Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Nowadays tolerance optimization is increasingly becoming an important tool for manufacturing and mechanical design. This seemingly, arbitrary task of assigning dimension tolerance can have a large effect on the cost and performance of manufactured products. With the increase in competition in today’s market place, small savings in cost or small increase in performance may determine the success of a product. In practical applications, tolerances are most often assigned as informal compromises between functional quality and manufacturing cost. Frequently the compromise is obtained interactively by trial and error. A more scientific approach is often desirable for better performance. In this paper particle swarm optimization (PSO) is used for the optimal machining tolerance allocation of over running clutch assembly to obtain the global optimal solution. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. The result obtained by PSO is compared with the geometric programming (GP) and genetic algorithm (GA) and the performance of the result are analyzed .     

Optimization of clearance variation in selective assembly for components with multiple characteristics

Quality is an important aspect of any manufacturing process. Only high quality products can survive in the market. The consumer not only wants quality, precision and trouble-free products, but also wants them at attractive prices. When a product consists of two or more components, the quality of that product depends upon the quality of the mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance or interference between the mating parts. In some high precision assemblies, it may not be possible to have closer assembly clearance variation with interchangeable system. Selective assembly meets the above requisite and gives an enhanced solution. Selective assembly is a method of obtaining high precision assemblies from relatively low precision components. In this paper, a new selective assembly method is proposed to minimize the clearance variation and surplus parts for a complex assembly which consists of the components viz. piston, piston ring. In this assembly, each component will have more than one quality characteristic contributing for the assembly, for e.g., piston diameter will assemble with cylinder inner diameter; piston groove diameter will assemble with piston ring inner diameter, etc. In selective assembly each quality characteristic will fall in different groups. Non-dominated sorting genetic algorithm (NSGA) is used to find the best combination to obtain the minimum clearance variation in this assembly.     

Optimum tolerance synthesis for complex assembly with alternative process selection using Lagrange multiplier method

Any part cannot be manufactured to the required nominal dimensions due to inherent variations in workmanship, material, and machine. The specification of tolerance on part dimensions plays a major role on performance, quality, and cost of the product. Distribution of tolerance among the components of an assembly is known as tolerance allocation. The selection of alternative processes for tolerance allocation also plays a vital role in reducing manufacturing cost. Near-optimal allocated tolerances are obtained using nontraditional optimization techniques in which the solutions are achieved randomly. Also, there is a chance for omitting the better process for allocation. The results of successive run of the program based on these techniques will not yield consistent results. An attempt has been made in this work to solve the above problem using Lagrange multiplier method for complex assemblies with univariate search method. The methodology has been demonstrated on wheel mounting assembly. The example product after implementing the proposed method would yield 1.4% savings in manufacturing cost as compared with the cost obtained by Singh.     

Optimal control of inventory accumulation in selective assembly processes

Assembly is a type of production process in which a number of components are combined to yield a final product. Although the concept of interchangeable parts has long been known as the fundamental principle of assembly processes, randomly picking some bulked components with dimensions varying in predefined tolerances may not be a valid approach to obtain special final products with considerably tighter tolerances. Therefore, each of the components needs to be measured and classified into dimensional groups in advance so that quality products can be obtained by matching components from suitable groups. This assembly scheme is called as “selective assembly.” In this work, we consider an assembly case with a pair of components in which one is manufactured on a given number of parallel processes whose settings can be changed to affect the dimensional distribution of the yield while the other component with a slightly bigger tolerance is manufactured on a single process with constant settings. In order to minimize the number of components which could not have been matched with their counterparts, we develop a nonlinear mathematical model to determine the optimal machine settings corresponding to the nominal mean of the component dimension which follows a normal distribution when it is machined. The solution of the mathematical model not only provides the individual settings for the parallel processes producing the same type of component but also the optimal batch sizes at each trial. We have finally used a simulation model of the whole production system, in order to prove that the solution of the mathematical model is able to provide the machine settings which minimize the number of unmatched parts at each trial.     

A hybridized approach to optimal tolerance synthesis of clutch assembly

Specifying proper tolerances for manufactured goods results in greater savings and improved performance, which may ultimately determine whether a product succeeds or fails in the marketplace. In the past, tolerance specification has been more an art than a science, and is largely dependent upon experiences. A more scientific and reliable approach is presented in this paper. A hybrid of Nelder-Mead simplex method and particle swarm optimization (NM-PSO) is introduced for the design of tolerance of the machine elements of an overrunning clutch assembly. The objective is to obtain tolerances of the individual components so that the cost of manufacturing and quality loss is minimized. Experimental results demonstrate that hybrid NM-PSO is extremely effective and efficient in locating best-practice optimal solutions compared to geometric programming (GP), genetic algorithm (GA), and particle swarm optimization (PSO) methods.     

基于Witness仿真软件的分组装配适配率预测

分组装配技术是一种对按经济加工精度设定配合尺寸公差,对完工后的配合尺寸检测、分组、同组号零件装配的方法。基于不同理论的分组方法都是在保证装配精度的前提下,以较低制造成本获得预期的适配率。在讨论等批量配合零件统计分组装配技术的基础上,提出了一种根据配合零件批的统计参数采用Witness仿真软件对分组装配适配率进行预测的方法,并据此来确定投入加工的配合零件批的数量,从而为编制生产计划提供了指导。     

Particle swarm optimization algorithm to maximize assembly efficiency

The demands on assembly accuracy require accurate operations both in machining and assembly in order to achieve the high performance of products. Although advanced machining technologies can be used to satisfy most of the demands on precision assembly, the corresponding manufacturing cost will also be increased. Selective assembly provides an effective way for producing high-precision assembly from relatively low-precision components. The accuracy of selective assembly is mainly based on the number of groups and the range of the group (allocated equally on the design tolerance). However, there are often surplus parts in some groups due to the imbalance of mating parts, especially in the cases of undesired dimensional distributions, which makes the methods developed and reported in the literature often not suitable for practice. In this work, a particle swarm optimization (PSO) algorithm is proposed by applying batch selective assembly method to a complex assembly with three mating components (as in ball bearing: an inner race, ball and outer race), to minimize the surplus parts and thereby maximizing the assembly efficiency. Due to the continuous nature of particle swarm optimization algorithm, standard PSO equations cannot be used to generate discrete combination of mating parts. An effective encoding scheme is developed to make the combination of mating parts feasible. The evolution performance of the PSO algorithm with different control parameter values is also analysed.     

A concurrent engineering based method for developing a baby carriage

An integrated concurrent engineering methodology for developing a baby carriage is proposed. The development process is divided into three stages for considering the design, manufacture, and assembly problems. The product is first designed based on design criteria to ensure the best matching of needs and requirements. Then the parts and machine tools are coded using group technology (GT). The machine groups and the parts to be processed are given in matrix form using the production flow analysis (PFA) method. The machine cells are arranged based on the balance analysis of process time, and a flexible manufacturing system (FMS) is planned. Finally, the assembly line is planned based on the relationship of the parts. Some related parts are collected as a subassembly system. After the subassembly is decided, the work stations are arranged based on the required assembly time to obtain a high-performance assembly line. The results show that not only is the production cost of the product reduced, but also that the competitive properties are improved. This model can also be applied to develop other products.     

Optimal tolerance design of assembly for minimum quality loss and manufacturing cost using metaheuristic algorithms

Tolerance allocation is a design tool for reducing overall cost of manufacturing while meeting target levels for quality. An important consideration in product design is the assignment of design and manufacturing tolerances to individual component dimensions so that the product can be produced economically and functions properly. The allocation of tolerances among the components of a mechanical assembly can significantly affect the resulting manufacturing costs. In this work, the tolerance allocation problem is formulated as a non-linear integer model by considering both the manufacturing cost of each component by alternate processes and the quality loss of assemblies so as to minimise the manufacturing cost. Metaheuristics techniques such as genetic algorithm and particle swarm optimisation are used to solve the model and obtain the global optimal solution for tolerance design. An example for illustrating the optimisation model and the solution procedure is provided. Results are compared with conventional technique and the performances are analysed.     

Tolerance design of mechanical assembly using NSGA II and finite element analysis

The technological and financial limitations in the manufacturing process are the reason for non-achievability of nominal dimension. Therefore, tolerance allocation is of significant importance for assembly. Conventional tolerance allocation methods are limited by an assumption that all parts are rigid. Every mechanical assembly consists of at least one or more flexible parts which undergo significant deformation due to inertia effect. Finite element analysis is used to determine the deformation of components in an assembly. Therefore, integration of statistical tolerance design with finite element analysis will guarantee that the optimal tolerance values of various components of the assembly obtained as end product of the tolerance design will remain within tolerance variation. Then the product can function as intended under a wide range of operating conditions for the duration of its life. In this paper, tolerance design of a piston cylinder assembly is done to demonstrate the proposed methodology.     

Optimal tolerance design for mechanical assembly considering thermal impact

In this paper, a cost–tolerance model based on neural network methods is proposed in order to provide product designers and process planners with an accurate basis for estimating the manufacturing cost. Tolerance allocation among the assembly components is carried out to ensure that the functionality and design quality are satisfied considering the effect of dimensional and geometric tolerance of various components of the assembly by developing a parametric computer aided design (CAD) model. In addition, deformations of various components of mechanical assembly due to inertia and temperature effects are determined and the same is integrated with tolerance design. The benefits of integrating the results of finite element simulation in the early stages of tolerance design are discussed. The proposed method is explained with an application example of motor assembly, where variations due to both dimensional and geometric tolerances are studied. The results show that the proposed methods are much effective, cost, and time saving than the ones considered in literature.     

A multi-plant assembly sequence planning model with integrated assembly sequence planning and plant assignment using GA

In a multi-plant collaborative manufacturing system, the manufacturing operations and assembly operations for producing a product can be distributed at different plants at various locations. The components are assembled with assembly operations performed in a multi-plant assembly sequence. In this research, a multi-plant assembly sequence planning model is presented by integrating (1) assembly sequence planning, and (2) plant assignment. In assembly sequence planning, the components and assembly operations are sequenced according to the assembly constraints and assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants to achieve multi-plant cost objectives. The feasible assembly sequences are generated using the developed graph-based models of assembly precedence graphs and matrices. A genetic algorithm (GA) method is presented to evaluate the multi-plant assembly sequences with an objective of minimizing the total of assembly operational costs and multi-plant costs. The main contribution lies in the new model for multi-plant assembly sequence planning and the new GA encoding scheme for simultaneous assembly sequence planning and plant assignment. Example products are tested and discussed. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem.     

An automated assembly environment in feature-based design

In the integration of design and manufacturing, one of the primary techniques is to evaluate the parts in the assembly so that the labour cost can be reduced. In this paper, the mechanical parts within the design and assembly are examined, and an integrated architecture of an assembly model is proposed which includes a proper assembly environment, the interface between the assembly model and feature-based design, and a knowledge-based expert system for assembly analysis. In order to determine satisfactory plans for assembly and to achieve the required functions in the product model, explicit relational information between the major components is implemented. The evaluation of interference checking of the parts and the geometric tolerances (such as perpendicularity, parallelism, and angularity) of the parts in the assembly model are also studied.     

尺寸精度约束下再制造产品零部件选配优化方法研究

合理选配再利用件、再制造件、新件等不同来源形式的零部件,是保障再制造产品质量与成本的关键环节。针对待选配零部件尺寸公差对再制造产品质量损失、成本的双重影响特征,通过设计零部件尺寸公差的精度分级机制,实现再制造产品各零部件尺寸精度不低于原新品,获取可行选配方案集;进一步以产品尺寸链精度不低于原新品为约束,建立关于零部件尺寸公差的再制造产品尺寸链精度损失函数、再制造产品总成本函数,构建面向再制造产品成本与质量协同优化的多目标选配优化模型,采用协同进化算法获取最终的选配优化方案,实现在全面提升再制造产品及其零部件尺寸精度的同时,降低产品成本。最后以某机床的再制造进给箱零部件优化选配为例,验证了所提出方法能够有效解决零部件来源形式多、尺寸精度与成本差异性大环境下,以不低于新产品尺寸精度为约束的再制造零部件选配优化问题。     

Minimizing clearance variations and surplus parts in multiple characteristic radial assembly through batch selective assembly

An assembly consists of two or more mating parts. The quality of any assembly depends on quality of its mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance between the mating parts. All precision assemblies demand for a closer clearance variation. A significant amount of research has already been done to minimize clearance variation using selective assembly. Surplus part is one of the important issues, which reduces the implementation of selective assembly in real situation. Surplus parts are inevitable while the assembly is made from components with undesired dimensional distributions. Batch selective assembly is introduced in this paper to reduce surplus parts to zero and it is achieved by using nondominated sorting genetic algorithm-II. For demonstrating the proposed algorithm, a complex assembly which consists of piston, piston ring and cylinder is considered as an example problem. The proposed algorithm is tested with a set of experimental problem datasets and is found outperforming the other existing methods found in the literature, in producing solutions with minimum clearance variations with zero surplus parts.     

Disassembly modeling, planning, and leveling for a cam-operated rotary switch assembly: a case study

The manufacturing organizations have been witnessing a paradigm shift on designing products and services from manufacturing to sustainability. Product disassembly is a vital strategy for industrial recycling and remanufacturing which generates the desired parts and/or subassemblies by means of separation of a product into its elements. This article presents the appropriate methods for modeling planning and leveling the disassembly process of a cam-operated rotary switch assembly. The methodology involves disassembly modeling using a graphical approach. Disassembly process planning has been done using reverse assembly approach. Disassembly leveling has been done to maintain profitability and environmental features at a desired level. An optimal disassembly sequence has been derived using disassembly leveling. The resultant cost of disassembly for reusable components has been computed. The article presents a unique contribution to the field of designing for disassembly from the perspective of rotary switches.     

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.     

Optimum assembly using a component dimensioning method

Many tolerance allocation methods based primarily on cost optimisation have been developed in recent years. However, the dimensions of the assembly parts are usually determined by convenience or design constraints other than the requirements of the assembly itself. The dimensions are often determined to give sufficient clearance during assembly without much consideration given to their tolerances. This is usually followed by a tolerance analysis to check for interference. The dimensions are then adjusted to eliminate any interferences. There is no control over the size of the clearance of an assembly.This paper presents a new approach of dimensioning the components of an assembly which would allow real control over the size of clearance or interference in an assembly. A unique algorithm in conjunction with a relationship matrix is developed to formulate the dimensions in a tolerance chain into a linear equation. This linear equation is then used to develop the tolerance analysis module. The dimensioning module is accomplished using a separate algorithm in conjunction with the linear equation established. The tolerance analysis and the dimensioning modules are subsequently tested on a number of examples.     

A methodology to define a reconfigurable system architecture for a compact heat exchanger assembly machine

Due to the unexpected, fast, and constant changes of market requirements and the hypercompetency, robust manufacturing systems are needed that adjust easily to operational variability and the customized product supply. The simply substitution of components, software, hardware, and/or their adaptation by parameters resetting are an attractive option to face this challenge. Short product life cycles are an undeniable consequence and evidence of this. For this reason, to develop products or services profitably in the product manufacturing field, it is common to use the product family concept, which involves sharing components, functional features, and manufacturing process, both to make a cheaper product development process and to obtain customized products. A new generation of manufacturing systems that deploy characteristics such as adaptability and flexibility responding to the market dynamics called reconfigurable manufacturing systems (RMS) are required by market according to manufacturing experts. The manufacturing systems with modular architecture are the best way to meet flexible and adaptable RMSs because they allow reconfiguration by a simple module substitution or by resetting module operation parameters. This paper presents a design methodology developed to obtain modular RMS. The method integrates the utilization of modular architecture principles, selection algorithms (analytical hierarchical process), clustering algorithms (average linkage clustering algorithm), family product features and functional system analysis in the classical product design process. The methodology proposed allows defining the most adequate modular system architecture and the modular definition of the reconfiguration variables that are needed to reach the flexibility required. A real study case about a heat exchanger assembly machine is presented where this methodology is applied in order to present an evidence of its usefulness.     

Manufacturing performance evaluation for IC products

As we known, the product diversity and complexity in the production line will gradually increase. When the multiple products were alternately produced at the same line, the manufacturing performance will be difficult to evaluate. In particular, traditional process capability analysis and related process capability indexes cannot be directly employed to the IC manufacturing process. As we know, the yield has a direct effect on the manufacturing cost. Hence, yield is frequently used by most IC manufacturers to evaluate manufacturing performance. The diversity of function will become another analytic consideration due to that the component density, wafer area and product complexity of an IC product rapidly increase. Hence, the diversity of function can be regarded as the evaluated factor. Additionally, the defects on a wafer will begin to cluster as the wafer area gradually increases. Therefore, only using the yield to represent manufacturing performance may not lead to an appropriate judgment. In particular, only using the yield to evaluate the process’s stability and the product’s maturity can not provide a meaningful resolution. The primary reason is that the inherent features in the processes or products are not included into analyzing. For instance, even though the defect count, defect size and defect distribution are the same, the yield loss of the complicate manufactured product will be less than that of the simple manufactured product. In this study, we propose a simple performance evaluation index to assess the manufacturing performance in the IC manufacturing industry. This evaluation index is constructed according to a modified Poisson yield model, and the related parameters regarding the process or product (e.g., the minimum linewidth, the area of a die, the number of manufactured process or layer, the degree of defect clustering, and so on.) are taken into consideration. In addition, an integrated evaluation procedure is also suggested to evaluate the performance of the manufacturing of multiple IC products. According to the result obtained from the illustrative example, the index and the procedure can overcome the drawback of separately using yield or defect count in the analysis. The rationality and the feasibility of the proposed evaluated index and the procedure can be verified by demonstrating the illustrative example.