J. Mech. Des. Nov 2020, 142(11): 111705
Renato Picelli, Scott Townsend, H. Alicia Kim
J. Mech. Des. Nov 2020, 142(11): 111705
The shape optimization of microstructural cells is investigated in this paper. Microstructure optimization yields the design of an architected material with a desired material property. Material usage and cost can be reduced by inserting holes into the microstructure. However, depending on the hole shape, the macroscale structure can fail because of the mechanical stress observed in the microstructural cell. This work investigates the possible stresses present in a microstructure and explores how shape optimization can be used for obtaining improved (optimal or nearly-optimal) cell configurations that have lower mechanical stress. The mechanical (von Mises) stress is evaluated using the finite element method. Cell shape optimization using mathematical programming is achieved using the level set method. As a result of this study, engineers have greater insight about, and flexibility when, designing microstructures.
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Roham Sadeghi Tabar, Kristina Wärmefjord, Rikard Söderberg, Lars Lindkvist
J. Mech. Des. Oct 2020, 142(10): 102001 (8 pages)
Paper No: MD-19-1377 https://doi.org/10.1115/1.4046436
The availability of big data has made the role of digital twins in manufacturing more prominent. This paper introduces a geometry assurance digital twin - created from the scanned data of individual components - to define and improve an assembly’s geometrical quality. The joining sequence in a sheet metal assembly impacts geometrical quality and determining the optimal joining sequence is computationally expensive. Meta-heuristic optimization techniques like genetic algorithms often require many simulations, which can increase computational cost. This work improves the optimization process by combining a model-based heuristic algorithm - based on contact displacement minimization – with the meta-heuristic algorithm. Contact modeling avoids part penetration in adjacent areas, and the joining sequences that provide minimal penetration states are used to populate the initial solution for the meta-heuristic algorithm. This approach is demonstrated on two sheet metal assemblies and a reduction in sequence time of 60-80% is achieved. By using a digital twin, optimal joining solutions can be achieved with greater efficiency.
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Merel van Diepen and Kristina Shea
J. Mech. Des 141(10), 101402; doi: 10.1115/1.4043314
Soft locomotion robots are intrinsically compliant and have a large number of degrees of freedom. However, the hand-design of soft robots is often a lengthy trail-and-error process. This paper presents the computational design of virtual, soft locomotion robots using an approach that integrates simulation feedback. The Computational Design Synthesis (CDS) approach consists of three stages: (1) generation, (2) evaluation through simulation, and (3) optimization.
Designs are generated using a spatial grammar that explicitly guides the type of solutions generated and excludes infeasible designs. The soft material simulation method is stable and sufficiently fast for use in a highly iterative simulated annealing search process. The resulting virtual designs exhibit a large variety of expected and unexpected gaits, thus demonstrating the capabilities of the method. Finally, the optimization results and the spatial grammar are analyzed to understand and map the challenges of the problem and the search space.
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Design and Optimization of Graded Cellular Structures with Triply Periodic Level Surface-Based Topological Shapes
Dawei Li, Ning Dai, Yunlong Tang, Guoying Dong, and Yaoyao Fiona Zhao
J. Mech. Des 141(7), 071402
Periodic cellular structures with excellent mechanical properties widely exist in nature. Examples include shark skin, bone structure, etc. This research introduces a generative design and optimization method for triply periodic level surface (TPLS)-based functionally graded cellular structures. In the proposed method, the density distribution is controlled so that the TPLS-based cellular structures can achieve better structural or thermal performances without increasing the weight of the structure. A series of different design specifications are used for validating the design and optimization methods introduced in this work. Effectiveness and robustness of the obtained structures are analyzed using both finite element analysis and experiments. Results from these studies show that the functional gradient cellular structure is much stiffer and has better heat conductivity than the uniform cellular structure.
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Automated Design of Energy Efficient Control Strategies for building clusters using reinforcement learning
Philip Odonkor; Kemper Lewis
J. Mech. Des. 2018; 141(2):021704-021704-9.
From smartphones to electric cars, lithium-ion batteries allow us power our favorite devices. When used in our homes, they allow us to store cheap electricity for later use. These batteries, however, can be prohibitively expensive. But what if a single battery can intelligently be shared by multiple homes? In this paper, we demonstrate the feasibility of this idea by developing an algorithm to autonomously learn the consumption behaviors of multiple real-world homes. This insight is leveraged to produce use strategies, allowing multiple homes to simultaneously enjoy the benefits of a single battery system.
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Co-design is the integrated optimization of the physical plant and controller for an engineering system. The challenge in co-design is determining both time-invariant (physical design) variables and time-variant (control) variables. In co-design, as the size of the problem (number of variables) becomes large, the problem can become too difficult for an all-at-once solution. Our approach extends earlier research by creating a class of multi-subsystem co-design problems where both design and control are formulated and solved. A scalable test problem is used for comparing the proposed decentralized co-design optimization approach against a centralized approach. Results of this study show that the computational time of the proposed decentralized approach increases approximately linearly with respect to an increase in the number of subsystems (variables), while the computational cost of the centralized approach increases nonlinearly.
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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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Cityplot is a new visualization technique for engineering design that uses a dimensionally-reduced representation of the design decisions to represent the mapping from the decisions to the criteria upon which a design is judged. The shown Cityplot depicts possible CubeSat constellations to support the 2007 Earth Science Decadal Survey. Each constellation is comprised of up to 4 CubeSats and each CubeSat can select from a list of 7 instruments. Possible CubeSat constellations are “cities” and are placed in a 2d space to be visualized. An individual constellation can also be seen as a table of instruments (rows) being present (black) on a given CubeSat (columns). The benefits, costs and risks of each possible constellation are represented as color-coded “buildings” in each “city”. The criteria in this example are: a tiered count of satisfied Decadal objectives (blue), the average CubeSat Technology Readiness Level (red), lifecycle cost (green), maximum number of lost instruments upon loss of a single satellite (black). A taller building indicates the possible constellation performs better in that criteria. Dark purple “roads” between two designs indicate that only one instrument is either added to or removed from one CubeSat to make one constellation identical to the other. Cityplot simultaneously shows sensitivity of criteria to decisions, criteria tradeoffs and design families via a quick intuitive view of the design space.
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The synthesis of functional molecular mechanisms is constrained by the notorious difficulties in fabricating nano-links of prescribed shapes and sizes. Thus, the classical mechanism synthesis methods, which assume the ability to manufacture any designed links, cannot provide a systematic process for designing molecular mechanisms. We propose a new approach to build functional mechanisms with prescribed mobility by only using elements from a predefined "link soup". The resulting synthesis procedure is the first of its kind that is capable of systematically synthesizing functional linkages with prescribed mobility constructed from a soup of primitive entities. Furthermore, the proposed systematic approach outputs the ATLAS of candidate mechanisms, which can be further processed for downstream applications. Although the scope of this technique is rather general, its immediate application is the design of molecular machines assembled from nano-links that either exist in nature or can be fabricated.
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Darren J. Hartl, Edgar Galvan, Richard J. Malak and Jeffrey W. Baur
J. Mech. Des 138(3), 031402; doi: 10.1115/1.4032268
This work is the first demonstration of the use of a parameterized approach to design optimization on a complex engineering problem. Parameterized optimization solves a family of optimization problems as a function of exogenous parameters. When applied to a subsystem of interest, it results in general knowledge about the capabilities of the subsystem rather than a restrictive point solution. The motivation is that it often is necessary to advance the development of a subsystem independent of system-level specifics. This is true during initial research and development efforts or in sensitive military and competitive industrial design environments in which compartmentalization of information is common and necessary. It also is important in systems development projects when the need for concurrency often requires subsystem designers to make progress in the absence of full information about other interfacing subsystems. We solve this specialized design problem using the predictive parameterized Pareto genetic algorithm (P3GA). The approach is demonstrated for the multifunctional design of a structurally-integrated liquid metal circuit intended to provide integrated cooling functionality. A family of optimal design solutions associated with values of external parameters (bounded real numbers) is computed efficiently using P3GA. The demonstration employs both high- and low-fidelity multi-physical engineering models seamlessly and results in general knowledge about the subsystem as a function of parameters associated with other interfacing subsystems.
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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.