Mixed Screw Assembly Floor Plan
Shop Talk
Capturing this week's zeitgeist
Button marvels at Gitlin’s love for mingling among the folks wielding torque wrenches and screwdrivers to fashion AC units and chillers. “He goes to all 47 plants at least once a year, and spends as much time on the floor as he does meeting with managers and customers,” says Button. Gitlin, notes the operations head, will stand by a station to check if a welder, say, is meeting the “Takt Time” allotted for a given task. “You can tell so much from being on the factory floor how efficiently a plant is running, what ‘good’ looks like,’” adds Button. “Dave knows what ‘good’ looks like, and lets you know if it’s happening.”
From “He turned down the chance to run Boeing—now he’s the best industrial CEO in America” - Fortune
To solve manufacturing problems there is no replacement for time on the floor observing processes firsthand, talking with operators, and seeing how systems function in practice. The most elegant solutions often emerge from understanding the subtle details and daily challenges that only become apparent when you’re physically present where the work happens. So, when your $45 screw catches the outrage of Twitter you know exactly why that came to be.
Elon Musk has overcome decades of complacency in aerospace and automotive, by often exclaiming, “The best part is no part” and “The best process is no process.” Those opportunities cannot be identified without continuously walking the gemba.
Assembly Line
This week's most influential Industry 4.0 media.
Mixed-Model Lines Enable Multiple Power Train Configurations
Mixed-model automotive assembly plants must have the workstations, tools and components necessary to efficiently build hybrids in the same facility as electric and internal combustion engine (ICE) vehicles. However, assembling cars with different power trains is much harder than making multiple types of pens, syringes or toothbrushes on the same assembly line. Mixed-model assembly lines typically also need to be laid out differently than lines that only produce one type of vehicle.
“Mixed-model assembly is used regularly across industries, most notably the discrete electronics industry where products such as smart phones with multiple form factors and internal configurations are manufactured,” notes Khalid Sebti, executive vice president and managing director of Capgemini Engineering. “Use in the auto industry is different, but not necessarily more complex, due to designs shifting to multi-use platforms to accommodate different power train options.
One complex problem that Porsche had to solve involved fastening. The marriage of a gas or hybrid vehicle requires the underbody and chassis to be screwed together in 20 places. However, in the all-electric variant, there are 50 joints that need to be tightened. Porsche engineers developed an automatic screw loading system that, depending on the product line and fittings, can handle any screw size and shape, torque and angle at high speeds. A measuring device regularly passes along the assembly line to check the screw spindles during ongoing production operations. As a result, there are virtually no idle times or delays.
Foxconn Expands Blackwell Testing and Production With New Factories in U.S., Mexico and Taiwan
To meet demand for Blackwell, now in full production, Foxconn, the world’s largest electronics manufacturer, is using NVIDIA Omniverse. The platform for developing industrial AI simulation applications is helping bring facilities in the U.S., Mexico and Taiwan online faster than ever.
In the construction process, the Foxconn teams use the Omniverse digital twin as the source of truth to communicate and validate the accurate layout and placement of equipment. Virtual integration on Omniverse offers significant advantages, potentially saving factory planners millions by reducing costly change orders in real-world operations.
Mastering Robotic Arm Design: Efficient Leak Detection & More
AI robots work together to perform autonomous synthesis and analysis
Two AI-driven mobile robots have been programmed to autonomously and cooperatively perform and analyse chemical reactions. A team led by Andrew Cooper, at the University of Liverpool, UK, has developed a workflow incorporating an advanced AI decision-making model that interprets data from multiple analytical tools to improve experimental design. The researchers behind the work believe it offers the potential for rapid discoveries in chemical manufacturing and drug discovery research.
Cooper’s team has now expanded the capabilities of their system to tackle three challenges in exploratory chemistry: conducting reactions, product analysis and data-driven decision making. They constructed a seamless synthesis to characterisation workflow using 1.75m tall mobile robots to operate a Chemspeed ISynth synthesis module, an ultrahigh-performance liquid chromatography-mass spectrometer (UPLC-MS) and a benchtop nuclear magnetic resonance (NMR) spectrometer. Notably, the robots are collaborative, meaning they can work alongside humans. ‘Labs are designed to be used by humans and if you hardwire things into a fixed workflow then it makes human cohabitation difficult. Here we have these instruments in the lab and students can use them while the robot isn’t,’ adds Cooper.
New Product Introduction
Highlighting new and innovative facilities, processes, products, and services
Hands free tool changes for smarter machining
Using conventional tool management methods, a lathe could run without human input for around four-to-eight hours. After that, the tool would reach the end of its life and a human worker would need to stop what they’re doing to manually switch the tool with a new replacement. With the ATC solution, machines can run for up to 16 hours without an operator worrying about them.
After using ATC technology for several years, Sandvik Coromant has now developed a solution for its customers that enables hands-free tool changes in turning operations. The new ATC system for machine-adapted clamping units (MACU) in turning centers fully automates the tool-changing process, eliminating the need for manual intervention. This leads to higher machine utilisation and improved productivity.
The immediate benefit is the time it saves human workers. Running machines for up to 16 hours — four times longer than before — without the need for operator intervention allows operators to focus on other tasks, rather than being occupied with constant tool wear inspections.
ATC also helps optimise tool wear. When operators manually change tools while juggling multiple other tasks, they often replace tools prematurely, before they’ve reached their full potential. With ATC, tools are changed only when they’re fully worn, maximising tool life and reducing waste. ATC also delivers significant sustainability benefits, including lower energy consumption, enhanced resource efficiency reduced waste and improved overall equipment efficiency. By keeping machines running at optimal levels, ATC minimises idle time and energy waste, increases output with fewer resources and reduces the frequency of breakdowns and downtime.
C-Hawk Technology Pioneers Robotic Plastic Welding with New Roberto™ Platform for Semiconductor Equipment Manufacturing
C-Hawk Technology, a global leader in the manufacturing of components, assemblies and processes for the semiconductor and healthcare industries, announced its new patent-pending Roberto™ platform, an integrated robotics system that automates plastic welding and sets new benchmarks for quality, precision, scalability and safety in manufacturing for plastic welded assemblies and subassemblies. Early performance data shows that Roberto outperforms manual welding with a 25% increase in production speed and product consistency, a 30% improvement in output efficiency and a 50% decrease in error rates.
C-Hawk is the first manufacturer in the semiconductor capital equipment supply base to implement an integrated robotics solution designed explicitly for plastic welding. Its robotics solutions are tailored to its manufacturing process, offering better customization and integration with existing operations. Further, the technology is location agnostic and can be deployed to any geography, supporting local manufacturing close to customer sites. Leveraging robotics allows C-Hawk’s customers to benefit from faster lead times, consistent quality in high-precision tasks and reduced production costs, helping them meet higher demand without sacrificing quality.
Hexagon takes the guesswork out of 3D printing precision metal components with Advanced Compensation technology
Hexagon’s Manufacturing Intelligence division has unveiled new capabilities in 3D printing with a new technology that eliminates costly trial-and-error in precision metal part production. The innovative Advanced Compensation approach uses advanced geometry compensation to ensure large or complex metal parts distort into their intended shape during the printing process. By combining process simulation and 3D scan compensation, the most challenging parts with tight quality tolerances can be printed successfully with just one prototype build.
3D printer OEM Additive Industries was able to print a stainless-steel jet engine exhaust mixer with 0.2mm precision with just one prototype build. Alex Redwood, Head of Applications & Additive Studios at Additive Industries explains: “Thanks to Advanced Compensation, we successfully printed a large 316L steel component with an impressive surface tolerance of +/-0.2 mm – despite natural distortion of over 3 mm in previous builds. Achieving this precision required only one previous trial build, reducing time, material waste and allowed us to reduce the supports structure to the bare minimum. This level of control and efficiency opens up new possibilities for large-scale additive manufacturing applications.”
Sony Semiconductor Solutions to Release an Industrial CMOS Image Sensor with Global Shutter for High-Speed Processing and High Pixel Count
Sony Semiconductor Solutions Corporation (SSS) announced the upcoming release of the IMX925 stacked CMOS image sensor with back-illuminated pixel structure and global shutter. This new product offers 394 fps high-speed processing and a high, 24.55-effective-megapixel*1 count and is optimized for industrial equipment imaging.
With factory automation progressing, demand continues to grow for machine vision cameras capable of fast, high-quality imaging for a variety of objects in the industrial equipment domain. By employing a global shutter capable of capturing moving subjects free of distortion together with a proprietary back-illuminated pixel structure, SSS’s global-shutter CMOS image sensors deliver superb pixel characteristics, including high sensitivity and saturation capacity. They are mainly being used to recognize and inspect precision components such as electronic devices.
Business Transactions
This week's top funding events, acquisitions, and partnerships across industrial value chains.
Pickle Robot Closes $50 Million Series B Funding and Secures New Orders for 30+ Unload Robots
Pickle Robot Company, a pioneer in Physical AI delivering robotic automation systems that unload trucks, announced it has raised $50 million in series B funding with participation from a strategic customer, Teradyne Robotics Ventures, Toyota Ventures, Ranpak, Third Kind Venture Capital, One Madison Group, Hyperplane, Catapult Ventures, and others.
Additionally, in Q3 2024 six customers placed orders for over 30 production robots to deploy in the first half of 2025. The new orders include pilot conversions, existing customer expansions, and new customer adoption. The funding will be used to accelerate the development of new feature sets and build out the company’s commercial teams to unlock new markets and geographies for global robotic truck unloading customers.
Pickle Robot is laser-focused on applying its Physical AI technology to one of the most labor-intensive, physically demanding, and highest turnover work areas in logistics operations: truck unloading. Pickle’s Physical AI combines a powerful vision system with generative AI foundation models trained on millions of data points from real logistics and warehouse operations that enable Pickle’s robotic hardware platform to perform physical work at human-scale or better.
Prism Worldwide Raises $40 Million in Series A and A1 Funding Co-Led by Columbia Pacific Advisors and former Costco CEO Jim Sinegal
Prism Worldwide, a leader in innovative material solutions that is dedicated to advancing end-of-life tire recycling technology, announced it has raised $40 million in Series A and Series A1 funds. Prism is using the funds to consolidate operations, make capital investments in technology and equipment, and expand its team. To date, the funding has provided a foundation from which Prism has reached commercial viability, demonstrated by an initial round of sales and sales commitments for its thermoelastic polymers (TPEs). The development of these TPEs marks the first time any company has successfully created a sustainable, high-performance polymer derived from end-of-life tires. The funding rounds were co-led by return investors Columbia Pacific Advisors, a Seattle alternative investment firm, and Jim Sinegal, co-founder and former CEO of Costco. The combination Series A and Series A1 funding round also includes participation from Robert “Spike” Anderson, former CEO of Anderson Daymon Worldwide. Anderson is Chairman of Prism Worldwide’s board of directors.
The first deployment of Series A capital was for the acquisition of CRC Polymer Systems, a custom compounding company that supplies compounds, resins, and colors to the plastics industry. The acquisition enabled Prism to increase its capacity for product development, manufacturing and distribution throughout the United States and North America.
Prism breathes new life into end-of-life rubber tires, which traditionally have limited high-value uses. Tires are often burned or ground into final-stage materials used on playground surfaces or sporting fields. With its environmentally friendly technology, Prism reverses the polymerization of the end-of-life tire rubber in a novel, energy-efficient, and low capital-intensive manner that allows the end material to be functional and flexible in a wide range of elastomer and plastic applications. Notably, Prism makes it possible to integrate a higher concentration of recycled materials to be used in both rubber-based and plastic-based products without conceding product quality. Its technology can help companies scale revenues and profitability while helping them achieve sustainability goals by reducing carbon emissions and reliance on virgin petrochemical-based materials.
Teleo Raises $16.2 Million in Series A Extension Funds Led by UP.Partners
Teleo, a company building autonomous technology for heavy equipment, announced it has raised $16.2 million in Series A extension funds in addition to the company’s previously announced Series A funding round. The company will primarily use the funds to scale customer deployments and continue its expansion in new industries that use heavy machinery, such as wheel loaders, terminal tractors, excavators, and more. Teleo will also use the funds to enhance its AI capabilities, including advancing autonomous features; integrating large language models (LLMs) to further unlock operator efficiency; and collecting real-world data to continue training AI models. Teleo’s first extension round, totaling $9.2 million, was led by UP.Partners, with participation from other investors, including new investor Trousdale Ventures and return investor F-Prime Capital, among others. The second extension, totaling $7 million, was also led by UP.Partners, with participation from new investor Triatomic Capital, as well as returning investors F-Prime Capital and Trucks Venture Capital, among others. Since inception in 2019, Teleo has raised $29.8 million.
Teleo converts any make, model, and vintage of heavy equipment, such as bulldozers, wheel loaders, and excavators, into autonomous and remote-operated robots. The combination of remote and autonomous operations, called Supervised Autonomy, allows one operator to supervise multiple autonomous machines working simultaneously by enabling the operator to remotely perform complex tasks as needed. The operator sits at a central command center that can be stationed locally or thousands of miles away. There are many industries that use heavy machines to perform repeatable and predictable tasks where Teleo’s technology can help, especially amid historic labor shortages. For example, the Associated General Contractors of America estimates that 91% of construction firms are having a hard time finding workers to hire, driving up costs and project delays.
Juna.ai wants to use AI agents to make factories more energy-efficient
At its core, Juna.ai wants to help manufacturing facilities transform into smarter, self-learning systems that can deliver better margins and, ultimately, a lower carbon footprint. The company focuses on “heavy industries” — industries such as steel, cement, paper, chemicals, wood and textile with large-scale production processes that consume lots of raw materials.
The Berlin-based startup said that it has raised $7.5 million in a seed round from Silicon Valley venture capital firm Kleiner Perkins, Sweden-based Norrsken VC, and Kleiner Perkins’ chairman John Doerr.
Juna.ai’s software integrates with manufacturers’ production tools, like industrial software from Aveva or SAP, and looks at all its historical data garnered from machine sensors. This might involve temperate, pressure, velocity, and all the measurements of the given output, such as quality, thickness, and color.
Kongsberg Digital Announces Availability of Kognitwin on the Databricks Data Intelligence Platform
Kongsberg Digital’s digital twin, Kognitwin, is now available on the Databricks Data Intelligence Platform. This partnership combines Databricks’ data intelligence with Kognitwin’s interface, allowing businesses to integrate real-time data, reduce costs, and optimize operations for better decision-making.
Customers can now also fully utilize their data-driven digital twin with Databricks Delta Sharing. Delta Sharing simplifies the process by eliminating the need to replicate data, making data analysis and secure access smoother. This integration ensures that users always work with the most current data and simulations.