Bonsai And Siemens Demonstrate Applicability Of AI To Industry By Reducing Machine Calibration Time More Than 30x In A Joint Proof-Of-Concept

Unique deep reinforcement learning platform combines the best of human and machine intelligence to train machine tools

Berkeley, CA (GLOBE NEWSWIRE) - AI startup Bonsai today announced, together with Siemens, a technological highlight in deploying AI on a real-world machine in a test environment. Using Bonsai’s AI Platform, Siemens subject matter experts trained an AI model to auto-calibrate a Computer Numerical Control (CNC) machine more than 30x faster than an expert human operator (estimation performed by Commonwealth Center for Advanced Manufacturing (CCAM) in Virginia). This marks the first time deep reinforcement learning has been successfully applied to auto-calibrate real-world CNC machines.

CNC machines, or computer-controlled machine tools, have revolutionized manufacturing since their inception in the 1940s. However, the value that CNC machines provide global manufacturers is constrained by high maintenance costs. To achieve highest possible quality of production, CNC machines need to be recalibrated frequently, as even minor friction leads to errors that result in costly manufacturing imperfections. Manufacturers have to fly in specialist engineers to do the job, which can take hours. While machines are decommissioned for maintenance, downtime and service costs can run up to several thousand dollars. Costs run especially high when unplanned errors arise outside the regular maintenance schedule.

Balancing Human And Machine Intelligence
To explore the possibilities of AI for their CNC business (see editor’s note), Siemens partnered with Bonsai, which has pioneered an AI platform based on deep reinforcement learning. At the platform’s core is an innovative ‘Machine Teaching’ technique, which enables subject matter experts such as specialist engineers to train machines to efficiently perform complex task. Using a simple scripting language, experts can design the ‘lessons’ and ‘rewards’ required to train each task. Bonsai’s AI Engine supports a wide range of state-of-the-art deep reinforcement learning algorithms, along with the logic for choosing the best-fit algorithms and guiding the training. In this way, the experts are able to leverage AI without themselves having to gain a deep understanding of machine learning.

Teaching A CNC Machine To Calibrate Itself Significantly Faster
To build this proof of concept, the team used Bonsai’s AI engine to build a predictive model that would calibrate the CNC machine. Each model produced by Bonsai is referred to as a BRAIN (Basic Recurrent Artificial Intelligence Network). The AI engine trains each BRAIN using cutting-edge deep reinforcement learning algorithms.

After six months of PoC, including training of the algorithms in a simulation environment, CCAM tested a BRAINs’ ability to calibrate a Siemens CNC controlled machine. The results were a great step forward. The most successful BRAINs calibrated a CNC machine more than 30x faster than the human operators, while achieving precision of less than two microns (as estimated by Commonwealth Center for Advanced Manufacturing (CCAM) in Virginia).

Siemens is delighted with the results, which are an important step in using AI to improve significantly the productivity, cost and quality of the CNC machine calibration process. Michal Skubacz, Siemens Vice President and Head of Industry Software at Siemens Motion Control concluded, “The results we achieved using Bonsai demonstrate that organizations can deploy the latest AI technologies in a noisy real-world system. The solution possible based on the proof-of-concept with Bonsai could augment and scale the work of our best operators. Instead of having operators carry out the same work repeatedly, they can focus on training the machines to perform better and more advanced tasks.”

Mark Hammond, CEO and co-founder of Bonsai, “Our successful project with Siemens represents a huge milestone in industrial AI, demonstrating the powerful results that can be achieved by combining machine teaching and machine learning. The beauty of this approach is that it balances the best of human and machine intelligence. Applied across the whole industrial manufacturing sector, the implications are staggering.”

Resources
Read the full Siemens case study: https://bons.ai/resources
Watch the video: https://bons.ai/blog/siemens-cnc-result
Get started with Bonsai: https://bons.ai/getting-started

About Bonsai
Bonsai offers an AI solution that empowers enterprises to build and deploy intelligent systems. By completely automating the management of complex machine learning libraries and algorithms, Bonsai enables enterprises to program AI models that improve system control and enhance real-time decision support. Businesses use these models today to increase automation and improve operational efficiency of industrial systems including robotics, manufacturing, supply chain, logistics, energy and utilities. Based in Berkeley, CA, Bonsai is backed by leading investors including NEA, Microsoft Ventures, ABB, Samsung NEXT and Siemens. To learn more, please visit: https://bons.ai/ or follow on Twitter @BonsaiAI.

About Siemens AG
Siemens AG (Berlin and Munich) is a global technology powerhouse that has stood for engineering excellence, innovation, quality, reliability and internationality for 170 years. The company is active around the globe, focusing on the areas of electrification, automation and digitalization. One of the world’s largest producers of energy-efficient, resource-saving technologies, Siemens is a leading supplier of efficient power generation and power transmission solutions and a pioneer in infrastructure solutions as well as automation, drive and software solutions for industry. With its publicly listed subsidiary Siemens Healthineers AG, the company is also a leading provider of medical imaging equipment – such as computed tomography and magnetic resonance imaging systems – and a leader in laboratory diagnostics as well as clinical IT. In fiscal 2017, which ended on September 30, 2017, Siemens generated revenue of €83.0 billion and net income of €6.2 billion. At the end of September 2017, the company had around 377,000 employees worldwide. Further information is available on the Internet at www.siemens.com.

Editor’s Note:
CNC milling machines are the most commonly used machine tools for shaping metal and other solid materials in cars, airplanes, household items, medical items, cellphones and countless other everyday items. Early milling machines were manually or mechanically automated. In today’s CNC machines, the functions formerly performed by human operators are now handled by a computer control module. Traditional CNC mills have three axes that control horizontal and vertical movements. Modern CNC mills, like the one used in Siemens’ project with Bonsai, have five or six axes, which make them capable of greater movement, including turning. These machines significantly improve production efficiency and reduce waste.

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