Analysis and optimization of assembly variations for non-rigid parts

Traditional variation analysis methods are not applicable to non-rigid assemblies due to possible part deformation during the assembly process. This paper presents the use of finite element methods to simulate assembly deformation. The relationship between the parts’ variation and the variation of the key points in final assembly for quality control is set up by calculating the spring back deformation after assembly. Moreover, the optimization method for non-rigid assembly variations based on finite element analysis is presented. The optimal objective is to reduce the manufacturing cost. The approach is implemented by using ANSYS and MATLAB. The test example shows that the proposed method is effective and applicable.

Assembly variation analysis of flexible curved surfaces based on Bézier curves

Assembly variation analysis of parts that have flexible curved surfaces is much more difficult than that of solid bodies, because of structural deformations in the assembly process. Most of the current variation analysis methods either neglect the relationships among feature points on part surfaces or regard the distribution of all feature points as the same. In this study, the problem of flexible curved surface assembly is simplified to the matching of side lines. A methodology based on Bézier curves is proposed to represent the side lines of surfaces. It solves the variation analysis problem of flexible curved surface assembly when considering surface continuity through the relations between control points and data points. The deviations of feature points on side lines are obtained through control point distribution and are then regarded as inputs in commercial finite element analysis software to calculate the final product deformations. Finally, the proposed method is illustrated in two cases of antenna surface assembly.

Assembly Variation Identification of High Pressure Spool by Response Surface Based Model Updating Using IV-optimal Designs

This article, in order to improve the assembly of the high-pressure spool, presents an assembly variation identification method achieved by response surface method （RSM）-based model updating using IV-optimal designs. The method involves screening out non-relevant assembly parameters using IV-optimal designs and the preload of the joints is chosen as the input features and modal frequency is the only response feature. Emphasis is placed on the construction of response surface models including the interactions between the bolted joints by which the non-linear relationship between the assembly variation caused by the changes ofpreload and the output frequency variation is established. By achieving an optimal process of selected variables in the model, assembly variation can be identified. With a case study of the laboratory bolted disks as an example, the proposed method is verified and it gives enough accuracy in variation identification. It has been observed that the first-order response surface models considering the interactions between the bolted joints based on the IV-optimal criterion are adequate for assembly purposes.

Minimizing assembly variation in selective assembly for auto-body parts based on IGAOT

Purpose–Dimensional quality of sheet metal assemblies is an important factor for the final product.However,the part tolerance is not easily controlled because of the spring back deformation during the stamping process.Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components.Therefore,the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox(IGAOT)in MATLAB.Design/methodology/approach–The method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation.Afterwards,the IGAOT is proposed to generate optimal selective groups,which consists of advantages of genetic algorithm for optimization toolbox(GAOT)and simulated annealing.Findings–The cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups.These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.Originality/value–The research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly.The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

Study on quality evaluation model in multi-station assembly processes

Based on features of dimension variation propagation in multi-station assembly processes,a new quality evaluation model of assembly processes is established. Firstly,the error source of multi-station assembly system is analyzed,the relationship of dimension variation propagation in multi-station assembly processes is studied and the state equation for variation propagation is constructed too. Then,the feature parameters which influence variation propagation and accumulation in multi-station assembly processes are found to evaluate quality characteristic of the assembly system. Through the derivation of equation on dimension variation propagation,station coefficient matrices which are combined and conversed to determine the max eigenvalue are educed. The max eigenvalue is multiplied by the weight coefficient to establish the quality evaluation model in multi-station assembly processes. Furthermore,assembly variation indexes are proposed to judge of the assembly technology process. Finally,through the practical example,the application of the model and assembly variation indexes are presented.

Population transcriptomics reveals a potentially positive role of expression diversity in adaptation

While it is widely accepted that genetic diversity determines the potential of adaptation,the role that gene expression variation plays in adaptation remains poorly known.Here we show that gene expression diversity could have played a positive role in the adaptation of Miscanthus lutarioriparius.RNA-seq was conducted for 80 individuals of the species,with half planted in the energy crop domestication site and the other half planted in the control site near native habitats.A leaf reference transcriptome consisting of 18,503 high-quality transcripts was obtained using a pipeline developed for de novo assembling with population RNA-seq data.The population structure and genetic diversity of M.lutarioriparius were estimated based on 30,609 genic single nucleotide polymorphisms.Population expression（Ep） and expression diversity（Ed）were defined to measure the average level and the magnitude of variation of a gene expression in the population,respectively.It was found that expression diversity increased while genetic Resediversity decreased after the species was transplanted from the native habitats to the harsh domestication site,especially for genes involved in abiotic stress resistance,histone methylation,and biomass synthesis under water limitation.The increased expression diversity could have enriched phenotypic variation directly subject to selections in the new environment.