Many-Objective Optimization and Decision-Making Method for Selective Assembly of Complex Mechanical Products Based on Improved NSGA-III and VIKOR

In Industry 4.0, data are sensed and merged to drive intelligent systems. This research focuses on the optimization of selective assembly of complex mechanical products (CMPs) under intelligent system environment conditions. For the batch assembly of CMPs, it is difficult to obtain the best combinations of components from combinations for simultaneous optimization of success rate and multiple assembly quality. Hence, the Taguchi quality loss function was used to quantitatively evaluate each assembly quality and the assembly success rate is combined to establish a many-objective optimization model. The crossover and mutation operators were improved to enhance the ability of NSGA-III to obtain high-quality solution set and jump out of a local optimal solution, and the Pareto optimal solution set was obtained accordingly. Finally, considering the production mode of Human–Machine Intelligent System interaction, the optimal compromise solution is obtained by using fuzzy theory, entropy theory and the VIKOR method. The results show that this work has obvious advantages in improving the quality of batch selective assembly of CMPs and assembly success rate and gives a sorting selection strategy for non-dominated selective assembly schemes while taking into account the group benefit and individual regret.

Development of a selective assembly card of the «Koadut» lens

The work is devoted to the development of a selective assembly card of the «Koadut» lens, depending on the actual thickness of each of the seven lenses included in the optical scheme of the objective. Criteria for sorting options are substantiated, a macro for processing a file with the results of measurements of actual lens thicknesses, which forms an assembly card, is created. The macro was tested on a test file with a normal distribution of deviations from the nominal value of a batch of one hundred lenses.

Simulation Model of Selective Assembly of the Conrod-Piston Group Unit of Internal Combustion Engines, Taking into Account Measurement Errors During Sorting

The process of selective assembly of the conrod-piston group of an internal combustion engine, which provides the required accuracy of the connections, is considered. The functioning of a unit consisting of three main elements is described, the scheme of the assembly set-making is shown. The simulation model of a one-parameter selective assembly of three elements has been modernized by taking into account measurement errors arising from sorting into size groups. The modeling of the assembly process of the unit for a specific engine model has been carried out. A dependence is obtained that connects the required output indicator of the technological process with the parameter of the measurement quality. This research will allow solving the metrological problem associated with the choice of the optimal accuracy of measuring equipment under technical and economic constraints.

Harmony Search Algorithm for Minimizing Assembly Variation in Non-linear Assembly

The proposed work aims to acquire the maximum number of non-linear assemblies with closer assembly tolerance specifications by mating the different bins’ components. Before that, the components are classified based on the range of tolerance values and grouped into different bins. Further, the manufacturing process of the components is selected from the given and known alternative processes. It is incredibly tedious to obtain the best combinations of bins and the best process together. Hence, a novel approach using the combination of the univariate search method and the harmony search algorithm is proposed in this work. Overrunning clutch assembly is taken as an example. The components of overrunning clutch assembly are manufactured with a wide tolerance value using the best process selected from the given alternatives by the univariate search method. Further, the manufactured components are grouped into three to nine bins. A combination of the best bins is obtained for the various assembly specifications by implementing the harmony search algorithm. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The results show that the contribution of the proposed novel methodology is legitimate in solving selective assembly problems.

Parallel evolution between genomic segments of seasonal human influenza viruses reveals RNA-RNA relationships

The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To explore putative intersegmental RNA-RNA relationships, we quantified similarity between phylogenetic trees comprising each vRNA segment from seasonal human IAV. Intersegmental tree similarity differed between subtype and lineage. While intersegmental relationships were largely conserved over time in H3N2 viruses, they diverged in H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, suggesting that IAV evolution could also be driven by RNA-RNA interactions. Finally, we used confocal microscopy to determine that colocalization of highly coevolved vRNA segments is enriched over other assembly intermediates at the nuclear periphery during productive viral infection. This study illustrates how putative RNA interactions underlying selective assembly of IAV can be interrogated with phylogenetics.

Identification of the 5′-Terminal Packaging Signal of the H1N1 Influenza A Virus Neuraminidase Segment at Single-Nucleotide Resolution

The genome of the influenza A virus is an eight-segmented negative-strand RNA (vRNA). Progeny vRNAs replicated in the nucleus selectively assemble into a single set of eight different segments, probably in the cytoplasm, and are packaged into progeny virions at the cell membrane. In these processes, a region of approximately 100 nucleotides at both ends of each segment is thought to function as a selective assembly/packaging signal; however, the details of the mechanism, such as the required sequences, are still unknown. In this study, we focused on the 5′-terminus of the sixth neuraminidase gene segment vRNA (Seg.6) to identify the essential sequence for selective packaging. The 5′-terminal region of the A/Puerto Rico/8/34 strain Seg.6 was divided into seven regions of 15 nucleotides each from A to G, and mutations were introduced into each region by complementary base substitutions or synonymous codon substitutions. Mutant viruses were generated and compared for infectious titers, and the relative ratios of the eight segments packaged into virions were measured. We also ascertained whether mutant vRNA was eliminated by competitive packaging with wild-type vRNA. Mutations in the A–C regions reduced infectious titers and eliminated mutant vRNAs by competition with wild-type vRNA. Even under non-competitive conditions, the packaging efficiency of the A or B region mutant Seg.6 was reduced. Next, we designed an artificial vRNA with a 50-nucleotide duplication at the 5′-terminal region. Using this, a virus library was created by randomly replacing each region, which became an untranslated region (UTR), with complementary bases. After selecting proliferative viruses from the library, nine wild-type nucleotides in the A and B regions were identified as essential bases, and we found that these bases were highly conserved in Seg.6 vRNAs encoding the N1 subtype neuraminidase. From these results, we conclude that the identified bases function as the 5′-terminal packaging signal for the N1 subtype Seg.6 vRNA.

Minimizing Cost of Assembly of an Interrelated Dimensional Chain Product Using ABC Algorithm

Selective assembly is a method where components made with wider tolerance are grouped into a number of bins. Based on the best combination of the bin, the corresponding group components are randomly selected and matched together to make an assembly. Existing techniques focused on equal group number partitioning of components, equal probability, equal group width, and equal area methods to minimize either clearance variation or surplus parts using different optimization techniques. Mostly, simple assemblies with two or three components are worked by various authors in the literature without considering their original dimension by considering only their component’s tolerance. In the present work, components are classified into different unequal group numbers based on their tolerance values. The interrelated dimensional assemblies are made in a single stage by matching the parts based on the best bin combination obtained by the artificial bee colony algorithm. A simple linear assembly and a three-armed knuckle joint assembly are considered examples of problems to demonstrate the effectiveness of the proposed method by minimizing the manufacturing cost.