Problem: Verne Global wanted well-researched, long-form blog content to inform their customers how HPC computing power was changing their industries and could benefit them.
Solution: We created a blog strategy that explored HPC and machine learning use cases in a range of fields. These blogging efforts help spur rapid growth at Verne Global .
Machine Learning Applications
in Process Manufacturing (part 1)
Machine learning is a key part of the emerging paradigm of manufacturing often referred to as “Industry 4.0,” where it’s used to extract insight and value from the large datasets collected by sensor-equipped robotic equipment. According to a report by PWC entitled Industry 4.0: Building the Digital Enterprise, this new paradigm of manufacturing is fast becoming a reality, with more than half of the respondents to their survey planning to double the digitization of their operations in the next five years.
Machine learning can be used to reach greater efficiency and reliability at every step of the manufacturing process, even in highly complex manufacturing environments like the semiconductor industry. A good example of this is Solido Design Automation, an electronic design automation (EDA) company based in Saskatoon, Canada that’s developed a suite of machine learning tools aimed at helping semiconductor manufacturers improve the speed and reliability of semiconductor design. Solidio, which has received both significant investment, uses machine learning to help semiconductor manufacturers reduce the resource-intensive cell, memory, and I/O characterization processes, aspects of IC design that become more difficult as integrated circuits shrink in size.
Machine learning is having an effect on more traditional industries as well, such as the automotive industry. Continental AG, a world leader in automotive components parts, has started to use machine learning to help design car tires that provide better traction in inclement weather, while American tire manufacturer Goodyear has proposed a very high-concept tire design called the “Eagle 360” that integrates machine learning into the tire itself, allowing it to adapt to road condition by making adjustments to an external membranes of sensors. Assisted by its technology partner IBM, Schaeffler, the manufacturer of bearings for the automotive and aerospace industry, has embarked upon a wholesale “digital transformation” to integrate machine learning into its production. In particular Schaeffler has begun to explore machine learning application in bearing maintenance, and how machine learning can help produce greater accuracy and reliability in the bearing manufacturing process.
Larger companies are forgoing the partnership route to build machine learning capacities of their own. For example, General Electric has made a very public pivot toward machine learning, hiring a large team of machine learning researchers to help develop its Predix software, an platform designed to help manufacturers monitor, record, and analyze each stage of their manufacturing process. In pursuit of its ambition to become a global top-ten software company, GE has been gaining machine learning expertise through acquisition too, like data integration platform Bit Stew and machine learning company Wise.io, both of which it purchased in 2016. So far, GE has attracted over 270 companies to employ its Predix software suite, including oil giant BP, which has outfitted 650 of its oil wells with sensors to feed data into the platform.
In Germany, Siemens has developed its own solution, which it calls Mindsphere. The Mindsphere platform is an open IoT operating system that offers product lifecycle management, digital twin capability, and a robust machine learning capability. In the case of Siemens, Mindsphere is just the latest incarnation of a long-standing commitment to machine learning, which stretches back over twenty years. According to Siemens, Mindsphere has already been able to reduce the emissions of their gas turbines an additional 10% to 15% beyond what their human engineers had previously thought possible.
This just a sampling of the many early use cases for machine learning in the field of manufacturing, and there are plenty of others. Australian software company 1Ansah, for example, has developed a natural language processing platform to help consolidate equipment manuals and maintenance logs into a single, intelligent source of knowledge. The company has had early success helping Airbus engineers maintain their helicopters. Because Airbus has equipped the latest generation of A350 with over 10,000 sensors in each wing to capture data, 1Ansah and machine learning companies like them are in a great position to provide on-going, synergistic support to Airbus, and possibly disrupt the field of aerospace maintenance in the process.
We’re still at the very early stages of this new data-driven era of industry. As the number of industrial robots continues to grow, to nearly 2.6 million in the next couple years, the desire for more intelligence that enables to them accomplish more complex tasks will increase along with it. This creates a very exciting situation for companies in the field of industrial machine learning, and the data center providers like Verne Global that are committed to helping them develop and refine their technologies with low-cost, carbon-neutral compute power.