Weihang Zhu

Five-axis pencil-cut planning and virtual prototyping with 5-DOF haptic interface

Abstract In this paper, techniques of 5-axis pencil-cut machining planning with a 5-DOF (degree of freedom) output haptic interface are presented. Detailed techniques of haptic rendering and tool interference avoidance are discussed for haptic-aided 5-axis pencil-cut tool path generation. Five-axis tool path planning has attracted great attention in CAD/CAM and NC machining. For efficient machining of complex surfaces, pencil-cut uses relatively smaller tools to remove the remaining material at corners or highly curved regions that are inaccessible with larger tools. As a critical problem for 5-axis pencil-cut tool path planning, the tasks of tool orientation determination and tool collision avoidance are achieved with a developed 5-DOF haptic interface. A Two-phase rendering approach is proposed for haptic rendering and force-torque feedback calculation with haptic interface. A Dexel-based volume modeling method is developed for global tool interference avoidance with surrounding components in a 5-axis machining environment. Hardware and software implementation of the haptic pencil-cut system with practical examples are also presented in this paper. The presented technique can be used for CAD/CAM, 5-axis machining planning and virtual prototyping.

Dexel-based force-torque rendering and volume updating for 5-DOF haptic product prototyping and virtual sculpting

This paper presents new techniques of Dexel-based force-torque rendering and volume-updating for haptic virtual sculpting of complex surfaces with a developed 5-DOF (degree of freedom) haptic interface. In the proposed methodology, 5-axis tool motion and analytical tool swept volume are formulated for updating the virtual stock material, which is represented with the Dexel volume model. Based on the tool motion analysis, a Dexel-based collision detection method and a force-torque feedback algorithm are proposed for virtual sculpting. A lab-built 5-DOF force-torque output haptic interface system is developed for the proposed haptic sculpting system. With the proposed methodology, a user can virtually sculpt a volume stock to get an intuitive design by using the haptic interface. From the haptic sculpting system, both the corresponding tool motion of the creative process and the sculpted model can be recorded and output. The output STL models of the haptic sculpting system can be processed for machining planning. Based on the proposed techniques, hardware and software implementation of the haptic sculpting system as well as the illustrative examples are also presented in this paper.

Massively parallel differential evolution--pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems

This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.

Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

This paper presents a massively parallel Ant Colony Optimization — Pattern Search (ACO-PS) algorithm with graphics hardware acceleration on nonlinear function optimization problems. The objective of this study is to determine the effectiveness of using Graphics Processing Units (GPU) as a hardware platform for ACO-PS. GPU, the common graphics hardware found in modern personal computers, can be used for data-parallel computing in a desktop setting. In this research, the classical ACO is adapted in the data-parallel GPU computing platform featuring ‘Single Instruction — Multiple Thread’ (SIMT). The global optimal search of the ACO is enhanced by the classical local Pattern Search (PS) method. The hybrid ACO-PS method is implemented in a GPU+CPU hardware platform and compared to a similar implementation in a Central Processing Unit (CPU) platform. Computational results indicate that GPU-accelerated SIMT-ACO-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ACO-PS with GPU acceleration.

GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space

In this paper, the attractive features of evolutionary algorithm and Markov Chain Monte Carlo are combined into a new Differential Evolutionary Markov Chain Monte Carlo (DE-MCMC) method for multi-objective optimization problems with continuous variables. The DE-MCMC evolves a population of Markov chains through differential evolution (DE) toward a diversified set of solutions at the Pareto optimal front in the multi-objective function space. The computational results show the effectiveness of the DE-MCMC algorithm in a variety of standardized test functions as well as a protein loop structure sampling application. Moreover, the DE-MCMC algorithm can efficiently take advantage of the massive-parallel, many-core architecture, where its implementation on GPU can achieve speedup of 14~35.

SIMD tabu search for the quadratic assignment problem with graphics hardware acceleration

This paper presents a single instruction multiple data tabu search (SIMD-TS) algorithm for the quadratic assignment problem (QAP) with graphics hardware acceleration. The QAP is a classical combinatorial optimisation problem that is difficult to solve optimally for even small problems with over 30 items. By using graphic hardware acceleration, the developed SIMD-TS algorithm executes 20 to 45 times faster than traditional CPU code. The computational improvement is made possible by the utilisation of the parallel computing capability of a graphics processing unit (GPU). The speed and effectiveness of this algorithm are demonstrated on QAP library problems. The main contribution of this paper is a fast and effective SIMD-TS algorithm capable of producing results for large QAPs on a desktop personal computer equivalent to the results achieved with a CPU cluster.

Particle Swarm with graphics hardware acceleration and local pattern search on bound constrained problems

This paper presents a Particle Swarm - Pattern Search Optimization (PS2) algorithm with graphics hardware acceleration for bound constrained nonlinear optimization problems. The objective of this study is to determine the effectiveness of using Graphics Processing Units (GPU) as a hardware platform for Particle Swarm Optimization (PSO). GPU, the common graphics hardware which can be found in many personal computers, can be used for desktop data-parallel computing. The classical PSO is adapted in the data-parallel GPU computing platform featuring ‘Single Instruction - Multiple Thread’ (SIMT). PSO is also enhanced by adding a local Pattern Search (PS) improvement. The hybrid PS2 optimization method is implemented in the GPU environment and with a Central Processing Unit (CPU) in a PC. Computational results indicate that GPU-accelerated SIMT-PS2 method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid PS2 with GPU acceleration.

DEMCMC-GPU: An Efficient Multi-Objective Optimization Method with GPU Acceleration on the Fermi Architecture

In this paper, we present an efficient method implemented on Graphics Processing Unit (GPU), DEMCMC-GPU, for multi-objective continuous optimization problems. The DEMCMC-GPU kernel is the DEMCMC algorithm, which combines the attractive features of Differential Evolution (DE) and Markov Chain Monte Carlo (MCMC) to evolve a population of Markov chains toward a diversified set of solutions at the Pareto optimal front in the multi-objective search space. With parallel evolution of a population of Markov chains, the DEMCMC algorithm is a natural fit for the GPU architecture. The implementation of DEMCMC-GPU on the pre-Fermi architecture can lead to a ~25 speedup on a set of multi-objective benchmark function problems, compare to the CPU-only implementation of DEMCMC. By taking advantage of new cache mechanism in the emerging NVIDIA Fermi GPU architecture, efficient sorting algorithm on GPU, and efficient parallel pseudorandom number generators, the speedup of DEMCMC-GPU can be aggressively improved to ~100.

Material side tracing and curve refinement for pencil-cut machining of complex polyhedral models

In this paper, a new Material-Side-Tracing method and a pencil-cut curve refinement technique are proposed for 3-axis pencil-cut path generation. Pencil-cut machining has been used to remove remaining material at highly curved regions or corners after the finishing process. Procedures of evaluating and extracting valid pencil-cut points are developed by taking practical cases into account. With the strategy of using material-side information in the tracing process, smooth and clean pencil-cut curves can be generated even if the actual adjacent pencil-cut curves are very close. A technique of pencil-cut curve refinement is presented to overcome the limitation due to the discrete CL-net intervals, and the smooth pencil-cut paths are made complete at sharp corners. Computer implementation and practical examples are also presented in this paper. The proposed techniques can be used in the CAD/CAM systems to generate pencil-cut paths for machining complex polyhedral models.

A multi-objective optimization model for gas pipeline operations

Abstract Natural gas is usually transported by pipeline networks. This paper presents a multi-objective optimization model in optimizing the operation of natural gas pipeline networks. A mathematical model is established for different network configurations, namely linear, branch and looped topologies. Two conflicting objectives, power consumption minimization and gas delivery flow rate maximization, are considered subjected to pipeline and compressor constraints. The decision variables are the pressure at the nodes and the rotational speed of the compressors. A set of constraints is developed based on gas flow equations and compressors operating conditions. Due to the nonlinearity of the constraints and the objective, the developed model is a Nonlinear Programming problem. The optimization of the models is performed with NSGA-II algorithm. The solution obtained is a set of Pareto optimal points from which a decision maker can select a specific preferred solution. Sensitivity analysis was performed to determine the impact of parameter changes.