Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

Modern manufacturing processes need high production rates, low costs, and high product quality. Generally, surface roughness is a good reference to determine the performance in machined products. The use of optimization systems can determine the optimum machining parameters in the machining process, especially in milling operations. The present study integrates the least square model based on feed rate, cutting speed, and grain size with a genetic optimization algorithm to provide the optimal process parameter. The NSGA II algorithm was applied due to its coverage and easily to optimize the micro milling of hardened steel. The responses were Fy Force and Mz Torque. The results show that the feed rate was the most significant factor for minimizing Fy force and Mz Torque.     

Optimization of the multi-objective dynamic cell formation problem using a scatter search approach

This paper addresses the dynamic cell formation (DCF) problem with multiple conflicting objectives. Recent researches have mainly focus on single-objective cell formation procedures that deals with the identification of part families and associated machine groups for constant demands. However, varying market demands and fluctuations of the business environment have caused cellular manufacturing systems to operate under dynamic conditions. Thus, the optimal configuration of manufacturing cells in each period is different and the reconfiguration of cells is required. This paper proposes a nonlinear multi-objective mathematical model of the DCF problem by giving weighing to three conflicting objectives including the machine relocation cost in the process of reconfiguring cells, the utilization rate of machine capacity, and the total number of intercell moves over the entire planning horizon. To solve the nonlinear multi-objective model, a scatter search approach is developed, which redesigns the common components of scatter search and incorporates diversification generator, global criterion method, local search method, and other improvement mechanisms to provide a wide exploration of the search space through intensification and diversification. The proposed approach is compared with the commercial solver CPLEX on 10 test problems, some of which are large dimensions. Computational results have demonstrated the effectiveness of the scatter search approach.     

A systematic approach to manufacturing packaging logistics

Packaging is one of the most important activities in the distribution systems and supply chains. Nevertheless, it attracts little attention of people, especially in the manufacturing sector because packaging has been intensively used as a protective agent only. One of the prime factors that inhibit manufacturing efficiency and productivity is treating packaging activities as an individual task. In other words, traditional packaging is usually considered as a cost driven center rather than a value added component throughout the manufacturing and distribution processes. If we reconsider the packaging design in a systematic approach, it is easy to develop more cost effective solutions for manufacturing processing that can support handling and distribution as well as providing protection of the product. By the same token, it will bring additional values to a company because of the improvement in manufacturing and distribution efficiency. The aim of the paper is to review and reorganize the major functions and roles of the packaging played in the manufacturing and logistics system and suggest a methodology for a systematic approach to packaging logistics. The proposed approach is demonstrated through a case study. How to transform an existing packaging system to the proposed one is also suggested.     

Optimization of multi-pass turning operations using hybrid teaching learning-based approach

This paper presents a novel hybrid optimization approach based on teaching–learning based optimization (TLBO) algorithm and Taguchi’s method. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing area. This research is the first application of the TLBO to the optimization of turning operations in the literature The proposed hybrid approach is applied to two case studies for multi-pass turning operations to show its effectiveness in machining operations. The results obtained by the proposed approach for the case studies are compared with those of particle swarm optimization algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing, and Hooke–Jeeves patter search.     

Optimization of mixed casting processes considering discrete ingot sizes

Using linear programming (LP), this research devises a simple and comprehensive scheduling methodology for a complicated, yet typical, production situation in real foundries: a combination of expendable-mold casting, permanent-mold casting and automated casting for large-quantity castings. This scheduling technique to determine an optimal casting sequence is successfully applied to the most general case, in which various types of castings with different alloys and masses are simultaneously produced by dissimilar casting processes within a predetermined period. The methodology proves to generate accurate scheduling results that maximize furnace or ingot efficiency. For multivariable and multi-constraint optimization problems per se, it provides an extremely practical solution which is readily implemented in most real-world casting plants. In addition, incorporating ingot adjustment from the reality of discrete ingot size, this LP scheduling can assist the casting industry in strengthening its competence by heightening ingot utilization as well as satisfying due dates. This paper was presented at the 9^th Asian International Conference on Fluid Machinery (AICFM9), Jeju, Korea, October 16–19, 2007.recommended for publication in revised form by Associate Editor Young-Seog Lee Yong Kuk Park received his B.S. degree in Metallurgical Engineering from Seoul National University, Korea, in 1987. He then received his M.S. degree in Industrial and Operations Engineering from University of Michigan, U.S.A., in 1988 and Ph.D. degree in Manufacturing Engineering from Ohio State University, U.S.A., in 1995. Dr. Park is currently an Associate Professor at the School of Mechanical and Automotive Engineering at Catholic University of Daegu, Korea. He worked for Ford Motor Co., U.S.A., and Renault-Samsung Motors, Korea. Dr. Park’s research interests include casting, forging, sheet metal forming, scheduling in production engineering, mechanical design, die failures, and fatigue analysis. Jung-Min Yang received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1993, 1995, and 1999, respectively. From March 1999 to February 2001, he was a Senior Member of the Engineering Staff at Electronics and Telecommunications Research Institute (ETRI), Korea. Since March 2001, he has been with the Department of Electrical Engineering, Catholic University of Daegu, Korea, where he is currently an Associate Professor. His research interests are in control of asynchronous sequential machines, fault-tolerance in real-time systems, and scheduling of casting process.     

Optimization of flatbed trailer frame using the ground beam structure approach

An alternative lightweight flatbed trailer design is achieved through a multi-stage optimization procedure. Topology optimization is used to obtain the optimal layout of flatbed trailer frame beams that provide minimum compliance when subjected to bending loads and exhibits maximum torsional natural frequency. The ground structure approach is used to define the trailer frame layout by generating numerous beams connected to predefined points in the trailer. Topology optimization is formulated as a multi-objective problem subject to a mass constraint. Responses and sensitivities are evaluated using ANSYS, and the optimization problem is solved using the moving asymptotes method. The thicknesses, widths, and heights of the C-channel beams are optimized for further weight reduction while at least maintaining the structural performances of the original design. Size and shape optimizations are performed using OptiStruct. The new optimal design is approximately 13% (275 kg) lighter than and as stiff as the original design for bending loads. However, the former has 3.5 times higher torsional natural frequency than the latter. Moreover, the new optimal design has positive manufacturability because the channel beams will be made out of commercially available sheet metals. The same fabrication technology as for a conventional flatbed trailer is possibly to be used.     

食品包装机中组合机构的优化设计

通过对某厂食品包装机中控制机械手运动的固定凸轮连杆组合机构的最佳传动角和凸轮的较好的压力角进行优化设计，实现了精确停歇的运动规律，结构紧凑，取得了满意的效果。     

Optimization of slider air bearing contours using the combined genetic algorithm-subregion approach

A hybrid optimization approach, the combined genetic algorithm-subregion method, which combines the advantage of the genetic algorithm and the subregion approach, is presented. Using a binary string to represent a selected design space, the combined genetic algorithm-subregion method adopts the genetic algorithm to perform the optimization process. Starting from a pico slider design originally flying at 14 nm, optimized designs were obtained for sliders with target flying heights of 7, 5 and 3.5 nm, respectively. The results show that the combined genetic algorithm-subregion method has good convergence with a substantial reduction of computation time.     

模糊制造系统中的不同尺寸工件单机批调度优化

将工件尺寸不同的单机批调度问题扩展到模糊制造系统中,建立了基于模糊批加工时间和模糊批间隔时间的制造跨度模型,提出了一种集成粒子群优化和差异演化的混合算法,将制造跨度最小化.为提高算法的收敛速度,设计了基于工件优先值向量的统一编码方式,并采用线性的缩放因子以确保足够的差异化信息;为解决差异演化算法早熟收敛的问题,将粒子群优化的全局搜索技术嵌入了差异演化算法;最后,在解码时利用批调度的启发式算法,将混合算法的个体加以优化分批.仿真实验结果验证了该混合算法的求解性能优于目前文献中的其他算法.     

An empirical approach to modelling fluid dispensing for electronic packaging

Fluid dispensing is a popular process in semiconductor manufacturing industry which is commonly used in die-bonding as well as microchip encapsulation for electronic packaging. Modelling the fluid dispensing process is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Previous studies of fluid dispensing mainly focus on the development of analytical models. However, an analytical model for fluid dispensing, which can provide accurate results, is very difficult to develop because of the complex behaviour of fluid dispensing and high degree of uncertainties of the process in a real world environment. In this project, an empirical approach to modelling fluid dispensing was attempted. Two common empirical modelling techniques, statistical regression and neural networks, were introduced to model fluid dispensing process for electronic packaging. Development of neural network based process models using genetic algorithm (GA) and Levenberg−Marquardt algorithm are presented. Validation tests were performed to evaluate the effectiveness of the developed process models from which a multiple regression model and a GA trained neural network with the architecture of 3-15-1 were identified to be the process models of the fluid dispensing respectively for the encapsulation weight and encapsulation thickness.     

Optimization of delamination factor in drilling medium-density fiberboards (MDF) using desirability-based approach

Medium-density fiberboards are finding increased applications in furniture and domestic industries. Drilling of these materials cannot be avoided. Delamination associated in drilling is the serious problem and this problem should be addressed further. This paper presents the systematic experimental investigation, analysis and optimization of delamination factor in drilling of medium-density fiberboards (MDF). Experiments were conducted on CNC drilling machine at various cutting conditions. The parameters considered for the experiments are cutting speed, feed rate and drill diameter. An empirical model has been developed for predicting the delamination factor at entry and exit of the holes in drilling of MDF boards. Desirability function based approach is employed for the optimization drilling parameters for minimizing the delamination factor at entry and exit in drilling of MDF boards. The influences of different parameters and their interactions are studied in detail and presented in this study.     

Assembly planning using a novel immune approach

Inspired by the vertebrate immune system, artificial immune system (AIS) has emerged as a new branch of computational intelligence. This paper explores the application of AIS in the problem of assembly planning and proposes a novel approach, called the immune optimization approach (IOA), to generate the optimal assembly plan. Based on the bionic principles of AIS, IOA introduces manifold immune operations including immune selection, clonal selection, inoculation and immune metabolism to derive the optimal assembly sequence. Maintenance of population diversity, attention to the local as well as the global search, and employment of heuristic knowledge to direct the search of optimized assembly sequences are the major concerns of IOA. The details of IOA are presented and the immune operations are discussed. Two practical products are taken as examples to illustrate the validity of IOA in assembly planning, and encouraging solutions in quality and efficiency are achieved. Comparisons with genetic algorithm demonstrate that IOA finds the optimal assembly solution or near-optimal ones more reliably and more efficiently, indicating that IOA has potential and advantages in dealing with assembly planning.     

Measuring the sizes of nanospheres on a rough surface by using atomic force microscopy and a curvature-reconstruction method

One of the advantages of atomic force microscopy (AFM) is that it can accurately measure the heights of targets on flat substrates. It is difficult, however, to determine the shape of nanoparticles on rough surfaces. We therefore propose a curvature-reconstruction method that estimates the sizes of particles by fitting sphere curvatures acquired from raw AFM data. We evaluated this fitting estimation using 15-, 30-, and 50-nm gold nanoparticles on mica and confirmed that particle sizes could be estimated within 5% from 20% of their curvature measured using a carbon nanotube (CNT) tip. We also estimated the sizes of nanoparticles on the rough surface of dried cells and found we also can estimate the size of those particles within 5%, which is difficult when we only used the height information. The results indicate the size of nanoparticles even on rough surfaces can be measured by using our method and a CNT tip.     

Scheduling decisions in FMS using a heuristic approach

The paper deals with the multilevel scheduling decisions of a Flexible Manufacturing System (FMS) to generate realistic schedules for the efficient operation of the FMS. The primary concern of an Operations Management System (OMS) for a FMS is production scheduling, Material Handling System (MHS) scheduling, Automated Storage/Retrieval System (AS/RS) operation and control and tool management. Scheduling is a critical issue and determines how efficiently the production resources are utilised and how the selected parts are affected in the system. In this paper, the integrated scheduling of FMS, namely, the production scheduling conforming with the MHS scheduling, is addressed. An enumerative heuristic is used, namely Giffler and Thompson, which is an evolutionary combining a Genetic Algorithm (GA) and a stochastic neighborhood search technique using a Simulated Annealing (SA) algorithm is employed.A. Noorul Haq received his PhD in Manufacturing Management from the Indian Institute of Technology (IIT), New Delhi, India, a Master of Engineering degree from Madras University, and a Bachelor of Engineering degree from Annamalai University, India. He is currently an assistant professor in the Department of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His research interests include aggregate production planning, facility layout and scheduling and optimisation techniques.T. Karthikeyan received his Master's degree from Bharathidhasan University. Currently, he is a research scholar in the Department of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His research interests includes facility layout, FMS and simulation.M. Dinesh received his bachelor's degree in engineering from Bharathiar University, India and is currently working on his master's degee in engineering in the Department Of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His current research interests are in optimisation techniques, facility layout and scheduling.     

Insulin and C-peptide (proinsulin) packaging: A quantitative immunocytochemical approach

C-peptide and insulin antigenic sites in rat pancreatic β cells were both labeled by the protein A and immunogold techniques. Gold bead density in the secretory granules was correlated with granule size distribution. Our results demonstrate that the distribution patterns of antigenic sites for C-peptide (which also represents proinsulin sites) and insulin are similar when granule size distribution is correlated to the density of antigenic sites. It was found that the ratio of C-peptide to insulin in granules is independent of granule size. Examination of frequency histograms by a Kolmogorov-Smirnov analysis excluded the possibility that the antigenic sites of C-peptide and insulin are distributed in the same manner. Mean gold bead density for insulin antigen (97 ± 24) was slightly higher than C-peptide density (64 ± 18). The efficiency of labeling was found to be very low; less than 0.1% of the theoretically estimated antigenic sites were labeled.     

Optimization of a multi-link steering mechanism using a continuous genetic algorithm

A steering mechanism is one of the main components of a vehicle that is responsible of controlling the directional wheels. The optimal design of a steering mechanism is not a trivial problem because the equations involved in the modeling are highly nonlinear. Therefore, this paper proposed a method for global optimization using a genetic algorithm for a multi-link steering mechanism, which can also be applied to other problems of optimal synthesis of mechanisms.     

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.     

Optimization of vacuum casting process parameters to enhance tensile strength of components using design of experiments approach

Vacuum casting is one of the widely used methods for small-volume production of plastic parts. The main challenge of this method is to choose the optimal w     

Optimization of a reversed trapezoidal fin using a 2-D analytic method

A reversed trapezoidal fin with variable fin base thickness is optimized based on the fixed fin volume by using a two-dimensional analytic method. The variation of temperature along the normalized Y position at the fin tip is presented. For fixed fin volumes, the maximum heat loss, the corresponding optimum fin effectiveness, fin base height and fin tip length as a function of the fin base thickness, fin shape factor and the fin volume are presented. One of the results shows that both the optimum heat loss and the optimum fin length increase with the increase of the value of fin shape factor.