Digital manufacturing with Dean Bartles
Our guest of this episode is Dr. Dean Bartles, a seasoned operations professional with four decades of management experience in manufacturing and technology. He is currently the President and CEO of the Manufacturing Technology Deployment Group, Inc., a not-for-profit holding company of both the National Center for Defense Manufacturing and Machining (NCDMM) and Advanced Manufacturing International, Inc. (AMI), a new subsidiary and sister company to NCDMM.
Dr. Bartles’ skills in advanced manufacturing technology areas include Digital Manufacturing, Model Based Enterprise, Industry 4.0, smart manufacturing, advanced robotics and brilliant factory concepts.
In our episode we discuss industry 4.0, optimization and control of processes, automatization and gathering data and how the companies can benefit from them. We will also talk about the place of AI and ML in manufacturing and how to prepare a manufacturing business for digital transformation.
Michał Grela: Hello and welcome to another episode of I.T. Insights by Future Processing, where we talk about business and I.T. challenges with tech leaders.
My guest today is Dr. Dean Bartles, a seasoned operations professional with four decades of experience in manufacturing technology. Dean, would you be so kind as to introduce yourself?
Dr. Dean Bartles: My name is Dean Bartles and I am currently the CEO and president of a not-for-profit called the Manufacturing Technology Deployment Group (MTDG). I formed this about two years ago as a parent company over the National Center for Defense Manufacturing and Machining (NCDMM), which has been around for 20 years.
We also run the National Additive Manufacturing Innovation Institute, branded as America Makes. My career started at the Fairchild Republic company making the A-10 airplane, followed by 31 years at General Dynamics, where I set up manufacturing lines in Egypt and Turkey.
I later served as the executive director of the Digital Manufacturing and Design Innovation Institute in Chicago, worked at the University of New Hampshire, and served as president of the National Tooling Machining Association.
Michał Grela: Digital manufacturing, smart manufacturing, and the Model-Based Enterprise are all on your plate right now. What exactly does smart manufacturing mean today?
Dr. Dean Bartles: I think it is synonymous with the German term Industry 4.0. The first step is connectivity – getting machines connected to understand what is happening by collecting data. You can use an IoT edge device, which is very inexpensive, to get data off any machine tool regardless of its age.
Once you analyze that data to improve efficiency, the next step is customer transparency. Large companies want to see where their parts are in real-time within a subcontractor’s process. In the future, we will see machines that can detect drift in quality from a previous operation and automatically compensate for it in a closed-loop fashion.
It also involves predictive maintenance, where you can fix a bearing on a weekend before it fails during a production shift.
Michał Grela: Another topic is Industrial AI. What is that about?
Dr. Dean Bartles: The term was coined by my friend, Professor Jay Lee. It involves developing algorithms that take manufacturing data to detect patterns and make closed-loop adaptive control adjustments.
This leads to the future of manufacturing with less human intervention and higher quality parts.
Michał Grela: Which companies can tap into these benefits?
Dr. Dean Bartles: All manufacturing companies can benefit, but it has been a harder sell for small-to-medium businesses. Large companies like Siemens and Rockwell can spend millions on experts, but small guys only have a few machines to learn from. I believe the future is a multi-sided sharing platform.
My company is working on one where over 200 companies share data from 3D printing processes. Eventually, we want to convince them to share that data with “smart guys” who can develop algorithms so that everyone on the platform benefits from the resulting machine learning.
Michał Grela: How can companies ensure they don’t stay behind?
Dr. Dean Bartles: It is going to be driven by customer push. For example, the Department of Defense (DoD) was leery about buying 3D printed metal parts because of variability between different machines and suppliers. They conducted a study giving different plants the same machine model and the same powder lot to build the same part, and they still found variability.
Now, the DoD requires suppliers to provide a Json file containing all key parameter data – such as laser beam power, scanning speed, spot size, and humidity – so their engineers can interrogate the data to ensure quality.
Michał Grela: Are there specific use cases for this technology today?
Dr. Dean Bartles: Yes, we developed a prototype additive manufacturing supply chain for the DoD. When the DoD puts out an RFQ, companies must agree to provide that Json data file along with the part. Some companies consider their data “secret sauce,” but the DoD may still require them to maintain that data for 10 years so they can perform a root cause analysis if a part fails in the field.
We have already provided over 1,200 parts to the DoD through this program. Big OEMs like Raytheon, Lockheed Martin, General Dynamics, and Northrop Grumman are on our advisory board and are seeing the value in sourcing parts through this data-rich supply chain.
Dr. Dean Bartles: I’m also excited about ICME (Integrated Computational Material Engineering). Using simulation software, if you provide a machine model and the morphology of the powder being used, the software can tell you exactly which parameter settings to use to achieve a specific microstructure and mechanical properties.
By controlling grain size and orientation through machine settings, you control the final quality of the part.
Michał Grela: That sounds like a very exciting direction for the industry. Thank you, Dean, for sharing your insights.