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.



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.



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.


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.