Over the past few years, two manufacturing sectors have been dominating markets: semiconductors and electric automobiles. Cities and states are hoping to capitalize on the boom by enticing manufacturers to build and renovate factories near their communities. This time around they are focusing on factors like workforce and infrastructure rather than massive subsidies as in the Wisconsin-Foxconn arrangement.
The Midwestern United States, a long standing stronghold of automotive manufacturing, is racing to make the transition to electric vehicles.
- In Michigan, General Motors is renovating is footprint of factories to retool for electric vehicles while the State looks to build out an electric vehicle charging network by 2030.
- Illinois seeks to build from the success of Rivian in Normal by creating the “Reimagining Electric Vehicles in Illinois, or REV Act, passed the General Assembly with near-unanimous bipartisan support during the recently concluded fall session. It provides tax credits for income tax withheld for EV manufacturers and costs to train new or retained employees.”
- Ohio can’t get enough of the Lordstown plant, with Foxconn and Fisker set to takeover from Lordstown Motors for EV development.
- Tennessee continues its push into the automotive industry with a new Ford EV project and strong economic development of the industry.
The Southwestern United States is more focused on building out its semiconductor fabrication capabilities, but is also making inroads into electric vehicle development and manufacture.
- Texas welcomed Tesla to Austin, and a short drive away in Taylor they recruited Samsung to build a chip plant.
- Arizona has been enticing semiconductor companies such as TSMC and Intel to the Phoenix area. Arizona is also looking to being a player in electric vehicle manufacture with a Lucid Motors factory.
- Nearby Nevada, is looking to monetize its abundant natural resource of lithium to maintain its lead in battery manufacturing.
Beyond the United States, Japan has recently decided to subsidize advanced battery factories while luring TSMC for chips. Germany is investing billions in semiconductor production while maintaining its prowess in the transition to electric vehicles.
As the build out progresses in these two industries, I bet the winning regions are active participants in recruiting the best workforce and deploying sufficient supply chain infrastructure rather than just providing subsidies. Once the physical footprint of these newer industries are established, the supply lines may be permanently changed. The race is on.
How Japan Won Lithography (& Why America Lost)
What Is Generative Design, and How Can It Be Used in Manufacturing?
The primary use case of generative design in manufacturing is to automatically trigger design options that are pre-validated to meet the requirements you’ve established. That can be especially important for efficient manufacturing. Sometimes a part or tool must fit into an entrenched workflow or pipeline—methodologically or physically—as part of a larger device or process.
Apollo Tyres Moves to AWS to Build Smart, Connected Factories
Apollo Tyres needed to upgrade its infrastructure to develop new ways of engaging with fleet operators, tyre dealers, and consumers, while delivering tyres and services efficiently at competitive prices. The company’s first step was to create a data lake on AWS, which centrally stores Apollo Tyres’ structured and unstructured data at scale. This data lake provides the foundation for an integrated data platform, which enables Apollo Tyres’ engineers around the world to collaborate in developing cloud-native applications and improve enterprise-wide decision making. The integrated data platform enables Apollo Tyres to innovate new products and services, including energy-efficient tires and remote warranty fulfillment.
Hexagon industrialises high quality additive manufacturing with open ecosystem strategy
Hexagon’s Manufacturing Intelligence division has revealed its plans to build the industry’s most flexible and open additive manufacturing (AM) ecosystem to help overcome complexities in 3D printing processes and support customers in effectively building their product development and manufacturing workflows.
“Just as large manufacturers drove the provision of open factory automation, it’s important we vendors now break down barriers to new manufacturing technologies that offer more flexibility and efficiency. Instead, open data standards should be seen as a growth enabler.”
Benefits of 3D Printed End-Use Parts in a Yacht
3D printing allows the company to make any number of different parts to fit and match exactly with the various spaces onboard a yacht. The CAD model can be created according to the space allowed and fits the needed requirements. With the advancements in filaments and precise high-quality printers like the FUNMAT HT, Nick and Adam are able to have a high control on cost, produce parts faster than traditional manufacturing, and use materials that are better suited to the intended function than in conventional methods. The FUNMAT HT is an open material system that doesn’t come at an extra cost, thus allowing them to test many types of filaments.
Using blockchain to share and monetize telecoms assets
Weaver Labs will be the open telecommunications partner in the Track & Trust project, which aims to deliver a scalable, cost-efficient communications platform and network combining satellite, IoT mesh and blockchain components, serving mostly supply chain use cases. The end solution will be a modular product that will provide a plug and play communication network that allows for end-to-end tracking of the supply chain. This will start from the initial supply of goods/aid and extend all the way to the last-mile shipments, even when limited or no telecommunication infrastructure is available.
New Ultrasonic Welder Mode Uses Real-time Adjustments to Improve Welds
Ultrasonic welding, including the single-parameter weld modes, let electronics manufacturers meet high levels of assembly quality, especially for products built from rigid, molded plastic components. But companies that assemble products from components with more dimensional, flexural, or material-related variability have faced a tougher challenge, one typically met by in-house modifications to ultrasonic welding equipment.
To use a welder equipped with dynamic mode, operators select the single-parameter weld mode that provides the best application results to date. Then, they enter two application-specific “scores,” which act as limits for dynamic mode activity. The first is a material “density” score that characterizes the hardness or resistance of the material that is to receive the welded, staked, or inserted part. Low density scores equate to harder, more resistant materials. The second, the “reactivity” score, affects the reaction time needed to get the desired density setting. In operation, dynamic mode monitors each weld cycle, using the density and reactivity limits to adjust and improve the cycle in response to specific part-to-part variabilities throughout the production run.
Transfer learning with artificial neural networks between injection molding processes and different polymer materials
Finding appropriate machine setting parameters in injection molding remains a difficult task due to the highly nonlinear process behavior. Artificial neural networks are a well-suited machine learning method for modelling injection molding processes, however, it is costly and therefore industrially unattractive to generate a sufficient amount of process samples for model training. Therefore, transfer learning is proposed as an approach to reuse already collected data from different processes to supplement a small training data set. Process simulations for the same part and 60 different materials of 6 different polymer classes are generated by design of experiments. After feature selection and hyperparameter optimization, finetuning as transfer learning technique is proposed to adapt from one or more polymer classes to an unknown one. The results illustrate a higher model quality for small datasets and selective higher asymptotes for the transfer learning approach in comparison with the base approach.