PCB assembly planning using genetic algorithms

Printed circuit boards (PCB) are used extensively in industry for the manufacture of electronic and electromechanical products. One of the primary concerns in the manufacture of PCBs is the determination of the optimal assembly plan. This paper presents work that leads to the development of an approach to PCB assembly planning using genetic algorithms (GAs). The approach takes into consideration component insertion priority and sequencing decision rules. A polygamy reproduction mechanism with dual mutation has been proposed and implemented. Details of the approach are described. A PCB model extracted from the literature was used for performance evaluation. Details of the evaluation are presented.     

Wear of the blade diamond tools in truing vitreous bond grinding wheels: Part II. Truing and grinding forces and wear mechanism

Truing and grinding forces and the wear mechanism of particle and rod diamond blade tools used to generate precise and intricate forms on rotating vitreous bond silicon carbide grinding wheels are presented. A Hall effect sensor was used to measure the change of grinding spindle power during truing and grinding. A signal processing procedure was developed to identify individual truing passes and to extract the average, peak-to-valley, and standard deviation of the variation of truing force for each pass. The truing force data and SEM micrographs of worn surfaces on blade tools reveal micro- and macro-fracturing of the diamond. The attritious and erosion wear of the diamond rod and particle, erosion of the metal bond, and pulling-out of the diamond particle are also identified. Grinding force data shows that, for the same truing parameters, a wheel trued by the rod diamond blade tool has higher grinding forces than one trued by a particle diamond blade tool.     

Wear of the blade diamond tools in truing vitreous bond grinding wheels: Part I. Wear measurement and results

The wear of stationary blade diamond tools used to generate a precise and intricate form on the vitreous bond grinding wheel is presented. Two types of blade tools made of rod and particle diamond were used. A method to measure the wear of the blade diamond tool in the μm-scale range using the size difference of two parts ground before and after truing was introduced. Two sets of experiments with four truing feeds and four tool traverse speeds across the grinding wheel were conducted on the rod and particle blade diamond tools, respectively. Experimental results showed the wear rate of blade diamond tools was improved at higher truing feeds and traverse speeds due to the brittle fracture of the abrasive and vitreous bond.     

Developing a genetic optimisation approach to balance an apparel assembly line

In apparel manufacturing, it is difficult to achieve line balance because the production rate of each workstation is different. This difficulty is particularly prominent in the labour-intensive assembly process. The development of a line balancing technique using genetic algorithms is thus proposed for optimising the assignment of operators in an assembly line. The impact of a different level of skill inventory SI_n on the assembly makespan is also investigated in order to find out the optimal number of task skills an operator should possess in the apparel assembly process. Experimental results will be discussed to demonstrate the performance of the proposed genetic optimisation approach.     

An optimisation approach to the contour error control of CNC machine tools using genetic algorithms

A contour control strategy has been studied in this paper to improve the contour error of CNC machine tools. First, a single axis controller is analysed and then a velocity feedforward controller is added in the velocity loop. To further reduce the contour error, a cross-coupled controller is adopted and an algorithm for an on-line estimation of the contour error in arbitrary curved contouring is proposed. These controller parameters are all optimised by an efficient robust optimisation technique using genetic algorithms. Experimental results are provided to illustrate the proposed methods.     

In-process dressing of fine diamond wheels for tool grinding

In tool grinding operations, especially ceramic cutting tool materials, two aims are important: high production efficiency and high-precision cutting edges with minimum surface and subsurface damage. Using conventional grinding conditions, these two aims cannot be combined without essential restrictions. In-process dressing enables efficient use of fine-grained grinding wheels to obtain superior cutting edge quality.     

工程陶瓷恒力磨削金刚石砂轮钝化规律研究

通过一系列试验建立了恒力磨削条件下砂轮全钝化周期的钝化曲线,并通过相应的数学手段对各个阶段的砂轮钝化特征和磨削能力进行分析.结果表明:初期钝化阶段的钝化曲线随时间的变化是由凹到凸的过程,与砂轮磨损过程有很大的区别.初期钝化阶段砂轮钝化速度大大低于其他两个阶段,砂轮具有很强的磨削能力,这一特征的潜在应用价值非常高.正常磨损阶段砂轮的钝化与砂轮磨损规律一致,呈线性增大趋势.     

树脂结合剂金刚石砂轮磨削铁氧体陶瓷磨削力的实验研究

通过一系列的实验，研究了树脂结合剂金刚石砂轮磨削铁氧体陶瓷材料时磨削力的变化规律及其特点。通过磨削对比实验方法分析磨削铁氧体陶瓷时，磨削用量对磨削力大小的影响。通过砂轮速度，磨削深度，横向进给速度和纵向进给速度等因素影响磨削力大小变化的数据及磨削力信号特征处理的分析和比较，分析了对铁氧体材料磨削时产生的磨削力影响的一些规律，表明铁氧体陶瓷磨削时磨削力变化的特有规律．     

Ants colony algorithm approach for multi-objective optimisation of surface grinding operations

An ant colony based optimisation procedure has been developed to optimise grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using a multi-objective function model with a weighted approach for the surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. The results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) techniques.Nomenclature a _p down feed of grinding (mm/pass) - a _w total thickness of cut (mm) - A _o initial wear flat-area percentage (%) - b _e empty width of grinding (mm) - b _s width of wheel (mm) - b _w width of workpiece (mm) - B _k positive definite approximation of the Hessian - doc depth of dressing (mm) - c _d cost of dressing ($) - c _s cost of wheel per mm³ ($/mm³) - CT total production cost ($/pc) - CT ^* expected production cost limit ($/pc) - d _g grind size (mm) - D _e diameter of wheel (mm) - f _b cross feed rate (mm/pass) - G grinding ratio - k _a constant dependent on coolant and wheel grind type - k _u wear constant (mm^-1) - k _c cutting stiffness (N/mm) - k _m static machine stiffness (N/mm) - k _s wheel wear stiffness (N/mm) - L lead of dressing (mm/rev) - L _e empty length of grinding (mm) - L _w length of workpiece (mm) - M _c cost per hour labour and administration ($/h) - N _d total number of pieces to be grouped during the life of dressing (pc) - N _t batch size of workpieces (pc) - N _td total number of workpieces to be grouped during the life of dressing (pc) - P number of workpieces loaded on the table (pc) - R _a surface roughness (µm) - R _a* surface finish limit during rough grinding (µm) - R _c workpiece hardness (Rockwell hardness number) - R _em dynamic machine characteristics - S _d distance of wheel idling (mm) - S _p number of spark out grinding (pass) - t _sh time of adjusting machine tool (min) - t _i time of loading and unloading workpiece (min) - T _ave average chip thickness during grinding (µm) - U specific grinding energy (J/mm) - U ^* critical specific grinding energy (J/mm³) - V _r speed of wheel idling (mm/min) - V _s wheel speed (m/min) - V _w workpiece speed (m/min) - VOL wheel bond percentage (%) - WRP workpiece removal parameter (mm³/min-N) - WRP ^* workpiece removal parameter limit (mm³/min-N) - WWP wheel wear parameter (mm³/min-N) - W _i weighting factor, 0W _i1 (W ₁+W ₂+W ₃=1)     

Stochastic U-line balancing using genetic algorithms

The advantages of U-type lines are very well known in industry. They offer improved productivity and quality, and are considered as one of the better techniques in implementing just-in-time (JIT) systems. There is a growing interest in the literature to organize traditional assembly lines as U-lines for improved performance. U-type assembly line balancing is an extension of the traditional line balancing problem, in which tasks can be assigned from both sides of the precedence diagram. Although there are many studies in the literature for the design of traditional straight assembly lines, the work on U-type lines is limited. Moreover, in most of the previous studies, task times are assumed to be deterministic. In this paper, a new multiple-rule-based genetic algorithm (GA) is proposed for balancing U-type assembly lines with stochastic task times.     

Optimization of manual fabric-cutting process in apparel manufacture using genetic algorithms

In apparel manufacturing, experience and subjective assessment of production planners are used quite often to plan the production schedules in their fabric-cutting departments. The quantities of cut-pieces produced by fabric-cutting departments based on these non-systematic schedules cannot fulfil the cut-piece requirements of the downstream sewing lines and minimize the makespan. This paper proposes a genetic algorithms (GAs) approach to optimize both the cut-piece requirements and the makespan of the conventional fabric-cutting departments using manual spreading and cutting methods. An optimization model for the manual fabric cutting process based on GAs was developed. Two sets of production data were collected to validate the performance of the model and the experimental results were obtained. From the results, it can be found that both the makespan and cut-piece fulfilment rates are improved in which the latter is improved significantly.     

Using genetic algorithms for the optimization of mechanisms

This work examines the possibility of using genetic algorithms for the optimization of the loads transmitted in mechanisms. The variables of design are the relative positions of the various connections, considered in a comparative manner. The minimization of the loads transmitted in the connections is achieved by optimizing the respective positions of those lead to less expensive solutions for bearings and sections of beams. The examples show that using this stochastic method is an efficient way to minimize loads in mechanisms.     

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.     

Ultra-precision grinding of optical glasses using mono-layer nickel electroplated coarse-grained diamond wheels. Part 2: Investigation of profile and surface grinding

After finishing the precision conditioning of mono-layer nickel electroplated coarse-grained diamond wheels with 151 μm (D151), 91 μm (D91) and 46 μm (D46) grain size, resp., profile and surface grinding experiments were carried out on a five-axis ultra-precision grinding machine with BK7, SF6 optical glasses and Zerodur glass ceramic. A piezoelectric dynamometer was used to measure the grinding forces, while an atomic force microscopy (AFM), white-light interferometer (WLI)) and scanning electron microscope (SEM) were used to characterize the ground surface quality in terms of micro-topography and subsurface damage. Moreover, the wear mechanics of the coarse-grained diamond wheels were analyzed and the grinding ratio was determined as well, in aiming to evaluate the grinding performance with the conditioned coarse-grained diamond wheels. Finally, the grinding results were compared with that of the fine-grained diamond wheels with regard to the ground specimen surface quality, process forces and wheel wear as a function of stock removal. The experimental results show that the precision conditioned coarse-grained diamond wheels can be applied in ductile mode grinding of optical glasses with high material removal rates, low wheel wear rates and no dressing requirement yielding excellent surface finishes with surface roughness in the nanometer range and subsurface damage in the micrometer range, demonstrating the feasibility and applicability of the newly developed diamond grinding technique for optical glasses.     

Experimental study on fabrication and evaluation of micro pyramid-structured silicon surface using a V-tip of diamond grinding wheel

A mechanical fabrication of micro pyramid-structured silicon surface is proposed using crossed grooving with a 60° V-tip of diamond grinding wheel. It can obtain high form-accuracy, good surface quality and efficient productivity in contrast to laser machining and etching, and also assure a high aspect ratio in contrast to other mechanical processes. In order to describe its micro-structured topography, a white-light interferometer was employed, and its measured point cloud was matched using an Iterative Closest Point (ICP) algorithm. In micro grinding, a novel CNC mutual-wear truing was first developed to sharpen the wheel V-tip; then, the effects of microscopic wheel topography, silicon crystal-orientation and grinding parameter were investigated on ground micro-topography, truing ratio and material removal ratio; finally, its form-accuracy, pyramid top radius, groove tip radius, surface roughness and aspect ratio were evaluated. It is shown that better microscopic grain protrusion topography on wheel V-tip produces much larger material removal ratio and much better micro-structured topography in micro grinding, but it leads to much less truing ratio in finer GC truing. In micro grinding, silicon crystal-orientation has little effect on micro-structured topography due to diamond crystal-orientations that are randomly distributed on wheel V-tip. Although the micro pyramid-structured form error is only about 3.4 μm, its V-groove bottom and pyramidal top have very large form errors (23.1-47.9 μm) due to the sharpness of wheel V-tip and the frangibility of micro pyramid top. On increasing feed speed, its pyramid top radius decreases and its groove tip radius slightly increases, ultimately leading to an increase in aspect ratio, whereas its surface quality descends. It is concluded that the micro-pyramid arrays may be precisely patterned on silicon surface using a SD600 wheel with crossed tool paths, on-machine V-tip truing and the depth of cut in 1 μm.     

Design of PID-type controllers using multiobjective genetic algorithms

The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases.     

Intelligent aircraft maintenance support system using genetic algorithms and case-based reasoning

The maintenance of aircraft components is crucial for avoiding aircraft accidents and aviation fatalities. To provide reliable and effective maintenance support, it is important for the airline companies to utilise previous repair experience with the aid of advanced decision support technology. Case-based reasoning (CBR) is a machine learning method that adapts previous similar cases to solve current problems. To effectively retrieve similar aircraft maintenance cases, this research proposes using a CBR system to aid electronic ballast fault diagnosis of Boeing 747-400 airplanes. By employing genetic algorithms (GA) to enhance dynamic weighting and the design of non-similarity functions, the proposed CBR system is able to achieve superior learning performance as compared to those with either equal/varied weights or linear similarity functions.     

Joint optimization of spare parts inventory and maintenance policies using genetic algorithms

In general, the maintenance and spare parts inventory policies are treated either separately or sequentially in industry. However, since the stock level of spare parts is often dependent on the maintenance policies, it is a better practice to deal with these problems simultaneously. In this study, a simulation optimization approach using genetic algorithms (GAs) has been proposed for the joint optimization of preventive maintenance (PM) and spare provisioning policies of a manufacturing system operating in the automotive sector. A factorial experiment was carried out to identify the best values for the GA parameters, including the probabilities of crossover and mutation, the population size, and the number of generations. The computational experiments showed that the parameter settings given by the proposed approach achieves a significant cost reduction while increasing the throughput of the manufacturing system.     

Precision modeling of form errors for cylindricity evaluation using genetic algorithms

A heuristic approach is proposed in this paper to model form errors for cylindricity evaluation using genetic algorithms (GAs). The proposed GAs method shows good flexibility and excellent performance in evaluating the engineering surfaces via measurement data involved with randomness and uncertainty. The numerical-oriented genetic operator is used as a basic representation for error modeling in the paper. The theoretical basis for the proposed Gas-based cylindricity evaluation algorithms is first presented. The performance of the method under various combinations of parameters and the precision improvement on the evaluation of cylindricity are carefully analyzed. One numerical example is presented to illustrate the effectiveness of the proposed method and to compare the Gas-based modeling results with those obtained by the least-squares method. Numerical results indicate that the proposed GAs method does provide better accuracy on cylindricity evaluation. The method can also be extended for solving difficult form error minimization and profile evaluation problems of various geometric parts in engineering metrology.     

基于改进遗传算法的工艺路线优化

工艺路线优化是实现计算机辅助工艺设计的关键技术之一,遗传算法是一种能较好解决工艺路线优化的方法。介绍了遗传算法及其在工艺路线优化中的应用,分析了目前流行的遗传算法应用于工艺路线优化中存在的问题,提出了改进的遗传算法,并给出了相应实例予以证明,较好解决了工艺路线优化问题。