Assembly Quality Control Research Articles

Multilayer prefabrication in practice

Pre-cast reinforced concrete walls manufacturing is still associated in Poland with large-panel construction, all the advantages of which have been crossed out by technological imperfection, terrible workmanship and boring, repetitive architecture. Meanwhile, modern large-panel prefabrication provides almost unlimited creative possibilities and ensures much better quality than traditional construction. The article describes application of modern multi-layer pre-cast elements made of reinforced concrete and light-weight expanded clay concrete in construction with the incorporation of BIM technology. It presents the advantages of prefabricated construction on specific examples at various scales - from single-family houses to large residential and apartment buildings. It also presents in detail the implementation process from design, through BIM modelling, production and quality control of elements and assembly.

Easy analysis of bacterial whole-genome sequencing data for clinical microbiologists using open-source Galaxy platform: Characterization of ESBL-producing Enterobacterales from bloodstream infections

ObjectivesClinical microbiologists require easy-to-use open access tools with graphical interfaces to perform bacterial whole-genome sequencing (WGS) in routine practice. This study aimed to build a bioinformatics pipeline on the open-source Galaxy platform, facilitating comprehensive and reproducible analysis of bacterial WGS data in a few steps. We then used it to characterize our local epidemiology of ESBL-producing Enterobacterales isolated from patients with bacteremia. MethodsWe built a bioinformatics pipeline consisting of the following sequential tools: Fastp (input data trimming); FastQC (read quality control); SPAdes (genome assembly); Quast (quality control of genome assembly); Prokka (gene annotation); Staramr (ResFinder database) and ABRicate (CARD database) for antimicrobial resistance (AMR) gene screening and molecular strain typing. Paired-end short read WGS data from all ESBL-producing Enterobacterales strains isolated from patients with bacteremia over one year were analysed. ResultsThe Galaxy platform does not require command line tools. The bioinformatics pipeline was constructed within one hour. It only required uploading fastq files and facilitated systematization of de novo assembly of genomes, MLST typing, and AMR gene screening in one step. Among the 66 ESBL-producing strains analysed, the two most frequent ESBL genes were blaCTX−M-15 (62.1%) and blaCTX−M-27 (13.6%). ConclusionsThe open-access Galaxy platform provides a graphical interface and easy-to-use tools suitable for routine use in clinical microbiology laboratories without bioinformatics specialists. We believe that this platform will facilitate fast and low-cost analysis of bacterial WGS data, especially in resource-limited settings.

Integrated error modeling and evaluation of the effect for the Wolter-I grazing incidence focusing mirror imaging quality

The imaging quality determines the performance of the Wolter-I grazing incidence focusing mirror. When the design parameters are specified, the factors affecting the imaging quality are mainly machining and assembly errors; finding the error sources and evaluating the main errors are critical fundamental issues to achieve process optimization and control and improve imaging quality. This paper constructs a model of surface distribution error based on Nurbs surface reconstruction by non-contact measurement. It proposes an integrated error modeling method based on the surface distribution error model, taking into account the typical assembly errors such as decenter error, defocus error, and tilt error formed during the assembly process. First, the theoretical modeling and analysis of imaging quality based on ideal surface shape are carried out, and the sensitivity analysis of each error is carried out by using the Monte Carlo ray tracing method; then, an integrated optical simulation model containing assembly errors based on the surface distribution error is established, and the imaging quality of the focused spot is evaluated; finally, the experimental validation is carried out. The results of the experiment and the integrated optical simulation model show overall consistency, where the decenter error and defocus error have less influence, and the tilt error has the greatest impact, with the 10′ tilt error causing a decrease in the angular resolution of the focused spot of about 50″. The method can provide effective modeling and computational approaches to support the optimization of the precision assembly process and quality control of the Wolter-I grazing incidence focusing mirror.

A digital twin-based assembly model for multi-source variation fusion on vision transformer

For the manufacture and assembly of the mechanical products, quality management is the key feature to improve the service performance, reduce the overall costs and enhance the sustainability of the manufacturing systems. While, with the emergence of advanced data-driven and digital twin technologies, the Zero-Defect Manufacturing (ZDM), which corrects, predicts, and prevents product defects based on multi-sources of data, is of great significance in improving assembly quality. As the specific application for the utilization of ZDM in product assembly, the critical performance index of the assembly results is predicted by taking into account the multi-source factors such as geometric deviation of the parts, material properties, assembly sequences and process boundary conditions during assembly. In this paper, we address the high computational cost and low computational efficiency of numerical simulation methods under multi-source factors, and propose a data-driven approach named DTA-VIT based on the fusion of heterogeneous variables for digital twin assembly modeling of products. Firstly, the geometric and performance variables of the assembly process are analyzed and modelled. Secondly, a multi-source assembly data fusion network under the Vision Transformer framework is developed. This network takes the parameter space, which fuses multi-source variables from the assembly process as input and the assembly result as output. Finally, a case study of the assembly process of composite bolted joint structures in aircraft assembly is conducted to verify the effectiveness and feasibility of the proposed method. The methodology provides a solid foundation for subsequent assembly quality control and prevent by predicting assembly performance efficiently, ultimately enabling the production of high-quality products.

JCVI: A versatile toolkit for comparative genomics analysis.

The life cycle of genome builds spans interlocking pillars of assembly, annotation, and comparative genomics to drive biological insights. While tools exist to address each pillar separately, there is a growing need for tools to integrate different pillars of a genome project holistically. For example, comparative approaches can provide quality control of assembly or annotation; genome assembly, in turn, can help to identify artifacts that may complicate the interpretation of genome comparisons. The JCVI library is a versatile Python-based library that offers a suite of tools that excel across these pillars. Featuring a modular design, the JCVI library provides high-level utilities for tasks such as format parsing, graphics generation, and manipulation of genome assemblies and annotations. Supporting genomics algorithms like MCscan and ALLMAPS are widely employed in building genome releases, producing publication-ready figures for quality assessment and evolutionary inference. Developed and maintained collaboratively, the JCVI library emphasizes quality and reusability.

Chapter 15: Quality Control of Assembly and Mounting

Chapter 15: Quality Control of Assembly and Mounting

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

The current assembly process of marine diesel engines is low in intelligence and the control chart pattern classifier with unstable performance, which makes it difficult to control and identify the quality control chart pattern. This paper proposes a new assembly quality control diagram recognition method based on an adaptive decision model to address these problems. Through characteristics and changes of the diesel engine assembly process analyses, the triangular norm is used to fuse the extracted shape features and statistical features to reduce the influence of data fluctuation and imbalance on pattern recognition. An adaptive decision fusion model of the assembly process is established by defining multiple weights with considering the complexity and uncontrollability of the diesel engine assembly process. Based on these, the fusion coefficients within the adaptive decision model are optimized by the Ant Lion Optimization algorithm (ALO) to improve the decision efficiency and classification precision. To validate the proposed model, diesel engine exhaust pressure is selected as a case for abnormal pattern recognition, and the ability of the model is discussed in terms of recognition accuracy and stability.

Extracting Technicians' Skills for Human-Machine Collaboration in Aircraft Assembly.

Research on the efficiency and quality issues faced in aircraft assembly was conducted in this article. A new method of human-machine collaborative riveting was proposed, which combined the flexibility of manual collaboration with the precise control of automatic riveting. The research works include: (1) a theoretical model of pneumatic hammer riveting was established to clarify the principle and parameters of riveting process. (2) A smart bucking bar was designed to support the data collection and extraction of manual collaborative riveting process. (3) An automatic riveting experimental platform was designed to test the automatic riveting process incorporating the extracted manual riveting process parameters, and further an optimization strategy was proposed for the automatic riveting process. (4) A human-machine collaborative riveting experimental platform was developed to conduct the verification work. Through the theoretical analysis, experimental research, system scheme design, and process parameters optimization, the application and verification of human-machine collaborative assembly technology have been achieved. This technology is expected to be comprehensively promoted in the field of aircraft manufacturing, and for breaking through the current difficulties of low production efficiency and poor assembly quality control.

A Review on Variation Modeling of Aircraft Assembly

Abstract: Purpose; The purpose of this paper is to provide a state-of-art review on variation modeling and propagation in aircraft assembly based on process-oriented strategy. And the main focus is on classifying and delineating different approaches, methods, and techniques with critical appraisals of their theories, applications, and limitations, based on which future research directions and corresponding methodologies are proposed.
 Design/methodology/approach; To facilitate understanding of the background, this paper starts with a brief description of aircraft assembly. Afterwards, this paper presents a comprehensive review of practical solutions in aircraft variation modeling. Their characteristics are summarized, which serves as a basis for the discussion of deviation control strategies. Thereafter, the possible trends are discussed to facilitate assembly quality control in future research.

Mechanism of orphan subunit recognition during assembly quality control

Cells contain numerous abundant molecular machines assembled from multiple subunits. Imbalances in subunit production and failed assembly generate orphan subunits that are eliminated by poorly defined pathways. Here, we determined how orphan subunits of the cytosolic chaperonin CCT are recognized. Several unassembled CCT subunits recruited the E3 ubiquitin ligase HERC2 using ZNRD2 as an adaptor. Both factors were necessary for orphan CCT subunit degradation in cells, sufficient for CCT subunit ubiquitination with purified factors, and necessary for optimal cell fitness. Domain mapping and structure prediction defined the molecular features of a minimal HERC2-ZNRD2-CCT module. The structural model, whose key elements were validated in cells using point mutants, shows why ZNRD2 selectively recognizes multiple orphaned CCT subunits without engaging assembled CCT. Our findings reveal how failures during CCT assembly are monitored and provide a paradigm for the molecular recognition of orphan subunits, the largest source of quality control substrates in cells.

Recognition of the CCT5 di‐Glu degron by CRL4DCAF12 is dependent on TRiC assembly

Assembly Quality Control (AQC) E3 ubiquitin ligases target incomplete or incorrectly assembled protein complexes for degradation. The CUL4‐RBX1‐DDB1‐DCAF12 (CRL4DCAF12) E3 ligase preferentially ubiquitinates proteins that carry a C‐terminal double glutamate (di‐Glu) motif. Reported CRL4DCAF12 di‐Glu‐containing substrates include CCT5, a subunit of the TRiC chaperonin. How DCAF12 engages its substrates and the functional relationship between CRL4DCAF12 and CCT5/TRiC is currently unknown. Here, we present the cryo‐EM structure of the DDB1‐DCAF12‐CCT5 complex at 2.8 Å resolution. DCAF12 serves as a canonical WD40 DCAF substrate receptor and uses a positively charged pocket at the center of the β‐propeller to bind the C‐terminus of CCT5. DCAF12 specifically reads out the CCT5 di‐Glu side chains, and contacts other visible degron amino acids through Van der Waals interactions. The CCT5 C‐terminus is inaccessible in an assembled TRiC complex, and functional assays demonstrate that DCAF12 binds and ubiquitinates monomeric CCT5, but not CCT5 assembled into TRiC. Our biochemical and structural results suggest a previously unknown role for the CRL4DCAF12 E3 ligase in overseeing the assembly of a key cellular complex.

Quality control of protein complex assembly by the ubiquitin-proteasome system.

The majority of human proteins operate as multimeric complexes with defined compositions and distinct architectures. How the assembly of these complexes is surveyed and how defective complexes are recognized is just beginning to emerge. In eukaryotes, over 600 E3 ubiquitin ligases form part of the ubiquitin-proteasome system (UPS) which detects structural characteristics in its target proteins and selectively induces their degradation. The UPS has recently been shown to oversee key quality control steps during the assembly of protein complexes. We review recent findings on how E3 ubiquitin ligases regulate protein complex assembly and highlight unanswered questions relating to their mechanism of action.

Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration

Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.

Application of Change Management in Quality Control of Mining Machinery Assembly

Application of Change Management in Quality Control of Mining Machinery Assembly

A Study of the Segment Assembly Error and Quality Control Standard of Special-Shaped Shield Tunnels

Large-section special-shaped shield tunnels feature many advantages, such as versatility and a large space utilization rate in energy transmission and public transport; however, guaranteeing the quality of segments’ assembly is difficult. Based on the quasi-rectangular shield tunnel project of Hangzhou Line 9 in China, this study investigated the formation mechanism and control measures of lining segment assembly defects. By quantifying the manufacturing error and positioning error, a simulation program for segment assembly is developed to calculate the error. Furthermore, considering the relative accumulative error between the upper and lower T blocks, the finite element model of key blocks (T-LZ block) is established to perform mechanical analysis, based on which the relative error control standard of the key block under the corresponding working conditions is proposed. The results show that the assembly quality can be effectively improved by assembling the LZ block first and applying corresponding error control measures, and the displacement of the segment along the rZ direction should be carefully controlled during the construction. The error caused by normal assembly will not damage the LZ block, and the corresponding control standard under the action of multi-degree-of-freedom error (extreme case) is 9.8 mm. Using this method to predict the assembly quality of segments can provide a basis for actual construction control of segment assembly.

PREVIS - A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control

We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry, via exploiting standard interpolation methods. Further, we allow a real-time preview of user-driven parameter changes in the displacement field via visual interpolation. This allows for fast and accountable online change management. We demonstrate the effectiveness with an ex-ante optimization of an automotive engine hood.

AI-enhanced vision system for dispensing process monitoring and quality control in manufacturing of large parts

Assembly is one of the cornerstones of today’s manufacturing; thus, efficient automated assembly systems have become a necessity in the recent past. Challenges that have traditionally been present in such systems involve positioning accuracy, stable gripping, accommodation of several variants by the same resources and prevention of error aggregation. In robot-based applications the detection of parts and their components is a common task, but when it comes to large objects which cannot be portrayed in single camera frames, the complexity becomes significantly larger. This paper presents an AI based perception system which employs a 3D vision sensor to implement a smart dispensing application and perform online process quality control of large parts assembly. The aim of the system is to provide execution autonomy for a robotic manipulator that is responsible for glue dispensing on different types of aluminium profiles of varying cross-sections, length, and associated glue patterns. By using an eye-in-hand RGB-D camera and employing deep learning methods (Yolov3 algorithm), the framework recognizes the type of profile that is going to be processed, and depth information is used to extract the profile’s points (starting-ending points) on which glue must be dispensed. Point Cloud manipulation (DBSCAN clustering, plane segmentation) and 2D image-based processing were tested during the quality inspection, in order to validate the success and accuracy of the process (detection of glue discontinuities, calculation of the glue volume level). The system has been deployed on a case study involving the assembly of components on buses and the results indicate that a robust recognition and adhesive application can be obtained throughout each operation cycle.

UCEasy: A software package for automating and simplifying the analysis of ultraconserved elements (UCEs).

BackgroundThe use of Ultraconserved Elements (UCEs) as genetic markers in phylogenomics has become popular and has provided promising results. Although UCE data can be easily obtained from targeted enriched sequencing, the protocol for in silico analysis of UCEs consist of the execution of heterogeneous and complex tools, a challenge for scientists without training in bioinformatics. Developing tools with the adoption of best practices in research software can lessen this problem by improving the execution of computational experiments, thus promoting better reproducibility.New informationWe present UCEasy, an easy-to-install and easy-to-use software package with a simple command line interface that facilitates the computational analysis of UCEs from sequencing samples, following the best practices of research software. UCEasy is a wrapper that standardises, automates and simplifies the quality control of raw reads, assembly and extraction and alignment of UCEs, generating at the end a data matrix with different levels of completeness that can be used to infer phylogenetic trees. We demonstrate the functionalities of UCEasy by reproducing the published results of phylogenomic studies of the bird genus Turdus (Aves) and of Adephaga families (Coleoptera) containing genomic datasets to efficiently extract UCEs.

Species-Specific Quality Control, Assembly and Contamination Detection in Microbial Isolate Sequences with AQUAMIS.

Sequencing of whole microbial genomes has become a standard procedure for cluster detection, source tracking, outbreak investigation and surveillance of many microorganisms. An increasing number of laboratories are currently in a transition phase from classical methods towards next generation sequencing, generating unprecedented amounts of data. Since the precision of downstream analyses depends significantly on the quality of raw data generated on the sequencing instrument, a comprehensive, meaningful primary quality control is indispensable. Here, we present AQUAMIS, a Snakemake workflow for an extensive quality control and assembly of raw Illumina sequencing data, allowing laboratories to automatize the initial analysis of their microbial whole-genome sequencing data. AQUAMIS performs all steps of primary sequence analysis, consisting of read trimming, read quality control (QC), taxonomic classification, de-novo assembly, reference identification, assembly QC and contamination detection, both on the read and assembly level. The results are visualized in an interactive HTML report including species-specific QC thresholds, allowing non-bioinformaticians to assess the quality of sequencing experiments at a glance. All results are also available as a standard-compliant JSON file, facilitating easy downstream analyses and data exchange. We have applied AQUAMIS to analyze ~13,000 microbial isolates as well as ~1000 in-silico contaminated datasets, proving the workflow’s ability to perform in high throughput routine sequencing environments and reliably predict contaminations. We found that intergenus and intragenus contaminations can be detected most accurately using a combination of different QC metrics available within AQUAMIS.

Combining Visual Analytics and Machine Learning for Reverse Engineering in Assembly Quality Control

The authors introduce an integrative approach for the analysis of the high-dimensional parameter space relevant for decision-making in the context of quality control. Typically, a large number of parameters influence the quality of a manufactured part in an assembly process, and our approach supports the visual exploration and comprehension of the correlations among various parameters and their effects on part quality. We combine visualization and machine learning methods to help a user with the identification of important parameter value settings having certain effects on a part. The goal to understand the influence of parameter values on part quality is treated from a reverse engineering perspective, driven by the goal to determine what values cause what effects on part quality. The high-dimensional parameter value domain generally cannot be visualized directly, and the authors employ dimension reduction techniques to address this problem. Their prototype system makes possible the identification of regions in a high-dimensional parameter value space that lead to desirable (or non-desirable) parameter value settings for quality assurance. They demonstrate the validity and effectiveness of our methods and prototype by applying them to a sheet metal deformation example.