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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Rigid-body mechanisms (RBMs) and compliant mechanisms (CMs) are traditionally treated in significantly different ways. In this paper, we present an approach to the synthesis of both RBMs and CMs. In this approach, RBMs and CMs are generalized into mechanisms that consist of five basic modules, including Compliant Link (CL), Rigid Link (RL), Pin Joint (PJ), Compliant Joint (CJ), and Rigid Joint (RJ). The link modules and joint modules are modeled with beam and hinge elements, respectively, in a geometrically nonlinear finite element solver, and subsequently a discrete beam-hinge ground structure model is established. Based on this discrete beam-hinge model, a procedure that follows topology optimization is developed, called module optimization. Particularly, in the module optimization approach, the states (both presence or absence and sizes) of joints and links are all design variables, and one may obtain a RBM, a partially CM, or a fully CM for a given mechanical task. The proposed approach has thus successfully addressed the challenge in the type and dimensional synthesis of RBMs and CMs. Three design examples of the path generator are discussed to demonstrate the effectiveness of the proposed approach.

Nicholas Meisel
School of Engineering Design, Technology, and Professional Programs (SEDTAPP)
The Pennsylvania State University
213J Hammond Building, University Park, PA, 16802, U.S.A.
nam20@psu.edu
ASME Member
 
Christopher Williams
Design, Research, and Education for Additive Manufacturing Systems Laboratory,
Virginia Tech
413D Goodwin Hall, 635 Prices Fork Road, Blacksburg, VA, 24061, U.S.A.
cbwill@vt.edu
ASME Member

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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​Collecting data on user needs can result in overwhelming amounts of data, especially if user groups are large and diverse. Additional analysis is necessary to prioritize a small subset of needs for further consideration. This study presents a simplified quality metric and online interface appropriate to initially screen and prioritize lists exceeding 500 statements for a single topic or product area. Over 20,000 ratings for 1697 need statements across three common product areas were collected in 6 days.  A series of analyses tested whether particular characteristics of users and groups affect the number of high-quality needs that can be generated.  The evaluated characteristics were user group size, needs submitted per person, and expertise and experience levels of users.  The results provided important quantitative evidence of fundamental relationships between the quantity and quality of need statements. Increased quantities of high-quality need statements resulted both due to increasing user group size and due to increasing counts per person using novel content-rich methods to help users articulate needs. However, a user’s topic-specific expertise (self-rated) and experience level (self-rated hours per week) were not significantly associated with increasing need quality.

Julia M. O'Rourke and Carolyn C. Seepersad
J. Mech. Des 137(9), 091101; doi: 10.1115/1.4030877

​Energy- and materials-efficient designs are highly valued in the context of sustainable product design, but realizing products with significant changes in efficiency is difficult. One means to address this challenge is to use biological analogies during ideation. The use of biological analogies in the design process has been shown to greatly increase the novelty of concepts generated, and many authors in the bioinspired design (BID) community contend that efficiency-related benefits may be conferred as well. However, there is disagreement in the field as to when, how, and why efficiency-related benefits might arise in BIDs. This work explores these issues in-depth. A review of BID literature and an empirical study of BIDs lead to a better understanding of the types of efficiency advantages conferred by BID and set the stage for the development of tools and methods to systematically generate more energy- and materials-efficient design concepts using biological analogies. 

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​Adding redundancy is a widely used method in engineering to improve the system reliability. How to add redundancy, (i.e., to meet the reliability requirement with the minimum cost), is an interesting topic in system design. Traditionally, the optimal redundancy allocation scheme is obtained under two simplified assumptions, i.e., binary states of each component and no failure dependency between components. The binary-state assumption assumes that each component and the entire system can only have two states: fully operational and completely failed. The failure independency assumption assumes no failure interaction between components, i.e., one component failure will not affect the failure process of other components. Although those two assumptions can simplify the analysis, they may lead to inaccurate reliability predictions and thus results in doubtful and misleading redundancy allocation scheme which in fact may not meet the reliability requirement. This work proposes a method to obtain the optimal redundancy allocation scheme by using the Semi-Markov process and optimization techniques without those two simplified assumptions. The target system is a type of commonly-seen system having multiple states and failure interactions. The target system contains a main subsystem providing the required output and an auxiliary subsystem helping the main subsystem function normally, such as the rotating subsystem and the lubricating subsystem, the computer mother board and the fan, and so on. A case study of a shipboard power electronic cabinet demonstrates the applicability of the proposed approach. 

​We have developed an improved deformable Underconstraint Eliminator (UE) linkage for removing underconstraint, which causes unwanted resonances and reduced stiffness at large displacements, in linear flexure bearings.  Linear flexure bearings deform to permit high repeatability, fine resolution translational motion.  This new linkage alleviates many of the problems associated with current linkage solutions such as static and dynamic performance losses and increased bearing size.  The nested linkage design is shown through analysis and experiment to work as predicted in selectively eliminating the underconstrained degrees of freedom (DOF) in linear flexure bearings.  The improved bearing shows a >10x gain in the resonance frequency and >100x gain in static stiffness of the underconstrained DOF, as designed. Analytical expressions are presented for designers to calculate the performance of the new UE linkage.  The linear nested linkage concept is also generalized to a rotary flexure design.

​Fig. 1.  a) Flexure bearing with the new nested underconstraint eliminator (UE) linkage. This linkage selectively removes the underconstraint inherent in the bearing design by linking the motion of the intermediate and final stage. b) Schematic of the UE linkage, this is the triangular structure in the center, enabled by flexures (11, 12, and 2), which does not impede the motion of the bearing flexures (m).  The possible motion for the structure is shown in the equivalent linkage model in c). 

Defining functions when dissecting a product is an important method to learn how it works.  There are three primary methods for identifying functions in a product.  In this study, we tested the hypothesis that one of the methods is superior to the others.  We tested the three methods in a within-subjects design of experiments to see how each method compared to the others, as measured by the size, shape, and content of each function tree.  We found that there was no dominant method for discovering functions in a dissected device.  This implies that any method is appropriate for understanding how a device works. Additionally, we found generalized aspects of creating function diagrams that are difficult for novice designers.

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products.  The authors hypothesize that i) lead users are discoverable from large scale social media networks and ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect  ~2.1 billion social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation smartphone designs.

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