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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.

The assembly of remanufactured machine tools is crucial for ensuring their accuracy. Poor assembly accuracy, low remanufacturing resource utilization, and inefficient assembly have become common issues in remanufacturing processes. This limitation is addressed in the study, wherein Taguchi’s mass loss function (QLF) and a cost function for the remaining parts are employed. Both assembly accuracy and minimum cost are ensured through the establishment of a comprehensive matching model with closed-loop dimensional chains as constraints. The solution process of the PSO-GA model is outlined, effectively reducing uncertainty and resource waste associated with impractical matching schemes. Practical results from the CAK6163 headstock demonstrate that not only is the low success rate issue in matching remanufactured parts resolved by the proposed method, but resource utilization is also significantly enhanced, and costs are reduced.

For cylinder shell parts produced in batches, computer-aided selective assembly can not only obtain higher product matching accuracy, but also reduce the remaining number of parts, ensuring the welding assembly quality and improving the production efficiency. Aiming at the selective assembly problem for spinning shells with electron beam welding, a selective assembly model based on an improved genetic simulated annealing algorithm was proposed. By analyzing the assembly process characteristics of spinning shells, mapping association matrix of assembly constraints was built to describe the assembly relationship between the different cylinder of spinning shells. Considering the multi-assembly quality loss function using SNR and assembly yield, a multi-objective comprehensive optimization model was established. Based on the measured internal diameter of the parts, a specific coding method and the adaptive cross mutation operator based on the sigmoid curve is introduced to apply an improved genetic simulated annealing algorithm (IGSAA), solving the assembly selection problem of 5 shell parts case. The results show that the model established has a good applicability to the spinning shell parts matching problem, which can effectively improve the success rate of parts matching and assembly accuracy, and meet the production needs of enterprises. Moreover, the produced assembly difference through improved genetic simulated annealing algorithm (IGSAA) is even better than manual selection in matching accuracy and efficiency.

The minimization of surplus components with normal dimensional distributions while making selective assemblies was the only objective considered in the previous research works carried out by various researchers in different periods. Seldom works have been found on selective assembly by considering all dimensional distributions. In this proposed work, a novel method is developed for making assemblies with zero surplus components and minimum clearance variation by considering arbitrary distribution, to demonstrate the greater improvement in the results than the past literature. Krill Herd algorithm has been implemented for identifying the best combination of groups. Computational results showed that the proposed krill herd algorithm outperformed as compared with existing literature and as well as the results by gaining-sharing knowledge-based algorithm, differential evolution algorithm, and particle swarm optimization algorithm.

In this study, we developed a system for selective self-assembly of millimeter-scale components differentiated by adhesive patterns. This was achieved by designing concentric circular patterns having different radii but the same total length of peripheries. Small polymer sheets having solder adhesive patterns in these designs were simply attached to the millimeter-scale components to be assembled in a stirring container. This strategy was effective in avoiding an overlap between different patterns and enforcing the selective bonds between identical patterns among three types of components. Finally, the selective assembly of a functional structure (i.e., poly(N-isopropylacrylamide) gel actuator) was demonstrated.

In the process of selective assembly for precision and complex mechanical products, it is necessary to perform the selective material kitting one by one and conduct check repeatedly according to the optimal matching result to ensure quality. The traditional manual picking and checking method by Bill of Material is inefficient and laborious. Currently, there is still no consensus reached on how to better transmit information and implement the material kitting in the course of selective assembly. In this paper, we pioneer in testing the performance between mainstream methods (Pick-by-Voice, Pick-by-Light, Pick-by-AR) and traditional methods (Pick-by-Paper) in terms of task time, errors, workload, and information perception for the selective material kitting. We developed an AR (augmented reality) on the level of material individual information level and verified its applicability. Meanwhile, we enhanced the effect of material individual information expression from different visual perspectives. We found that Pick-by-AR can outperform others in terms of selective assembly, which means expanding the dimension of information expression and enhancing users’ perception of information can help users quickly and accurately select parts with the same appearance but different quality characteristics fast and accurately, thus providing a viable option for material kitting in the selective assembly process.

Aerospace engine parts are complex precision-engineered products with tighter assembly tolerances produced by conventional and non-conventional manufacturing processes. Variations in these manufacturing processes have to be controlled, process risks mitigated, and managed effectively, to facilitate the ease of aero-engine assembly to reduce overall variation and improve the assembly quality. One such technique is the application of the Selective Assembly as a Design for Manufacturing and Assembly (DfMA) tool. The paper details the methodology of Selective Assembly, its applications, benefits, and limitations in the aerospace industry along with a framework case study with a focus on ease of assembly and meeting the design intent of the assembly fit with the detailed study on the current traditional assembly process. The focus is to analyze the current challenges faced on the application of the Selective assembly of Aero-engine components through a case study on the development and application of an Assembly Installation Operation Selective (AIOS) assembly tool, that be applied both as a built-in tool for Selective Assembly data acquisition and optimal selection. The study also investigates whether the concept of decimal roundoff in the measurement reporting process has any significant impact on the selective process capability and the pin-hole assembly fit quality. The objectives are to optimize and enhance the current assembly process to meet the customer design intent and First Time Right (FTR) quality of the assembly fit and to promote the application of the Selective Assembly concept as a potential quality and cost optimization technique in assembly processes highly subjected to variations by emphasizing its application as an Aerospace Design for Manufacturing and Assembly (ADFMA) guideline for effectively controlling and mitigating assembly variations of shafts and holes in aerospace engine parts and reducing reworks and rejections in the aerospace industry due to high process variability and need for better process control in fabrication tolerances.

Selective assembly is the method of obtaining high precision assemblies from relatively low precision components. For precision instruments, the geometric error on mating surface is an important factor affecting assembly accuracy. Different from the traditional selective assembly method, this paper proposes an optimization method of selective assembly for shafts and holes based on relative entropy and dynamic programming. In this method, relative entropy is applied to evaluate the clearance uniformity between shafts and holes, and dynamic programming is used to optimize selective assembly of batches of shafts and holes. In this paper, the case studied has 8 shafts and 20 holes, which need to be assembled into 8 products. The results show that optimal combinations are selected, which provide new insights into selective assembly optimization and lay the foundation for selective assembly of multi-batch precision parts.

3D printing is an additive manufacturing process that has an untold number of applications in fields as diverse as medical prostheses to military vehicle manufacturing not to mention its long term use in component prototyping [1][2]. With the creation of robotic arm based printers, the 3D printing process can be improved in terms of flexibility and time-efficiency, but with the potential trade-off being lower resolution in some areas of printer workspace. To counteract the resolution reduction, we are studying the use of continuously variable transmissions (CVTs) coupled to the robot's traditional revolute joints [3]. This paper will show that CVTs allow our SCARA-based robotic printer called DEXTER to achieve a resolution as good or better than a traditional desktop style 3D printer.

The planetary roller screw mechanism (PRSM) is a novel precision transmission mechanism that realizes the conversion between linear and rotary motions. The contact characteristics of helical surfaces directly determine PRSM’s performance in load-carrying capacity and transmission accuracy. Therefore, studying the contact characteristics of PRSM forms the fundamental basis for enhancing its transmission performance. In this study, a three-dimensional parametric analysis method of contact characteristics is proposed based on the PRSM meshing principle and PyVista (a high-level API to the Visualization Toolkit). The proposed method considers the influence of machining errors among various thread teeth. The effects of key machining errors on contact positions and axial clearance, as well as their sensitivities, are analyzed. With excellent solution accuracy, this method exhibits higher calculation efficiency and stronger robustness than the analytical and numerical meshing models. The influence of nominal diameter and pitch errors of the screw, roller, and nut on the axial clearance follows a linear relationship, whereas flank angle errors have negligible effects on the axial clearance. The corresponding influence coefficients for these three machining errors on the axial clearance are 0.623, 0.341, and 0.036. The variations in contact positions caused by individual errors are axisymmetric. Flank angle errors and roller diameter errors result in linear displacements of the contact points, whereas pitch errors cause the contact points to move along the arc of the roller diameter. Based on the proposed three-dimensional parametric contact characteristics analysis method, the Fuzzy C-Means clustering algorithm considering error sensitivity is utilized to establish a component grouping technique in the selective assembly of critical PRSM components, ensuring the rational and consistent clearances based on the given component’s machining errors. This study provides effective guidance for analyzing contact characteristics and grouping in selective assembly for PRSM components. It also presents the proposed method’s potential applicability to similar calculation problems for contact positions and clearances in other transmission systems.