The Exponential Industry “Mapped” series is back with a look at all the amazing factory tours available around the world. More than fifty famous tours are pinned spanning every continent except Antarctica. Each pin icon has been updated to reflect the product being produced at the factory. Automotive factories are the most common, headlined by the Ford Rouge Factory Tour which sees over a hundred thousand visitors each year. Drill down into each pin to get a detailed tour description and a link to sign up for a tour!
Click the map to enter the interactive version and add your favorite tour! This map is open for public editing, so please add any of your favorites that I may have missed.
A factory tour is an immersive experience and great for all ages. I’ll never forget my first time watching a production line in primary school and asking a million questions. Little did I know that I would eventually build a career in engineering and manufacturing technologies and work in many of these factories throughout my career.
Factory tours matter for a variety of reasons. They matter to inspire the next generation of industrial leaders. They matter for executives and engineers to “build a better understanding of a site’s performance potential; to assess a competitor; to rally the frontline workforce; and to communicate the company’s performance, strategy, and current challenges.” They matter to help us all understand how modern society produces the goods we consume.
If you need you need tips for how to organize a tour at your factory we have that covered too! And best of all, factory tours are often free or reasonably priced, and who doesn’t love free?
Capturing this week's trending industry 4.0 and emerging industrial technology media
How Volkswagen and Google Cloud are using machine learning to design more energy-efficient cars
Volkswagen strives to design beautiful, performant, and energy efficient vehicles. This entails an iterative process where designers go through many design drafts, evaluating each, integrating the feedback, and refining. For example, a vehicle’s drag coefficient—its resistance to air—is one of the most important factors of energy efficiency. Thus, getting estimates of the drag coefficient for several designs helps the designers experiment and converge toward more energy-efficient solutions. The cheaper and faster this feedback loop is, the more it enables the designers.
This joint research effort between Volkswagen and Google has produced promising results with the help of the Vertex AI platform. In this first milestone, the team was able to successfully bring recent AI research results a step closer to practical application for car design. This first iteration of the algorithm can produce a drag coefficient estimate with an average error of just 4%, within a second. An average error of 4%, while not quite as accurate as a physical wind tunnel test, can be used to narrow a large selection of design candidates to a small shortlist. And given how quickly the estimates appear, we have made a substantial improvement on the existing methods that take days or weeks. With the algorithm that we have developed, designers can run more efficiency tests, submit more candidates, and iterate towards richer, more effective designs in just a small fraction of the time previously required.
How to Speed Up EV Cable Assembly
High-voltage connectors used in EV harness applications have many components that require precise assembly. Automation can improve productivity, quality and throughput when stripping and crimping cables. High-voltage connectors require several production steps that must be performed in a specific sequence. While most engineers want to automate every process, the cost of a fully automatic system cannot always be justified. Some process steps are more challenging and require more precision. For instance, removing the foil layer or cutting the shield is critical, because connector performance or safety may be affected significantly. In addition, some process steps are required for almost all connectors and cable types, while other steps are required only for certain connectors.
To achieve precision and throughput, manufacturers must invest in automation. It can provide not only high precision, but complete flexibility so that processing requirements can change in the future. It is important that systems can be expanded so they can grow and adapt as demand changes. Different connectors often have very different individual process steps because of their unique functions and constructions. However, there are some basic steps that apply to almost all of them. These steps pertain to properly stripping the cable and loading the ferrules.
Ford's Vijayakumar Kempuraj on Digital Twin Adoption | Future Says
AGVs for Automating Heavy Load Manufacturing Conveyance
For right now’s heavy producers, conveyance automation methods should be extraordinarily sturdy and able to transporting high-capacity payloads, but additionally ship the excessive ranges of flexibility, security and scalability anticipated from right now’s cell robotic methods. Trendy automated guided automobiles can do exactly that.
Trendy automated guided automobiles mix the capabilities set of autonomous cell robots – flexibility, security and scalability – with the load capability of towline conveyors. As such, they supply producers with the most effective of each worlds, a cheap, versatile, heavy load conveyance answer for manufacturing construct traces designed for manufacturing as it’s performed right now, and that may meet the manufacturing calls for of tomorrow.
Psyonic makes advanced prosthetics accessible using additive manufacturing
The Ability Hand is designed and manufactured in-house at Psyonic with hybrid manufacturing methods, including 3D printing, injection and silicone moulding, and CNC machines. Psyonic says that the Ability Hand is promising to restore life and mobility back to what it was for patients.
Using the Formlabs SLA 3D printers, Psyonic says that it was able to create an FDA-registered, medicare-covered, industry-defining upper-limb prosthesis from scratch. The machine also allows for collection of customer feedback followed by rapid prototyping in-house to improve design and functionality.
Data-driven fault identification is key to more sustainable facilities management
HVAC units are central to a building and constitute roughly 50% of a building’s energy consumption. As a result, they are well instrumented and generally follow a rules-based approach. The downside: this approach can lead to many false alarms and building managers rely on manual inspection and occupants to communicate important faults that require attention. Building managers and engineers focus significant time and budget on HVAC systems, but nevertheless HVAC system faults still can account for 5% to 20% of energy waste.
A building’s data model, and the larger building management schema, are established when the building first opens. Alerts, alarms, and performance data are issued through the BMS and a manager will notify a building services team to take action as needed. However, as the building and infrastructure ages many alarms become endemic and are difficult to remedy. Alarm fatigue is a term often used to describe the resulting BMS operator experience.
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
JITX Launches General Availability And Announces $12M Series A From Sequoia Capital
Today we’re announcing the general availability of JITX and that we raised a $12M Series A round, led by Sequoia Capital, with participation from Y Combinator, Funders Club and Liquid 2.
Hardware engineers need a credible way out of the trap they find themselves in. JITX helps by letting them write code that automates their engineering process. To get ahead they can’t just do one design after another – they need reusable code that designs hardware for them. To illustrate this point: a software engineer can upload code to GitHub and thousands of people can reuse that code in their own projects. Using a traditional hardware design flow, each one of those thousands of engineers would have to re-design and re-analyze the same circuit to make sure the design will behave correctly in their product. JITX brings the productivity of software to hardware.
At the same time we were working with enterprise design teams like Northrop Grumman. It turns out that they also needed JITX to address some specific problems. Like everyone else, their biggest challenge is finding and retaining skilled engineers. There just aren’t enough experts to go around, and even entry level positions are getting harder to fill (turns out new EE graduates are more interested in AI than drafting circuit boards). So they use JITX as a way to make their existing experts more productive. They find a lot of value out of checking designs automatically – a manual derating analysis on a complex FPGA board can take months but JITX automates the whole procedure. They are also excited about using code as a more efficient way to coordinate across different teams in the organization. At the end of our iteration process we were quickly designing boards that were at the limit of what traditional factories could build (our thanks to Gerry Partida for an 8/4 stacked microvia with sub 70um trace and space!). For example we built this silicon validation board that included 2500 pins in a complex 300um grid.
Invisible AI Raises $15 Million Series A to Rapidly Scale Innovative Computer Vision Platform Across Manufacturing Facilities
Invisible AI, a leader in state-of-the-art AI solutions for manufacturing, today announced the close of a $15 million Series A funding round, bringing the company’s total amount raised to $21 million. New investor Van Tuyl Companies (VTC) led the round with participation from new investor FM Capital and existing investors 8VC, Sierra Ventures, K9 Ventures and Vest Coast Capital. The funding will be used to grow the team and meet rapidly growing demand from existing and new customers.
Invisible AI’s technology uses edge-based AI devices with a built-in AI chipset, 1 TB of storage, and a high-resolution 3D camera to track activity across manufacturing facilities without using the cloud or any bandwidth. The self-contained AI device processes body motion data to identify potential for high-stress injuries and prevent simple defects in real-time, which generates millions in savings for customers. The software is entirely anonymized and privacy-centric by design and can be deployed in minutes without any coding or engineering expertise, allowing customers to scale to thousands of cameras with ease.
ServiceNow, Honeywell Back Noodle.ai with $25M Series C to End Global Supply Chain Crisis
Noodle.ai, creator of the world’s leading supply chain system of intelligence, today announced it has closed a $25M Series C funding round, including participation from the venture arm of ServiceNow ( NYSE: NOW) and Honeywell Ventures. The investments provide further validation of Noodle.ai’s innovative AI-driven platform, Inventory Flow, a supply chain system of intelligence that enables companies to profitably navigate some of the world’s most complex supply chain challenges.
The latest round brings Noodle.ai’s total funding to more than $100M, including previous rounds from TPG Growth, Dell Technologies Capital, Mitsubishi and SMS group. In a tight investment landscape, the recent funding is notable, reinforcing the urgent need for innovation in global supply chains. Noodle.ai will use the funds to accelerate product innovation and technology integrations to support a growing customer base of companies across a wide range of industries heavily reliant on the global supply chain, including consumer packaged goods (CPG), food and beverage, beauty and cosmetics, metals, automotive, paper and pulp, and more.
Synopsys helps semiconductor designers accelerate chip design and development on Google Cloud
EDA software is a large consumer of high performance computing capacity in the cloud. With the release of Synopsys Cloud bring-your-own-cloud (BYOC) solution on Google Cloud, chip designers can now scale their Google Cloud infrastructure with Synopsys’s leading EDA tools under the flexible FlexEDA pay-per-use model and access unlimited EDA software license availability on-demand by the hour or minute.