Capturing this week's trending industry 4.0 and emerging industrial technology media
A Data-Driven Approach to Sustainability in Industry 4.0 Using MQTT
The MQTT protocol is the de-facto standard for IoT messaging. It works following the publish/subscribe (Pub/Sub) pattern. Many manufacturing and industry 4.0 companies use MQTT as it is lightweight, supports bi-directional messaging, can scale to millions of connected devices, works well over unreliable networks, and allows secure communication.
At the Internet of Things World, Berlin event, HiveMQ, SVA, and Splunk demonstrated the complete cycle of a connected car platform. In this small practical demonstration, we showed how combining data movement and communication efforts could accelerate sustainability in the automotive industry and other verticals. The demo exhibited a HiveMQ broker connecting several autonomous racing cars. The data published by these cars were forwarded to Splunk using SVA’s HiveMQ Splunk Extension. Splunk’s Sustainability dashboard visually brought key sustainability metrics like C02 emissions and fuel efficiency to life.
Rhinestahl Thriving amid complexity in manufacturing
Intro to deep learning to track deforestation in supply chains
In my experience, I have observed that it’s common in machine learning to surrender to the process of experimenting with many different algorithms in a trial and error fashion, until you get the desired result. My peers and I at Google have a People and Planet AI YouTube series where we talk about how to train and host a model for environmental purposes using Google Cloud and Google Earth Engine. Our focus is inspiring people to use deep learning, and if we could rename the series, we would call it AI for Minimalists since we would recommend artificial neural networks for most of our use cases. And so in this episode we give an overview of what deep learning is and how you can use it for tracking deforestation in supply chains.
Prototyping for the Circular Economy: Impact of Sustainability Regulations on Packaging Development
New environmental regulations are going to require manufacturers to redesign packaging so they are only one material (monomaterial) – which allows for increased ability to recycle as opposed to packaging made from multi-materials. This creates the need for rapid product development in order to completely redesign bottles and caps to be made of different materials than ever before. More companies are leaning on HDPE bottles and caps rather than the traditional PET bottle, which is going to cause a necessary redesign as the mechanical and physical properties of the materials are different.
Along with mono materials, tethered caps and closures are another shift in the world of manufacturing, designed to keep caps with their bottles to decrease the amount of litter made from single-use containers. These types of caps are pushing designers to get creative and develop entirely new caps and closures. This blog is going to give designers, product developers, and industry professionals the proper information for the future of packaging and how to leverage 3D printed tooling to stay ahead of the competition while maintaining proper prototyping procedures.
Yorii Automobile Plant, Saitama Factory, Honda Motor Co., Ltd.
Market Dynamics, Technologies Drive Automotive Design
The ground underneath is constantly shifting: Supply chain constraints, software defined architectures, functional safety requirements, and the changing dynamics among original equipment manufacturers (OEMs), tier 1 suppliers, and semiconductor companies are altering the landscape of automotive electronics. This dynamic environment was the subject of discussion in a recent panel hosted by ProteanTecs, and, judging from that talk, “changing” may be an understatement.
“For each and every little functionality, there’s a single ECU,” that’s about to change drastically as OEMs move to a domain-based architecture with high-performance computers. Sustainability is also going to be viewed through a new lens because of data, as the car now has so many sources that will inform optimal charging times and where charging stations are placed.
How a universal model is helping one generation of Amazon robots train the next
In short, building a dataset big enough to train a demanding machine learning model requires time and resources, with no guarantee that the novel robotic process you are working toward will prove successful. This became a recurring issue for Amazon Robotics AI. So this year, work began in earnest to address the data scarcity problem. The solution: a “universal model” able to generalize to virtually any package segmentation task.
To develop the model, Meeker and her colleagues first used publicly available datasets to give their model basic classification skills — being able to distinguish boxes or packages from other things, for example. Next, they honed the model, teaching it to distinguish between many types of packaging in warehouse settings — from plastic bags to padded mailers to cardboard boxes of varying appearance — using a trove of training data compiled by the Robin program and half a dozen other Amazon teams over the last few years. This dataset comprised almost half a million annotated images.
The universal model now includes images of unpackaged items, too, allowing it to perform segmentation across a greater diversity of warehouse processes. Initiatives such as multimodal identification, which aims to visually identify items without needing to see a barcode, and the automated damage detection program are accruing product-specific data that could be fed into the universal model, as well as images taken on the fulfillment center floor by the autonomous robots that carry crates of products.
Automated Optical Inspection
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
Startups Look for Ways to Bring Down the Cost of Green Hydrogen
Companies are pouring a lot of money into the idea that hydrogen can help decarbonize the fossil-fuel-based economy. One drawback to hydrogen as a form of green energy, however, is that nearly all of the world’s hydrogen is produced in a greenhouse-gas-intensive process: heating natural gas with steam to split it into hydrogen and carbon dioxide. This type of hydrogen is known as gray hydrogen, or sometimes blue hydrogen if the factory has carbon-capture technology.
Green hydrogen currently costs between approximately $3 per kilo and $26 per kilo, according to data from S&P Global. The Energy Department has said it needs to cost about $1 per kilo to unlock new industrial applications. Closing that gap with current technology depends on renewable electricity becoming a lot cheaper. The Hydrogen Council, an industry group, says the cost of making hydrogen with electrolyzers could fall to $1.40 a kilogram by 2030 in the right circumstances, such as renewable electricity being available for as little as $13 per megawatt hour.
Infinitum Secures $30M in Additional Growth Capital to Expand and Fully Automate Production Facility in Mexico
Infinitum, creator of the sustainable, breakthrough air core motor, today announced $30 million in additional growth capital from Riverstone Holdings Latin America, Alliance Resource Partners, Caterpillar Venture Capital and Cottonwood Technology Fund. The funds will be used to expedite commercial and industrial motor production by expanding and fully automating assembly at the company’s 65,000 square foot facility in Mexico to meet a significant increase in demand.
Electric motors consume more than half of the world’s electricity, with the general industry segment consuming 38 percent. Infinitum’s motor is 50 percent smaller and lighter, uses 66 percent less copper and no iron, and consumes 10 percent less energy. Infinitum motor components can be reused, allowing them to stay in service for decades.
BOWE GROUP leads an $8.2M investment round in robot software innovator MOV.AI
BOWE GROUP, a leading provider of integrated automation technology announced today an $8.2M investment round in MOV.AI – a startup revolutionizing Autonomous Mobile Robots (AMR) software. The round is led by BOWE GROUP and includes MOV.AI’s existing investors State of Mind Ventures, NFX, and Viola Ventures.
MOV.AI’s Robotics Engine Platform changes how AMRs are built, separating software from hardware and offering both AMR manufacturers and automation integrators the enterprise-grade tools they need for advanced automation. The Robotics Engine PlatformTM helps AMR manufacturers quickly develop and differentiate their robots. Automation integrators can deploy in days, not months, and ensure secure, uninterrupted operation in constantly changing business and operational environments.
TOffeeAM banks £5 million
The round was led by Sumitomo Corp subsidiary Presidio Ventures Europe and London-based East Innovate. UK-based deeptech investor IQ Capital has returned from TOffeeAM’s seed round, investing alongside US-based fund Type One Ventures, Exor Seed, Italy’s Excellis and assorted angel investors.
TOffeeAM will use the series A capital to scale its 3D printing platform into new overseas markets; specifically seeking in-roads into the massive US market and also across East Asia.
Koch Teams With Startup to Build Giant Battery Factory in Georgia
Norwegian startup Freyr Battery and energy conglomerate Koch Industries Inc. are accelerating their plan to build a multibillion-dollar battery plant that will be among the largest to tap incentives in President Biden’s climate, tax and spending plan, Freyr said.
Koch’s chief executive long opposed environmental regulation and subsidies while funding groups that questioned climate change. The company and Freyr will likely invest more than $2.6 billion in two phases for the Georgia plant, which will supply batteries primarily for the U.S. power grid.
LG, Altair build AI-powered validation platform for automotive parts
LG Electronics Inc., an industry frontrunner in applying artificial intelligence to home appliances, said on Wednesday it has joined forces with Altair Engineering Inc., a US tech firm, in developing an AI-powered validation platform for automotive parts.
Integrating AI technology into the vehicle component development process will provide LG’s clients with more reliable and high-quality solutions for products, including infotainment systems, LG said. The South Korean electronics company said the new platform leverages a machine learning algorithm to accurately predict and measure product performance from an early stage of the design validation process.