As the chief engineer and head of the department for digital transformation of manufacturing technologies at the Laboratory for Machine Tools and Production Engineering (WZL) within RWTH Aachen University, I've seen a lot of technological advancements in the manufacturing industry over my tenure. I hope to help other manufacturers struggling with the complexities of AI in manufacturing by summarizing my findings and sharing some key themes. The WZL has been synonymous with pioneering research and successful innovations in the field of production technology for more than a hundred years, and we publish over a hundred scientific and technical papers on our research activities every year. The WZL is focused on a holistic approach to production engineering, covering the specifics of manufacturing technologies, machine tools, production metrology and production management, helping manufacturers test and refine advanced technology solutions before putting them into production at the manufacturing edge. In my team, we have a mix of computer scientists, like me, working together with mathematicians and mechanical engineers to help manufacturers use advanced technologies to gain new insights from machine, product, and manufacturing data.

The manufacturing industry includes a broad range of businesses varying in size, from small manufacturers producing limited quantities of items to multi-national organisations involved in the large-scale production of manufactured goods. This industry encompasses manufacturers of food and beverages to textiles, chemical manufacturing and even heavy machinery. As an industry, manufacturing contributes around $100 billion to the Australian GDP annually (ABS, 2020). Thus, an industry with such varying degrees of complexity, breadth and scale is one that seeks to maximise the broad benefits of cloud technologies. Regardless of the end product, commonly faced challenges experienced within the manufacturing industry include innovation, risk management, operational efficiency and cost optimisation, remaining sustainable and time to market.

Matt Kirchner spent his career running manufacturing companies. Now, he shares his knowledge with listeners of Webinar Wednesday and the TechEd Podcast. In this article, Matt gives us 11 predictions for how artificial intelligence will totally transform manufacturing. To view this article as a video presentation, click here. You know, there's a handful of things that have differentiated world class manufacturers from average ones in the last several decades. The organizations that embraced change surged ahead, while the others were left behind.

According to a report on the website of the National Institute of Standards and Technology on November 24, a multi-institutional team from the National Institute of Standards and Technology, the University of Maryland and the Stanford Linear Accelerator Center (SLAC) of the U.S. Department of Energy has developed a closed-loop material exploration and optimization based on artificial intelligence The system (CAMEO) algorithm aims to use the self-learning characteristics of the algorithm to discover complex new materials with specific properties through fewer experiments, to help scientists minimize the time of trial and error in experiments and improve the efficiency of new material development. The research team connected the X-ray diffraction equipment to a computer equipped with the CAMEO algorithm and imported the existing material database into the algorithm. After many iterations of learning, only a small amount of routine measurement can be used to find The best material for specific properties. Using this method, researchers discovered new nanocomposite phase change memory materials among 177 possible materials. The number of test iterations required was reduced to 1/10 of the original, and the time required was shortened from 90 hours.

When we think of artificial intelligence (AI) going rogue, prime examples from the movies include HAL 9000 from 2001: Space Odyssey and Skynet from The Terminator, which were mainframe computers that reacted to the real-world problems in unexpected ways. From industrial manufacturing to autonomous vehicles, machine learning models are becoming increasingly embedded in our lives. Researchers are thus exploring pre-emptive ways to avoid harm from unexpected AI decisions when machine learning models are deployed to act in the real-world--an area of machine learning known as reinforcement learning (RL). "While deep RL has indeed been very successful in achieving state-of-the-art performance in curated academic environments, it has yet to be thoroughly tested in the presence of real-world complexities," said Abhishek Gupta, a Scientist at the Singapore Institute of Manufacturing Technology (SIMTech) and one of the study's senior authors. The work, which was principally conducted by Nanyang Technological University (NTU) graduate student Xinghua Qu and jointly overseen by Gupta and NTU professor Yew-Soon Ong, focused on the performance of vision-based AI, which is likely to be critical for the safe use of AI in applications such as autonomous vehicles.

Robots already assemble and weld products in factories. Can they make the components parts themselves, too? Although it may not be obvious, there's a close link between manufacturing technology and innovation. Elon Musk often talks of the "machines that build the machines" as being the real enabler in both his space and automotive businesses. Using less-expensive, more scalable processes allows Space X to launch missions on budgets and with speed that would be unthinkable using NASA's old-school manufacturing methods.

Every new technology that comes to prominence has always made the life of humans better. Remember, the time fire was first discovered by your ancestors to cook food. And then came the wheel. Now, it is digital payments and internet of things. Are you a person who keeps a keen eye on the scientific developments happening in the world?

Of all the pain points in manufacturing platforms, pricing is one of the most upfront. Potential customers want to know, rightly, what a project will cost. Detailed inquiries also help service bureaus understand what users want. But neither party really wants to wait around while technicians manually go through every inquiry by hand to figure out costs for each job by tallying prices for materials, machine and operator time, finishing processes and time, and shipping. With more agility coming to manufacturing, there's thinking that this new year will see an upswing for manufacturing platforms. Among these is 3D Hubs, which has its origins in 3-D printing.

Intel has sold more than 220 million Xeon processors over the past 20 years, generating $130 billion in revenues. But the latest $1 billion -- generated by sales for artificial intelligence applications -- may be the most important. Navin Shenoy, executive vice president at Intel, said at an Intel event that Intel's AI chip business is strategically important for the company as it shifts to becoming a data-centric company. Five years ago, Intel's revenues were about a third data-centric, and now the sector is close to half the business. But AI isn't a business that Intel dominates yet.

The Blockchain market is forecast to grow in a 61.5% Compound Annual Growth Rate (CAGR) between 2016 and 2021, developing from $.2B to $2.3B in 2021. The largest segments are in the company and financial services and technologies, telecom and media. The biggest protocols comprise Bitcoin, Ethereum, and Ripple. Deloitte discovered that banks have allegedly stored between $8B to12B annually with blockchain technology to enhance operational efficiencies. The Artificial Intelligence (AI) market is predicted to rise from $8B in 2016 to $72B from 2021, reaching a 55.1 percent CAGR.