Can Data Connectivity Catapult AM Forward? (Authentise Weekly News-In-Review – Week 76)

AM is a manufacturing technology like many other but, unlike most, has numerous variables at play in making the final part. Most are controlled by the initial setup by the lab technician, but after that there is very little that goes in the way of making sure that the best result is achieved. In-print monitoring is crucial yet still hard to apply properly. Techniques like machine learning enable automated pinpointing of potential issues, stopping before precious time and resources are wasted. This will be made possible thanks to a slew of sensors that power computer-vision algorithms. The bandwidth required for these applications will be huge, something that coming 5G networks will be able to support, together with other IIoT applications previously impossible. In the future, self-correcting printers will make AM much more reliable and efficient. There is already so much that the data coming from printers can teach to improve operational performance. At Authentise we have developed smart analytical tools to help you leverage all that data, and are now moving towards letting you control printer directly, with remote and automated tools.

Machine Learning and Metal 3D Printing Combine for Real-Time Process Monitoring Algorithm

Two researchers from the College of Engineering at Carnegie Mellon University (CMU) have figured out how to combine 3D printing and machine learning for real-time process monitoring, a practice which can detect anomalies inside a part while it’s being 3D printed. Their research could one day lead to self-correcting 3D printers.

Read the full story here.

New whitepaper examines smart metrology for additive manufacturing

If factories are to become faster and more flexible, inspection is a bottleneck to overcome, especially in industries where 100% inspection is required. In this new whitepaper by Autodesk and Faro, smart metrology for the additive manufacturing industry. Components made by additive manufacturing technologies (AM) have more variables than machined parts. Faster inspection for additive manufacturing is more challenging because AM processes are not as accurate as cutting metal. Better metrology for AM will help reduce feedstock and costs.

Check out the whitepaper here.

How Will 5G Change Robotics and the IIoT?

As efficient and effective as 4G technology is, it pales in comparison to the faster, more reliable platform of 5G. If the new protocol meets its advertised speeds of 100 gigabits per second, this rates 5G at a speed of 1,000 times faster than 4G. Given the increasing size of datasets, the greater need for real-time data processing and more reliance on large-scale and long-term data storage, it’s easy to see how 5G benefits everyone.

Read the full article here.

 

Follow us on Twitter to keep updated on AM & IIoT related news as well as updates to Authentise’s services!

Rapid + TCT

We are going to be present at this year’s Rapid + TCT show from the 23rd to 26th of April in Fort Worth, Texas.

Our CEO Andre Wegner and Software Engineer Anusha Iyer will hold a speech titled “Machine Learning in Additive Manufacturing (Intermediate)”.

When: Wednesday, April 25

Time: 1:00 PM – 1:25 PM

Please don’t hesitate to reach us if you wish to discuss the future of AM production.

Business inquiries: Frank Speck, CMO – frank@authentise.com

The Trifecta of Manufacturing Agility: Software, Hardware and the IIOT (Authentise Weekly News-In-Review – Week 65)

The world of today’s economy requires businesses to keep a quick pace with the demands of the market. The globalization of products and services made it so that, to stay competitive, product iteration and deployment must be quick and effective. Fortunately, we have the technological foundation to enable this kind of model. A combination of hardware, software and analytical tools put businesses in the position to close the iterative circle of prototyping and manufacturing in a lean fashion. Manufacturing technologies like 3D printing and hybrid manufacturing platforms give the tools needed to experiment and ultimately produce finished goods for almost any circumstance. The digital world we have weaved enables CAD and software to travel and be shared, creating an ecosystem in which everyone is uplifted. Finally, the IIoT is empowering everyone through the might of data-driven insights, interconnecting information hotspots and putting processing power to work on spotting operational inefficiencies.

Engineers Create 4D Printer that Combines Four 3D Printing Techniques

Engineers Create 4D Printer that Combines Four 3D Printing Techniques

[3D printing] Still somewhat in its infancy, the last decade has witnessed a generous body of research that seeks to exploit its uses more than we could have ever imagined. One example comes from a team from the Georgia Institute of Technology, led by Professor H. Jerry Qi from the University’s George W. Woodruff School of Mechanical Engineering. Their aim was simple: 4D printing. Or put in a different way, to create a machine that combines multiple materials into one 3D printer.

Read the full article here and the paper here.

3D Life Launches 3D Anatomical Heart Library

Justin Ryan, Research Scientist at the Cardiac 3D Print Lab, Phoenix Children’s Hospital, holds a 3D printed heart model. Photo via Philips USA.

[3D Life] are meeting the demand for anatomical models by launching a 3D anatomical heart library, providing medical professionals with access to 3D printing. The USA’s National Institutes of Health offers a similar library covering a broader range of medical models freely available as .STL files, but without the printing services offered by 3D Life.

Leonardo Bilalis, Design Engineer, hopes that the library will promote “better knowledge of [how 3D printed] organs can be used for surgery preparation for complex problems”, “making operations shorter and more efficient.”

Read more about it here.

IIoT Analytics are Just Numbers, Unless You Solve a Business Problem

IIoT-Analytics-are-Just-Numbers-Unless-You-Solve-a-Business-Problem

There is lots of excitement about analytics and machine learning. It’s moving through its hype-cycle but still faces many challenges. Putting aside other challenges, solving real business issues is still a major shortcoming. If your reporting and analytics is counting “things” – just buy a calculator. Find a business problem to solve, and then you will see real value.

 

Read the full article here.

 

Follow us on Twitter to keep updated on AM & IIoT related news as well as updates to Authentise’s services!

5 Ways You Can Use Data to Improve Your Additive Manufacturing Operations in 2018

In the run-up to the New Year, we’re summarizing our key learnings of 2017 into a quick checklist for your 2018 Additive Manufacturing IT endeavors. Taking these seriously will reduce your Total Cost of Ownership and make sure you are production ready.

Let’s start with the easiest:

 

Performance Measurement

The simplest initiative is to start measuring your Overall Equipment Efficiency, Failed Build levels or other Key Performance Indicators using data from your machines. With our Machine Analytics module, you can start today with an existing dashboard or incorporate the data into your own solutions.

 

Transparency

Many of you work in organizations that already operate many printers, though you might not know where they are, what they do and how often they do it. Using device data to track your assets and processes is the first step to creating a more transparent network and learning from your experience. Our Machine Analytics module includes a Gantt chart of all historical prints fed simply by data from machines.

 

Process Automation

To see immediate ROI from your data, start by identifying manual steps you could cut out with data: How can your sales team see when the printers are available? Can we alert customers or sales teams automatically if the print fails or once it goes into production? These and many other options are part of our MES solution.

 

Traceability

Additive production has many advantages over subtractive and other processes. Among them is your level of data access – let’s use it. Provide all required traceability documentation – and more – to your customers by extracting data from the machines and digitalizing process events. MES does this, and we’re augmenting it every day: We’re excited about announcements forthcoming in 2018.

Quality

This, of course, is the big one: Using all the data generated to draw conclusions about quality that could influence the incidence of testing. There’s still plenty of work to do, but it is never too late to start developing strategies to capture all that data in order to make the necessary abstractions.

 

Call us to discuss how we can help you optimize data to improve your additive manufacturing process in 2018. The industry has tremendous opportunities if we use it wisely.

Merry Christmas and a Happy New Year from the Authentise Team!

Machine-Driven Performance for the Digital Thread (Authentise Weekly News-In-Review – Week 32)

Machine-learning methods are transforming image recognition and problem-solving skills in computers with hardware and simulation algorithms that are capable of providing actionable insights. Businesses are already starting to employ these new tools to gain a more efficient and productive workflow, automating the digital thread beyond simple dematerialization, as well as stepping into smart decision-making.

Machine Learning “Surfnet” Creates 3D Models From 2D Images

The SurfNet process. Image via Purdue University Mechanical Engineering.
The SurfNet process. Image via Purdue University Mechanical Engineering.

New research has developed AI technology that can transform 2D images into 3D content. The method, called SurfNet, has great potential in the field of robotics and autonomous vehicles, as well as creating digital 3D content. The research was led by Purdue University’s Donald W. Feddersen Professor of Mechanical Engineering, Karthik Ramani.

Karthik Ramani explains this process:

“If you show it hundreds of thousands of shapes of something such as a car, if you then show it a 2D image of a car, it can reconstruct that model in 3D”.

Read more about Surfnet here.

 

MIT’s Robotic Arm 3D Printers Take The Stress Out of Architecture

4 self-supporting gridshell test designs, 3D printed in plastic using a robotic arm. Image via 3D Printing and Additive Manufacturing journal.

Stress Line Additive Manufacturing (SLAM) is an architectural 3D printing concept out of MIT. It challenges the typical FDM approach to construction, accounting for structural stresses caused by the act of depositing material layer-by-layer. […] In further development, the researchers will apply further architectural theory to the designs and make solid filled objects. They also hope to be able to integrate sensors into the system so the robotic arm intelligently adapts the design as it prints.

Read more about it at 3D Printing Industry.

 

Geometric search engines – How useful are they?

Digitisation presents challenges as well as opportunities: On the one hand we’re surrounded by more data than ever before, yet on the other, we have more efficient tools to manage the onslaught. […] In the process of searching for similar designs, while we have traditional search methods like text based and keyword based, they do fall short at times. Geometric Search Engines (GSEs) can significantly improve speed and efficiency of the digital thread in additive manufacturing to help solve these challenges.

Read the full article at Develop3D.

 

Don’t forget to come back next week for another news’ roundup. In the mean time, our Twitter feed should keep you updated with the latest AM/IIoT news!

The Hybrid Future in Human-Robot Relationships (Authentise Weekly News-In-Review – Week 29)

The manufacturing plant is now more than ever the product of synergies derived from multiple, different actors playing their part for the greater objective. There is no “killer app” in the manufacturing industry and AM will need traditional manufacturing just as much as robots will still need human input to get around their limitations. The non-zero-sum game nature of manufacturing is exemplified by the international efforts to find balances in which new production processes can get the best of both worlds. For example, 11 partner groups from Germany and the Netherlands are starting new research efforts to explore the potential of hybrid manufacturing, particularly helpful for complex products like electronics. On a broader perspective, human-robot relationships have never been stronger. Those people afraid of giving up their jobs to robotic counterparts can put their hearts at ease (for now): automation is bringing greater productivity by putting tireless androids able to execute the most boring tasks under the human supervision. Similarly, deep learning automation is helping businesses deploy their time and resources more intelligently, using machine vision and actuation where the humans could be better employed doing something higher level.

German company Neotech AMT announces two new fully additive 3D printed electronics projects

A circuit board created using 3D printing technology. Image via Neotech.

German electronic 3D printing company Neotech AMT GmbH has announced it will engage in two new projects to advance additive manufacturing. The first project, known as ‘Hyb-Man’, will bring together 11 partner groups from Germany and the Netherlands with the aim of developing hybrid manufacturing techniques. While the second project – AMPECS – will focus on the printing of ceramic substrates.

The resultant process lines will address the needs for low volume agile manufacture within a single platform. – Dr. Martin Hedges, Managing Director of Neotech

Read more about the projects here.

Online Retail Boom Means More Warehouse Workers, And Robots To Accompany Them

There’s a good chance something you’ve bought online has been in the hands of a “picker” first. These are the workers in warehouses who pick, pack and ship all those things we’re ordering. At Amazon and other companies, they’re working side by side with robots. Experts say while the robots are replacing some human workers, the machines aren’t quite ready to take over completely.

Read the full article at NPR.

Two Apple Engineers Want To Create The Brain For Fully-Automated Manufacturing

Assembling TV sets

Anna-Katrina Shedletsky, along with another former Apple engineer, Samuel Weiss, have founded manufacturing startup Instrumental. The Los Altos, California-based startup builds a camera system that takes high-definition pictures of the product during various stages of the assembly process and sends it back to the company. Instrumental software then lets companies remotely track how their products are being assembled. But the bigger picture vision for the company is introducing more automation into what is a still very manual process. Instrumental has begun deploying machine learning techniques to pick out any manufacturing anomalies and track where things go wrong.

Read more about Instrumental and their goals here.

 

We hope to see you again next week as we publish another edition of our News-In-Review! Also, check out our Twitter feed for more AM/Automation/IIoT related news and insights.