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# Accepted Manuscripts

BASIC VIEW  |  EXPANDED VIEW
Technical Brief
J. Mech. Des   doi: 10.1115/1.4037303
Additive manufacturing (AM) has many potential industrial applications because highly complex parts can be fabricated with little or no tooling cost. However, one barrier to widespread use of additive manufacturing is that many designers lack detailed information about the capabilities and limitations of each process. A need exists for comprehensive, statistically meaningful metrology studies to be performed on AM technologies. This research focuses on characterizing the process of polymer powder bed fusion (PBF). A test part is designed to evaluate the accuracy and resolution of the polymer selective laser sintering (SLS) process for a wide variety of features. The unique construction of this test part allows it to maximize feature density while maintaining a small build volume so that it can easily fit into most SLS builds. Using this custom test part, a metrology study can be conducted to better understand the process or to evaluate the accuracy of specific machines.
TOPICS: Resolution (Optics), Polymers, Metrology, Additive manufacturing, Tooling, Density, Lasers, Machinery, Sintering, Construction
Technical Brief
J. Mech. Des   doi: 10.1115/1.4037304
This paper discusses the Design for Additive Manufacturing of a system of five light-weight, topologically optimized components that serve as an engine mount in SpaceIL's GLPX lunar landing craft that will participate in the Google Lunar XPrize challenge. The craft containing the system of topologically optimized components is intended for launch in 2017. The system of components was designed specifically for Additive Manufacturing in AlSi10Mg on an EOS M290 machine, which dictated their maximum size (250mm [9.84in] x 250mm [9.84in] x 325 mm [12.79in]). In addition to size considerations, the components were designed with specific AM build orientations in order to minimize the number of support structures as well as to minimize thermal stresses imparted during the powder-bed, laser-melting additive process. After design verification by successfully passing a precise Finite Element Analysis routine, the Additively Manufactured artifacts and in-process testing coupons have undergone rigorous verification and qualification testing in order to to deliver high quality structural components suitable for the loading requirements encountered in the lunar mission. Measured ultimate tensile strength, yield strength, elongation, and density were all well within the acceptance limits for the mission. The final Additively Manufactured assembly consists of five components (four legs and one hub) built in 3 builds with assembled dimensions of 785 mm [30.9 inches] diameter by 305 mm [12 inches] tall.
TOPICS: Design, Optimization, Space vehicles, Topology, Additive manufacturing, Testing, Elongation, Finite element analysis, Yield strength, Tensile strength, Density, Weight (Mass), Lasers, Machinery, Engines, Dimensions, Manufacturing, Structural elements (Construction), Melting, Thermal stresses
Review Article
J. Mech. Des   doi: 10.1115/1.4037305
The lattice structure is a type of cellular material with truss-like frames which can be optimized for specific loading conditions. The fabrication of its intricate architecture is restricted by traditional manufacturing technologies. However, Additive Manufacturing (AM) enables the fabrication of complex structures by aggregation of materials in a layer-by-layer fashion, which has unlocked the potential of lattice structures. In the last decade, lattice structures have received considerable research attention focusing on the design, simulation, and fabrication for AM techniques. And different modeling approaches have been proposed to predict the mechanical performance of lattice structures. This review introduces the aspects of modeling of lattice structures and the correlation between them; summarizes the existing modeling approaches for simulation; and discusses the strength and weakness in different simulation methods. This review also summarizes the characteristics of AM in manufacturing cellular materials and discusses their influence on the modeling of lattice structures.
TOPICS: Modeling, Additive manufacturing, Manufacturing, Simulation, Trusses (Building), Equipment performance, Design, Manufacturing technology
research-article
J. Mech. Des   doi: 10.1115/1.4037306
To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small would fail to cover all possible designs; while bounds that are too large would waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded input data space. We propose a data-driven adaptive sampling technique -- epsilon-margin sampling, which both learns the domain boundary of feasible designs, while also expanding our knowledge of the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling Design Manifolds with epsilon-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.
TOPICS: Design, Space, Dimensions, Manifolds, Sampling methods, Uncertainty, Tradeoffs
research-article
J. Mech. Des   doi: 10.1115/1.4037307
Prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems. Sustained high-amplitude pressure and temperature oscillations may cause stresses in structural components of the combustor, leading to thermomechanical damage. Therefore, the design of combustion systems must take into account the dynamic characteristics of thermoacoustic instabilities in the combustor. From this perspective, there needs to be a procedure, in the design process, to recognize the operating conditions (or parameters) that could lead to such thermoacoustic instabilities. However, often the available experimental data are limited and may not provide a complete map of the stability region(s) over the entire range of operations. To address this issue, a Bayesian nonparametric method has been adopted in this paper. By making use of limited experimental data, the proposed design method determines a mapping from a set of operating conditions to that of stability regions in the combustion system. This map is designed to be capable of: (i) predicting the system response of the combustor at operating conditions at which experimental data are unavailable, and (ii) statistically quantifying the uncertainties in the estimated parameters. With the ensemble of information thus gained about the system response at different operating points, the key design parameters of the combustor system can be identified; such a design would be statistically significant for satisfying the system specifications. The proposed method has been validated with experimental data of pressure time-series from a laboratory-scale lean-premixed swirl-stabilized combustor apparatus.
TOPICS: Design, Combustion chambers, Combustion systems, Pressure, Stability, Temperature, Structural elements (Construction), Stress, Design methodology, Time series, Thermomechanics, Uncertainty, Damage, Oscillations
research-article
J. Mech. Des   doi: 10.1115/1.4037308
Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a more effective and nuanced search for solutions.
TOPICS: Mining, Manufacturing, Engineering design, Design, Theoretical methods, Data mining, Shapes, Topology
research-article
J. Mech. Des   doi: 10.1115/1.4037309
Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as Computational Fluid Dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based on 3-Dimensional Convolutions that predicts Functional Quantities of digital design concepts. This work defines the term Functional Quantity to mean a quantitative measure of an artifact's ability to perform a function. Several research questions are derived from this work: i) Are learned 3D Convolutions able to accurately calculate these quantities, as measured by rank, magnitude and accuracy? ii) What do the latent features (that is, internal values in the model) discovered by this network mean? iii) Does this work perform better than other deep learning approaches at calculating Functional Quantities? A case study is presented a proposed network design is tested for its ability to predict several functions (Sitting, Storing Liquid, Emitting Sound, Displaying Images, and Providing Conveyance) based on test form classes distinct from training class. A study is presented which evaluates several approaches to this problem based on a common architecture, with the best approach achieving F Scores of >0.9 in 3 of the 5 functions identified. Testing trained models on novel input also yields accuracy as high as 98% for estimating rank of these functional quantities. This method is also employed to differentiate between decorative and functional head-wear, which yields an 84.4% accuracy and 0.786 precision.
TOPICS: Wear, Computational fluid dynamics, Design, Testing, Neural network models
research-article
J. Mech. Des   doi: 10.1115/1.4037302
A current issue in metal-based additive manufacturing (AM) is achieving consistent, desired process outcomes in manufactured parts. When process outcomes such as strength, density, or precision need to meet certain specifications, changes in process variable selection can be made to meet these requirements. However, the changes required to achieve a better part performance may not be intuitive, particularly because process variable changes can simultaneously improve some outcomes while worsening others. There is great potential to design the additive manufacturing process, tailoring process variables based on user requirements for a given part. In this work, the tradeoffs between multiple process outcomes are formalized and the design problem is explored throughout the design space of process variables. Based on user input for each process outcome considered, P-V (Power-Velocity) Process Design Charts are introduced that map the process space and identify the best combination of process variables to achieve a user's desired outcome.
TOPICS: Process design, Additive manufacturing, Performance, Design, Tradeoffs, Feature selection, Density, Metals
J. Mech. Des   doi: 10.1115/1.4037250
Prior research has shown that powder bed fusion (PBF) additive manufacturing (AM) can be used to make functional, end-use components from powdered metallic alloys, such as Inconel® 718 superalloy. However, these end-use components and products are often based on designs developed for more traditional subtractive manufacturing processes and do not take advantage of the unique design freedoms afforded by AM. In this paper, we present a case study involving the redesign of NASA's existing "pencil" thruster used for spacecraft attitude control. The initial pencil thruster was designed for and manufactured using traditional subtractive methods. The main focus in this paper is to (a) identify the need for and use of both opportunistic and restrictive Design for Additive Manufacturing (DfAM) concepts and considerations in redesigning the thruster for fabrication with PBF AM and (b) compare the resulting DfAM thruster with a parallel development effort redesigning the original thruster to be manufactured more effectively using subtractive manufacturing processes. The results from this case study show how developing end-use AM components using specific DfAM guidelines can significantly reduce manufacturing time and costs while enabling new and novel design geometries.
TOPICS: Metals, Design, Additive manufacturing, Manufacturing, Machining, Alloys, Superalloys, Space vehicles
research-article
J. Mech. Des   doi: 10.1115/1.4037251
Additive manufacturing (AM) technologies have become integral to modern prototyping and manufacturing. Therefore, guidelines for using AM are necessary to help users new to the technology. Many others have proposed useful guidelines, but these are rarely written in a way that is accessible to novice users. Most guidelines 1) assume the user has extensive prior knowledge of the process, 2) apply to only a few AM technologies or a very specific application, or 3) describe benefits of the technology that novices already know. In this paper, we present a 1-page, visual design-for-additive-manufacturing worksheet for novice and intermittent users which addresses common mistakes as identified by various expert machinists and additive manufacturing facilities who have worked extensively with novices. The worksheet helps designers assess the potential quality of a part made using AM and indirectly suggests ways to redesign it. The immediate benefit of the worksheet is to filter out bad designs before they are printed, thus saving time on manufacturing and redesign. We implemented this as a go-no-go test for a high-volume AM facility where users are predominantly novices, and we observed an 81% decrease in the rate of poorly designed parts. We also tested the worksheet in a classroom, but found no difference between the control and the experimental groups. This result highlights the importance of motivation since the cost of using AM in this context was dramatically lower than real-world costs. This second result highlights the limitations of the worksheet.
TOPICS: Design, Additive manufacturing, Manufacturing, Errors, Filters
research-article
J. Mech. Des   doi: 10.1115/1.4037252
Rapid advancement of sensor technologies and computing power has led to wide availability of massive population-based shape data. In this paper, we present a Taylor expansion based method for computing structural performance variation over its shape population. The proposed method takes four steps: 1) learning the shape parameters and their probabilistic distributions through the statistical shape modeling; 2) deriving analytical sensitivity of structural performance over shape parameter; 3) approximating the explicit function relationship between the FE solution and the shape parameters through Taylor expansion; 4) computing the performance variation by the explicit function relationship.To overcome the potential inaccuracy of Taylor expansion for highly nonlinear problems, a multi-point Taylor expansion technique is proposed, where the parameter space is partitioned into different regions and multiple Taylor expansions are locally conducted. It works especially well when combined with the dimensional reduction of the principal component analysis in the statistical shape modeling.Numerical studies illustrates the accuracy and efficiency of this method.
TOPICS: Shapes, Modeling, Principal component analysis, Sensors
research-article
J. Mech. Des   doi: 10.1115/1.4037253
The primary motivation in this paper is to understand decision-making in design under competition from both prescriptive and descriptive perspectives. Engineering design is often carried out under competition from other designers or firms, where each competitor invests effort with the hope of getting a contract, attracting customers, or winning a prize. One such scenario of design under competition is crowdsourcing where designers compete for monetary prizes. Within existing literature, such competitive scenarios have been studied using models from contest theory, which are based on assumptions of rationality and equilibrium. Although these models are general enough for different types of contests, they do not address the unique characteristics of design decision-making, e.g., strategies related to the design process, the sequential nature of design decisions, the evolution of strategies, and heterogeneity among designers. In this paper, we address these gaps by developing an analytical model for design under competition, and using it in conjunction with a behavioral experiment to gain insights about how individuals actually make decisions in such scenarios. The contributions of the paper are two fold. First, a game-theoretic model is presented for sequential design decisions considering the decisions made by other players. Second, an approach for synergistic integration of analytical models with data from behavioral experiments is presented. The proposed approach provides insights such as shift in participants' strategies from exploration to exploitation as they acquire more information, and how they develop beliefs about the quality of their opponents' solutions.
TOPICS: Design, Decision making, Equilibrium (Physics), Engineering design
research-article
J. Mech. Des   doi: 10.1115/1.4037243
This paper applies linear elastic theory and Castigliano's first theorem to design nonlinear (stiffening) flexures used as load cells with both large force range and large resolution. Low stiffness at small forces causes high sensitivity while high stiffness at large forces prevents over-straining. With a standard 0.1 $\mu$m deflection sensor, the nonlinear load cell may detect 1\% changes in force over 5 orders of force magnitude. In comparison, a traditional linear load cell functions over only three orders of magnitude. We physically implement the nonlinear flexure as a ring that increasingly contacts rigid surfaces with carefully chosen curvatures as more force is applied. We analytically describe the load cell performance as a function of its geometry. We describe methods for manufacturing the flexure from a monolithic part or multiple parts. We experimentally verify the theory for two load cells with different parameters.
TOPICS: Stress, Resolution (Optics), Bending (Stress), Stiffness, Theorems (Mathematics), Sensors, Manufacturing, Design, Deflection, Geometry
research-article
J. Mech. Des   doi: 10.1115/1.4037245
Passive dynamic systems have the advantage over conventional robotic systems that they do not require actuators and control. Brachiating, in particular, involves the swinging motion of an animal from one branch to the next. Such systems are usually designed manually by human designers and often are bio-inspired. However, a computational design approach has the capability to search vast design spaces and find solutions that go beyond those possible by manual design. This paper addresses the automated design of passive dynamic systems by introducing a graph grammar based method that integrates dynamic simulation to evaluate and evolve configurations. In particular, the method is shown to find different, new solutions to the problem of the design of two-dimensional passive, dynamic, continuous contact, brachiating robots. The presented graph grammar rules preserve symmetry among robot topologies. A separation of parametric multi-objective optimization and topologic synthesis is proposed, considering four objectives: number of successful swings, deviation from cyclic motion, required space and number of bodies. The results show that multiple solutions with varying complexity are found that trade-off cyclic motion and the space required. Compared to research on automated design synthesis of actuated and controlled robotic systems, this paper contributes a new method for passive dynamic systems that integrates dynamic simulation.
TOPICS: Robots, Simulation, Design, Dynamic systems, Robotics, Biomimetics, Pareto optimization, Tradeoffs, Space, Actuators, Separation (Technology)
research-article
J. Mech. Des   doi: 10.1115/1.4037246
With the recent advances in information gathering techniques, product usage data, including time-dependent product performance feature data and field data (i.e., working conditions), can be continuously collected during the product usage stage. Product reliability can be improved by incorporating product usage data for making design decisions. Since influences of product usage data on design quality are seldom studied in the past, a new decision-making approach is introduced in this research to improve quality of design based on analysis of product usage data. In this approach, a hierarchical product function model is built first to describe the relationships among functions and multiple performance features for each function. Second, the time-dependent data of performance features for each function are explored to assess function health degradation using the Gaussian mixed model, and functions with rapid and severe degradation are identified. Third, the abnormal field data that cause the severe function degradation are found by clustering of field data. Finally, a redesign necessity index (RNI) is defined for each design parameter related to severely degraded functions based on the relationships between design parameters and abnormal field data. An associate relationship matrix is constructed to calculate the RNI of each design parameter for identifying the to-be-modified design parameters. The effectiveness of this new approach is demonstrated through a case study for the redesign of a large tonnage crawler crane.
TOPICS: Design, Decision making, Cranes, Product reliability
research-article
J. Mech. Des   doi: 10.1115/1.4037249
Styling or product appearance is well known for holding great influence on its differentiation, branding, and overall success in the market. However, the styling process is difficult due to the intuitive and subjective way in which designers evaluate designs. In particular, negotiating iterations between designers and engineers is challenging since engineers have objective, data driven approaches to rationalize decisions whereas designers rely on instinct and intuition. While the literature shows sustained interest in this issue, and provides methods to analyze appearance objectively, many approaches rely on abstracted or simplified versions of a product's appearance as the basis for analyses, ignoring the holistic nature of product appearance. This article contributes by proposing an improvement employing digital shape comparison tools applied to 3D geometry of products, and generating data on differentiation in product shape–that is, the Holistic Styling Analysis (HSA). The HSA provides objective assessment of difference in appearance to form the basis for designers to rationalize styling to other stakeholders during the design process. The HSA is tested through an automotive industry case study. Results show the method adds objectivity to decision-making by providing objective reference measures for differentiation in the styling of previous and competing products. Such measures can be used to inform styling goals, and to identify intended degrees of difference in key features while highlighting areas to maintain consistency. As such, we contribute by providing a means for styling designers to use data to drive their activities in the same manner as other stakeholders.
TOPICS: Engineers, Automotive industry, Design, Decision making, Geometry, Product design, Shapes
Technical Brief
J. Mech. Des   doi: 10.1115/1.4037109
The early conceptual design phase often focuses on functional requirements, with a limited consideration of the manufacturing processes that will be needed to turn design engineers' conceptual models into physical products. In the past, design and manufacturing engineers often worked in close physical proximity. Today, the geographically distributed manufacturing paradigm has slowed the feedback cycle and increased product lead-time. Design for manufacturability (DFM) techniques have been adopted to overcome this problem. DFM feedback is critical for faster convergence to a manufacturable design. DFM tools give feedback in several modalities, including textual and graphical. However, since information modality may affect interpretability, empirical evidence is needed to understand how manufacturability feedback modalities affect design engineers' work. A user study was conducted with novice design engineers to evaluate how their design performance, workload, confidence, and feedback usability were affected by textual, two-dimensional (2D) and three-dimensional (3D) feedback modalities. Results showed that graphical feedback significantly improved performance and reduced mental workload compared to textual and no feedback. Differences between 3D and 2D feedback were mixed. 3D was generally better on average, but not significantly so. However, the usability of 3D was significantly higher than 2D. Conversely, providing feedback in textual modality was often no better than not providing any feedback. The study will benefit manufacturing industries by demonstrating that early 3D manufacturability feedback improves novice design engineers' performance with less mental workload, and streamlines the design process resulting in cost-saving and reduction of product lead-time.
TOPICS: Design, Design for Manufacturing, Feedback, Engineers, Manufacturing, Cycles, Manufacturing industry, Conceptual design
research-article
J. Mech. Des   doi: 10.1115/1.4036997
Noise, vibration, and harshness performances are always concerns in design of an automotive belt drive system. The design problem of the automotive belt drive system requires minimum transverse vibration of each belt span and minimum rotational vibrations of each pulley and the tensioner arm at the same time, with constraints on tension fluctuations in each belt span. The auto-tensioner is a key component to maintain belt tensions, avoid belt slip, and absorb vibrations in the automotive belt drive system. In this work, a dynamic adaptive particle swarm optimization and genetic algorithm (DAPSO-GA) is proposed to find an optimum design of an auto-tensioner to solve this design problem and achieve design targets. A dynamic adaptive inertia factor is introduced in the basic PSO to balance the convergence rate and global optimum search ability by adaptively adjusting the search velocity during the search process. GA-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the PSO to further exploit optimal solutions generated by the PSO. These operators are used to diversify the swarm and prevent premature convergence. The objective function is established using a weighted-sum method and the penalty function method is used to deal with constraints. Optimization on an example automotive belt drive system shows that the system vibration is greatly improved after optimization compared with that of its original design.
TOPICS: Design, Particle swarm optimization, Belts, Vibration, Optimization, Inertia (Mechanics), Fluctuations (Physics), Noise (Sound), Probability, Pulleys, Tension, Genetic algorithms
Announcements
Amy Suski
J. Mech. Des   doi: 10.1115/1.4025965
TOPICS: Design

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