Big Tech eyes Industrial AI and Robotics


One of South Korea’s “Big Tech” players, SK Group, enters into the industrial AI market with the creation of Gauss Labs. Gauss Labs is targeting the semiconductor sector by aligning with SK hynix, a leader in DRAM chips. The hope is that if they can bring improvements to the complex semiconductor sector they can scale quickly to other manufacturing verticals. But what really caught my eye is was the response on a question about recruiting talent to Gauss Labs, “There’s no Google, Microsoft, or Amazon in the industrial AI area. It will be a tough journey, but the opportunity to become the world’s top player exists.” While no one has become the top player yet, all of the American Big Tech players have been investing heavily into industrial AI technologies.

Let’s start with Google. Google’s parent company, Alphabet, launched a new industrial robotics company, Intrinsic, just this week! This should come as no surprise as Google’s strength in AI, particularly reinforcement learning, has been applied to robotics to manipulate objects such as those in an eCommerce fulfillment center or a manufacturing facility. In the cloud, Google Cloud launched a visual inspection AI service for “faster, more accurate quality control” last month. Google is playing to their strengths in robotics and deep learning as they approach the industrial space.

Amazon and Amazon Web Services (AWS) are major players in the Industrial Internet of Things (IIoT) and robotics. For Amazon’s fulfillment operations, robotics are key to scaling economically, and they recently showcased Robin, “one of the most complex stationary robot arm systems Amazon has ever built, brings many core technologies to new levels and acts as a glimpse into the possibilities of combining vision, manipulation and machine learning.” AWS complements their robotics strategy with reinforcement learning products of their own, SageMaker and RoboMaker. Also with AWS, they seek to get manufacturers embedded into their ecosystem with numerous data acquisition (AWS IoT), transformation, and applications services such as Looker for predictive maintenance and IoT SiteWise Edge for processing industrial equipment data on premises. Amazon is applying industrial AI into their fulfillment operations and then releasing it back out as a service through AWS to the rest of industry.

Microsoft is leveraging their unique position as a dominant platform for large enterprises to extended their traditional information technology (IT) and operational technology (OT) products to the cloud and into the factory. They recently launched the Microsoft Cloud for Manufacturing that builds on top of open standards: Open Manufacturing Platform, the OPC Foundation, and the Digital Twins Consortium. They are also partnering with emerging start-ups in the industrial AI space to enhance their core business offerings and value propositions to existing IT and OT customers. Like Google and Amazon, Microsoft is pursuing a strategy that works for their unique position in the market.

A new entrant into Big Tech, NVIDIA, is using their position as a leader in graphics processing unit (GPU) technology to bring high performance computing (HPC) and AI applications to industry. Their Jetson familty of products is the leading-edge for deployment of trained AI to the point of impact like picking robots or inspection machines. They partner with emerging AI startups with their NVIDIA Inception program to get new industrial AI technologies to market faster. Lastly, they offer a robust set of developer tools for deep learning. Overall, NVIDIA continues to grow from their hardware foundation in GPUs up the technology stack into the software space to bring AI applications to market.

Other big tech players are more quiet about their in-roads with industrial AI. Apple has struggled with automation for their products. Facebook open sourced a robotics development platform this week, but it does not have any known applications within manufacturing or industry.

Ultimately, Big Tech is heavily involved with industrial AI, but no one has a dominant position in the market. Exponential Industry will keep you up-to-date with the revolution. Subscribe below:

Visual Inspection

Manufacture Plastic Bottles. Now Imagine Manufacturing 20,000 in an hour

Acoustic Monitoring

Assembly Line

3D Vision Technology Advances to Keep Pace With Bin Picking Challenges

📅 Date:

✍️ Author: Jimmy Carroll

🔖 Topics: machine vision, convolutional neural network

🏢 Organizations: Zivid, CapSen Robotics, IDS Imaging Development Systems, Photoneo, Universal Robots, Allied Moulded

When a bin has one type of object with a fixed shape, bin picking is straightforward, as CAD models can easily recognize and localize individual items. But randomly positioned objects can overlap or become entangled, presenting one of the greatest challenges in bin picking. Identifying objects with varying shapes, sizes, colors, and materials poses an even larger challenge, but by deploying deep learning algorithms, it is possible to find and match objects that do not conform to one single geometrical description but belong to a general class defined by examples, according to Andrea Pufflerova, Public Relations Specialist at Photoneo.

“A well-trained convolutional neural network (CNN) can recognize and classify mixed and new types of objects that it has never come across before,”

Read more at A3

Introducing Intrinsic

📅 Date:

✍️ Author: Wendy Tan-White

🔖 Topics: robotics

🏢 Organizations: Google, Alphabet, Intrinsic

Intrinsic is working to unlock the creative and economic potential of industrial robotics for millions more businesses, entrepreneurs, and developers. We’re developing software tools designed to make industrial robots (which are used to make everything from solar panels to cars) easier to use, less costly and more flexible, so that more people can use them to make new products, businesses and services.

Read more at Medium

Accelerating the Design of Automotive Catalyst Products Using Machine Learning

📅 Date:

✍️ Authors: Tom Whitehead, Flora Chen, Christopher Daly, Gareth Conduit

🔖 Topics: generative design, machine learning

🏭 Vertical: Automotive

🏢 Organizations: Intellegens, Johnson Matthey

The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation.

Read more at Ingenta Connect

How Companies Oversee IoT Device Management

📅 Date:

✍️ Author: Tammy Xu

🔖 Topics: IIoT

🏢 Organizations: MxD, FourKites, Industrial Internet Consortium, SOTI

Companies have been using Internet of Things (IoT) devices for a long time, from agricultural companies monitoring weather and crop conditions to industrial companies tracking the output and safety within manufacturing facilities.

IoT can accelerate processes by giving companies real-time data and visibility, but having a lot of devices can be a maintenance and security headache. In those cases, it’s even more important to have the right procedures in place for IoT management.

Read more at Built In

How and Why Pharmaceutical Manufacturers Are Applying Artificial Intelligence

📅 Date:

✍️ Author: David Greenfield

🔖 Topics: quality assurance, predictive maintenance

🏭 Vertical: Pharmaceutical

🏢 Organizations: AspenTech

“Opportunities to reduce manufacturing costs exist across all stages of the product lifecycle. Advanced analytics can reveal those opportunities, allowing pharma companies to take informed action to save money,” said Richard Porter, global director, pharmaceuticals, at AspenTech. “Whether using multivariate analytics to identify process degradation and its impact on quality or predicting final product quality to reduce lab testing lag times, these techniques offer pharmaceutical companies a competitive advantage.”

A purified water system at a pharmaceutical manufacturing facility.“The company tried to avoid batch losses—with each batch valued between $250,000-$300,000—as frequent shutdowns to replace the seals limited capacity,” said Porter. “As the company needed to ramp up capacity, it purchased two additional mills. Adopting Aspen Mtell, which connects to OPC UA supported devices, for predictive maintenance allowed the company to reduce supply chain disruptions from seal replacements and cut lifecycle maintenance costs by 60%. In addition, the company reduced capital expenditures and associated lifecycle maintenance costs by 50%.”

Read more at Automation World

Davey Textiles Shows Digital Transformation Can Be Affordable and Effective

📅 Date:

✍️ Author: Michael Guilfoyle

🔖 Topics: digital transformation, IIoT

🏭 Vertical: Textiles

🏢 Organizations: Davey Textile, Uptake

If something interrupted operations, the Uptake Fusion’s Downtime Tracker sent an alert to the operator. Due to the noise levels on the floor, the solution sent the alert via Twitter, ensuring operators could be notified directly through their hearing protection devices.

The company could also now visualize production data to examine trends and anomalies for products, days, shifts, equipment, room locations, and other key variables. They now had new insight into causes of lost production, enabling them to eliminate issues that undermined operational optimization. Uptake Fusion also managed all of this using a single-pane view, minimizing user complexity.

Read more at Arc Advisory Group

Optimizing manufacturing processing and quality management with digital twins, IIoT

📅 Date:

🔖 Topics: Digital Twin, IIoT, Quality Management System

🏭 Vertical: Primary Metal

🏢 Organizations: Industrial Internet Consortium

The application of IIoT and digital twin technologies in production process and quality management in steel production processes with the following characteristics:

  • Integrate process design data, quality specification data, equipment operational real time data, quality measurement data into a holistic end-to-end closed-loop system, enabling comprehensive online monitoring and analytics of production process and supporting product quality traceability.
  • Combine digital twin and Industrial Internet technology seamlessly into a holistic platform to support such an application.
  • Enable digital twin for both equipment and product alike, dynamically bind product digital twins with equipment digital twins to enabling product process and quality online tracking, monitoring and traceability.
  • Combine online data and analytic technologies with Lean management and Six Sigma concepts and best practice for production process and quality management, creating a digital Lean capability.

Read more at Plant Engineering

The Cost of Unplanned Downtime for Refineries

📅 Date:

🔖 Topics: predictive maintenance, machine health

🏭 Vertical: Petroleum and Coal

🏢 Organizations: Gecko Robotics

According to the American Institute of Chemical Engineers (AlChE), the cost of missed production for a U.S. refinery with an average-sized fluid catalytic cracking unit of 80,000 barrels per day will range from $340,000 a day at profit margins of $5 per barrel, to $1.7 million a day at profit margins of $25 per barrel, based on a conservative estimate. A single, unplanned shutdown that lasts hours can lead to the release of a year’s worth of emissions into the atmosphere, according to John Hague, Aspen Technology Inc.

One type of innovative inspection process is Rapid Ultrasonic Gridding (aka RUG), which creates data-rich visual grid maps that identify areas of corrosion and other damage mechanisms. It is 10 times faster than traditional gridding and competing methods. In most situations, the operator can quickly make the decision of whether to proceed with maintenance measures to resolve the issue, or to return the inspected asset to operation.

Read more at Gecko Robotics Blog

AWS IoT SiteWise Edge Is Now Generally Available for Processing Industrial Equipment Data on Premises

📅 Date:

🔖 Topics: manufacturing analytics, edge computing

🏢 Organizations: AWS

With AWS IoT SiteWise Edge, you can organize and process your equipment data in the on-premises SiteWise gateway using AWS IoT SiteWise asset models. You can then read the equipment data locally from the gateway using the same application programming interfaces (APIs) that you use with AWS IoT SiteWise in the cloud. For example, you can compute metrics such as Overall Equipment Effectiveness (OEE) locally for use in a production-line monitoring dashboard on the factory floor.

Read more at AWS News Blog

Robotic 3D manufacturing providing greater flexibility

📅 Date:

✍️ Author: Tanya Anandan

🔖 Topics: additive manufacturing, robotics

🏢 Organizations: Lincoln Electric, MX3D, ABB

Robots are extending their reach. These multiaxis articulators are taking 3D manufacturing and fabrication to new heights, new part designs, greater complexity and production efficiencies. Integrated with systems to extend their reach even further, their flexibility is unmatched. Robots are virtually defying gravity in additive manufacturing (AM), tackle complex geometries in cutting, and collaborate with humans to improve efficiencies in composite layup. This is the future of 3D.

3D printing is already a multibillion-dollar industry, with much of the activity focused on building prototypes or small parts made from plastics and polymers. For metal parts, one additive process garnering lots of attention is robotic wire arc additive manufacturing (WAAM).

Read more at Plant Engineering

Aiming for the Top in Industrial AI, SK’s First AI Company Gauss Labs

📅 Date:

🔖 Topics: metrology

🏭 Vertical: Semiconductor

🏢 Organizations: Gauss Labs, SK hynix

Gauss Labs has been developing AI solutions aimed at maximizing production efficiency using the massive amount of data generated at SK hynix’s production sites. SK hynix wishes to make the overall semiconductor production process more intelligent and optimized across all procedures including process management, yield prediction, equipment repair and maintenance, materials measurement, and defect testing and prevention.

Read more at Sk hynix Newsroom