Reactive Extrusion additive manufacturing (REAM) is an additive manufacturing (AM) process in which a liquid thermoset feedstock is deposited and cured in situ. The REAM process has many unique advantages such as the ability to fabricate parts with isotropic mechanical properties, initiate curing without external energy input, and utilize high extrusion rates. However, there are many complex factors such as curing kinetics, feedstock rheology, and thermal gradients that can influence the capabilities of the REAM system and the resulting dimensional accuracy and mechanical properties of the fabricated parts. Understanding the processing parameters of an AM process is crucial to resolving complex features and producing high-quality parts repeatably. While REAM has been documented in the literature, no in-depth studies investigating these processing parameters exist. In this work parameters such as the extrusion rate, deposition speed, and the elapsed time between layers are investigated, and their effect on resulting part properties are characterized. Additionally, the ability to fabricate unsupported overhangs and bridges is also studied as a function of different processing parameters. By understanding these variables and their effects, the system can be tuned to improve accuracy, repeatability, and feature resolution.

Radio Frequency Additive Manufacturing (RFAM) is an additive manufacturing process that utilizes Radio Frequency (RF) radiation as the sole heat source to heat and sinter an entire object simultaneously. Parts are fabricated selectively from powders, similarly to powder bed fusion but with RF radiation replacing laser or electron beams as the energy source. Typical polymer powders, such as nylon 11 or 12, are relatively transparent to RF energy sources, but polymer powders that are doped with conductive additives selectively absorb RF energy. By depositing electrically conductive dopants into selective regions of an insulating polymer powder bed, those regions of the powder bed can be sintered quickly and volumetrically via RF radiation into engineered parts. Previous work demonstrated that heating uniformity is a challenge related to the dopant density and the geometry of the part, but simulations suggested that it can be addressed by functionally (spatially) grading the dopant density. In this work, those simulation-based, functionally graded designs are fabricated for the first time via a combination of binder jetting additive manufacturing and sintering in an RF heating apparatus. The heating uniformity and geometric accuracy of the functionally graded samples are evaluated and compared to that of uniformly doped samples. The results show that functionally graded samples exhibit enhanced heating uniformity and improved geometric accuracy.

This editorial is the first in a series of editorials exploring the future of engineering design from the perspective of thought-leaders within the engineering design community. Five early- and mid-career researchers were invited to present their perspectives at a meeting of the Design Society in March 2025 on the campus of the Georgia Institute of Technology. Jessica Menold focused on teaming and collaboration in the context of artificial intelligence (AI); Kosa Goucher-Lambert on design cognition; Astrid Layton on sustainable and resilient design; Mohsen Moghaddam on virtual reality (VR)/augmented reality (AR)/extended reality (XR) in engineering design; and Zhenghui Sha on the design of complex sociotechnical systems. Each presentation was followed by roundtable discussions of the challenges and opportunities posed by the speaker. The presentations and audience discussions are summarized in this editorial, along with a brief overview of some of the opportunities for further work. The goal of this editorial series is to inform the broader community of the progress, challenges, and opportunities associated with important themes within our research community and to offer a starting point for those who seek to investigate these topics and continue to advance the state-of-the-art in our field.

Topology optimization is one of the most flexible structural optimization methodologies. However, in exchange for its high level of design freedom, typical topology optimization cannot avoid multimodality, where multiple local optima exist. This study focuses on developing a gradient-free topology optimization framework to avoid being trapped in undesirable local optima. Its core is a data-driven multifidelity topology design (MFTD) method, in which the design candidates generated by solving low-fidelity topology optimization problems are updated through a deep generative model and high-fidelity evaluation. As its key component, the deep generative model compresses the original data into a low-dimensional manifold, i.e., the latent space, and randomly arranges new design candidates over the space. Although the original framework is gradient free, its randomness may lead to convergence variability and premature convergence. Inspired by a popular crossover operation of evolutionary algorithms (EAs), this study merges the data-driven MFTD framework and proposes a new crossover operation called latent crossover. We apply the proposed method to a maximum stress minimization problem in 2D structural mechanics. The results demonstrate that the latent crossover improves convergence stability compared to the original data-driven MFTD method. Furthermore, the optimized designs exhibit performance comparable to or better than that in conventional gradient-based topology optimization using the P-norm measure.

The Journal of Mechanical Design Reviewers with Distinction Award is given to reviewers who have made a meritorious contribution to the journal in terms of the number, quality, and turnaround time of reviews completed during the past year. The prize is an award certificate. Winners are announced in the journal, posted on the ASME Digital Collection and the JMD companion website. The 2023 winners of this award are as follows:Xiaoping Du—Indiana University-Purdue University Indianapolis, USAAhmad Najafi—Drexel University, USAAnurag Purwar—Stony Brook University, USASandipp Krishnan Ravi—–GE Aerospace Research, USAZhufeng Shao—Tsinghua University, ChinaZequn Wang—University of Electronic Science and Technology of China, ChinaHao Wu—University of Illinois Urbana-Champaign, USAWe would like to congratulate all the award recipients and look forward to continuing to work with the entire ASME community of editors, authors, reviewers, and staff to bring JMD to the next level of excellence.

This paper examines the emissions tradeoffs of additive manufacturing (i.e., 3D printing) using plastic waste in fused granular fabrication (FGF) versus traditional fused filament fabrication (FFF) and injection molding (IM). A ‘cradle-to-gate’ life cycle assessment (LCA) was utilized to compare these methods, built in OpenLCA v1.11.0 with the Ecoinvent v3.9.1 database. Different scenarios were used to evaluate the impacts of varying transportation and material inputs, highlighting critical emission contributors in manufacturing plastic goods. FGF with waste plastic can significantly reduce climate impact by 82.1% relative to FFF and 70.6% relative to IM for a specified unit product. Even with varied transportation and materials, FGF is a lower CO2-equivalent emitting method. Utilizing FGF with waste plastic as a manufacturing method could reduce emissions and divert plastic from landfills and the environment, thereby contributing to a circular plastic economy.

In this work, a large-scale, high-viscosity vat photopolymerization additive manufacturing system is designed and fabricated to print 3D structures as large as 370 x 300 x 370 mm(3) out of high-viscosity, low-reactivity elastomeric resins. A detailed overview is presented of the printer's design and capabilities, including a resin processing sub-system that stores and spreads high-viscosity resin; a roll-to-roll variable tensioning system to mitigate the separation forces after printing each layer; and a light patterning system that generates high-intensity light patterns across an area of 370 x 300 mm(2) with a resolution of 3840 x 4320 pixels. The ability to print with both high-viscosity and low-reactivity resins and resins that require high-intensity light enables additive manufacturing of new classes of materials that could not be printed previously using vat photopolymerization techniques. These materials include highly reinforced silica nanoparticle composites, high-molecular-weight polymers such as silicones and acrylate or methacrylate resins, and low-reactivity resins such as photocurable platinum-catalyzed liquid silicone rubber.

This work details the design and fabrication of a large-volume LCD 3D printer that is capable of printing structures as large as 370 × 300 × 370 mm3 from high-viscosity, paste-like elastomeric resins as well as low-reactivity resins that require orders of magnitude greater exposures of light than traditional SLA resins for curing. A detailed overview of the printer design and capabilities is presented, including a novel method of recoating high-viscosity resins for SLA and a roll-to-roll film tensioning technique for reducing the separation forces experienced by the printed structure between each layer. This work also describes the scaling of a novel light-patterning technique that uses dual LCDs with wire grid polarizers that can generate high-intensity UV 2D patterns over areas exceeding 370 × 300 mm2 with a resolution of 3840 × 4320. This technique enables the polarization and patterning of high-intensity UV light, resulting in shorter curing times and the ability to print a wider range of stereolithography feedstocks. The ability to print with both highviscosity elastomers and low-reactivity resins enables additive manufacturing of new classes of materials that could not previously be printed using vat polymerization techniques. These materials include highly reinforced silicone nanoparticle composites, high-molecular-weight polymers such as silicones and acrylate or methacrylate resins, and low-reactivity resins such as photocurable platinum-catalyzed liquid silicone rubber.

This research describes the design for additive manufacturing (AM) of a pneumatic soft robotic actuator that is uniquely enabled by a novel vat photopolymerization (VPP) AM process. Specifically, the device is a PneuNet style bending mechanism that is designed specifically for a high-viscosity, large-scale VPP AM system. This novel system is capable of fabricating high performance elastomeric materials, such as polydimethylsiloxane (PDMS) loaded with fumed silica nanoparticles or other fillers that result in viscosities too high to be printed with most existing VPP processes. Herein, a tough double network PDMS (DN-PDMS) is designed for printing pneumatic actuators. The mechanical properties of the DN-PDMS, along with the geometric resolution of resulting features, are characterized through experiments, and the results are used to guide the design process of the pneumatic actuator. The results show that the broad design space accessible through the high-viscosity VPP process corresponds with the target geometry identified in this research (determined by maximum bending angle). This design would be challenging or impossible to manufacture using other means; however, this VPP process enables printing of high viscosity materials, large overhangs, internal features, and a fully enclosed hollow structure. Results show that altering the chamber shape and incorporating internal structures leads to a 29.8% improvement in bending angle when compared with the original PneuNet actuator geometry simulated with the same material and pressurization.

Topology optimization is one of the most flexible structural optimization methodologies. However, in exchange for its high degree of design freedom, typical topology optimization cannot avoid multimodality, where multiple local optima exist. This study focuses on developing a gradient-free topology optimization framework to avoid being trapped in bad local optima. Its core is a data-driven multifidelity topology design (MFTD) method, in which design candidates generated by solving low-fidelity topology optimization problems are updated based on evolutionary algorithms (EAs) through high-fidelity evaluation. The key component of the data-driven MFTD is a deep generative model that compresses the dimension of the original data into a low-dimensional manifold, i.e., the latent space. In the original framework, convergence variability and premature convergence problems arise as the generative process is performed randomly in the latent space. Inspired by a popular crossover operation, we propose a data-driven MFTD framework incorporating a new crossover operation called latent crossover. We apply the proposed method to a maximum stress minimization problem in 2D structural mechanics. The results demonstrate that the latent crossover improves convergence stability compared to the original method. Furthermore, the optimized designs exhibit performance comparable to or better than that in conventional gradient-based topology optimization using the P-norm measure.

While additive manufacturing offers unprecedented design freedom to engineers, challenges associated with part accuracy and quality prevent additive manufacturing from becoming as widespread as traditional manufacturing techniques. Statistical quality control is typically applied to mass-produced parts in which large numbers of identical (or similar) parts are fabricated in a production line context, but for customized, additively manufactured parts, part qualification must be performed with a much more limited number of samples — ideally, just one customized part — or the benefit of customized production is lost to the expense of repeated production and refinement. Building upon previous work that established a statistical database of geometric design allowables for a laser sintering additive manufacturing process via extensive geometric metrology, this research establishes transfer learning techniques for transferring those databases to new materials, machines, or parts. The goal of this research is to utilize transfer learning to maximize the accuracy of design allowables for new materials, machines, or parts, while limiting the required number of expensive new metrology studies. Transfer learning is implemented via a two-stage Gaussian process regression method. In the first stage, a trend model is built using pre-existing metrology data to capture general geometric characteristics of the process. In the second stage, a deviation model is trained on the difference between the pre-existing data and a small amount of data gathered from the new material/machine/process. The trend and deviation models are then aggregated to create a surrogate model referred to as the transfer learning model. The proposed method is evaluated using a multi-material polymer selective laser sintering metrology dataset. The performance of the transfer learning model is compared to that of simpler alternative models. Results show that transfer learning models are generally more accurate in predicting geometric dimensions and less sensitive to changes in training data points.

Abstract Additive manufacturing (AM) offers expansive design freedoms for realizing parts that are more complex and customized than their conventionally fabricated counterparts, but all AM technologies impose restrictions on buildable geometries and features. Design rules capture those restrictions in the form of best practices to successfully design for AM. This article discusses how design rules can potentially support and accelerate the process of developing part geometry for AM. The discussion provides examples of design rules that are independent of any specific AM process and then discusses design rules specific to particular AM processes.

Radio Frequency Additive Manufacturing (RFAM) is an additive manufacturing process that utilizes Radio Frequency (RF) radiation as the sole heat source to heat and sinter an entire object simultaneously. By depositing electrically conductive dopants into an insulating polymer powder bed, the resultant mixture can be sintered quickly and volumetrically via RF radiation. Preliminary tests demonstrate that heating uniformity is a challenge related to the dopant density and the geometry of the part. In this work, the heating uniformity issue is addressed by functionally grading the dopant density. The functionally graded dopant density map specific to each geometry is generated through a heuristic tuning algorithm. Functionally graded samples are fabricated via a combination of binder jetting additive manufacturing and sintering in an RF heating apparatus. The geometric accuracy of the functionally graded samples is evaluated and compared to that of uniformly-doped samples. The results show that functionally graded samples exhibit enhanced heating uniformity and improved geometric accuracy.

Reactive extrusion additive manufacturing (REAM) is a recently developed process that utilizes reactive thermoset resin-hardener systems that are mixed inside a shearing element, deposited layer by layer to form a structure, and cured in-situ without external energy. An externally powered active mixing element was developed and used to demonstrate REAM with a highly viscous resin that was filled with 10 wt% chopped carbon fibers. This was achieved by adding fumed silica and increasing the temperature of the fiber-resin mixture to enable effective in-situ mixing while maintaining shape retention upon deposition. Tensile properties of fiberreinforced and reference REAM parts were measured and explained using their fiber alignment and length distribution. Finally, a mechanics model was utilized to determine the optimal fiber content for strength and stiffness, considering the degradation of fiber length at higher volume fractions due to the mixing.

Reactive extrusion additive manufacturing (REAM) is a process in which a motion-controlled nozzle mixes and immediately deposits a multi-part thermoset resin to create layered parts that cure rapidly in the ambient environment. By mixing particle-filled and neat resins on demand, it is possible to functionally grade both the material structure and the resulting properties of the parts. This research investigates a two-part shape memory polymer in which an epoxy resin is combined with a curing agent to yield a thermoset polymer part with structural integrity and shape memory properties. Magneto-active iron oxide particles are added to the epoxy resin to create a filled resin that is mixed on demand with neat epoxy resin and curing agent; control over the mixing and deposition results in a customized distribution of iron oxide content spatially within a part. After characterizing the shape memory properties of the matrix epoxy, the magneto-active composites are shape programmed and actuated via the application of thermal and magnetic fields, respectively, and the effect of the functional grading on the magneto-active properties of the shape memory polymer is demonstrated. Analytical models are derived to predict the magnetic field-responsive behavior of the thermoset composites.

The Journal of Mechanical Design Reviewers with Distinction Award is given to reviewers who have made a meritorious contribution to the journal in terms of the number, quality, and turnaround time of reviews completed during the past year. The prize is an award certificate. Winners are announced in the journal, posted on the ASME Digital Collection, and on the JMD companion website. The 2022 winners of this award are as follows:Courtney Cole—Penn State University, USAXiaoping Du—Indiana University-Purdue University Indianapolis, USADaniel Hulse—NASA Ames Research Center, USAChen Jiang—Huazhong University of Science and Technology, ChinaNoe Vargas Hernandez—University of Texas Rio Grande Valley, USAVivek Rao—University of California Berkeley, USAHongyi Xu—University of Connecticut, USAWe would like to congratulate all the award recipients and look forward to continuing to work with the entire ASME community of editors, authors, reviewers, and staff to bring JMD to the next level of excellence.

As I begin my term (January 2023–December 2027) as Editor-in-Chief of the ASME Journal of Mechanical Design, I would like to offer a few thoughts on the current state of the journal and the many opportunities for growth in the coming years. I am truly honored and humbled to continue the tradition of excellence established by previous editors—Professors Michael McCarthy (University of California, Irvine) (2003–2007), Panos Papalambros (University of Michigan) (2008–2012), Shapour Azarm (University of Maryland) (2013–2017), and Wei Chen (Northwestern University) (2018–2022)—whose vision, hard work, and dedication built JMD into one of the leading journals in our field.Over the past five years, my predecessor, Professor Wei Chen, has worked tirelessly on a number of impactful initiatives, for which the JMD community is truly grateful. The journal’s impact factor has risen to 3.441 (2021), and the average time elapsed from submission to editor decision has decreased to less than 1.5 months, making it one of the leading journals in the ASME portfolio. JMD has begun hosting quarterly webinars to highlight timely topics and encourage in-depth discussion of emerging research threads. Under Professor Chen’s leadership, JMD expanded the publication of special issues to highlight strategic, emerging research themes, including a new annual special issue that fast tracks some of the most highly reviewed papers from the ASME IDETC conference. JMD’s companion website1 has continued to thrive, hosting featured articles and serving as an important repository and bulletin board for all aspects of the journal. Professor Chen also implemented several initiatives to enhance inclusivity, including a double-blind review process, an increasingly diverse cohort of associate and guest editors (with more than 30% of AEs identifying as women or members of underrepresented groups), a DEI advocate, and special efforts to encourage participation from international and underrepresented members of the community.Over the next five years, I would like to build on these initiatives and grow the journal community in several ways. While it will take some time to become completely familiar with all of the facets of the journal, I’ve identified a few focus areas as a starting point. First, I would like to build an advisory board, separate from the suite of associate editors and populated with former associate editors and other scholars who are passionate about JMD, to help us catalyze some new initiatives. One example is an expanded view of inclusivity to include not only underrepresented populations but also new authors and early career members of the community. Work is needed to amplify their voices and demystify and support the process of successfully publishing a paper in JMD as a new author. Another initiative is a concerted effort to publish broad, insightful reviews of the status of research in the mechanical design community, the impact of our research, and the challenges facing the community. If our work is successful, these reviews will serve as required reading for incoming graduate students and scholars, much as the Finger and Dixon reviews educated previous generations. Together with the advisory board and the associate editors, I would like to host roundtable discussions at major conferences, such as IDETC and ICED, to seek feedback and suggestions from the community. Finally, we will strive to continue the efforts of previous editors to raise the impact factor and decrease the time for review while simultaneously emphasizing the need for a fair and thoughtful review process.None of these things would be possible without an exceptional collection of Associate Editors, reviewers, and authors, and one of the most superb assistants in publishing, Ms. Amy Suski. I’m also grateful for the exceptional behind-the-scenes efforts of our featured articles editor and webinar organizer, Professor Faez Ahmed, and my co-editor, Professor Qiaode Jeff Ge, who handles all of the mechanism-related submissions. Their dedication and hard work are an invaluable contribution to the community, and I am humbled to work with them every day.Please continue to support the journal by sending us your very best papers, contributing thoughtful and constructive reviews, and contacting me any time with ideas for improving the journal or leveraging the journal platform to support the community in meaningful ways. The mechanical design community has always been a warm and welcoming professional home for me, so it’s an incredible privilege to work with all of you to raise our journal to the next level of excellence.

We are pleased to announce the two (2) winners for the Journal of Mechanical Design 2022 Editors’ Choice Paper Award:In the Category of Design Methods:Eamon Whalen and Caitlin Mueller (September 21, 2021). “Toward Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses.” ASME. J. Mech. Des. February 2022; 144(2): 021704. https://doi.org/10.1115/1.4052298In the Category of Machine Design:Abdullah Aamir Hayat, Lim Yi, Manivannan Kalimuthu, M. R. Elara, and Kristin L. Wood (February 15, 2022). “Reconfigurable Robotic System Design With Application to Cleaning and Maintenance.” ASME. J. Mech. Des. June 2022; 144(6): 063305. https://doi.org/10.1115/1.4053631In addition, one (1) paper was awarded an Honorable Mention in the Category of Machine Design:Merel van Diepen and Kristina Shea (June 13, 2022). “Co-Design of the Morphology and Actuation of Soft Robots for Locomotion.” ASME. J. Mech. Des. August 2022; 144(8): 083305. https://doi.org/10.1115/1.4054522The selection of these papers was based on the recommendations of the Associate and Guest Editors and guided by the following criteria: (i) fundamental value of the contribution, (ii) expectation of archival value (e.g., expected number of citations), (iii) practical relevance to mechanical design, and (iv) quality of presentation. Nominated papers were considered in two category tracks by two separate ad hoc committees: one for design methods and one for machine design. The paper by Whalen et al. was awarded in the category of design methods and the papers by Hayat et al. and van Diepen et al. were awarded in the category of machine design.Plaques will be awarded to each of the authors of the Editors’ Choice Award, and certificates will be awarded to the authors of the paper with an Honorable Mention. We would like to congratulate all the award recipients and look forward to continuing to work with the entire ASME community of editors, authors, reviewers, and staff to bring the Journal of Mechanical Design to the next level of excellence.

Polydimethylsiloxane (PDMS) elastomers are silicone rubbers that find widespread application in both academic and industrial settings. This study investigates the use of photosensitive platinum catalysts for UV-activated hydrosilylation of PDMS, which enables the additive manufacturing of PDMS objects through techniques such as stereolithography. Specifically, this study focuses on understanding the aging behavior of Pt-catalyzed, UV-curable PDMS samples, including their mechanical behavior under thermal and UV-accelerated aging conditions. The results show that the Pt-catalyzed, UV-curable PDMS introduced in this research is 'under-cured' after being printed and will slowly evolve over time resulting in increased stiffness and decreased elongation, likely due to the formation of additional crosslinks. FTIR spectroscopy indicates that no new chemical bonds or functional groups are generated through the aging process, suggesting that no thermal degradation or polymer chain scissions occur in the polymer matrix. Overall, this PDMS formulation exhibits greater mechanical strength and significantly less reduction in strength with aging, compared with the commonly used, UV-curable thiol-ene PDMS.