Dr. Lee Clemon, P.E. is a research scientist in advanced manufacturing and high consequence design and licensed professional engineer. He focuses on the interplay of materials, design, and manufacturing for a more reliable and environmentally conscious industrial world. His current research interests are in process improvement and material property manipulation in advanced manufacturing processes, with an emphasis on additive and hybrid additive-subtractive manufacturing through particulate, wire, layer, and ensemble fabrication methods.
Lee M Clemon received his Ph.D. and M.S. in Mechanical Engineering from the University of California at Berkeley, and his B.S. in Mechanical Engineering from the University of Kansas. He was a staff member at Sandia National Laboratories from 2011 to 2017 as a design and R&D engineer on hazardous substance processing systems and manufacturing process development. In May 2018, Lee became a Lecturer at the University of Technology Sydney, in the School of Mechanical and Mechatronic Engineering and is a core member of the Centre for Advanced Manufacturing.
Lee is also active in industry challenges as a member of professional societies, codes & standards committees, and in peer-reviewing research.
- Additive Manufacturing
- Rapid Prototyping Journal
- Journal of Cleaner Production
- International Journal of Precision Engineering and Manufacturing - Green Technology
- Engineers Australia
Codes and Standards
- Resource Development Group, ASME BPVC VIII Div 3
- Standards Australia, MT-006
Can supervise: YES
- Additive Manufacturing
- Sustainable Manufacturing
- Materials processing
- Mechanical Design
- Impulsively loaded pressure systems
- Design in hazardous environments
- Mechanical Design
Song, R, Clemon, L & Telenko, C 2019, 'Uncertainty and Variability of Energy and Material Use by Fused Deposition Modeling Printers in Makerspaces', Journal of Industrial Ecology, vol. 23, no. 3, pp. 699-708.View/Download from: Publisher's site
© 2018, Yale University. Desktop-grade fused deposition modeling (FDM) printers are popular because of compact sizes and affordable prices. If we are moving toward a future where desktop FDM printers are in every school and office, like conventional printers, then these machines will consume a large amount of energy and material. However, it is very difficult to evaluate the environmental impacts of FDM printers since there are so many different brands and types of printers using different raw materials under different scenarios. This study uses data from two different printing sites to evaluate the scenario and parameter uncertainty and variability in energy and material balances for FDM printers. Data from the two makerspaces provide insight into the material and energy consumption data using polylactic acid and acrylonitrile butadiene styrene (ABS) with four types of printers. The use of actual performance data allowed for the additional study of scrap ratio. Regressions provide insight into predictive factors for energy and material consumption. Monte Carlo simulations show the range of energy life cycle inventory values for the desktop-grade FDM printers. From the regressions, Type A Pro was the most energy-intensive machine. For material waste, an open-access makerspace using ABS was associated with higher scrap ratio. Regression analysis indicates that the rate of material usage is not a strong predictor of waste rates. The amount of waste generated across both sites indicates that more ubiquitous access to FDM printing may create a significant addition to the waste stream.
Clemon, LM & Zohdi, TI 2018, 'On the tolerable limits of granulated recycled material additives to maintain structural integrity', Construction and Building Materials, vol. 167, pp. 846-852.View/Download from: Publisher's site
© 2018 Elsevier Ltd Production and maker spaces are increasingly generating mixed plastic material waste of varying quality from 3-D printers. Industrial interest is growing in embedding granulated recycled particulate material additives into a virgin binding matrix. Examples include the introduction of granulated mixed recycled materials into 3-D printer material, concrete, and pavement. The stress load-sharing between the particulate additive and the binding matrix is an important factor in design and development of these composite materials. With mixed material additives, a designer is interested in the variation of such predicted load-sharing. However, experimental development is costly and time-consuming, thus analytical and semi-analytical estimates are desired for accelerated development. In this work, we expand on previous analytically correlated phase-averaged micro- and macrostructural loading to include variational effects present in mixed recycled material. In addition, model trade-offs are provided to aid designers in quickly selecting application specific mixtures. This framework identifies the stress contributions, and their variation, to reduce product development time and costs, which could greatly accelerate material recycling and reuse for improved infrastructure materials, low-cost 3-D printer filament, and reduced waste towards a more circular economy.
Yang, N, Yee, J, Zheng, B, Gaiser, K, Reynolds, T, Clemon, L, Lu, WY, Schoenung, JM & Lavernia, EJ 2017, 'Process-Structure-Property Relationships for 316L Stainless Steel Fabricated by Additive Manufacturing and Its Implication for Component Engineering', Journal of Thermal Spray Technology, vol. 26, no. 4, pp. 610-626.View/Download from: Publisher's site
© 2016, ASM International. We investigate the process-structure-property relationships for 316L stainless steel prototyping utilizing 3-D laser engineered net shaping (LENS), a commercial direct energy deposition additive manufacturing process. The study concluded that the resultant physical metallurgy of 3-D LENS 316L prototypes is dictated by the interactive metallurgical reactions, during instantaneous powder feeding/melting, molten metal flow and liquid metal solidification. The study also showed 3-D LENS manufacturing is capable of building high strength and ductile 316L prototypes due to its fine cellular spacing from fast solidification cooling, and the well-fused epitaxial interfaces at metal flow trails and interpass boundaries. However, without further LENS process control and optimization, the deposits are vulnerable to localized hardness variation attributed to heterogeneous microstructure, i.e., the interpass heat-affected zone (HAZ) from repetitive thermal heating during successive layer depositions. Most significantly, the current deposits exhibit anisotropic tensile behavior, i.e., lower strain and/or premature interpass delamination parallel to build direction (axial). This anisotropic behavior is attributed to the presence of interpass HAZ, which coexists with flying feedstock inclusions and porosity from incomplete molten metal fusion. The current observations and findings contribute to the scientific basis for future process control and optimization necessary for material property control and defect mitigation.
Murray, R, Foy, G & Clemon, L 2019, 'Dimensional Comparison Of A Cold Spray Additive Manufacturing Simulation Tool', SFF Symposium Preceedings, Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, University of Texas, Austin, Texas USA, pp. 1333-1339.
High-velocity particle spray greatly increases metal additive manufacturing deposition speed over
other commercial methods. Accurate prediction and measurement of this process will improve process
control. A LightSPEE3D machine fabricated symmetric copper components. On-board software
predicts the build geometry (.stl) given the input geometry and the build settings. Assessment of
prediction accuracy is needed to enable rapid part design and print setting optimization. White-light
3D-scanning and high-fidelity optical microscopy scans are compared to the simulation and intended
20mm cubes using hausdorf distance:
1. Control-repeated scans: 0.38±0.48mm, max:2.25mm
2. Intended-original vs. scans: 1.42±1.58mm, max:6.72mm
3. Software-predicted vs. scans: 0.44±0.66mm, max:3.97mm
Discrepancies up to 6.72mm and asymmetric fabrication artifacts were identified. The reduction in the
hausdorf distance for simulation vs intended-original, and larger distance of the simulation compared
to control, indicate the simulation tool may enable rapid optimization given over/under spray
quantification. Recommendations for reducing asymmetric fabrication artifacts and over/underspray
Clemon, L 2016, 'Energy and Emission Estimation Uncertainty in Fused Deposition Modeling for a Job-Shop', SFF Symposium Preceedings, Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, University of Texas, Austin, Texas USA, pp. 1878-1889.
Solid freeform fabrication has the potential to affect both financial and environmental
concerns for manufacturing enterprises. However, when planning for installation of a new machine
tool, accurate energy usage estimation relies heavily on the data and model selections of the
estimator. This project used a variety data sources and model decision options to examine the
spread of energy consumption and global warming potential estimates for a fused deposition
modeling machine. In addition to primary and secondary data sources, the use of similar machines
was explored as proxy estimates for the target machine. A Monte Carlo simulation was constructed
to vary the model selections, machine utilization, and data sources. The results indicated data
sources and model decisions had large effects on the output and that most model estimates were
Zheng, B, Yang, N, Yee, J, Gaiser, K, Lu, WY, Clemon, L, Zhou, Y, Lavernia, EJ & Schoenung, JM 2016, 'Review on Laser Powder Injection Additive Manufacturing of Novel Alloys and Composites', LASER 3D MANUFACTURING III, Conference on Laser 3D Manufacturing III, SPIE-INT SOC OPTICAL ENGINEERING, San Francisco, CA.View/Download from: Publisher's site
Clemon, L, Sudradjat, A, Jaquez, M, Krishna, A, Rammah, M & Dornfeld, D 2013, 'PRECISION AND ENERGY USAGE FOR ADDITIVE MANUFACTURING', PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2013, VOL 2A, ASME International Mechanical Engineering Congress and Exposition (IMECE2013), AMER SOC MECHANICAL ENGINEERS, San Diego, CA.
The most widely used methods for toolpath planning in fused deposition 3D
printing slice the input model into successive 2D layers in order to construct
the toolpath. Unfortunately slicing-based methods can incur a substantial
amount of wasted motion (i.e., the extruder is moving while not printing),
particularly when features of the model are spatially separated. In recent
years we have introduced a new paradigm that characterizes the space of
feasible toolpaths using a dependency graph on the input model, along with
several algorithms to search this space for toolpaths that optimize objective
functions such as wasted motion or print time. A natural question that arises
is, under what circumstances can we efficiently compute an optimal toolpath? In
this paper, we give an algorithm for computing fused deposition modeling (FDM)
toolpaths that utilizes Monte Carlo Tree Search (MCTS), a powerful
general-purpose method for navigating large search spaces that is guaranteed to
converge to the optimal solution. Under reasonable assumptions on printer
geometry that allow us to compress the dependency graph, our MCTS-based
algorithm converges to find the optimal toolpath. We validate our algorithm on
a dataset of 75 models and show it performs on par with our previous best local
search-based algorithm in terms of toolpath quality. In prior work we
speculated that the performance of local search was near optimal, and we
examine in detail the properties of the models and MCTS executions that lead to
better or worse results than local search.