This is the second part of a two part interview with Lonnie Love from Oak Ridge National Laboratory. Check out the first part, here.
Outside of large scale metal systems, are there other areas you are exploring? Tell us about any upcoming technologies in the AM space that you think have the capability to bring scale, volume and size to make AM economically viable for volume production.
A couple:
Data analytics and artificial intelligence (AI): AM is very data intensive but few are really using that data to improve the process or designs. I believe there is an enormous potential to collect data and use it to validate and improve the process, as well as qualify additive parts. At the MDF, we have key experts working with other government agencies and industry to develop new strategies, software tools, and qualification frameworks increasing the confidence of additive components.
Microfactories: I believe we are already seeing the start of hybrid systems where additive is a component in an integrated work cell. There is tremendous potential in the area of hybrid machines where you are printing systems rather than parts. AM can be one part of a system that includes subtractive, pick and place, multi-material, etc. Traditional factories are geared towards manufacturing one thing a million times. This leads to centralization (e.g. massive automotive assembly plants). I think the microfactory could enable massive decentralization where a factory can produce a million different things one at a time, enabling local manufacturing. At the core, it’s really getting us back to pre-industrial revolution societies where every town had a blacksmith, a carpenter, etc., where you locally made what your town needed with local talent and local resources. I think this is what the fourth industrial revolution could enable.
PROCESS MODELING AND VALIDATION FOR METAL BIG AREA ADDITIVE MANUFACTURING
An extended summary of the publication by Srdjan Simunovic, Andrzej Nycz, Mark W. Noakes (Oak Ridge National Laboratory, Oak Ridge, TN, USA) Charlie Chin and Victor Oancea (Dassault Systemès SIMULIA Corporation, Johnston, RI, USA) at 2017 Science in the Age of Experience
The AM process simulation framework lately developed by Dassault Systèmes is validated for the Laser Direct Energy Deposition (LDED) process. ORNL used the new simulation framework to simulate another large-scale metal additive manufacturing process that uses a wire fed arc process and then ORNL validated the simulation results against experimental measurements as part of research activities that are funded by the Department of Energy’s Advanced Manufacturing Office. A continuously fed metal wire is melted by an electric arc that forms between the wire and the substrate, and deposited in the form of a bead of molten metal along the predetermined path. This process is modeled by computational simulation of material deposition with heat transfer first, followed by the structural analysis based on the temperature history for predicting the final deformation and stress state.
A partially clamped curl bar was printed with six thermocouples drilled into each side of the build plate and three additional thermocouples attached to the table on each side of the build plate. The temperature from thermocouples was compared to simulation results. With simple choices of constant convection coefficients, the curves compare well. The temperature histories from heat analysis were mapped into subsequent structural analysis. The overall upward bending distortion at the top of the bar caused by material contraction during cooling was captured well with simulation.
A bigger thin-wall structure was also printed and simulated. In the early stages, there is more conduction into the massive build plate and positioning table which is reflected in lower temperatures. As the print builds up, the intensity of the heat conduction from the heat source into the build plate and table is reduced, so that the overall temperature increases. Using the constant film coefficients for the printed part and the build
bar, respectively, the simulated temperatures again matched well the experimental thermocouple data for the first hour of simulated printing. Afterwards, the simulations exhibited slower cooling rates which is associated with increasing effect of the radiative heat transfer as the wall grows higher. Using the temperature dependent combined heat transfer model, developed for a similar AM process, good correlation with the experiment was then found.
Finally, a 2.1m high excavator arm was printed. The real-world printing time for this part is around 4.6 days. Simulation is a clear incentive to replace the physical print of this part (a demonstrative model with ~2.2 million elements takes ~6 hrs. of simulation time). It was shown that simulations can be effectively used to assess the temperature history, final distortions and the residual stresses in the printed part, and investigate efficiency of various printing strategies.
In summary, validated parts range from small (0.01m high) to large (2m high). The small parts were used to develop the best simulation practices and to calibrate the process and boundary condition models. Large parts demonstrated the feasibility of computational modeling for simulating practical large-scale metal systems manufacturing problems. Results show that with minimal calibration efforts a good correlation with the physical experiments was achieved.
ABOUT OAK RIDGE NATIONAL LABORATORY
Oak Ridge National Laboratory is managed by UT-Battelle for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time.
This research is supported by DOE’s Office of Energy Efficiency and Renewable Energy-Advanced Manufacturing Office under the Manufacturing Demonstration Facility at ORNL. AMO supports early stage applied research and development of new materials, information, and processes that improve American manufacturing’s energy efficiency, as well as platform technologies for manufacturing clean energy products.
Lonnie J. Love received his B.S. and M.S. degree in mechanical engineering from Old Dominion University, and a Ph.D. in mechanical engineering from the Georgia Institute of Technology. He is currently a distinguished research scientist in the Energy and Transportation ScieDivision and group leader of the Manufacturing Systems Research Group at the Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL). He has made major contributions at ORNL as a researcher, a leader, and an innovator in advanced robotics and additive manufacturing (AM). His research has most recently focused on largescale and highspeed advanced AM and 3D printing.
