V. Jayabalan

A new grouping method to minimize surplus parts in selective assembly for complex assemblies

Selective assembly is the method of obtaining high precision assemblies from relatively low precision components. The mating parts are manufactured with wide tolerances. The mating part population is then partitioned to form selective groups. The corresponding selective groups are then assembled interchangeably. The mating parts are manufactured in different processes and in different machines. The standard deviation of the mating parts will be different. The probability of the number of parts in the selective group cannot be the same. A large number of surplus parts is expected according to the difference in standard deviations of the mating parts. In this paper, a complex assembly with three mating parts (as in a ball bearing: an inner race, ball and outer race) is considered for analysis. A new method is proposed for partitioning the lots to form selective groups. By this method, the number of surplus parts is reduced to a large extent. The variation in clearance is minimized and the total number of groups is also less when compared with traditional methods.

Genetic algorithm for minimizing assembly variation in selective assembly

Selective assembly is a method of obtaining high-precision assemblies from relatively low-precision components. In selective assembly, the mating parts are manufactured with wide tolerances. The mating part population is partitioned to form selective groups, and corresponding selective groups are then assembled interchangeably. If the mating parts are manufactured in different processes and in different machines, their standard deviations will be different. It is impossible that the number of parts in the selective group will be the same. A large number of surplus parts are expected according to the difference in the standard deviations of the mating parts. A method is proposed to find the selective groups to minimize the assembly variation and surplus parts when the parts are assembled linearly. A genetic algorithm is used to find the best combination of the selective groups to minimize the assembly variation. Selective assembly is successfully applied using a genetic algorithm to achieve high-precision assemblies without sacrificing the benefit of wider tolerance in manufacturing.

A New Method in Selective Assembly to Minimize Clearance Variation for a Radial Assembly Using Genetic Algorithm

Selective assembly is the method of obtaining high-precision assemblies from relatively low-precision components. A relatively smaller clearance variation is achieved than in interchangeable assembly, with the components manufactured with wider tolerance. In selective assembly, the mating parts are partitioned to form selective groups with smaller tolerance, and the corresponding groups are assembled interchangeably. The mating parts are manufactured in different machines, using different processes, and with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. In selective assembly, the number of parts in the corresponding selective groups is not similar and will result in surplus parts. The clearance variation is also very high. In this article, a new method is proposed in selective assembly. Instead of assembling components from corresponding selective groups, the components from different combination of selective groups can be assembled to achieve minimum clearance variation. Genetic algorithm is used to find the best combination of the selective groups for minimizing the clearance variation. A case of hole and shaft (radial) assembly is analyzed in this article, and the best combination is obtained to minimize assembly clearance variation. The assembly is done in three stages to completely use all the components. The best combination for the selective groups and the resulting clearance variations are tabulated. The surplus parts are minimized to a large extent.

Modelling and analysis of selective assembly using Taguchi's loss function

An assembly is the integrative process of joining components to make a completed product. It brings together the upstream process of design, engineering and manufacturing processes. The functional performance of an assembled product and its manufacturing cost are directly affected by the individual component tolerances. But, the selective assembly method can achieve tight assembly tolerance through the components manufactured with wider tolerances. The components are segregated by the selective groups (bins) and mated according to a purposeful strategy rather than being at random, so that small clearances are obtained at the assembly level at lower manufacturing cost. In this paper, the effect of mean shift in the manufacturing of the mating components and the selection of number of groups for selective assembly are analysed. A new model is proposed based on their effect to obtain the minimum assembly clearance within the specification range. However, according to Taguchis concept, manufacturing a product within the specification may not be sufficient. Rather, it must be manufactured to the target dimension. The concept of Taguchis loss function is applied into the selective assembly method to evaluate the deviation from the mean. Subsequently, a genetic algorithm is used to obtain the best combination of selective groups with minimum clearance and least loss value within the clearance specification. The effect of the ratio between the mating part quality characteristics dimensional distributions is also analysed in this paper.

A new algorithm for minimizing surplus parts in selective assembly by using genetic algorithm

Precision assemblies are produced from low precision subcomponents by partitioning and assembling them randomly from their corresponding groups. Surplus part is one of the important issues, which reduces the implementation of selective assembly in real situations. A new algorithm is introduced in this present paper to reduce surplus parts almost to zero and it is achieved in two stages by using a genetic algorithm. For demonstrating the proposed algorithm, a gearbox shaft assembly is considered as an example problem in which the shaft and pulley are manufactured in wider tolerance and partitioned in three to nine bins. The surplus parts are divided into three bins equally and a best combination of groups is obtained for both cases. It is observed that nearly 995 assemblies are produced out of one thousand subcomponents with the manufacturing cost savings of 19.5% for T max and 992 assemblies are produced with 13.5% saving in manufacturing cost for 0.9T max.

Construction of closed-form equations and graphical representation for optimal tolerance allocation

The Lagrange multiplier method (LM) is currently used to allocate tolerance for optimum manufacturing cost. This is a tedious iterative process and sometimes it allocates a components tolerance outside its process tolerance limits. The present work develops a graphical representation which can help the process engineer to visualize the minimum and maximum values for assembly tolerances. The graphical representation developed can also help the process engineer to determine the exact total manufacturing cost of the assembly and help to fix the tolerance, which would not fall outside the limits prescribed. A simple C program is developed to construct the closed-form equations (CFE), and a single EXCEL graphical representation is derived for assembly tolerance, allocated tolerance, and total manufacturing cost. The developed algorithm has been demonstrated on a two- to five-component linear assembly, to help the process/manufacturing engineers visualization before determining the tolerance specification on component dimensions. The test results show a maximum percentage deviation of 0.09% of assembly tolerance and 0.33% of total manufacturing cost between the LM and the newly developed CFE method.

A new method in selective assembly for components with skewed distributions

A product consists of two or more components assembled together is an assembly. The quality of the product depends upon the quality of the assembly. The contributing quality characteristics of the mating parts play a major role. A good amount of research has been carried out to improve the quality of assembly using selective assembly, when the contributing quality characteristics confirms to normal distribution. However, in reality, the contributing quality characteristics of a component will have some skewness, which will make the models proposed by earlier researcher not suitable for practice. In this paper, a new method is proposed with component quality characteristics having skewness and selective assembly can be effectively used to meet the specification requirements without any surplus parts. The proposed method ensures that all the components of the mating part population is used and at the same time there is minimum variation in the assembly even there is skewness in the dimensional distribution of the mating parts. Genetic algorithm (GA) is used to find the number of components in selective group combinations for a given clearance variation.

Six Sigma inspired sampling plan design to minimise sample size for inspection

One of the important property of the c = 0 plan is that it provides a perception that defective product will not be tolerated, ideally a zero defect environment requirement. But this plan fails in discriminating the lots with non-conformities even with larger sample size. In this paper, a method based on Design for Six Sigma (DFSS) road map Plan, Identify, Design, Optimise and Validate (PIDOV) is proposed for problem solving. This procedure retains the simplicity of single sampling plan by attributes. The procedure uses a recursive form of hypergeometric probability formula for sampling plan design to meet the stipulated consumers risk and producers risk. New levels of AQL and LTPD are chosen and corresponding changes in the given specifications are made yielding to smaller sample size. The new tightened specification allows the probability of occurrence of the defective in a smaller size of the sample. The parameter Average Total Inspected (ATI) calculated shows considerable improvements from the existing sampling plans. Thus the proposed procedure shifts the application of acceptance sampling plan to acceptance control plan to ensure minimum deviations from the desired specification mean, an obvious objective of a Six Sigma program.

Designing an economic sampling plan: a QFD approach

Sampling inspection puts severe psychological pressure on producer to improve the process to avoid rejection. Even the critics on acceptance sampling agree with this feature. But this qualitative expectation is not addressed by the existing sampling systems. Although many strategic sampling procedures like economical sampling plans are available to attend these intrinsic needs, the difficulty lies in deciding which of these procedures is appropriate for the particular application. However a sampling plan can be modelled and developed as a product to meet both the extrinsic/quantifiable, intrinsic/intangible requirements. Quality function deployment (QFD) is a system for designing the product or service based on customer demands and involving all members of the producer or supplier organisation. In this paper using the QFD matrix, customer voices (qualitative) are translated into sampling plan specifications (quantitative) directed at customer satisfaction. A case analysis with numerical example is presented to highlight the use of this QFD matrix in developing an economic sampling procedure.