A Review of Elastic–Plastic Contact Mechanics

The present invention features methods for operating, such as methods for dispersing and clustering, robotic devices (i.e., “robots”) that employ adaptive behavior relative to neighboring robots and external (e.g., environmental) conditions. Each robot is capable of receiving, processing, and acting on one or more multi-device primitive commands that describe a task the robot will perform in response to other robots and the external conditions. The commands facilitate a distributed command and control structure, relieving a central apparatus or operator from the need to monitor the progress of each robot. This virtually eliminates the corresponding constraint on the maximum number of robots that can be deployed to perform a task (e.g., data collection, mapping, searching, dispersion, and retrieval). By increasing the number of robots, the efficiency in completing the task is also increased.

Systems and methods for dispersing and clustering a plurality of robotic devices

Additive manufacturing allows us to build almost anything; traditional CAD however restricts us to known geometries and encourages the re-usage of previously designed objects, resulting in robust but nowhere near optimum designs. Generative design and topology optimization promise to close this chasm by introducing evolutionary algorithms and optimization on various target dimensions. The design is optimized using either 'gradient-based' programming techniques, for example the optimality criteria algorithm and the method of moving asymptotes, or 'non gradient-based' such as genetic algorithms SIMP and BESO. Topology optimization contributes in solving the basic engineering problem by finding the limited used material. The common bottlenecks of this technology, address different aspects of the structural design problem. This paper gives an overview over the current principles and approaches of topology optimization. We argue that the identification of the evolutionary probing of the design boundaries is the key missing element of current technologies. Additionally, we discuss the key limitation, i.e. its sensitivity to the spatial placement of the involved components and the configuration of their supporting structure. A case study of a ski binding, is presented in order to support the theory and tie the academic text to a realistic application of topology optimization.

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State of the art of generative design and topology optimization and potential research needs

Numerical modelling of contact heat transfer problem with work hardened rough surfaces

This work discusses some of the benefits, techniques, challenges, and considerations associated with the incorporation of measured surfaces in finite element (FE) models including how much surface data to measure and import into the model, the shape of the surface geometry to create, the presence and effect of surface layers and impurities, the required mesh density for rough surfaces, the nature of the element formulations and material properties at small length scales, the differences between measurement and FE coordinate systems, the limitations and idealizations of the FE method, issues associated with boundary conditions and their ability to impose or prevent conformal contact, and issues associated with the size of the pinball region and the contact stiffness relative to the nature of the surface. It also describes some current and future research directions that can be used to validate and expand existing techniques and to improve our understanding of surface phenomena.

Considerations for the Incorporation of Measured Surfaces in Finite Element Models

ANSYS Mechanical APDL for Finite Element Analysis

Considerations for the incorporation of measured surfaces in finite element models

A modular robotic teaching tool including a plurality of modules that may be operated, for example, to play robot soccer, for introducing principles of mechanical engineering and robotics to prospective students. The teaching tool includes a modular robot that may be assembled in a plurality of configurations. The modular robot includes a body portion, first and second motors, first and second motor mounts, at least three wheels, two of which are mountable to the motors, at least one flipper module, and a remote control module for actuating the motors and the flipper module. The modular robot is advantageous in that it provides for an inexpensive, challenging, and entertaining introduction to some of the principles of robotics and mechanical engineering.

Modular robotic teaching tool

Surface roughness can affect the performance of components and systems in a wide variety of fields including tribology, fluid sealing, heat transfer, electronic packaging, dentistry, and medicine. However, the surface geometry of many components and systems is not always known and cannot always be measured. This paper presents methods for generating, using, and operating on nonuniform variates for the incorporation of probabilistic rough surfaces in ANSYS. These techniques, combined with the ability to model real surfaces in ANSYS, can be used to help researchers in material science, mechanical and electrical engineering, and beyond to better understand micro scale surface phenomena. in an external program and then imported into ANSYS or it can be created within ANSYS using APDL. The first method is more common in biomedical and dental research where specialized software is needed to convert CT scans into three dimensional geometry [13]. The second method is better suited to engineering and gives the analyst more control over the model. The procedure for creating rough surface geometry in ANSYS has three basic steps: 1. Create a two dimensional array (or table) which will hold the surface values using the *DIM command 2. Fill the array with measured or probabilistic surface values using the *DO and *VFILL commands 3. Use the array values to directly generate nodes and elements, to modify an existing finite element model, or to create solid model geometry. Details for how to create rough surface geometry based on surface array values are given in [1,14]. 3. Methods for Generating Probabilistic Surface Values in ANSYS A number of approaches can be used to generate nonuniform random variates to populate a rough surface array in ANSY including: importing probabilistic surface values from another program, using the pre-defined probability distributions available within ANSYS, numerical inversion using closed form inverse cumulative density functions (CDF), and numerical inversion of arbitrary distributions based on surface histogram data. The first three approaches will be discussed in this section. The numerical inversion of arbitrary distributions will be discussed in section 5. 3.1 Importing Probabilistic Surface Values Nonuniform random variates can be generated in a variety of programs such as Excel, Matlab, and statistical software packages. Once the variate has been generated, the resulting values can be entered into ANSYS in one of two ways: by using the *VREAD command or by embedding the data in ANSYS input

John M. Thompson

Papers

ANSYS Mechanical APDL for Finite Element Analysis

Considerations for the Incorporation of Measured Surfaces in Finite Element Models

Considerations for the incorporation of measured surfaces in finite element models

Modular robotic teaching tool

Methods for Generating Probabilistic Rough Surfaces in ANSYS