J. Mech. Des. May 2020, 142(5): 051702
John K. Ostrander, Conrad S. Tucker, Timothy W. Simpson, Nicholas A. Meisel
J. Mech. Des. May 2020, 142(5): 051702
Additive manufacturing (AM) is becoming more prevalent in the classroom. Most AM education, though, is limited to desktop-scale material extrusion printers since they are relatively safe, easy to use, and inexpensive. However, industry demand continues to rise for students skilled in more advanced forms of AM, such as laser-based, metal powder bed fusion. Unfortunately, the cost, infrastructure, and training necessary for using these complex systems in education present challenges for classroom implementation. This research proposes using virtual reality (VR) as a medium for teaching introductory concepts of AM without the need for a physical printing system located in the classroom. Changes in student knowledge are identified using a pre-/post-AM lesson evaluation and student-reported changes in self-efficacy. Results showed no overall significant difference between knowledge gained in the physical AM environment and knowledge gained in the VR environment. This suggests that VR could be used as a successful approach for teaching AM concepts if cost, space, or infrastructure make it difficult to implement a physical industrial-scale AM system. There was also no significant difference found in knowledge gained in a passive VR environment when compared with an interactive VR environment. This suggests that even simple, accessible, low-cost VR solutions (such as Google Cardboard) could be used to educate the future AM workforce.
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Yi Xiong, Pham Luu Trung Duong, Dong Wang, Sang-In Park, Qi Ge, Nagarajan Raghavan, David W. Rosen
J. Mech. Des. Oct 2019, 141(10): 101101
The capabilities of additive manufacturing offer significant potential for revolutionizing existing product development processes by creating products that are rich in shape, material, hierarchical, and functional complexities. However, searching through design solutions in such a multidimensional design space is a challenging task. In this study, the authors propose a holistic approach that applies data-driven methods in successive stages of design search and optimization. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed stages of product design. A Bayesian network classifier is used in embodiment design as the reasoning framework for exploring the design space. A Gaussian process regression model is then used as the evaluation function during optimization, allowing for the exploitation of the design space during the optimization of the detailed design. These models are constructed based on one dataset created using Latin hypercube sampling and then refined using Markov Chain Monte Carlo sampling. This cost-effective data-driven approach is demonstrated by designing a customized ankle brace that has tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses in different directions.
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Sheng Yang and Yaoyao Fiona Zhao
J. Mech Des 140(3):031702-031702-12. doi:10.1115/1.4038922
Part count reduction (PCR) is one motivation for using additive manufacturing (AM) processes. PCR helps simplify product structure, eliminate auxiliary connecters, and reduce assembly difficulties and cost. However, PCR may also increase manufacturing difficulty and the irreplaceability of failed subcomponents. This paper presents a pioneering investigation of how AM-enabled PCR (AM-PCR) impacts lifecycle activities. A new set of design rules and principles are proposed for PCR that lead to lowered cost and enhanced performance. The PCR problem is formulated as a combinatory optimization problem where the objective is minimizing lifecycle cost/performance ratio while ensuring conformance to all constraints (e.g. manufacturing, maintenance, and recycling). To address the challenge of computational cost, a dual-level screening and refinement product redesign framework is presented that first searches for the minimum grouping solution and then refines the remaining combinations using design optimization. This approach will help designers automate the part count reduction process enabled by additive manufacturing while exploring new design innovation opportunities.
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Additive manufacturing (AM) techniques provide designers with greater freedom in creating customized products with complex shapes. When major design changes are made to a part, undesirable high cost increments may be incurred due to AM process setting adjustments, challenging designers to explore AM-enabled design freedom while controlling costs at the same time. In this research, we introduce the concept of a variable product platform and its associated AM process setting platform, based on which the design and process setting adjustments can be restricted within a bounded feasible space in order to limit cost increments. Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is introduced to predict AM production costs based on process settings. The process setting adjustment’s feasible space boundary is identified by solving a multiobjective optimization problem. Design parameter limitations are computed in a Mamdani-type expert system and then used as constraints in the design optimization to maximize customer perceived utility. Case studies on designing an R/C racing car family illustrate the proposed methodology and demonstrate that the optimized additive manufactured variable platforms can improve product performances at lower costs than conventional consistent platform based design.
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Kurt Maute, Anton Tkachuk, Jiangtao Wu, H. Jerry Qi, Zhen Ding and Martin L. Dunn
J. Mech. Des 137(11), 111402; doi: 10.1115/1.4030994
Multi-material polymer printers allow the placement of different materials within a composite. The individual material phases can be spatially arranged and shaped in an almost arbitrary fashion. Utilizing the shape memory behavior of at least one of the material phases, active composites can be 3D printed such that they deform from an initially flat plate into a curved structure. To navigate this vast design space, systematically and efficiently explorer design options, and find an optimum layout of the composite this paper presents a novel design optimization approach. The optimization approach combines a level set method for describing the material layout and a generalized formulation of the extended finite element method (XFEM) for predicting the response of the printed active composite (PAC). This combination of methods yields optimization results that can be directly printed without the need for additional post-processing steps. The proposed optimization method is studied with examples where the target shapes correspond to a plate-bending type deformation and to a localized deformation. The optimized designs are 3D printed and the XFEM predictions are compared against the experimental measurements. The design studies demonstrate the ability of the proposed optimization method to yield a crisp and highly resolved description of the optimized material layout that can be realized by 3D printing.
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School of Engineering Design, Technology, and Professional Programs (SEDTAPP)
The Pennsylvania State University
213J Hammond Building, University Park, PA, 16802, U.S.A.
Design, Research, and Education for Additive Manufacturing Systems Laboratory,
413D Goodwin Hall, 635 Prices Fork Road, Blacksburg, VA, 24061, U.S.A.
The PolyJet material jetting additive manufacturing (AM) process is uniquely qualified to create complex, multi-material structures. However, key manufacturing constraints need to be explored and understood in order to guide designers in their use of the PolyJet process including 1) minimum manufacturable feature size, 2) removal of support material, 3) survivability of small features during cleaning, and 4) the self-supporting angle in the absence of support material. In this study, the authors used a series of experiments to identify statistically significant geometric and process parameters and how they impact part manufacturability. Support material removal was found to be limited by the cross-sectional area of small channels in the part; a minimum cross-sectional area approximately equal to the diameter of the cleaning water jet spray results in the highest percentage of support material removed from small channels (Figure 1). The process’s minimum resolvable feature size was shown to rely on surface finish and feature shape, as well as the interactions between surface finish and orientation, surface finish and feature direction, and orientation and feature direction. If a designer can account for the ideal configuration of these variables, then it is possible to manufacture features that are half the size of a more general “worst-case” scenario. Feature survivability during the cleaning process was tied to cross-sectional area (for rigid features) and feature connectivity (for flexible features), with flexible features requiring significantly larger feature diameters to survive when fixed at both ends. Finally, the self-supporting angle in the absence of support material was driven by the orientation of the surface with respect to the roller in the print head assembly, with y-dominated specimens offering better self-supporting angles. Experimental design studies such as these are crucial to provide designers with the knowledge to ensure that their proposed designs are manufacturable with the PolyJet process, whether designed manually or by an automated method, such as topology optimization.
Figure 1. Mean support material removed from channels of various areas
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This section includes brief descriptions of articles soon to be or recently published by the Journal of Mechanical Design. These featured articles highlight recent research developments and emerging trends in mechanical design. For Abstracts and Full Articles please see ASME's Digital Collection.