Breaking Disruption with Robot Agents

LLMs provide plain language explanations to instruction manuals and complex system faults. GenAI makes it easy to instruct robots after collecting a bit more data. Building the language of the yard.

Aug 25, 2024

Shop Talk

Capturing this week's zeitgeist

According to job listings, Tesla is hiring “data collection operators” to gather movement information and provide equipment feedback on their Optimus robots. Unitree’s G1 humanoid has been upgraded into a mass-production version and is ready for the 2028 Breaking competition.

Kaizen Blitz

🚦 Andon Status
- 🟢 CW Fletcher has installed 1,676 high-spec solar panels while GM announced a 15-year renewable energy purchase agreement with NorthStar Clean Energy to bolster each company’s green ambitions and drive cost savings.
- 🟡 How China Is Becoming a Money Pit for Foreign Automakers and Stellantis delays plans for Illinois assembly plant.
- 🚨 Deere reveals deep cuts to production and payrolls to avoid prolonged pain from market slump.
📊 Survey Says
- Dassault Systèmes, Siemens, and ABB lead the Robotics Offline Programming Software Competitive Ranking.
- Virtual industrial monitoring patents surge reflects mining sector growing emphasis on compliance, says GlobalData.
- ⛓️💰 Supply chain has become a staple in the venture world, representing ~15-20% of total VC activity globally according to The Kearney COO Innovation Radar.
🗣️ Town Hall
- 🦾 Teradyne Robotics president discusses how the industry is transforming
- 🖨️ Hamid Zarringhalam, Nikon Advanced Manufacturing CEO, talks 3D printing strategy, challenges, growth and parallels with the semiconductor industry.
- 🛩️ Lilium CEO: How 29 years at Airbus guides his vision for the eVTOL frontrunner
- 👨‍👩‍👧‍👦 The multigenerational leadership style behind Altech Corporation is built on listening to the market.
🏆 Golden Part
- 🚙 SES AI’s 100 Ah Li-Metal Is First to Successfully Pass the Global Electric Vehicle EV Safety Standard Test GB38031-2020.
- 🚙 Toyota to test Tesla-style EV gigacasting machine from Ube Machinery at Japan plant.
🏭💰 Production Planning
- Endress+Hauser invests €100M in the Campus 2030+ project at its oldest production site.
🏛️📜 Industrial Policy
- 🇩🇪 EU Commission approves €5B for German TSMC fab
- 🇺🇸 🇿🇲 AI Enters the Critical Mineral Race
- 🇮🇳 Foxconn meets Karnataka govt for potential investment talks while it builds large iPhone assembly plant

Assembly Line

This week's most influential Industry 4.0 media.

💬🦾 Can AI Deliver Fully Automated Factories?

✍️ Authors: Daniel Kuepper, Leonid Zhukov, Namrata Rajagopal, Yannick Bastubbe
🏢 Organizations: Boston Consulting Group

Manufacturers must prepare for the inevitable disruption — or risk falling behind.

Our client chose to adopt a “redesign for automation” approach for its process, products, and layout. This complete overhaul of factory operations added new process steps to improve automation feasibility while removing human-oriented process inefficiencies. For example, our client no longer had to sacrifice valuable floor space for storing inventory that humans can see and reach. Instead, they built second-story vertical storage areas that robots can easily access and navigate. With the freed-up space, they installed more machines to increase output by more than 30%.

Programming and integration is 50 to 70% of the cost of a robotic application. Generative AI interfaces are expected to significantly drive this cost down by providing natural language interface for even non-technical workers to instruct robots. The transformation would be drastic: Instead of one specialized worker for every eight robots, the factory would only require one non-specialized worker for every 25 robots. Industry applications have already emerged. For example, Sereact has already rolled out a voice or text command interface, PickGPT, to interact with robots using simple instructions such as “I need to pack the order.”

Read more at Harvard Business Review

📑 Could reading instruction manuals become a thing of the past?

✍️ Authors: Michael Dempsey, Will Smale
🔖 Topics: Large Language Model, Generative AI
🏢 Organizations: Aveva, Dozuki, SCG Chemicals

Simon Bennett, Aveva’s head of AI innovation, says the AI can locate where there has been, say, a power failure. It then delves into “a monster PDF manual”. From this, the AI - via a computer screen - generates different ideas of what the problem might be. It can also produce a 3D image of the affected machinery, such as a turbine, with Mr Bennett noting that engineers appreciate such visual responses to their questions.

Dozuki’s AI-powered system CreatorPro can automatically create a user guide based on an engineer making a video of him or her talking through and carrying out a process. “The user uploads the video, and a step-by-step instruction guide is automatically created,” says Allen Yeung, Dozuki’s vice president of product. “The AI chooses the text that accompanies each step, and it can automatically translate that into other languages.”

🧑‍🔧 Why Airlines Like American Are Scrambling To Make Engines Last Longer

📑 MIT researchers use large language models to flag problems in complex systems

✍️ Authors: Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille
🔖 Topics: Anomaly Detection
🏢 Organizations: MIT

In a new study, MIT researchers found that large language models (LLMs) hold the potential to be more efficient anomaly detectors for time-series data. Importantly, these pretrained models can be deployed right out of the box.

The researchers developed a framework, called SigLLM, which includes a component that converts time-series data into text-based inputs an LLM can process. A user can feed these prepared data to the model and ask it to start identifying anomalies. The LLM can also be used to forecast future time-series data points as part of an anomaly detection pipeline.

While LLMs could not beat state-of-the-art deep learning models at anomaly detection, they did perform as well as some other AI approaches. If researchers can improve the performance of LLMs, this framework could help technicians flag potential problems in equipment like heavy machinery or satellites before they occur, without the need to train an expensive deep-learning model.

In the future, an LLM may also be able to provide plain language explanations with its predictions, so an operator could be better able to understand why an LLM identified a certain data point as anomalous.

🖨️ Ford harnesses Formlabs SLA & SLS 3D printing technology to prototype Electric Explorer vehicle parts

✍️ Author: Sam Davies
🔖 Topics: Additive Manufacturing
🏭 Vertical: Automotive
🏢 Organizations: Ford, Formlabs

Ford was one of the first beta users of Formlabs’ Form 4, deploying the technology at its Ford Cologne facilities in Germany, where its engineers also have access to a Form 3L and Fuse 1+ 30W machine.

Among the parts to be prototyped with Formlabs 3D printing technology are a complex charging port, a cover for the charging port, a rearview mirror assembly, dashboard parts and exterior features. The company also 3D printed insert moulds for the injection moulding of two rubber components, required in the door handle design for their damping and insulation capabilities.

Having achieved this success with prototyping, Ford engineers also sought to combine the capabilities of 3D printing with injection moulding to produce crash test parts. These components must be made from the same material and process as in mass production, meaning the parts were always going to be manufactured with injection moulding. Ford saw the potential, however, in leveraging 3D printing for rapid tooling, producing the mould inserts for the rubber door handle assembly parts in weeks rather than months.

🦾 Fast and Accurate Relative Motion Tracking for Dual Industrial Robots

✍️ Authors: Honglu He, Chen-lung Lu, Glenn Saunders
🔖 Topics: Industrial Robot
🏢 Organizations: Rensselaer Polytechnic Institute, GE Aerospace

Industrial robotic applications such as spraying, welding, and additive manufacturing frequently require fast, accurate, and uniform motion along a 3D spatial curve. To increase process throughput, some manufacturers propose a dual-robot setup to overcome the speed limitation of a single robot. Industrial robot motion is programmed through waypoints connected by motion primitives (Cartesian linear and circular paths and linear joint paths at constant Cartesian speed). The actual robot motion is affected by the blending between these motion primitives and the pose of the robot (an outstretched/near-singularity pose tends to have larger path tracking errors). Choosing the waypoints and the speed along each motion segment to achieve the performance requirement is challenging. At present, there is no automated solution, and laborious manual tuning by robot experts is needed to approach the desired performance.

In this paper, we present a systematic three-step approach to designing and programming a dual robot system to optimize system performance. The first step is to select the relative placement between the two robots based on the specified relative motion path. The second step is to select the relative waypoints and the motion primitives. The final step is to update the waypoints iteratively based on the actual measured relative motion. Waypoint iteration is first executed in simulation and then completed using the actual robots. For performance assessment, we use the mean path speed subject to the relative position and orientation constraints and the path speed uniformity constraint. We have demonstrated the effectiveness of this method on two systems, a physical testbed of two ABB robots and a simulation testbed of two FANUC robots, for two challenging test curves.

New Product Introduction

Highlighting new and innovative facilities, processes, products, and services

Herøya: Line 2 is now operational

🏢 Organizations: Nel Hydrogen

Nel’s second production line at the Herøya factory in Norway, with a 500 MW capacity, is now fully operational. With this, we have an annual production capacity of 1 GW in Norway, making our facility at Herøya one of the world’s largest automated electrolyser manufacturing facilities.

🚛 Ryder and Terminal Digitize Yard achieve 99% accuracy with AI computer-vision

🏢 Organizations: Ryder System, Terminal Industries

Ryder System, Inc., a leader in supply chain, dedicated transportation, and fleet management solutions, and Terminal Industries, which develops artificial intelligence (AI) platforms to digitize yard operations, announce the first successful pilot program leveraging Terminal’s computer-vision technology to automatically index and analyze trucks and trailers flowing in and out of the warehouse yard. Since January, the ongoing pilot at a Ryder e-commerce fulfillment center in City of Industry, California, has processed more than 10,000 truck detections, achieving 99% accuracy in capturing license plates and Department of Transportation (DOT) numbers.

“We are building the language of the yard,” says Max Constant, CEO of Terminal. “Ryder’s data is a gold mine for training. Most people believe more data is the answer, but it’s really about having the right data – the noisy, real-world data. That’s a major key to developing our core technology, so we can leverage this powerful tool – not just for one use case but across a broad range of applications to optimize yard operations in ways not yet evident in the industry.”

Read more at American Journal of Transportation

Business Transactions

This week's top funding events, acquisitions, and partnerships across industrial value chains.

🇺🇸 Fortera Secures $85M to Accelerate the Global Deployment of Low-Carbon Cement Production

🔖 Topics: Funding Event
🏢 Organizations: Fortera, Khosla Ventures, Temasek

To meet the growing demand to lower the cement industry’s carbon emissions, advanced materials manufacturer Fortera secured $85 million in Series C funding to scale the deployment of its low-carbon cement technology that integrates with existing infrastructure. The round included participation from previous investors Khosla Ventures and Temasek, and first-time investments from Wollemi Capital, NOVA by Saint-Gobain, Presidio Ventures, and Alumni Ventures. With operations at the company’s Redding ReCarb Plant underway, Fortera is positioned to move forward with additional plants that will produce ReAct® green cement, which has 70% less carbon dioxide (CO2) per ton than ordinary cement.

Fortera’s ReCarb process bolts onto existing cement manufacturing plants, captures the industrial CO2 emissions from traditional cement production, and converts it to mineral form to achieve a ready-to-use low-carbon cement. Since the company’s process integrates into established infrastructure, including feedstocks, capital investments, logistics, and sales networks, the path to wide-scale commercialization is shorter and more cost-effective. Fortera’s ReCarb technology operates at a significantly reduced kiln temperature and is compatible with renewable energy integration, which would further reduce emissions and enable zero CO2 cement production.

🇬🇧 BeyondMath Raises $8.5 Million to Revolutionize Physics-Based Engineering with Groundbreaking AI-Powered Simulation Platform

🏢 Organizations: BeyondMath, UP Partners

BeyondMath, a leader in advanced engineering simulation, announced it has secured $8.5 million in seed funding led by UP.Partners, with significant participation from Insight Partners and InMotion Ventures, the investment arm of JLR. This funding supports BeyondMath’s ambitious mission to reshape engineering practices globally with its AI-driven multiphysics simulation platform, which accelerates engineering iterations by a factor of 1,000 compared to current solutions.

To help achieve this mission, BeyondMath is among the first to adopt an NVIDIA DGX H200 system to enhance the capabilities of its platform. DGX H200 systems provide advanced AI supercomputing, allowing BeyondMath to train its physics solver at industrial scales and helping it deliver even more groundbreaking solutions to its customers.

🇨🇭 Apheros secures $1.85M to cool down data centers, using high performance cooling systems

🏢 Organizations: Apheros, Founderful, ETH Zurich

Data centers are the backbone of the digital age, with unprecedented demand for digital infrastructure driven by the surge in the use of AI, machine learning, and supercomputing. However, their energy consumption is skyrocketing. By 2030, an estimated six percent of global energy consumption will be used specifically for cooling data centers. A shift from traditional cooling methods to more cost- and energy efficient liquid-based solutions is inevitable. Enabling this transition, deep tech startup Apheros is announcing a $1.85m funding round seizing this critical moment to introduce its innovative metal foam technology, offering a superior solution to this pressing industry challenge.

The pre-seed funding round, led by venture capital firm Founderful, will accelerate development and deployment of Apheros’ revolutionary metal foam-based cooling solutions.

The Apheros patented manufacturing process creates unique foam structures with completely open porosity and unparalleled surface area, surpassing traditional solutions by a factor of thousand, which translates into exceptional heat transfer and flow properties. Ideal for high performance cooling applications, Apheros’ metal foams are easily integrated within its customers’ existing cooling systems. They address customers’ urgent needs of reduced energy consumption and cooling costs.

AMD to Significantly Expand Data Center AI Systems Capabilities with Acquisition of Hyperscale Solutions Provider ZT Systems

🔖 Topics: Acquisition
🏢 Organizations: AMD, ZT Systems

AMD (NASDAQ: AMD) announced the signing of a definitive agreement to acquire ZT Systems, a leading provider of AI infrastructure for the world’s largest hyperscale computing companies. The strategic transaction marks the next major step in AMD’s AI strategy to deliver leadership AI training and inferencing solutions based on innovating across silicon, software and systems. ZT Systems’ extensive experience designing and optimizing cloud computing solutions will also help cloud and enterprise customers significantly accelerate the deployment of AMD-powered AI infrastructure at scale.

AMD has agreed to acquire ZT Systems in a cash and stock transaction valued at $4.9 billion, inclusive of a contingent payment of up to $400 million based on certain post-closing milestones. AMD expects the transaction to be accretive on a non-GAAP basis by the end of 2025.

Following transaction close, ZT Systems will join the AMD Data Center Solutions Business Group. ZT CEO Frank Zhang will lead the manufacturing business and ZT President Doug Huang will lead the design and customer enablement teams, both reporting to AMD Executive Vice President and General Manager Forrest Norrod. AMD will seek a strategic partner to acquire ZT Systems’ industry-leading U.S.-based data center infrastructure manufacturing business.

Automated Industrial Robotics Inc. Acquires Sewtec Automation

🏢 Organizations: Automated Industrial Robotics, Sewtec Automation

Automated Industrial Robotics Inc. announced the acquisition of Sewtec Automation, a leading industrial automation company based in the United Kingdom. The transaction expands AIR’s geographic footprint, strengthens its engineering capabilities and further positions the Company to capitalize on the increasing global demand for manufacturing automation solutions across a diversified customer base. The transaction was funded primarily by an additional investment from an Ares Management Private Equity fund.

Sewtec joins Totally Automated Systems and Modular Automation as a foundational asset of the AIR portfolio. With the acquisition of Sewtec, AIR now has over 400 employees and an automation hub footprint of approximately 275,000 square feet across the United States, Ireland and the United Kingdom. By leveraging the significant engineering experience and capabilities in each of these hubs, AIR is advancing its goal to seamlessly deliver differentiated industrial automation solutions and service to its global customer base. With Ares’ support, AIR expects to seek to further expand its platform through future strategic acquisitions of industrial automation companies with strong operational histories and tenured management teams, in addition to continued investment in its organic growth strategy.

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