to anticipate risk.

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This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.

The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents. Original equipment manufacturer, Sentry Equipment, evolved its SentryGuard sampling machine to provide guidance to operators using the Aveva System Platform to slash development time. It provides the ability to analyze sample data, provide alerts, and guide operators to resolution. But AI usage is happening more in some parts of the world than others, with the U.S. lagging behind. 51% of European manufacturers are implementing AI, compared with Japan at 30%, and the U.S. at 28%. The two most common use cases for AI in manufacturing, according to Capgemini, are maintenance and quality control.

Ways AI Is Improving Manufacturing In 2020

AI machines are also able to optimize production and figure out the root cause of a problem when there is an error. They store your data pretty cheaply, but when you start using computing resources, it becomes a lot more expensive. You want the ability to scale across different cloud providers or storage solutions, whichever is most cost effective.

One of very common problems in discrete manufacturing (owning huge production sites) is the issue of misplaced parts and tools. The AI continuum can be compared to an intelligence scale that allows us, to optimize our environment instead of making conclusions based on data. In other words, the real power of what we call AI is not found simply by organizing the data or presenting it smartly, or even in alerts that are merely based on the data. Every so often I get approached by manufacturing professionals who seem genuinely interested at what do at Plataine. After all, IIoT is not just an industry in tremendous growth, it’s also a trending buzzword. But when the word ‘AI’, enters the discussion things become even more interesting.

Advanced Technology Manufacturing

When IIoT is linked with cloud computing and virtual or augmented reality, businesses can communicate about industrial activities, share simulations, and send vital or relevant information in real-time, independent of location. Manufacturers now have the unmatched potential to boost throughput, manage their supply chain, and quicken research and development thanks to AI and machine learning. Well, there are a lot of use cases for artificial intelligence in everyday life, but what about AI in manufacturing? The effects of artificial intelligence in business heavily include manufacturing. Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year.

• These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.
• Delfos is a Brazilian startup that makes Delfos I.M., a renewable energy asset monitoring platform.
• Nexus uses these technologies to analyze real-world threats, exploits, and vulnerabilities and correlates threats with dark web chatter and asset information.
• RPA software is capable of handling high-volume or repetitious tasks, transferring data across systems, queries, calculations and record maintenance.
• Manufacturers can specify each product’s optimal supply chain solution using machine learning techniques.

The cost of running a production process can greatly decrease by using AI to analyze energy usage. Additionally, lower costs allow more cash to be set aside for resources for process innovation, improving quality and production. Supply chain and inventory management can better prepare for future component needs by forecasting yield. Production managers can be warned to extend production time to meet demand if the yield is predicted to be lower than projected.

Machine Learning, Neural Networks, and Deep Learning

The major advantage of AI as a service in a company is that it allows the reduction of the development cost of AI solutions. With industries like banking, education, gaming and retails being transformed by AI, it’s no surprise that the manufacturing industry is next. The use cases above prove that AI has immense potential in the manufacturing sector. Of course, the manufacturers themselves can benefit from its implementation – but so can the economy and environment. Manufacturers around the world have been using enterprise resource planning (ERP) systems for a long time already in order to optimize the usage of resources and maximize profit.

Brought by Plataine, the leading manufacturing optimization software for advanced manufacturing and Aerospace composites.Learn more or get the latest guide on How Smart your Factory Really Is. Reviewed by Anton Logvinenko, Web Team Leader at MobiDev
The Internet of Things (IoT), is all about connecting devices into networks that work together. Here are 11 innovative companies using AI to improve manufacturing in the era of Industry 4.0. Some manufacturers are turning to AI systems to assist in faster product development, as is the case with drug makers. For example, visual inspection cameras can easily find a flaw in a small, complex item — for example, a cellphone. The attached AI system can alert human workers of the flaw before the item winds up in the hands of an unhappy consumer.

Visual inspection equipment — such as machine vision cameras — is able to detect faults in real time, often more quickly and accurately than the human eye. Factories creating intricate products like microchips and circuit boards are making use of ‘machine vision’, which equips AI with incredibly high-resolution cameras. The technology is able to pick out minute details and defects far more reliably than the human eye. When integrated with a cloud-based data processing framework, defects are instantly flagged and a response is automatically coordinated.

Companies can monitor an object throughout its lifecycle and get critical notifications, such as alerts for inspection and maintenance. In the event of these types of complications, RPA can reboot and reconfigure servers, ultimately leading to lower IT operational costs. Manufacturers typically direct cobots to work on tasks that require heavy lifting or on factory assembly lines. For example, cobots working in automotive factories can lift heavy car parts and hold them in place while human workers secure them. While autonomous robots are programmed to repeatedly perform one specific task, cobots are capable of learning various tasks. They also can detect and avoid obstacles, and this agility and spatial awareness enables them to work alongside — and with — human workers.

No, Definitely not,we have AI for that, and more specifically machine learning! By processing historical data using pattern recognition and other algorithms, the Plataine system “learns” the cycle per each type of object and offers an automatic alert every time it deviates from its normal course. Again, context is key, parts moving through the shop floor are not simply data to be shown on a dashboard. They’re an intricate language of behaviors that can be understood, analyzed, predicted and optimized. This chapter introduces the development and applications of artificial intelligence in manufacturing.

It offers predictive analytics that can assist manufacturers in making better choices. Artificial intelligence has many advantages, from product design to customer management. These include improving process quality, streamlined supply chain, adaptability, etc. Manufacturing data’s prominence is fueled by AI and machine learning work well with it. Machines can more easily analyze the analytical data that is abundant in manufacturing. Hundreds of variables impact the production process, and while these are challenging for humans to examine, machine learning models can forecast the effects of individual variables in these challenging circumstances.

Even after the ML model is in production and continuously monitored, the job continues. Business requirements, technology capabilities and real-world data change in unexpected ways, potentially giving rise to new demands and requirements. Industrial titans like General Electric (GE -2.25%) and Siemens have embraced artificial intelligence as the technology offers a number of advantages, including minimizing defects and errors, reducing downtime, and saving on costs. Increasingly, technology plays a major role in how products get made on the factory floor.

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