Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Additive manufacturing of metallic components – Process, structure and properties

/pdf/the-theory-of-industrial-organization-19bejph0ej.pdf

The Theory of Industrial Organization

Two-photon excitation provides a means of activating chemical or physical processes with high spatial resolution in three dimensions and has made possible the development of three-dimensional fluorescence imaging, optical data storage, and lithographic microfabrication. These applications take advantage of the fact that the two-photon absorption probability depends quadratically on intensity, so under tight-focusing conditions, the absorption is confined at the focus to a volume of order λ3 (where λ is the laser wavelength). Any subsequent process, such as fluorescence or a photoinduced chemical reaction, is also localized in this small volume. Although three-dimensional data storage and microfabrication have been illustrated using two-photon-initiated polymerization of resins incorporating conventional ultraviolet-absorbing initiators, such photopolymer systems exhibit low photosensitivity as the initiators have small two-photon absorption cross-sections (δ). Consequently, this approach requires high laser power, and its widespread use remains impractical. Here we report on a class of π;-conjugated compounds that exhibit large δ (as high as 1, 250 × 10−50 cm4 s per photon) and enhanced two-photon sensitivity relative to ultraviolet initiators. Two-photon excitable resins based on these new initiators have been developed and used to demonstrate a scheme for three-dimensional data storage which permits fluorescent and refractive read-out, and the fabrication of three-dimensional micro-optical and micromechanical structures, including photonic-bandgap-type structures.

Two-photon polymerization initiators for three-dimensional optical data storage and microfabrication

Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.

The status, challenges, and future of additive manufacturing in engineering

Laser assisted machining process and machine utilizing multiple distributed laser units that are strategically distributed around the workpiece being machined to simultaneously heat the workpiece, creating a desired temperature distribution for laser assisted machining. Sequential incremental heating from different directions and positions are used, resulting in longer tool life and shorter machining time.

Laser assisted machining apparatus with distributed lasers

A novel algorithm based on the least squares (LS) method and genetic algorithm (GA) is proposed for autonomous learning and construction of FBFN's when training data are available. The proposed algorithms add significant fuzzy basis functions (FBF) at each iteration during training, based on error reduction measures. The adaptive least squares (ALS) algorithm based on the combined LS and GA, realizes hybrid structure-parameter learning without any human intervention. Simulation studies are performed with numerical examples for comparison with conventional algorithms. The ALS algorithm is applied to the construction of a fuzzy basis function network model for surface roughness in a grinding process using experimental data.

http://ieeexplore.ieee.org/iel5/7506/20424/00943638.pdf

Construction of fuzzy basis function networks using adaptive least squares method

This paper is concerned with the experimental and numerical study of the high speed face milling of Ti-6Al-4V titanium alloy. Machining is carried out by uncoated carbide and polycrystalline diamond cutters in the presence of an abundant supply of coolant. Experimental analysis is conducted in terms of cutting forces, chip morphology, surface integrity and tool wear. The experimental analysis is supplemented by simulations from the finite element analysis where needed. The highest cutting speed realized for both the cutting tool materials is 600 sfpm with the diamond cutter operating at feeds lower than that for carbide. Good surface integrity in terms of residual stress and surface finish is achieved under the machining conditions used with limited tool wear. Residual stresses imparted to the machined surface are compressive with the diamond tool yielding higher values and are the most sensitive to feed. Tool wear patterns are described in terms of various cutting conditions.Copyright © 2002 by ASME

A Study on the High Speed Face Milling of Ti-6Al-4V Alloy

Although many advanced signal processing techniques and novel machine learning algorithms have been applied to the monitoring of grinding processes in the literature, most of these techniques and algorithms are only effective under specific conditions and are unusable under other grinding conditions, such as varying wheel types or workpiece materials. This article proposes a robust grinding wheel wear monitoring system to eliminate these restrictions. Physical information generated during the grinding process is collected by a power sensor, accelerometers, and acoustic emission sensors. After the signals are preprocessed, features are extracted via different signal processing techniques, and a novel normalization scheme is applied to make these features independent of the wheel type, workpiece material, and grinding parameters. The features that are most related to wheel wear are selected according to the statistical criterion. An interval type-2 fuzzy basis function network is adopted to develop a wheel wear monitoring model, which is capable of predicting wheel wear under various grinding conditions and generating upper and lower prediction bounds according to the fluctuation of features. Based on the wheel wear model, a robust monitoring scheme to schedule timely wheel dressing and ensure workpiece surface finish could be established.

Robust Wheel Wear Monitoring System for Cylindrical Traverse Grinding

This paper presents the capability of an ultrasonic system for the in-process surface roughness measurement of machined parts. The system uses a focused ultrasonic transducer to measure the reflected amplitude variation of an ultrasonic beam incident on the surface. The robustness of the system is examined by applying the technique to different machined surfaces and investigating the effect of various coupling fluids. Experimental results show that the technique is applicable to the surfaces produced by most production processes. In addition, the capability of in-process measurements is demonstrated by adapting the system to a CNC machining center. Results show in-process measurements correlate well with off-line profilometer data. Tolerance to machine vibration is also shown by comparing the measurement data with the machine spindle on and off.

Yung C. Shin

Papers

Laser assisted machining apparatus with distributed lasers

Construction of fuzzy basis function networks using adaptive least squares method

A Study on the High Speed Face Milling of Ti-6Al-4V Alloy

Robust Wheel Wear Monitoring System for Cylindrical Traverse Grinding

In-Process Monitoring of Surface Roughness Utilizing Ultrasound