Capturing this week's zeitgeist
Looking out in the horizon to 2030 ‘xiaozhou2500’ explains what the future of manufacturing looks like:
“AI centric control systems run manufacturing plants by optimizing high-level objectives and following constraints and other instructions from human operators. Human operators spend most of their time on creative thinking and strategic planning, which may turn into new economic objectives for AI systems. Operators only need to monitor plant performance and intervene for critical operations, and AI systems automatically take care of all other things like process optimization, design of experiments, process variability, predictive maintenance in an optimal way.”
And over the next year, Daniel Ketyer, investor, Piva Capital makes his 2023 prediction in “The Crystal Ball: VCs, private equity investors, and tech founders predict what’s to come in 2023”
“Robotics, 3D printing, and automation industries will all get a boost as manufacturing firms bump into labor shortages as they increase US production and sourcing to take advantage of 45X tax credits. 45X, which directs $30 billion in tax credits over the next 10 years for US production of components such as solar panels, wind turbines, and batteries for electric vehicles, as well as the minerals that go into these products, sets up a labor challenge for these companies. With labor still in short supply, this will push robotic technologies to the forefront as companies try to keep pace with demands.”
Lastly, in the current moment where “Ukraine Has Digitized Its Fighting Forces on a Shoestring” from Sam Schechner and Daniel Michaels:
Ukrainian soldiers have been using 3-D printers to build compact plastic harnesses that snap onto popular commercial drones so that they can be armed with grenades, Mr. Perimov said. The harnesses have inexpensive light sensors attached to a mechanical clasp. When the operator tells the drone to flash lights on its belly, the sensors pick up the light, triggering the clasp to release a strap holding a grenade. Total cost for a 3-D-printed harness is about $10 to $15, Mr. Perimov said.
This week's most influential Industry 4.0 media
Fast, Easy Six-Axis Robot Integration Created by a Molder for Molders
For Scott and his staff, few tools are more critical to profitability and efficiency than automation, which is why Noble Plastics has Fanuc six-axis robots on all its injection machines. The integration was performed inhouse with the philosophy that, as Rogers puts it, “The robot should be a partner for the operator, not a hindrance.” After 20-plus years of robot integration experience and eight years as an authorized integrator for Fanuc robots, Noble Plastics is now launching a turnkey package of a robot, basic and intuitive user interface, end-of-arm tooling (EOAT)—if desired, integration with the injection machine controls, job-specific programming and operator training. “We can do all this faster and at lower cost than your average integrator,” Rogers says, “and the end result is easier for the operator to use.”
Systems can be delivered in as little as 2 to 4 weeks and commissioned in 1 to 2 days, vs. up to 4 to 6 months. All this adds up to what Rogers thinks is a unique set of capabilities to serve injection molding customers in need of highly flexible automation. Is six-axis an expensive solution? Not if you make good use of its capabilities, says Rogers. “Depending on how many shifts you run, it could be $2 to $5/hr. And there are some things you can do with a six-axis that you can’t do with human operators or any other kind of robot.”
Unlocking the industrial potential of robotics and automation
Some aspects of productive activity are more amenable to automation than others are, with routine tasks at the head of the line. Activities such as picking, packing, sorting, movement from point to point, and quality assurance are already automated to some extent, and these will continue to see heavy investment over the coming years. Conversely, activities such as assembly, stamping, surface treatment, and welding, all of which require high levels of human input, are less likely to be automated in the short to medium terms.
A standout message from the survey is that automation is not easy. Participants report that the primary challenges to adoption include the capital cost of robots and a company’s general lack of experience with automation, cited by 71 percent and 61 percent of respondents, respectively. Some say that business confidence in technology is low, leading to challenges around conviction and funding. Moreover, respondents’ expectations of production and reliability gains through automation are offset by the belief that such gains will eliminate jobs and may affect existing contracts. In fact, that is not the case since automation typically leads to changes in workplace roles rather than the creation of redundancies.
Hitachi Mining Excavators Factory Tour
The Ultimate Guide to AfterMarTech for Industrial OEMs
Industrial OEM manufacturers are increasingly making investments in technology to improve their customer experience, communication, marketing, and their ability to serve customers. This consolidated set of technology is focused on aftermarket, service, sales, marketing, and customer support – and can be collectively known as “AfterMarTech” – combining all the aspects of dealing with existing customers (i.e., Aftermarket) with “Marketing Technology” (or MarTech) – the term used by Marketers to describe the tech stack used to optimize the customer experience. In the equipment manufacturing industry, this has been driven by leaders within individual functions, leading to a variety of different tools used by different teams that are disconnected, cannot share data, and are unable to complement each other. For many organizations, this is now a challenge – too many applications, no single source of truth, overlapping or redundant functionality, and high cost. Hence, many organizations are looking to consolidate their “AfterMarTech” investments to achieve process efficiency, cost optimization, and most importantly, improve their customer experience – with a big focus on existing customers and the Installed Base. For OEMs who want to lead their verticals moving forward, Aftermarket is no longer defined as just fulfilling replacement parts orders – Aftermarket is the broader set of services, parts, and solutions that enable the end-users to drive maximum value from their relationship with an OEM and establish the foundation to build a true partnership that is highly beneficial for the OEM.
How Corning Built End-to-end ML on Databricks Lakehouse Platform
Specifically for quality inspection, we take high-resolution images to look for irregularities in the cells, which can be predictive of leaks and defective parts. The challenge, however, is the prevalence of false positives due to the debris in the manufacturing environment showing up in pictures.
To address this, we manually brush and blow the filters before imaging. We discovered that by notifying operators of which specific parts to clean, we could significantly reduce the total time required for the process, and machine learning came in handy. We used ML to predict whether a filter is clean or dirty based on low-resolution images taken while the operator is setting up the filter inside the imaging device. Based on the prediction, the operator would get the signal to clean the part or not, thus reducing false positives on the final high-res images, helping us move faster through the production process and providing high-quality filters.
Manufactured in the Metaverse: Mercedes-Benz Assembles Next-Gen Factories With NVIDIA Omniverse
Mercedes-Benz plans to start production of its new dedicated platform for electric vehicles at its plant in Rastatt, Germany. The site currently manufactures the automaker’s A- and B-Class as well as the compact SUV GLA and the all-electric Mercedes-Benz EQA. Experts from NVIDIA and Mercedes-Benz operations are setting up a “digital first” – planning process for the plant that won’t disrupt the current production of compact car models at the site. This blueprint will be rolled out to other parts of the global Mercedes-Benz production network for more agile vehicle manufacturing. By tapping into NVIDIA AI and metaverse technologies, the automaker can create feedback loops to reduce waste, decrease energy consumption and continuously enhance quality.
Weekly mergers, partnerships, and funding events across industrial value chains
Hexagon AB invests $100m into Divergent Technologies
Divergent Technologies has received 100m USD in investment from software technologies leader Hexagon AB. A portion of Hexagon’s investment is subject to certain regulatory approvals. It follows the automotive tier 1 manufacturer’s 160m USD Series C funding round in April of last year. With this funding from Hexagon, Divergent is seeking to accelerate its plans to build a global network of DAPS factories, which will each serve multiple OEM clients.
Profet AI Closes US$5.6m Series A Round To Fuel Regional Expansion And Product Development
Profet AI, a Taiwan-based developer of auto machine learning solutions, has raised US$5.6 million in a Series A funding round led by Darwin Ventures. Existing investors Hive Ventures, AUO and SVTI also participated in the round, joined by Harbinger Venture Capital, and Jensen-Capital Management. Profet AI will use this funding to accelerate its overseas expansion into Japan, Southeast Asia and China, further develop its AutoML Virtual Data Scientist Platform and Ready To Go Applications and accelerate new product development.