Automobile Assembly Research Articles

Conveyor-Less Urban-Car Assembly Factory with VaaC and Matrix System

The advent of autonomous electric vehicles (AEVs) will give drivers time and space instead of focusing on driving. Because of this, some drivers may want to personalize their car for their work, while others may want to customize their vehicle space to be more suitable for relaxation, which will accelerate the megatrend of mass individualization. However, the production of individualized cars faces several challenges. For example, since high-level automation during individualized car production is difficult, a stable skilled labor supply is essential, low-volume/high-variety production is required, and customer proximity or involvement is also important. These conditions can be satisfied by building a car assembly plant in an urban area. The problem is that urban areas are often spatially and environmentally constrained. However, it is be possible to overcome these urban limitations by implementing a conveyor-less micro factory. The objective of this study is to propose a new iterative matrix-system layout design method that can realize a conveyor-less urban car assembly factory with two technologies—VaaC (vehicle as a conveyor) and matrix assembly system. VaaC consists of three novel ideas: sensor skid, safety-sensor guidance system, and vehicle-powered devices, and this paper views each of them in detail. The proposed iterative matrix-system layout design method consists of four steps: (1) layout refinement, (2) simulation, (3) cost analysis, and (4) optimization check, and will examine how each step is performed through simple examples. The authors hope that this paper will arouse interest and provide elements to spur future research on the conveyor-less urban car assembly system.

Research on the Application of Digital Manufacturing Technology in Automobile Assembly Process

Research on the Application of Digital Manufacturing Technology in Automobile Assembly Process

Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes

Appropriate sensor deployment is the key to the efficient diagnosis of product variation. Yet, optimizing sensor placement in complex manufacturing systems remains challenging. We propose a variation propagation analysis (VPA)-based sensor deployment strategy for variation diagnosis in multistation assembly processes. A state-space model is employed to analyze the influences of fixture faults and workpiece dimensional deviations on assembly variation. Based on matrix transformation, the assembly variation propagation characteristics are quantified and a VPN-based causal graph is constructed to represent the causality between assembly variation and sensor measurement. To ensure the diagnosability of over-tolerance of assembly variation (OAV) and the economics of the sensor system, an optimal sensor deployment scheme is presented. It uses the enhanced shuffled frog-leaping algorithm to minimize the OAV unobservability per unit cost and the sensor cost under the constraint of detectability. Finally, the effectiveness of the proposed approach is illustrated by a case study of sensor deployment for variation diagnosis in a multistation automobile differential assembly process.

Digital ergonomics: an evaluation framework for the ergonomic risk assessment of heterogeneous workers

ABSTRACT In the era of smart factories, digital twin may be a key for improving ergonomics analyses related to manual assembly activities. The use of digital technologies can help in bypassing some biases related to observational methods and speeding up the ergonomics analyses. This paper proposes a framework based on the development of a digital environment for carrying out ergonomics simulations of complex working systems in which a number of workers and other variables have to be considered for ergonomics evaluation. Based on such framework, a case study considering heterogeneous workers in a manual car assembly system has been analysed, and the impact of the implementation of different job rotation strategies has been studied. The tool is demonstrated to be effective in evaluating the physical ergonomic risk of a large number of configurations, and the results highlighted that proper job rotation strategies may affect the final scores of the considered indices.

A multi-objective decomposition evolutionary algorithm based on the double-faced mirror boundary for a milk-run material feeding scheduling optimization problem

With the development of personalized and differentiated customization demands, mixed-model assembly lines have been increasingly adopted by manufacturing enterprises due to their low-cost and high-efficiency. However, this mode has brought great challenges to the material-feeding process which is highly coupled with the production of automobile assembly lines. This paper, therefore, proposes a milk-run material-feeding scheduling problem based on two-level logistics network for modern mixed-model assembly lines, focusing on the milk-run material-feeding process between the central warehouse and line-integrated supermarkets for the first time. An integer programming mathematical model is established with the objective of simultaneously minimizing the number of electric vehicles put into use and the maximum travelling distance of the electric vehicles. Then, a double-faced mirror boundary-based multi-objective decomposition evolutionary algorithm (MOEA/D-DFMB) is provided to solve the problem. The introduction of the double-faced mirror boundary theory helps overcome the uneven distribution of Pareto solutions. Besides, the adaptive crossover operator based on evolutionary potential judgment, the adaptive adjustment strategy of mutation probability, and the local search optimization operator are applied to the proposed algorithm, so as to enhance the search ability and the convergence speed. Finally, computational experiments of the proposed algorithm are carried out, and the feasibility and effectiveness of the proposed algorithm are evaluated by comparing with the non-dominated sorting genetic algorithm-II (NSGA-II), the strength Pareto evolutionary algorithm-II (SPEA-II), the multi-objective decomposition evolutionary algorithm based on differential evolution (MOEA/D-DE), the multi-objective red deer algorithm (MORD), and the multi-objective social engineering algorithm (MOSE). The results indicate that the proposed MOEA/D-DFMB outperforms the comparison algorithms both in solution quality and convergence rate when solving the milk-run material-feeding scheduling problem.

Analysis of Measurement and Calculation of MSD Complaint of Chassis Assembly Workers Using OWAS, RULA and REBA Method

Car assembly is a combination of all components that form one completed vehicle unit. The work process is manually done and repeatedly, which contributes to a risk of musculoskeletal disorders (MSD). Chassis assembly is a job with a high level of complexity and associated with MSD risk for its employees. This study consists of 30 assembly activities divided into six groups based on posture and working methods used during the work process. Group A consists of 7 assemblies, Group B consists of 8 assemblies, Group C consists of 5 assemblies, Group D consists of 2 assemblies, Group E consists of 5 assemblies, and Group F consists of 3 assemblies. This study aims to compare the measurement and calculation of the risk level of MSD workers by using the RULA, REBA, and OWAS methods. The results of the measurements and computations acquired using these three approaches yielded the same risk category: 83.33 % medium risk/dangerous in working groups A, C, D, E, and F, and 16.67 % very high risk/highly hazardous in working group B. These six groups, particularly group B, requires immediate attention to reduce worker complaints of MSD.

Did you even see that? visual sensory processing of single stimuli under different locomotor loads.

Modern living and working environments are more and more interspersed with the concurrent execution of locomotion and sensory processing, most often in the visual domain. Many job profiles involve the presentation of visual information while walking, for example in warehouse logistics work, where a worker has to manage walking to the correct aisle to pick up a package while being presented with visual information over data-glasses concerning the next order. Similar use-cases can be found in manufacturing jobs, for example in car montage assembly lines where next steps are presented via augmented reality headsets while walking at a slow pace. Considering the overall scarcity of cognitive resources available to be deployed to either the cognitive or motor processes, task performance decrements were found when increasing load in either domain. Interestingly, the walking motion also had beneficial effects on peripheral contrast detection and the inhibition of visual stream information. Taking these findings into account, we conducted a study that comprised the detection of single visual targets (Landolt Cs) within a broad range of the visual field (-40° to +40° visual angle) while either standing, walking, or walking with concurrent perturbations. We used questionnaire (NASA-TLX), behavioral (response times and accuracy), and neurophysiological data (ERPs and ERSPs) to quantify the effects of cognitive-motor interference. The study was conducted in a Gait Real-time Analysis Interactive Laboratory (GRAIL), using a 180° projection screen and a swayable and tiltable dual-belt treadmill. Questionnaire and behavioral measures showed common patterns. We found increasing subjective physical workload and behavioral decrements with increasing stimulus eccentricity and motor complexity. Electrophysiological results also indicated decrements in stimulus processing with higher stimulus eccentricity and movement complexity (P3, Theta), but highlighted a beneficial role when walking without perturbations and processing more peripheral stimuli regarding earlier sensory components (N1pc/N2pc, N2). These findings suggest that walking without impediments can enhance the visual processing of peripheral information and therefore help with perceiving non-foveal sensory content. Also, our results could help with re-evaluating previous findings in the context of cognitive-motor interference, as increased motor complexity might not always impede cognitive processing and performance.

Bi-objective green in-house transportation scheduling and fleet size determination in mixed-model assembly lines with mobile robots

PurposeFacing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well.Design/methodology/approachThis research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a Multi-objective Discrete Differential Evolution Algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem.FindingsThe performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem.Originality/valueThis study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

Detection of Physical Strain and Fatigue in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors

The detection and prevention of workers’ body straining postures and other stressing conditions within the work environment, supports establishing occupational safety and promoting well being and sustainability at work. Developed methods towards this aim typically rely on combining highly ergonomic workplaces and expensive monitoring mechanisms including wearable devices. In this work, we demonstrate how the input from low-cost sensors, specifically, passive camera sensors installed in a real manufacturing workplace, and smartwatches used by the workers can provide useful feedback on the workers’ conditions and can yield key indicators for the prevention of work-related musculo-skeletal disorders (WMSD) and physical fatigue. To this end, we study the ability to assess the risk for physical strain of workers online during work activities based on the classification of ergonomically sub-optimal working postures using visual information, the correlation and fusion of these estimations with synchronous worker heart rate data, as well as the prediction of near-future heart rate using deep learning-based techniques. Moreover, a new multi-modal dataset of video and heart rate data captured in a real manufacturing workplace during car door assembly activities is introduced. The experimental results show the efficiency of the proposed approach that exceeds 70% of classification rate based on the F1 score measure using a set of over 300 annotated video clips of real line workers during work activities. In addition a time lagging correlation between the estimated ergonomic risks for physical strain and high heart rate was assessed using a larger dataset of synchronous visual and heart rate data sequences. The statistical analysis revealed that imposing increased strain to body parts will results in an increase to the heart rate after 100–120 s. This finding is used to improve the short term forecasting of worker’s cardiovascular activity for the next 10 to 30 s by fusing the heart rate data with the estimated ergonomic risks for physical strain and ultimately to train better predictive models for worker fatigue.

Exhibition Review: Devour the Land: War and American Landscape Photography since 1970

Exhibition Review: <i>Devour the Land: War and American Landscape Photography since 1970</i>

Knowledge discovery using an enhanced latent Dirichlet allocation-based clustering method for solving on-site assembly problems

• We propose a text mining approach combining topic modeling, text clustering , and association rule identification algorithms for knowledge discovery. • We propose an enhanced latent Dirichlet allocation scheme to explore document-topic and topic-word distributions. • We introduce a refined DBSCAN method for text clustering. • The results demonstrate the capability and effectiveness of the proposed approach. Prompt responses to problems/faults arising in an assembly workshop are crucial in terms of production reliability and efficiency. However, human-dependent tasks are time-consuming and prone to error. In this paper, we propose a knowledge discovery approach. We extract the patterns of associations between texts in problem-solving records to generate appropriate solutions automatically. First, we use an enhanced latent Dirichlet allocation (EnLDA) technique to explore the document-topic and topic-word distributions of a text corpus recording assembly problems, causes, and solutions. To increase accuracy, we adjust the elements of the document-term matrix, and we assign term frequency-inverse document frequencies. Second, we use the Refining Density-based Spatial Clustering of Application with Noise (Rf-DBSCAN) algorithm for text clustering. This refines the distances among topic distribution vectors and incorporates noise objects into clustering. This clusters textual documents with similar semantic information, maximizing information retention. Third, we use the Apriori algorithm to identify pattern associations among document clusters that represent the problems, causes, and solutions. We perform a case study using field data from an automobile assembly workshop. The results show that the method retrieves hidden but valuable information from textual records. The decision support knowledge facilitates assembly problem-solving.

Flexible scheduling of diagnostic tests in automotive manufacturing

The car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

Approval status and characteristics of work-related musculoskeletal disorders among Korean workers in 2020.

This study aimed to investigate the characteristics of work-related musculoskeletal disorders (WRMSDs) in occupational disease claims and identify patterns of WRMSDs for each body part by industry and occupation. This study analyzed the raw data of occupational disease claims for musculoskeletal disorders deliberated by the Occupational Disease Decision Committee of the Korea Workers' Compensation & Welfare Service in 2020. The data was classified into 6 body parts with the highest numbers of occupational disease cases by using the complete enumeration data on principal diagnoses and 4 types of subdiagnoses in the raw data. The characteristics and approval rates of WRMSDs by body part, industry and occupation were examined and summarized. A total of 13,015 occupational disease cases for WRMSDs were classified, and lumbar spinal (back) diseases accounted for the largest proportion of claimed diseases, followed by shoulder, elbow, wrist, knee, and neck diseases in a descending order. The occupations with the highest and second highest numbers of occupational disease cases by body part were found to be automobile assemblers and production-related elementary workers for the neck, school meal service workers and cooks for the back, construction frame mold carpenters and school meal service workers for the shoulder, elementary workers in mining and food service workers for the elbow, food service workers and automobile parts assemblers for the wrist, and ship welders and school meal service workers for the knee. This study examined the characteristics and approval status of WRMSDs by body part and occupation. Based on the study results, management strategies for the prevention of WRMSDs should be established regarding occupations with a high risk of WRMSDs for each body part.

Procedure control and interface design of automobile connector automatic assembly device

Procedure control and interface design of automobile connector automatic assembly device

Unsupervised domain adaptive object detection for assembly quality inspection

A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data.

An Enhancement and Detection Method for a Glue Dispensing Image Based on the CycleGAN Model

During the active alignment focusing process of car camera assembly, lenses and holders need to be gummed by creamy white and translucent UV glue. The quality of glue dispensing can directly influence the performance of car cameras. Because of the translucency of UV glue, the glue dispensing image may present a low contrast situation, which increases the difficulty of vision detection. This paper proposes a method based on CycleGAN to enhance the glue dispensing image and effectively overcome the problems of blurred and low contrast edges. First, the glue part of the image is segmented into twenty regions. Second, the VGG16 model is used to divide the abovementioned twenty regions into high-contrast images and low-contrast images. Next, the CycleGAN model is trained to enhance the low-contrast images, and then convert them to high-contrast images. Finally, glue contours are extracted by using thresholding segmentation and edge detection to ensure that the quality of glue dispensing can be detected. The success rates of the VGG16 model and the CycleGAN model are 96% and 58%, respectively. The results show that the proposed method can effectively enhance the low contrast part of the glue region and improve the detection accuracy. Specifically, it can increase the gray value difference between the glue and the background from 20 to 55, while the background is substantially retained. The detailed information of the edges of the images is enriched. The accuracy of glue edge extraction can be increased to 99%, which is an approximately 75% improvement compared to the methods without enhancement.

Design and development of ball dispenser Machine through lean manufacturing tool Poka-Yoke technique in automobile industries

In the current scenario manufacturing is the most challenging task with high quality product in minimum time. In automobile assembly industries is lot of small–small part are assembling for make one product. If any small part is missing then the quality and safety of product affected. In this experimental paper discussed about the practical case study in automobile assembly industries. Ball dispenser machine design and developed due to Anything that could make the operator realize that the steel ball has fallen inside the housing if it has fallen. An arrangement that allows person from store to fill the container of balls, but should be out of reach of operator such that the operator could not take-out balls directly.

Irregularities recognition system for automotive pieces

The automotive industry is a growing sector in Mexico that requires many production processes. One of the most important is auto parts manufacturing with high-quality standards to avoid economic losses. Hence, the failure detection of pieces must be carried out in the early process, discarding those that do not reach the desired quality. This paper deals with an object recognition system to automatically find failures in circular automotive pieces. This is an open problem in the automobile assembly process to guarantee product quality. We detect imperfections (above 3.5 mm) on small pieces, such as scratches and dents on edges, by using an image processing stage, where no training is included, with a low-cost camera. The average processing time to detect failures is 2.7 seconds, which allows us to examine more pieces in a short time compared with other works and with manual inspections carried out by human experts. The proposed system reaches an accuracy of 98% and is implemented in the LabVIEW tool.

Leadership and Productivity: A Study of US Automobile Assembly Plants

Leadership and Productivity: A Study of US Automobile Assembly Plants

Non-Contact Articulated Robot-Integrated Gap and Flushness Measurement System for Automobile Assembly

Non-Contact Articulated Robot-Integrated Gap and Flushness Measurement System for Automobile Assembly