J. Mech. Des 141(7), 071402
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.
Full full article please see ASME's Digital Collection.
Andrew S. Gillman; Kazuko Fuchi; Philip R. Buskohl
J. Mech. Des. 141(4), 041401 (Jan 11, 2019)
Origami, the ancient art of paper folding, is finding numerous uses in scientific and engineering applications because of the combined advances in mathematics, computer science, and computational geometry. From deployment of solar arrays and antennas to design of robots and modeling of protein folding, origami provides an efficient means of compaction and coordinated motion. Many of the design and analysis tools for origami have relied on both rigid body mechanics and adaptation of well-known fold patterns for engineering applications. This work expands on these approaches through development of an automated design tool for fold pattern discovery, while accounting for non-rigid (deformable) facets through a novel nonlinear mechanics model. The nonlinearity presents challenges for finding the optimal design, and we employ an evolutionary algorithm for navigating this complex design space. With this framework, fold patterns satisfying targeted motions can be identified automatically and thus enables discovery of fold patterns designed specifically for engineering applications.
For the full article please see ASME's Digital Connection.
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.
For the Full Paper please visit ASME's Digital Collection.
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.