Manufacturing + Machines        

Manufacturing + Machines

October 17, 2018

By: Parth Bhatt, Mechanical Engineer, Beyond Design, Inc.

A Shift From Traditional Manufacturing
With technology evolving and being reinvented at its current pace, we’ve noticed a shift in our own practices and workflow and how we approach projects. Specifically, when it comes to manufacturing and machinery. As a mechanical engineer, I am captivated by manufacturing technologies, but is it slowly taking away responsibilities and jobs from the next generation of engineers? In this article, I will share how Beyond embraces new technologies when it comes to engineering and new trends, the direction of manufacturing in America, and the role of AI and robotics in our industry.

Navigating engineering trends is an uphill battle that makes us smarter engineers and keeps us on our toes for the future of manufacturing.

Rapid Paced Machine Learning
As we saw at the IMTS show last month, companies are putting their best foot forward in the 3D printing sector. Specifically, metal additive manufacturing has had an uptick in demand. HP has expanded their product line with the Jet Fusion metal 3D printer. It has an automated materials prep and post processing station keeping it extremely productive and powerful while also including metal printing capabilities. By 2024 there will be a 24% increase in the metal additive manufacturing industry and for good reason. The process increases sustainability and is a more effective way to rapid prototype.

Predictive Maintenance – The Advantages of Using IoT Data
In the same tune as additive manufacturing, predictive maintenance has also been on the rise due to its sustainability results. The wear and tear of equipment can be time consuming, costly and completely unexpected at times. With the emergence of new technologies that can anticipate these effects of overuse, data is collected and analyzed to determine when an issue or error will occur. Although the majority of machines come with the technology that captures this data, it hasn’t been analyzed and effectively used until recently. Machinery learning about itself (specifically it’s weaknesses) will contribute to longer machine life spans, excellent condition and overall health, and keeping productivity rolling steadily.

Spotlight – Railway: The railway industry has latched onto this resource to reduce repair costs and its predicted will spend $30 billion over the course of the next 15 years on more IoT ventures that will continue to optimize their system and keep travelers happy and on time.  GE installed 7,000 of their Trip Optimizer software on locomotives which captured train characteristics and fuel logs, eventually saving $200 billion in fuel costs. The value in these types of predictive IoT software is limitless.

Finite Element Analysis involves using FEM (Finite Element Method) to break down analytical representations of natural and physical phenomena into small elements (resulting in a mesh) and solving for each instance or cell. Nowadays, most of the mathematics of FEA is wrapped in a software that makes it quicker and easier to use (sometimes at the cost of precise control) and negates the need for creating mathematical models from scratch. Today, FEA is an integral part of Computer Aided Engineering and helps shorten design validation phases by cutting down the need for expensive tests that require prototypes to be fabricated and iterated upon.  Below, the FEA of an aircraft’s bearing bracket.

Spotlight – MIT’s Product Generator: Finding the perfect balance between aesthetics and functionality is a constant struggle as an engineer and designer. Theses usual pain points of designing a product (from home appliances to medical, to tools, etc.) can delay productivity and final results. MIT has developed a concept that takes out the human guesswork of this balancing act and incorporates itself into CAD creating several interactive designs and giving real-time feedback. The algorithm can work with almost any CAD and enhance existing workflows while increasing the speed at which decisions are reached and projects are finished.

Evolving Design – Space Hardware: NASA’s AI software designed an antenna that was created completely by a computer program with humans only inputting parameters. The antenna, seen above, is smaller than a quarter and stands at barely 2.5 x 2.5 centimeters. Originally sent into orbit in 2005, the tiny satellite antenna receives commands and sends data back down to Earth. After a series of redesigns, the software completed the final design process in 10 hours. The Project Lead on the program, Jason Lohn, says that, “the software also may invent designs that no human designer would ever think of,” and is now “using the software to design tiny microscopic machines, including gyroscopes, for spaceflight navigation.”

Outsourcing + Replacing
As we’ve seen at past manufacturing shows, and mentioned in our IMTS Expo Highlights last month, there has been a wave of manufacturing opportunities and employers leaving the United States. Companies like DMG Mori (pictured below), Germany’s largest manufacturers of cutting machine tools, has over 7,400 employees and locations in 76 countries as well as 13 worldwide training locations. While the U.S. has lost a staggering 5 million manufacturing jobs since the millennium, with technology as a major factor. The rapid advancement of manufacturing technology has created an intricate skill set that many Americans can’t keep up with or learn fast enough. And the jobs left behind are quickly being filled with AI mechanics.

Galvanizing U.S. Manufacturing 
Although we’ve seen this automation wave take away jobs, places like mHub, a manufacturing incubator in our own city, show signs of growth and promise to bringing manufacturing back to the states. Home to local manufacturers, entrepreneurs, and investors, last year mHUB created 350 jobs and developed 235 products with nearly $19 million in revenue. These efforts create optimism not only in local manufacturing but nationally as well.

mHUB recently hosted their first Women in Manufacturing Day earlier this month, bringing awareness to women in the industry and inspiring a talent pipeline of female STEM leaders. 

Best Bet: AutoML
Another helpful tool to utilize is Google’s AutoMl. Launched to the public this past summer, AutoML had an initial alpha period at the start of the year. The service allows developers, both with and without a machine learning background, to build and train self-learning models. The tool “enables you to perform supervised learning, which involves training a computer to recognize patterns from labeled data.”

Embracing Change
These developments significantly broaden the horizons for machine learning and will completely reshape the approach to model construction in the next years.  We have found that using our own in-house Stratasys printer has resulted in less time and manpower compared to using third-party supply chains. We’ve also found it extremely helpful when we need to go through a round of rapid prototyping for a client and overall we are using fewer materials. To compare, CNC machining uses a pre-determined chunk of material that’ll be cut down, while 3D printing creates layers as needed and builds using only the amount required. As a company that prides ourselves on finding new ways to stay environmentally friendly we take full advantage of our 3D printer and keeping things in house. Both for environmental reasons and the fact that it has greatly increased our productivity.

As we see manufacturing and engineering technologies evolving faster each year, there is only so much we can do as engineers to keep up with their strides. For now, efficient and dynamic workflows will still need a touch of human creativity and intelligence.  For more on our engineering capabilities and to discuss our findings, please drop us a note at Thanks for reading and stay tuned for our next article.

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