Multiscale simulation of nanometric cutting of single crystal copper and its experimental validation

In this paper a multiscale simulation study was carried out in order to gain in-depth understanding of machining mechanism of nanometric cutting of single crystal copper. This study was focused on the effects of crystal orientation and cutting direction on the attainable machined surface quality. The machining mechanics was analyzed through cutting forces, chip formation morphology, generation and evolution of defects and residual stresses on the machined surface. The simulation results showed that the crystal orientation of the copper material and the cutting direction significantly influenced the deformation mechanism of the workpiece materials during the machining process. Relatively lower cutting forces were experienced while selecting crystal orientation family {1 1 1}. Dislocation movements were found to concentrate in front of the cutting chip while cutting on the (1 1 1) surface along the View the MathML source cutting direction thus, resulting in much smaller damaged layer on the machined surface, compared to other orientations. This crystal orientation and cutting direction therefore recommended for nanometric cutting of single crystal copper in practical applications. A nano-scratching experiment was performed to validate the above findings.

Parallels between machine and brain decoding

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies. © 2012 Springer-Verlag.

Starbog: The Garden Virtuoso and the Turing Machine

This article will discuss a recent ensemble composition entitled Starbog which was toured and broadcast in Britain in 2006 . The composition of Starbog focused on developing working methods which combined computer-based techniques (using OpenMusic) with more subconscious means of generating musical ideas. The challenge in achieving this was as much aesthetic/philosophical as it was technical and the present article is intending as a ‘sounding’ which focuses on the influence OpenMusic has had on the composer’s music, rather than documenting the nature of the often simple application of algorithms.

Influence of microstructure on the cutting behaviour of silicon

We use molecular dynamics simulation to study the mechanisms of plasticity during cutting of monocrystalline and polycrystalline silicon. Three scenarios are considered: (i) cutting a single crystal silicon workpiece with a single crystal diamond tool, (ii) cutting a polysilicon workpiece with a single crystal diamond tool, and (iii) cutting a single crystal silicon workpiece with a polycrystalline diamond tool. A long-range analytical bond order potential is used in the simulations, providing a more accurate picture of the atomic-scale mechanisms of brittle fracture, ductile plasticity, and structural changes in silicon. The MD simulation results show a unique phenomenon of brittle cracking typically inclined at an angle of 45° to 55° to the cut surface, leading to the formation of periodic arrays of nanogrooves in monocrystalline silicon, which is a new insight into previously published results. Furthermore, during cutting, silicon is found to undergo solid-state directional amorphisation without prior Si-I to Si-II (beta tin) transformation, which is in direct contrast to many previously published MD studies on this topic. Our simulations also predict that the propensity for amorphisation is significantly higher in single crystal silicon than in polysilicon, signifying that grain boundaries eases the material removal process.

Diamond machining of silicon: A review of advances in molecular dynamics simulation

Molecular dynamics (MD) simulation has enhanced our understanding about ductile-regime machining of brittle materials such as silicon and germanium. In particular, MD simulation has helped understand the occurrence of brittle–ductile transition due to the high-pressure phase transformation (HPPT), which induces Herzfeld–Mott transition. In this paper, relevant MD simulation studies in conjunction with experimental studies are reviewed with a focus on (i) the importance of machining variables: undeformed chip thickness, feed rate, depth of cut, geometry of the cutting tool in influencing the state of the deviatoric stresses to cause HPPT in silicon, (ii) the influence of material properties: role of fracture toughness and hardness, crystal structure and anisotropy of the material, and (iii) phenomenological understanding of the wear of diamond cutting tools, which are all non-trivial for cost-effective manufacturing of silicon. The ongoing developmental work on potential energy functions is reviewed to identify opportunities for overcoming the current limitations of MD simulations. Potential research areas relating to how MD simulation might help improve existing manufacturing technologies are identified which may be of particular interest to early stage researchers.

Simulation of three-dimension cutting force and tool deflection in the end milling operation based on finite element method

This paper presents a 3D simulation system which is employed in order to predict cutting forces and tool deflection during end-milling operation. In order to verify the accuracy of 3D simulation, results (cutting forces and tool deflection) were compared with those based on the theoretical relationships, in terms of agreement with experiments. The results obtained indicate that the simulation is capable of predicting the cutting forces and tool deflection.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.