Automotive manufacturing is changing at an incredible pace. This week, Divergent secured access to additional funds “to industrialize its fully-integrated platform, combining generative design, additive manufacturing, and automated assembly” among automotive OEMs. This experiment in proprietary end to end additive manufacturing is in stark contrast to the traditional structure design and production process where components are designed and produced across thousands suppliers and assembled. Although seemingly crazy, 3D printing of an entire structures has been successful in other sectors. Relativity space is producing complete rockets through an end-to-end additive manufacturing process and many companies are pursuing the technology for housing construction. If 3D printing technology can address the tight specifications of those sectors, it seems viable in automotive manufacture.
In the meantime, BMW is rolling out a subscription service for heated seats. On the surface, this seems “nuts” as one commentator put it. However, I love the experimentation. Automotive OEMs need to figure out new ways of producing value to consumers over the life of the automobile. A modern car is effectively an advanced computer on wheels. In my opinion, the automotive companies that can adopt software business models while building consumer trust through the transition will be winners over the next century. Tesla started with this advantage from the start, but now competition is heating up.
Capturing this week's trending industry 4.0 and emerging industrial technology media
Can Realtime Robotics’ RapidPlan Software Break Through Industrial Automation’s Slow Progress?
Despite continuous advances in the field of robotics, complex motion through space still presents a stumbling block for bots. In oftentimes hectic industrial settings, complex motion that entails a robot getting from point A to point B to perform a task is a feat that generally involves weeks to months of programming time, resulting in movement that’s relatively slow and collision-prone. It’s a challenge that George Konidaris, cofounder and chief roboticist of Realtime Robotics, says has been a persistent hurdle for robotics researchers since 1979—and he thinks RapidPlan represents a significant leap of progress.
Part of the software’s promise is enabling robots to more quickly determine the best path of movement in dynamic environments like factories, which Konidaris says so far hasn’t been accomplished. Software users start with RapidPlan Create, which guides them through the robotic programming phase. Then RapidPlan Control runs the robots’ operations.
Data from Realtime indicates that the software can reduce programming time by 70-80 percent, increase throughput rate by 10-30 percent and decrease a bot’s life cycle cost by up to 50 percent.
Manufacturing Process Innovations: A “Bessemer Moment” For Titanium?
I had called Taso to talk about their process innovation for making titanium. It is a new method that uses hydrogen instead of carbon: hydrogen assisted metallothermic reduction (HAMR). HAMR promises to be both environmentally friendly as well as much lower cost, what Arima calls titanium’s “Bessemer moment.” The process was developed by metallurgist and Professor of Metallurgical Engineering at the University of Utah, Dr. Z. Zak Fang, under the sponsorship of the U.S. Department of Energy’s ARPA-E program, their version of DARPA. The HAMR process uses half the energy, cuts emissions by more than 30% (and to potentially zero if using renewable energy) to power the furnaces. It substantially reduces the cost of producing titanium. The majority of savings come from eliminating both the chlorination step and the vacuum distillation.
Miners Are Relying More on Robots. Now They Need Workers to Operate Them.
In this remote corner of western Australia, surrounded by clusters of low-lying scrub and red rocky outcrop, the world’s second-biggest mining company has built its most technologically advanced mine. For Rio Tinto, PLC finding the workers to run the new high-tech operation is a challenge.
Automation helped miners to become more efficient and avoid disruptions triggered by the pandemic, when sudden border closures marooned workers who used to jet in from afar for their shifts. But the companies’ investments are doing little to solve a broader labor crisis affecting an industry that still needs a large staff to keep their operations running smoothly.
Improving Yield With Machine Learning
Machine learning is becoming increasingly valuable in semiconductor manufacturing, where it is being used to improve yield and throughput.
Synopsys engineers recently found that a decision tree deep learning method can classify 98% of defects and features at 60X faster retraining time than traditional CNNs. The decision tree utilizes 8 CNNs and ResNet to automatically classify 12 defect types with images from SEM and optical tools.
Macronix engineers showed how machine learning can expedite new etch process development in 3D NAND devices. Two parameters are particularly important in optimizing the deep trench slit etch — bottom CD and depth of polysilicon etch recess, also known as the etch stop.
KLA engineers, led by Cheng Hung Wu, optimized the use of a high landing energy e-beam inspection tool to capture defects buried as deep as 6µm in a 96-layer ONON stacked structure following deep trench etch. The e-beam tool can detect defects that optical inspectors cannot, but only if operated with high landing energy to penetrate deep structures. With this process, KLA was looking to develop an automated detection and classification system for deep trench defects.
Velo3D Gives Us a Backstage Tour of its New Facilities in Germany
Sensor Noise and Straightforward Software Techniques To Reduce It
Sensor telemetry is at the heart of IoT. But while it can lead to amazing insights, it can also be noisy and inconsistent. There are two main sources of the problem. First, all sensors have hardware limitations and only measure to a certain degree of accuracy, with sequential readings having some amount of variance. (We call this variation in sensor readings, “sensor noise”.) Second, even if a sensor could measure with perfect accuracy and precision, the world itself that the sensor is measuring still presents variation; for instance, an IR distance sensor is affected by sunlight.
We can accept noise and inconsistency as a reality of IoT, but we can also take reasonable steps to reduce them. For instance, is there more accurate hardware available? Are there adjustable gain, sensitivity, positioning, or other calibrations to make on our sensors? Can we reduce environmental factors? Should we average out multiple readings over time? In many cases, these basic steps are enough to allow the data of interest to stand out.
TUNEL DE VIENTO PUESTA A PUNTO v2
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
Divergent Secures Up to $80M in New Financing for 3D Printed Car Operations
3D printed supercar startup Divergent Technologies announced that it has successfully completed two new financing agreements, for a total of up to $80 million. This follows the Southern California-based company’s $160 million Series C investment round, announced in April of this year.
This signals financial faith being shown not just in the AM sector, but, more broadly, in the technology’s ability to deliver wholly automated production lines. That has significance far beyond its implications for one company, as it is precisely what AM will have to display it can achieve, in order for the industry to scale up to the point where it is capable of handling mass production.
Industrial AI enterprise Detect Technologies raises $28mn in Series B funding led by Prosus Ventures
Detect Technologies, a leading AI-based SaaS enterprise, announced today that it raised $28 Mn in primary and secondary funding. Detect Technologies provides cloud-based applications to industries to automate and enhance visibility of industrial risks and improve productivity. Prosus Ventures led this round with significant participation from existing investors Accel and Elevation Capital, and continued support from other existing investors—Shell Ventures, Bharat Innovation Fund and Bluehill Capital.
Detect Technologies has grown rapidly to become a leading industrial AI and SaaS enterprise. The company will utilize this capital to further expand and strengthen its sales and operations across international markets in North America and Europe. Operating in the fast-evolving space of AI, the company has also allocated funds for enriching its product suite.
FORT’s $25MM Series B Funding Accelerates Expansion of its Machine Communications Platform
FORT Robotics (FORT), a pioneer in communications and control for smart machines, today announced the close of a $25 million Series B funding round, led by Tiger Global. The company will use the funds to accelerate the rollout of its machine communications platform, which aims to make autonomous machines safer and more secure.
FORT’s platform technology is the foundation for its hardware solutions, including wireless emergency stops and remote control systems, that are used by industry leaders including Agility Robotics, Hexagon, Moog, and hundreds of others. With the new investment, FORT plans to expand its platform functionality and enhance security offerings to address growing demand from end users.
Hoosun Completes Round D Financing
Li Yuanlin, Chairman and General Manager of Hoosun, spoke about the company’s efforts: “Hoosun will increase its investment in research and development in the future, continue to work on breakthroughs in key technologies for core equipment in the field of micro-nano materials and the process path of material processing, while leading the way in the domestic substitution of new materials and equipment.”