改进区域划分的圆Packing变分算法

通过改进基于Power图的区域划分,提出一种收敛速度更快的圆packing算法.首先固定容器面积,将输入圆缩小一定的倍数,随机撒在容器中;之后对圆心点进行三角化,并根据相邻圆的半径比值对容器进行区域划分;再让所有圆在不超出自己区域边界的条件下尽量等比例增长至最大;最后将划分区域-长大的过程迭代下去,得到最大增长倍数.实验结果表明,该算法能够使得圆packing的过程更快地达到收敛.国家自然科学基金(61472332);;福建省自然科学基金(2018J01104

A New Approach to CNC Programming of Plunge Milling

ABSTRACT A New Approach to CNC Programming of Plunge Milling Sherif Abdelkhalek, PhD. Concordia University, 2013. In current industrial applications many engineering parts are made of hard materials including dies, mold cavities and aerospace parts. Manufacturing these types of parts is classified as pocket milling. By using the regular machining methods, pocket milling takes a long time accompanied by high cost. Plunge milling, is a new machining strategy that has proven to have an excellent performance in the rough machining of hard materials. In plunge milling, the cutter is fed in the direction of the spindle axis, with the highest structural rigidity which showed a very interesting performance in removing the excess material rapidly in the rough operations. Mainly, according to the previous researchers, two directions are adopted to improve the efficiency of the plunge milling process. First, to reduce the cutting forces and increase chatter stability which attracts the majority of the researchers. Second, to optimize the tool path planning which has less attention. Therefore, in the first part of the research, a new practical approach is established in optimized procedures to generate the tool paths for plunge milling of pockets, even for these with free-form boundaries and islands. This innovative approach is proposed as follows: (1) fill a pocket with minimum number of specified radii circles which are tangent to each other and/or the pocket boundary without overlapping by building an algorithm using the maximum hole degree (MHD) theory for solving the circle packing problem. (2) cover the areas left between the non-overlapped circles by the same used specified radii. Finally, solve the travelling sales man problem (TSP) for the circles with the same radii by using the simulated annealing algorithm. According to the results, this approach significantly advances the tool path planning technique for pockets plunge milling. In the second part of the research, a new algorithm is proposed to calculate the global solution for constraint polynomial functions by using subtractive clustering which makes the results more accurate and faster to be obtained. This part is extremely useful to calculate the depth of cut for each plunging place in case of having a polynomial surface as a bottom of the machined pocket with high accuracy, and less calculation time to avoid gauging between the tool and the bottom surface. The polynomial function can be classified according to the number of variables. In the proposed research, the functions with one and two variables have more importance because they graphically represent curves and surfaces which are the cases under study. Since the polynomial function under study can be represented graphically according to the number of the variables, the change in the function’s shape can be detected by the feature recognition. The feature recognition is done for the function’s shape by calculating the surface or curve curvature at the data points. The main procedure is; (1) identifying the entire features of the objective function which are classified according to the curvature as convex, concave, plane, and hyperbolic, (2) applying the sub-clustering technique for convex and concave regions to find the approximated centers of these regions, and eventually, (3) the clusters’ centers are calculated and used as initial points for local optimization technique which gives the local critical point for each region. The local minima are calculated, the global minimum is the minimum of the local minima

Design of Tubular Network Systems Using Circle Packing and Discrete Optimization

In this thesis, we describe the design of tubular network systems that must occupy, as best as possible, regions that demonstrate some kind of longitudinal symmetry. In order to simplify the problem, the region of the container is discretized into a sequence of prism blocks . The problem is decomposed into two parts: 1. Pack tubes in these blocks, 2. Connect these packed tubes at the ends of each block. In the first part, since each block is prismatic, the problem of packing tubes is equivalent to the packing of circles in the cross-sectional area of each block. In this case, we assume that the cross-sectional area of each block is a polygon. We investigate a series of algorithms to pack circles, including a rather naive approach as well as the GGL [2] circle packing algorithm. Then we modify the GGL algorithm to pack circles in regions that are more complicated. Based on the GGL, we will also invent new algorithm that provides more satisfactory packing results. In the second part, we connect the packed tubes from Part one to form a complete network system. First we consider the simplest case -- constructing a tubular system in a container with no variations, i.e., a single block. We solve this problem in terms of the travelling salesman problem (TSP) which is a classical problem in discrete optimization. For containers with varying cross-sections, we connect tubes at end of each block independently instead of constructing a complete system. This problem can be reduced to a perfect matching (PM) problem at each end. We apply similar integer programming algorithms to both perfect matching problem and TSP. However, the design of complete tubular network system in a container exhibiting longitudinal symmetry remains an open problem for future work

Formulation space search for two-dimensional packing problems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The two-dimension packing problem is concerned with the arrangement of items without overlaps inside a container. In particular we have considered the case when the items are circular objects, some of the general examples that can be found in the industry are related with packing, storing and transportation of circular objects. Although there are several approaches we want to investigate the use of formulation space search. Formulation space search is a fairly recent method that provides an easy way to escape from local optima for non-linear problems allowing to achieve better results. Despite the fact that it has been implemented to solve the packing problem with identical circles, we present an improved implementation of the formulation space search that gives better results for the case of identical and non-identical circles, also considering that they are packed inside different shaped containers, for which we provide the needed modifications for an appropriate implementation. The containers considered are: the unit circle, the unit square, two rectangles with different dimension (length 5, width 1 and length 10 width 1), a right-isosceles triangle, a semicircle and a right-circular quadrant. Results from the tests conducted shown several improvements over the best previously known for the case of identical circles inside three different containers: a right-isosceles triangle, a semicircle and a circular quadrant. In order to extend the scope of the formulation space search approach we used it to solve mixed-integer non-linear problems, in particular those with zero-one variables. Our findings suggest that our implementation provides a competitive way to solve these kind of problems.This study was funded by the Mexican National Council for Science and Technology (CONACyT)

Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy

In Raman spectroscopy, data analysis occupies a large amount of time and effort; thus, it is paramount to have the proper tools to extract the most meaning from the Raman analysis. This thesis explores improved ways to analyse Raman data mostly by using machine learning techniques available in Python. The substrate used throughout this thesis has been patterned through an electrohydrodynamic process that patterns micrometric pillars onto the substrate, which, after being gold coated, can generate surface-enhanced Raman scattering. An initial theoretical background was laid for the electrohydrodynamic process and additional observations regarding the fluid mechanics. Furthermore, when the structures are fabricated, and Raman measurements are taken, we show that it is possible to create an effective convolutional neural networks that systematically evaluate these patterns’ surface morphology and extracts features responsible for the surface-enhanced Raman scattering phenomenon. Being able to predict 90% of the time from optical microscope images and 99% of the time with atomic force microscopy images Additionally, a thorough machine learning analysis of the Raman literature was done. The best machine learning algorithms were put together into a script combined with a graphical user Interface that can run multiple commands such as principal component analysis and self-organizing maps, all in a centralised way. This way, we managed to consistently extract information from Raman and surface-enhanced Raman scattering spectra to open possibilities for precise peak analysis methods using a multi-Lorentzian fit algorithm