If You Are Not in the Next Space, You Are Out of Business

Industry 4.0 technology adoption enables new business opportunities. 3D printers watch the manufacturing process and correct errors, while new algorithms improve operations planning and maintenance.

Aug 07, 2022

From your local machine shop down the road to the multinational conglomerate in the skyscraper in the city center, enterprises are racing to adopt emerging industrial technology. Gone are the days of making fixed investments into equipment and operating it the same way for decades. Today’s equipment is dynamic. Additive manufacturing printers now ‘watch the manufacturing process and then correct errors in how they handle the material in real-time’ while machine-side simulator applications are reducing setup time and preventing scrap through the use of digital twins. New algorithms are materializing to improve fulfillment planning and predict remaining useful life of machines. But what’s most astounding is the breadth of adoption of these technologies across both small and mid-size enterprises and large companies. This week’s newsletter highlights a variety of technology themes enabling the next generation of manufacturing businesses.

Industry 4.0 technology is transforming manufacturers both big and small.

Industry 4.0 technologies are being adopted across all manufacturing enterprises and enabling new business opportunities.

Assembly Line

Capturing this week's trending industry 4.0 and emerging industrial technology media

From Boeing Starliner to Goodyear tire, 3-D printing is becoming manufacturing reality

Date: July 31, 2022
Topics: Additive Manufacturing, 3D Printing
Organizations: Boeing, Goodyear

Tweet | Pocket | Instapaper

By 2030, Goodyear aims to bring maintenance-free and airless tires to market, and 3-D printing is part of that effort for the Akron-based tire-making leader founded in 1898 and named after innovator Charles Goodyear. Currently, about 2% of its production is through additive manufacturing and more integration into the mix is in sight.

Humtown Products, a 63-year-old, family-owned foundry near Youngstown, Ohio, adopted 3-D printing in 2014 as an efficient way to make industrial cores and molds. Today, its additive manufacturing division accounts for 55% of overall revenue and is growing by 50% annually. Pivoting to 3-D printing was the company’s “Kodak moment,” said owner and president Mark Lamoncha. “If you are not in the next space, you are out of business,” Lamoncha said. “This industry is at a tipping point to commercialization and in many disciplines it is the equivalent of driving a race car,” he said.

“For industry, it’s most definitely a competitive advantage because you can design in ways that you can’t with traditional production,” said Melissa Orme, has been vice president of additive manufacturing since 2019, a role that cuts across Boeing’s three business units making commercial airplanes, satellites and defense systems. She works with a team of 100 engineers, researchers and other specialists in advancing the technology’s development. Orme cited advantages in reduced lead times for production by a factor of ten, streamlined design into one large piece for assembly, and increased durability.

Using artificial intelligence to control digital manufacturing

Date: August 2, 2022
Topics: Additive Manufacturing, Computer Vision, AI
Organizations: MIT

Tweet | Pocket | Instapaper

MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time. They used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer. Their system printed objects more accurately than all the other 3D printing controllers they compared it to.

The work avoids the prohibitively expensive process of printing thousands or millions of real objects to train the neural network. And it could enable engineers to more easily incorporate novel materials into their prints, which could help them develop objects with special electrical or chemical properties. It could also help technicians make adjustments to the printing process on-the-fly if material or environmental conditions change unexpectedly.

How King Arthur Baking Produces 100 Million Pounds of Flour per Year

Grinding Simulation Enables Growth in Custom Tooling

Date: August 3, 2022
Author: Evan Doran
Topics: Simulation

Tweet | Pocket | Instapaper

Even the best grinding simulation has flaws — namely, a reliance on perfection. Real-world scenarios on the shop floor can diverge from the tested parameters, requiring adjustments to achieve the performance promised in the simulation. Gorilla Mill, a toolmaker based out of Waukesha, Wisconsin, relies on ANCA’s CIMulator3D software to control for these differing parameters.

By providing a virtual testing ground for complex custom designs, the software ensures tool quality, prevents scrap and streamlines the process of developing customer prints. A machine-side simulator application reduces setup time by highlighting how differences between ideal and actual circumstances will affect the ground part and by enabling machinists to adjust settings to achieve optimal results rather than regrind wheels.

Driving digital transformation in manufacturing at the edge

Optimizing Order Picking to Increase Omnichannel Profitability with Databricks

Date: August 4, 2022
Authors: Peyman Mohajerian, Bryan Smith
Topics: BOPIS, Operations Research
Organizations: Databricks

Tweet | Pocket | Instapaper

The core challenge most retailers are facing today is not how to deliver goods to customers in a timely manner, but how to do so while retaining profitability. It is estimated that margins are reduced 3 to 8 percentage-points on each order placed online for rapid fulfillment. The cost of sending a worker to store shelves to pick the items for each order is the primary culprit, and with the cost of labor only rising (and customers expressing little interest in paying a premium for what are increasingly seen as baseline services), retailers are feeling squeezed.

But by parallelizing the work, the days or even weeks often spent evaluating an approach can be reduced to hours or even minutes. The key is to identify discrete, independent units of work within the larger evaluation set and then to leverage technology to distribute these across a large, computational infrastructure. In the picking optimization explored above, each order represents such a unit of work as the sequencing of the items in one order has no impact on the sequencing of any others. At the extreme end of things, we might execute optimizations on all 3.3-millions simultaneously to perform our work incredibly quickly.

auton-survival: An Open-Source Package for Regression, Counterfactual Estimation, Evaluation

Date: August 4, 2022
Authors: Chirag Nagpal, Willa Potosnak
Topics: Operations Research, Predictive Maintenance

Tweet | Pocket | Instapaper

Real-world decision-making often requires reasoning about when an event will occur. The overarching goal of such reasoning is to help aid decision-making for optimal triage and subsequent intervention. Such problems involving estimation of Times-to-an-Event frequently arise across multiple application areas, including, predictive maintenance. Reliability engineering and systems safety research involves the use of remaining useful life prediction models to help extend the longevity of machinery and equipment by proactive part and component replacement.

Discretizing time-to-event outcomes to predict if an event will occur is a common approach in standard machine learning. However, this neglects temporal context, which could result in models that misestimate and lead to poorer generalization.

Capital Expenditure

Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain

Bosch’s new partnership aims to explore quantum digital twins

Date: August 4, 2022
Organizations: Bosch

Tweet | Pocket | Instapaper

Industrial giant Bosch has partnered with Multiverse Computing, a Spanish quantum software platform, to integrate quantum algorithms into digital twin simulation workflows. Bosch already has an extensive industrial simulation practice that provides insights across various business units. This new collaboration will explore ways quantum-inspired algorithms and computers could help scale these simulations more efficiently.

One of the most promising use cases for the new quantum algorithms is creating better machine learning models more quickly. Hernández Caballer said quantum computing shows tremendous promise in use cases with many combinations of parameters and materials. This early research could give Bosch a leg up in taking advantage of these new systems to improve machine learning and simulation.

Nexeon raises over $200m to fund battery materials manufacturing

Date: August 3, 2022

Tweet | Pocket | Instapaper

Further to the announcement of its strategic partnership with and investment from SKC on 26th January 2022, Nexeon, a battery materials developer and manufacturer, has completed the second round of its fundraising, resulting in a total investment of $170m. The capital raised will provide Nexeon with further resources to accelerate the expansion of its own manufacturing capabilities to mass produce tens of thousands of metric tonnes annually of its silicon-based anode materials for use in rechargeable Lithium-ion batteries.

SoftBank-backed Chinese robot maker, JAKA, to build plant in Toyota's backyard

Date: August 4, 2022
Topics: Cobot

Tweet | Pocket | Instapaper

JAKA is one of a number of companies looking to challenge Denmark-based Universal Robots’s lead in collaborative robots, also called co-robots, which play a complementary role to the hulking machines on automated assembly lines. Other contenders include Japan’s Fanuc and Chinese startup Elite Robot. JAKA’s advantage comes from the size of its home market, as well as its track record as a Toyota supplier. The company’s mean time between failure is 80,000 hours, the equivalent of one incident every nine years or so.

JAKA now makes all of its robots in Changzhou, China, at a factory with an annual capacity of about 10,000 units. For JAKA, the Nagoya plant is not only about serving Japanese buyers. In preparation for the expansion, JAKA Robotics raised a total of 1 billion yuan ($148 million) from investors including SoftBank Vision Fund 2 and Prosperity7 Ventures, a fund under Saudi Aramco.

Exponential Industry

Discussion about this post