Assembly Intelligence Required
Shop Talk
Capturing this week's zeitgeist
As part of our ongoing commitment to enhance the overall security of our products, we are introducing an authorization and authentication protection mechanism for the connection and control of Bambu Lab 3D printers. This step is a significant security enhancement to ensure only authorized access and operations are permitted.
via “Firmware Update Introducing New Authorization Control System”
Following intense backlash over the digital rights management implementation, Bambu issued a clarifying announcement addressing the community’s concerns:
We want to make it absolutely clear that all of these claims are entirely false:
- Bambu Lab will remotely disable your printer (“brick” it).
- Firmware updates will block your printer’s ability to print.
- AMS functionality will be restricted, and the use of third-party filament will be disabled.
- Bambu Lab firmware contains trojans or backdoors for unauthorized remote control.
- The printers have a timed killswitch that disables them after a certain period.
- All 3D files printed are monitored, duplicated, or stolen.
- A subscription will be mandatory to use your printer. via “Updates and Third-Party Integration with Bambu Connect”
Assembly Line
This week's most influential Industry 4.0 media.
Implementing a Workflow for Deploying and Integrating Deep Learning Networks on PLCs for Industrial Automation
Our team at Beckhoff Automation has implemented a new workflow that combines MATLAB® tools and Beckhoff Automation products to enable low-code design and AI model training—and simplifies the deployment and integration of those models on industrial targets. Working with MathWorks engineers, we developed this workflow and demonstrated it on an example quality control application that involved the visual inspection of hex nuts. While this simple application classifies hex nuts as either defective or not, demonstrating a straightforward use case, the steps in the workflow can be applied to accelerate the development and deployment of much more sophisticated and complex applications.
After collecting and preparing the data to be used in a deep learning application, the workflow’s first step is training a deep learning model. With MATLAB and Deep Learning Toolbox, there are several ways to do this, including training a network from scratch with Deep Network Designer app, defining a deep learning model as a function and using a custom training loop, or retraining a pretrained model with new data, also known as transfer learning. If there is a small amount of abnormal data, anomaly detection methods such as FCDD and PatchCore, which are included in the Automated Visual Inspection Library for Computer Vision Toolbox™, are also effective.
Rethinking the Box
But as the low-hanging fruit of last-mile optimization is picked, and companies hunt for small-but-mighty efficiency improvements that yield meaningful cost savings and sustainability benefits, they’re asking: How much scarce, costly warehouse square footage is eaten up by packaging inventory? How many more orders might fit on a truck if cartons were right-sized? How many truck trips might be eliminated? How much less fuel consumed? How much money saved in parcel freight charges? How many more orders could be filled by the same workforce in the same workday with less risk of damage?
The issue is not, however, always wasted space. Getting the pack wrong can also be costly. As an example, Larsen worked with a maker of dog food that was shipping its product in quantity in heavy-grade, lightweight poly-bags. While the bags had no void or fill, the total bag weight, size and sorting/handling limitations (they could not be run through the normal conveyor/sorter system) triggered high accessorial charges in the carrier rate tables. A shift in packaging strategy to low-profile boxes generated quick savings. On average, he says, shippers could see 5-20% savings from studying the tables closely and taking advantage of size or weight thresholds to split or reconfigure shipments to reduce dimensional or DIM rates and charges — or to challenge a carrier’s interpretation of the tables.
A Design Engineer’s Tour of Zebra Technologies’ R&D Facility
Human hands are astonishing tools. Here's why robots are struggling to match them
Human sensory systems are so complex and our perceptive abilities so adept that reproducing dexterity at the same level as the human hand remains a formidable challenge. But the level of sophistication is rapidly increasing. Enter the DEX-EE robot. Developed by the Shadow Robot Company in collaboration with Google DeepMind, it’s a three-fingered robotic hand that uses tendon-style drivers to elicit 12 degrees of freedom. Designed for “dexterous manipulation research”, the team behind DEX-EE hope to demonstrate how physical interactions contribute to learning and the development of generalised intelligence.
Roboticists have long dreamed of automata with anthropomorphic dexterity good enough to perform undesirable, dangerous or repetitive tasks. Rustam Stolkin, a professor of robotics at the University of Birmingham, leads a project to develop highly dexterous AI-controlled robots capable of handling nuclear waste from the energy sector, for example. While this work typically uses remotely-controlled robots, Stolkin is developing autonomous vision-guided robots that can go where it is too dangerous for humans to venture.
During the course of a day, however, human hands undertake thousands of different tasks, adapting in order to handle a variety of different shapes, sizes and materials. And robotics has some way to go to compete with that. One recent test of a robotic hand using open-source components costing less than $5,000 (£4,000) found that it could be trained to reorientate objects in the air. However, when confronted with a challenging object – a rubber duck shaped toy – the robot still fumbled and dropped the rubber duck around 56% of the time.
Aurubis completes largest planned maintenance shutdown at Hamburg plant
Development of an injection molding production condition inference system based on diffusion model
Plastic injection molding is a crucial process for the mass production of various products. However, traditional methods for setting production conditions have heavily relied on skilled operators to adjust parameters through trial and error. This approach is not only inefficient but also results in inconsistent quality control. To address these challenges, this study proposes a new machine learning based model that automatically infers process parameters, enabling real time adaptation to external environmental changes. A surrogate model is first developed to learn the relationship between process parameters, environmental variables, and product quality, predicting whether a given set of parameters will result in a good or defective product. Building on this, a diffusion model, a type of deep generative model, was employed to generate diverse sets of process parameters likely to yield defect free products under specific environmental conditions. The proposed diffusion model outperforms existing generative models such as generative adversarial network (GAN) and variational autoencoder (VAE) in both accuracy and diversity of generated parameters. Notably, the diffusion model achieved an error rate of 1.63%, significantly outperforming GAN and VAE, which exhibited error rates of 23.42% and 44.54%, respectively. Additionally, the applicability of the proposed diffusion model was experimentally validated in a real world testbed. Several experiments conducted under various external environmental conditions demonstrated that the quality of the products produced using the process parameters generated by the diffusion model matched the quality predicted by the model. This study introduces a novel approach to improving both the efficiency and quality of injection molding processes and holds promise for broader applications in manufacturing.
New Product Introduction
Highlighting new and innovative facilities, processes, products, and services
Chinese 'artificial sun' sets new record in milestone step toward fusion power generation
The Experimental Advanced Superconducting Tokamak (EAST), dubbed China’s “artificial sun,” maintained a steady-state high-confinement plasma operation for a remarkable 1,066 seconds, setting a new world record and marking a breakthrough in the quest for fusion power generation.
The duration of 1,000 seconds is considered a key step in fusion research. The breakthrough, achieved by the Institute of Plasma Physics under the Chinese Academy of Sciences (ASIPP), greatly improved the original world record of 403 seconds, which was also set by EAST in 2023.
Global scientists have worked for more than 70 years on trying to achieve this feat. However, only after reaching temperatures over 100 million degrees Celsius, sustaining stable long-term operation, and ensuring controllability can a nuclear fusion device successfully generate electricity.
Outrider deploys reinforcement learning AI to enhance distribution yard throughput
Outrider, the leader in autonomous yard operations for logistics hubs, announces its industry-first deployment of advanced reinforcement learning (RL) techniques to maximize freight throughput at customer sites. Outrider’s RL models increase path planning speed by 10x and enable the Outrider System to move freight more efficiently and safely through busy, complex distribution yards.
RL techniques involve creating a model that improves decision-making over time. Using years of data samples of behaviors, Outrider developed an RL curriculum of increasing difficulty for the model to learn. This technique reinforces preferred behaviors, such as following traffic rules and maintaining safe distances from other vehicles, and discourages undesirable behaviors. Once the RL models are tested extensively in simulation and on-vehicle at Outrider’s Advanced Testing Facility, the model and code are deployed into autonomous operations at customer sites.
Processing these data points through DL and RL models requires sophisticated computing hardware and a cost-effective hybrid cloud training environment that leverages public and private AI clouds. Outrider’s private AI cloud deployment utilizes NVIDIA DGX H200 GPUs installed at a secure, Denver-based data center owned and operated by Equinix.
Business Transactions
This week's top funding events, acquisitions, and partnerships across industrial value chains.
Sereact raises €25M to boost AI-powered robotics
At Sereact, we are on a mission to redefine what’s possible with AI-driven robotics. This marks a major milestone in our journey as we secure €25 million in Series A funding. The Series A round was led by Creandum, alongside significant participation from existing investors Point Nine and Air Street Capital, and prominent business angels, including former Formula 1 World Champion Nico Rosberg, Mehdi Ghissassi (ex Google DeepMind), Ott Kaukver (Skype), Lars Nordwall (ex neo4j), Rubin Ritter (ex Zalando), Torsten Reil and Niklas Köhler (both Helsing).
This funding will allow us to accelerate our mission in several key areas:
- Expanding R&D efforts to support additional robotic platforms, including mobile robots and humanoids.
- Developing AI solutions for more complex tasks beyond logistics and manufacturing.
- Expanding our U.S. presence, building strategic partnerships, and growing our local team.
H2, Inc. Secures $16 Million in Bridge Funding to Expand Flow Battery Manufacturing Capacity
H2, Inc., an industry-leading vanadium flow battery (VFB) developer and manufacturer headquartered in South Korea, successfully raised $16 million in recent bridge funding, finalized in the second half of 2024. This brings the company’s total accumulated funding to $77 million. The round was led by STIC Investments which is one of the largest and a leading private equity firm in Korea, with participation from KRUN Ventures and Lighthouse Combined Investment.
The newly raised capital will be primarily allocated to constructing the company’s new, state-of-the-art K2 Plant. This facility will significantly expand H2’s manufacturing capacity to 1.2 GWh per annum, marking a major milestone in the global flow battery and long-duration energy storage industry. Scheduled to begin operations in 2026, the K2 Plant will triple the current capacity of the K1 Plant, which stands at 330 MWh per year. The company has already secured the land for the K2 Plant to achieve its target annual production capacity as quickly as possible.
Basetwo Raises $11.5M Series A to Transform Chemical Manufacturing with Physics AI Platform
Basetwo, an AI platform for manufacturing engineers, announced it has raised USD $11.5M in Series A funding led by AVP with participation from existing investor Glasswing Ventures, Deloitte Ventures, Global Brain Ventures, Shimadzu Corporation, Chiyoda Corporation, and prominent UAE angel investors via Qora71. The investment allows the company to accelerate its mission to revolutionize how pharmaceutical and chemical manufacturers optimize their production processes.
When launching new drug compounds or chemical formulations to market, manufacturers must precisely determine numerous production parameters — from reactor temperatures to mixing speeds — while maintaining strict quality standards. At the commercial scale, teams must continuously verify production performance, identify issues, and implement corrective actions to ensure optimal batch quality. Traditional machine learning approaches relying solely on historical data struggle with these complex manufacturing processes, as they can only learn from correlations rather than the underlying physics and chemistry engineers use to control and troubleshoot these systems. This technology gap leads to significant inefficiencies, with 20 cents of every dollar spent in manufacturing going to waste — a staggering global loss of $8 trillion annually.
Basetwo’s Physics AI platform uniquely combines fundamental chemical engineering principles with artificial intelligence to optimize pharmaceutical and chemical manufacturing processes. This results in an up to 40% improvement in cycle times and raw material usage while helping customers achieve a 25% improvement in product quality. The platform enables manufacturers to run virtual experiments and simulate process changes before implementation, significantly reducing the time and cost traditionally required to optimize production processes and eliminating the risks associated with live testing.
The funding will accelerate the development of Basetwo’s AutoPilot technology for autonomous, real-time manufacturing control while expanding the company’s presence in the US, Japan, Europe, and the Middle East. Basetwo will continue growing its business development, AI, and software engineering teams to support increasing market demand.
Addis Energy Introduces Novel Technology to Unleash the Earth’s Potential for Clean Ammonia Production
Addis Energy introduced its technology platform, which harnesses the Earth’s chemical and thermal potential for clean ammonia production at low cost through a net energy-positive process. The platform unlocks energy abundance and affordability by combining next-generation chemical innovation with legacy expertise from the oil and gas industry, and will create new economic opportunities for domestic energy production with zero emissions. Addis Energy has raised $8.75 million total to date: $4.5 million from ARPA-E through its Vision OPEN program and $4.25 million in pre-seed funding from Engine Ventures, Pillar VC and Voyager Ventures. This month, the company’s co-founder Dr. Iwnetim Abate published a paper in the peer-reviewed scientific journal Joule outlining how ammonia can be produced directly from iron-rich rocks using only the injection of nitrate-source water into the Earth’s subsurface. The novel process leverages established oil and gas drilling techniques to access subsurface heat and pressure, offering a low carbon intensity alternative to conventional ammonia synthesis at cost parity.
ABB to acquire Sensorfact expanding its digital energy management offering
ABB is acquiring Sensorfact BV, a fast-growing energy management company headquartered in Utrecht, Netherlands. The acquisition further expands ABB’s digital energy management offering and is expected to close in Q1 2025. Financial terms were not disclosed.
Established in 2017, Sensorfact offers a scalable software as a service (SaaS) solution that helps small and medium sized enterprises use AI in their operations and energy management to lower costs and increase efficiency. The company has more than 250 full-time employees in Utrecht, Amsterdam, Barcelona, and Berlin and serves more than 1,900 customers across Europe.
Sensorfact’s SaaS solution includes plug-and-play sensors that measure consumption on machine-level and connect to a smart software platform. The company uses algorithms to analyze the data, identify energy-saving opportunities and provide easy-to-implement advice that is unique to each customer’s operations.