Hole-in-One Robots in Teaching and Metalforming
Shop Talk
Capturing this week's zeitgeist
“Manufacturing tolerances weren’t nearly as tight for golf balls [as they are today],” Parente said. “So [Spalding] had a robot down in Palm Desert — in the Palm Springs area — an old Iron Byron, and they told me they’d take 500 golf balls or 750 golf balls, at 110 mph, and they’d go out and take the balls that were plus or minus 30 yards greater right and left and discard them.
In celebration of Fanuc’s one-millionth robot and how far golf-ball manufacturing standards have come let’s celebrate with a robot hitting a hole-in-one!
Assembly Line
This week's most influential Industry 4.0 media.
Toyota Research Institute Unveils Breakthrough in Teaching Robots New Behaviors
The Toyota Research Institute (TRI) announced a breakthrough generative AI approach based on Diffusion Policy to quickly and confidently teach robots new, dexterous skills. This advancement significantly improves robot utility and is a step towards building “Large Behavior Models (LBMs)” for robots, analogous to the Large Language Models (LLMs) that have recently revolutionized conversational AI.
TRI has already taught robots more than 60 difficult, dexterous skills using the new approach, including pouring liquids, using tools, and manipulating deformable objects. These achievements were realized without writing a single line of new code; the only change was supplying the robot with new data. Building on this success, TRI has set an ambitious target of teaching hundreds of new skills by the end of the year and 1,000 by the end of 2024.
The treacherous path to trustworthy Generative AI for Industry
Despite the awesome first impact ChatGPT showed and the already significant efficiency gain programming copilots are delivering to developers as users2, making LLMs serve non-developers – the vast majority of the workforce, that is – by having LLMs translate from natural language prompts to API or database queries, expecting readily usable analytics outputs, is not quite so straightforward. Three primary challenges are:
- Inconsistency of prompts to completions (no deterministic reproducibility between LLM inputs and outputs)
- Nearly impossible to audit or explain LLM answers (once trained, LLMs are black boxes)
- Coverage gap on niche domain areas that typically matter most to enterprise users (LLMs are trained on large corpora of internet data, heavily biased towards more generalist topics)
ROBOFORMING: The Future of Metalworking?
It Takes Two: Why Digital Twins Need Both Humans and Machines
When Western Digital expanded its hard drive manufacturing site in Thailand, the first time the assembly lines were turned on wasn’t on the factory floor; it was on a laptop 8,000 miles away. Before any physical machinery found its place within the newly constructed walls, teams of engineers meticulously crafted its virtual counterpart. This digital twin could mimic the operations down to every tool, robot arm, and even the pace of human operators, flawlessly simulating the assembly of the company’s most advanced enterprise hard drives. Engineers could quickly test different layouts and operation scenarios without touching the production line.
For most projects, Sanguanpong could go into the factory and measure parameters like cycle times, yield, output, or level of automation. Here, she needed to extrapolate data from experts about machines and processes that had yet to materialize. “Because there is no physical operation in the building, we the advanced analytics team needed to validate our findings with the subject matter experts, making sure our simulation model fit the expected action,” she said. Data needed to constantly flow in and out of the model, relying not only on algorithms but on the capacities of human communication and imagination.
Your supplier’s supplier is not your supplier – Graph learning for transparency in deep-tier supply networks
Early graph neural networks have been applied to supply network data, but these are not as accurate as they could be because they focus only on supplier-buyer relationships and assume each company only produces one type of product. As a result, most companies with various types of products still lack visibility into risks involving deep-tier suppliers.
In this blog, we propose a graph representation learning method that models supply networks as heterogeneous graphs. The benefit of this model is that it can depict multiple relationships between companies and products, thus exposing the deep-tier supplier risk of companies with multiple products.
For companies within increasingly complicated supply networks, risk exposure extends far beyond direct suppliers. However, most companies lack risk transparency into their deep-tier supply networks because the supplier of their supplier is not their deep-tier supplier.
The FUTURE of engineering | Flow
It’s arrived: Commoditization for industrial process control
With the advent of industrial process-control commoditization has come technological advancements that have expanded the boundaries of modern manufacturing–right to the computing edge. Traditionally, administrators had to walk out to a control system–USB stick in hand–and apply an update manually. Today, thanks to the combined work of Intel Corporation, Schneider Electric, and Red Hat, manufacturers can enjoy an edge-ready, software-defined, industrial control system that relieves the burden of manual effort and runs on commodity hardware and a commodity operating system and uses commodity automation techniques.
Sanofi uses the industrial metaverse to revolutionize training and operational efficiency
Sanofi is a leader in the development and manufacturing of both prescription and over-the-counter medications. To meet an increased demand for Sanofi pharmaceuticals, the Paris-based organization needed to find a way to make its production lines more streamlined while training and onboarding more operators. Sanofi launched a pilot project in it packaging workshop in Lisieux, France to work in the metaverse using Microsoft HoloLens 2 with Microsoft Dynamics 365 Guides and Microsoft Dynamics 365 Remote Assist. The aim was to streamline employee training procedures, enhance operator efficiency, and integrate a digital approach within the manufacturing environment. Using HoloLens 2, Sanofi has reduced training time for new operators from eight weeks to six weeks. The organization is also using HoloLens 2 for remote maintenance and troubleshooting, enabling quicker resolution of equipment issues. With the success of the pilot, Sanofi is looking to expand the solution across its value chain, with a specific emphasis on data analysis.
AI camera steers ships away from collisions in fog and darkness
Japanese trading company Marubeni is partnering with Israeli startup Orca AI to equip ships with artificial intelligence cameras that aid visibility in fog or darkness to reduce collisions. Orca AI’s camera system, dubbed SeaPod, has accumulated over 20 million nautical miles worth of marine visual data – equivalent to 4,200 transpacific trips between Japan and Los Angeles. The AI analyzes the big data from the footage, as well as from other parameters such as weather-related delays, to improve situational awareness of potential collisions.
Industrial Policy
How governments are shaping the future industrial landscape.
🇮🇳 India plans over $2 billion in incentives for new manufacturing sectors
India is planning to offer incentives of up to 180 billion rupees ($2.2 billion) to spur local manufacturing in six new sectors including chemicals, shipping containers and inputs for vaccines, two government officials said. The proposal is part of the country’s 1.97-trillion-rupee production-linked incentive scheme (PLI), launched in 2020 which currently targets 14 sectors ranging from electronic products to drones, but has been successful only in a handful of them.
🇺🇸 Arizona governor says state in talks with TSMC on advanced packaging
Arizona is in talks with Taiwanese chipmaker TSMC on advanced packaging, Governor Katie Hobbs said on Tuesday, as the U.S. state seeks to attract more investment and address challenges that TSMC’s massive project has encountered there. TSMC is investing $40 billion to build two chip fabrication facilities, or fabs, in Arizona, supporting Washington’s plans to boost U.S. chipmaking capacity.
Business Transactions
This week's top funding events, acquisitions, and partnerships across industrial value chains
Dragos Raises an Additional $74M in Series D Round Extension
Dragos, Inc., the global leader in cybersecurity for industrial controls systems (ICS)/operational technology (OT) environments, announced a $74 million Series D extension, led by strategic operating and investing firm WestCap.
The Series D funding extension will bolster Dragos’s ability to make ICS/OT cybersecurity more accessible around the world. This year, Dragos has already expanded across Western Europe and the DACH region, building on its established presence in the UK. This summer, Dragos entered into an agreement with Macnica to provide Dragos’s cybersecurity solutions in Japan, signifying Dragos’s expansion in Asia-Pacific beyond its presence in Australia and New Zealand.
Lumafield Announces $35M Series B Funding, Major Product Upgrades, and New Board Appointments
Lumafield, a pioneering developer of accessible X-ray CT technology, today announced it has closed a $35M Series B funding round from new and existing investors, achieved a major new AI-driven performance breakthrough, and appointed two prominent executives to its board of directors. The company has raised a total of $67.5M to date, including a combined $32.5M in its Seed and Series A funding rounds. Spark Capital led the Series B round, which also included participation from existing investors Lux Capital, Kleiner Perkins, DCVC, and Future Shape.
Lumafield’s AI allows the company’s reconstruction process to achieve the same high-quality results with fewer two-dimensional X-ray images, reducing the time required to run a scan. New improvements in Lumafield’s software also make it possible to skip certain steps in the reconstruction process before performing automated analysis, which cuts processing time.
Austrian firm Metaloop raises a €16M Series A for scrap metal recycling software
The Austrian scrap metal marketplace, founded by Alexander Schlick and Jan Pannenbäcker, has just raised an oversubscribed Series A funding round of over €16 million. The funds will see the software firm source top talent to join its sales, recycling operations, product, data and development teams as it eyes further growth of its digital enterprise software platform.
The round was led by capital from the US as FirstMark Capital backed the metal recycling cause. The round also included participation by Silence VC and existing investors Statkraft Ventures and FJ Labs.
Boxbot Raises $12M Series A Led by Playground Global
Boxbot, the vertical automation solution that sorts and stores packages for last-mile carriers, announced today a $12 million Series A round, led by Playground Global. Maersk Growth (the venture arm of A.P. Moller), Toyota Ventures, Pear Ventures, and Artiman Ventures also participated in the round, which brings the company’s total funding to $29.5 million. The funds will be used to accelerate the company’s mission of enabling carriers to provide more efficient delivery experiences and bolster the team’s engineering and business operations. Playground Global Venture Partner Richard Peretz, former CFO at UPS, will join the company’s board of directors.
Boxbot transforms conveyors into intelligent, three-dimensional package handling systems. The platform can store, sort, and sequence size-agnostic, high throughput payloads while requiring a minimal physical footprint. Storage density is maximized by dynamically adapting the space required for each payload in real time. The system is designed for flexibility and can be easily installed in both new or existing facilities.
Treon secures €5.5 M Series A to accelerate expansion and innovation in IoT solutions
Tampere-based IoT startup Treon has successfully closed a €5.5 million Series A funding round led by Ventech, building on their initial seed investment in 2019. The startup specializes in providing intelligent edge products and scalable solutions for industrial, digital buildings, and logistics clients, facilitating data collection and analysis to enhance efficiency and decision-making. With plans to expand into the US, Denmark, and France, the funding will be used to strengthen sales and marketing efforts, enhance their product portfolio, and support their mission to become a global leader in massive IoT solutions.
Aspinity Raises $5 Million Series B Funding to Bring Breakthrough AI/ML Technology to a Global Market
Aspinity, the leader in near-zero power AI solutions, today announced that it has closed $5 million in Series B funding from current investors Anzu Partners, Birchmere Ventures, Mountain State Capital and Riverfront Ventures. Growing Aspinity’s total funding to more than $19 million, the round also includes new strategic investor and partner Unitrontech, a leading distributor of automotive semiconductors and key partner of leading automotive manufacturers.
Founded in 2015, Aspinity has taken a revolutionary new approach to power-conscious AI in the billions of devices that continuously analyze real-time sensor data. The company’s unique RAMP™ (Reconfigurable Analog Module Processor) technology platform merges the ultra-low power benefits of analog processing with the sophistication of machine learning and the versatility of software programmability.
LS ELECTRIC and Sight Machine Collaborate to Build AI-Based Big Data Analytics Platform for Manufacturing and Energy
LS ELECTRIC announced on September 18 that it has signed an MOU with Sight Machine to build an AI-based intelligent manufacturing and energy big data analysis platform at LS ELECTRIC Cheongju, Chungbuk, Korea. With this MOU, the two companies plan to conduct extensive collaboration in the field of integrated platforms aimed at the ESG market, including verification and analysis of manufacturing-energy big data connectivity using Sight Machine’s Manufacturing Data Platform, prediction of AI-based manufacturing process equipment control values, and construction of big data analysis platforms to help companies achieve smart manufacturing and power efficiency in the production process.