Automotive Factory Research Articles

Effect of sound absorption on noise reduction in the automotive industry

Industrial noise is one of the most common physical factors that cause annoyance and damage workers' health in the long term. Precautions should be taken to reduce noise and improve acoustic performance in industrial working environments. This paper aims to analyze the acoustic performance of the automotive industry and contribute to the global outcomes of sustainability and the development of strategies to improve the quality of the working environment through improvement scenarios. For this purpose, the automotive industry in Türkiye was examined as a case study. In-situ acoustic measurements were made in the seat manufacturing unit of an automotive factory and the current situation was transferred to the simulation program. The effects of acoustic improvements on A-weighted sound pressure level and reverberation time at mid-frequencies (500, 1000, 2000 Hz) were investigated through three different scenarios. In the investigations, noise distributions were carried out through noise mapping. The A-weighted sound pressure levels in the automotive industry were reduced by approximately 15 dB. As a result of the study, suggestions for noise control precautions and their effects on the automotive industry seat manufacturing unit are presented.

Effect of Sound Absorption on Noise Reduction in the Automotive Industry

Industrial noise is one of the most common physical factors that cause annoyance and damage to workers' health in the long term. Precautions should be taken to reduce noise and to improve acoustic performance in industrial working environments. This paper aims to analyze the acoustic performance of the automotive industry contributes to the global outcomes of sustainability and develop strategies for improving the quality of the working environment through improvement scenarios. For this purpose, the automotive industry in Türkiye was examined as a case study. In-situ acoustic measurements were made in the seat manufacturing unit of an automotive factory, and the current situation was transferred to the simulation program. The effects of acoustic improvements on A-weighted sound pressure level and reverberation time at mid-frequencies (500, 1000, 2000 Hz) were investigated through three scenarios. In the investigations, noise distributions were carried out through noise mapping. The A-weighted sound pressure levels in the automotive industry were reduced by approximately 15 dB. As a result of the study, suggestions for noise control precautions and their effects on the automotive industry seat manufacturing unit are presented.

The Relationship Between Stress and Burnout: A Sample of Automotive Factory Workers

The purpose of this research is to determine the relationship between stress and burnout in employees of an automotive factory. This research was conducted descriptively with 306 participants at Sango Automotive Products Industry and Trade Joint Stock Company in Kocaeli/Türkiye between April and July 2021. Data were collected using the Personal Information Form, Perceived Stress Scale and Burnout Scale-Short Version. The mean Perceived Stress Scale score of participants was 25.11±7.39; The average score of the Burnout Scale were 29.69±12.61. A statistically significant positive relationship was determined between the mean Perceived Stress Scale scores and Burnout Scale scores of participants (r=0.67; p

Nano coating containing SiO2 nanoparticles with non-stick properties for easy peeling of car paint from mild steel surfaces

Non-stick latex coating enhanced with mesoporous silica nanoparticles (SiO2 NPs) showed promising peeling properties. In order to see the effect of SiO2 NPs, coatings with and without SiO2 NPs applied on pieces of a hollow section mild steel beam as substrate. The grain size, crystal phase structure, specific surface area (SSA) and pore size distribution of the SiO2 NPs was determined using transmission electron microscopy (TEM), X-ray diffraction (XRD) pattern and Brunauer-Emmett-Teller (BET) analysis. Field emission scanning electron microscope (FESEM) and energy-dispersive X-ray spectroscopy (EDS) was used to observe the surface morphology and composition of the coated samples. The contact angle was measured for three different liquids on the surface of the samples namely, 1-bromonaphthalene (nonpolar), formamide and distilled water (polar) in order to calculate the surface energies per unit area and hysteresis contact angles. The result showed that adding SiO2 NPs increases the hydrophobicity and decreases the surface energy of the surface. One of the applications of such a coating is to protect the surface from paint sticking to it. For this purpose, the coating with and without SiO2 NPs is applied on a mild steel substrate and then painted by resin-based automotive paint. The pull off test showed that the adhesion is reduced by addition of SiO2 NPs. In addition, to see this application in industry, the coating with SiO2 NPs is applied to a skid and placed in the painting line of an automotive factory for 12 working days. After removing from the line, the coating and the paint on it were easily peeled from the skid either manually with a spatula or by a water jet cleaning machine (100 bar at spraying angle of 45°) compared to a bare skid.

A Descriptive Study of Pulmonary Function Comparison Between Spot Welding and Assembly Line Workers in a Large Automotive Factory in Iran

Background: This study seeks to define the pulmonary function of a considerably large group in one of the largest automotive companies in Iran. The aim of this study was to compare the two groups of spot-welding (exposed) and assembly line (unexposed) workers in terms of pulmonary function and conclude the probable impacts of metal fumes on pulmonary function in the two groups. Methods: A total of 1,798 spot-welding personnel and 2,160 assembly line personnel were compared in terms of pulmonary function. Moreover, 120 breathing zone samples were collected from the welders’ metal fumes and were analyzed. The parameters of age, anthropometric indices, smoking, and pulmonary symptoms were also compared with across the two groups. Results: Results revealed the FEV1 mean of the total population to be 3.9 ± 0.5 liters and FVC to have been 4.9 ± 0.7 liters. Besides, predicted FEV1 and FVC percentages across the whole population were close to 100%, suggesting the better condition of the studied population compared with normal individuals in the society. All the parameters of age, weight, height, and smoking status were similar in the two groups, and no statistically significant difference was observed between the two groups in terms of pulmonary functions. Conclusion: Implementation of professional health and health improvement programs, running screening programs, and hiring personnel with no history of mild pulmonary disorders, and healthy workers effect at the recruitment stage could be the reasons behind the lack of significant difference between the two exposed and unexposed groups to metal fumes in the automotive industry in terms of pulmonary function.

A portable inflatable soft wearable robot to assist the shoulder during industrial work.

Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions using soft materials and active controls. We present the design and evaluation of a portable inflatable shoulder wearable robot for assisting industrial workers during shoulder-elevated tasks. The robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units, and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback.

Carbon footprint analysis of Jaguar Land Rover Slovakia s.r.o.

The automotive industry around the world, including Slovakia, is taking steps to decarbonize their production in accordance with the Science Based Targets initiative (SBTi). Decarbonizing the operations of automotive Original Equipment Manufacturers (OEMs)requires a holistic and systemic understanding of all environmentally relevant resource flows and the implementation of technologically and economically affordable solutions to achieve their targets for reducing the carbon footprint (CF). The CF analysis of Jaguar Land Rover Slovakia s.r.o. (JLR SK) was performed using the Greenhouse Gas Protocol (GHG Protocol). The purpose of the study was to improve theunderstanding of CF within the context of an OEM. The authors of this study are not aware, as of the date of publication, of any other comparable outputs from CF analyses by GHG Protocol in Slovakia, making this study the first of its kind. The results showed that the total CF was 1,143,205 tCO2e(tonnes of carbon dioxide equivalent); of which SCOPE 1 emissions were 18,584 tCO2e (1.62%), SCOPE 2 emissions were 0.87 tCO2e (0.0001%) and SCOPE 3 emissions were 1,130,866 tCO2e (98.38%). Normalized CF values for all three SCOPES were 309.81 tCO2e per active employee; 3.08 kgCO2e per unit of turnover (EUR); and 14.95 tCO2e per vehicle produced. Current legislative standards and the active participation of OEMs can lead the automotive industry towards sustainability, including the achievement of net-zero carbon emissions despite rising consumption or production volume. With respect tosome ambiguities in the initial data inventory, the presented analysis shows the first approach to identifying the CF of an automotive factory based on GHG Protocol in Slovakia.

Integration of Autonomous Driving Functionalities for End-of-Line Production Transport Scenarios

Autonomous driving functionalities are increasingly being integrated into nowadays cars, which presents an opportunity for Automotive manufacturers to use these functionalities as part of the production process. This paper presents a case study of the integration of autonomous driving functionalities in a prototype vehicle for end-of-line production transport.The vehicle is equipped with sensor instrumentation, processing systems, and actuators to achieve automated driving, and the Robot Operating System (ROS) was utilized to achieve navigation on a pre-established route within a factory hall. The vehicle was also tested on a test track to emulate outdoor post-production testing stations. Complementary developed functions allow the vehicle to be effectively tracked and monitored inside and outside the demo factory.The use of autonomous driving functionalities in end-of-line production transport presents an opportunity for car manufacturers to streamline their production process. The successful integration of these functionalities in the vehicle using ROS and real-time tracking validates their use in a smart automotive factory. Further research and development in this area can lead to improved production efficiency and safety.

A Process Mining-based unsupervised Anomaly Detection technique for the Industrial Internet of Things

Industrial Internet of Things (IIoT) applications in Industry 4.0 collect and process Time Series (TS) originating from heterogeneous sources. Many data-driven techniques have been proposed over the years for unsupervised collective Anomaly Detection (AD) to detect anomalous TS and improve quality of service. These techniques build statistical and/or behavioral models through data-driven algorithms often based on machine and deep learning. However, these algorithms may have black-box behavior, may require too much computational effort, and may not be trustworthy. In order to address these challenges, this paper proposes and evaluates an unsupervised AD technique that pre-processes noisy and complex TS and detects anomalous patterns. Pre-processing handles TS complexity through Feature Extraction and Dimensionality Reduction based on Autoencoders (AEs), whereas anomalous patterns are classified through Process Mining (PM), which unsupervisedly captures TS patterns and compares unknown behavior to such patterns for collective AD. We apply the technique to a scale replica of an assembly plant adopted in a smart automotive factory to evaluate our technique with respect to several configurations to analyze their impact on both detection and timing performance. Our contribution improves and extends the existing state-of-the-art work in the literature regarding the application of PM to IIoT for collective AD in TS. Moreover, it compares the obtained results with a baseline approach based on AEs.

In Situ Indoor Air Volatile Organic Compounds Assessment in a Car Factory Painting Line

Proper working conditions must be one of the employers’ main concerns in any type of company but particularly in work locations where the employees are chronically exposed to hazardous compounds, like factories and production lines. Regarding this challenge, the present research addresses the mapping of a car factory painting line to possibly toxic volatile organic compounds emitted by all the coatings and chemicals used during the work shifts for the future evaluation of employees’ exposure. For the first time, a Gas Chromatography–Ion Mobility Spectrometry device was employed for the in situ detection of volatile organic compounds in an automotive factory. A total of 26 analytes were detected at nine different locations, of which 15 VOCs were accurately identified. Pure chemical-grade substances were used for the development of the VOC database. Although quantitative analysis was not the goal of this study, a calibration model was presented to one analyte for exemplificative purposes. Relative intensity profiles were plotted for all locations, revealing that some indoor VOCs can reach intensity levels up to 60 times higher than in outdoor air samples. The achieved results proved that the painting line has an abundant number of VOCs emitted from different sources and may lead to serious health risks for the employees. Additional studies shall be developed in the painting line for quantitative evaluation of the existing VOCs and their influence on the employees’ health conditions.

A Cyber-Physical Methodology to Automate Robotized Finishing Processes

Finishing operations represent a crucial stage in manufacturing, where it is necessary either to remove unwanted material from a part, or provide aspect features to it. This includes smoothing, polishing and machining-alike solutions. In this paper, a new methodology to approach the automation of finishing processes in manufacturing, is presented. Four main steps are defined: part measuring, path creation, finishing operation, and part verification. Two use cases, belonging to real workplaces in an automotive factory of the Stellantis group, are presented as validation scenarios. One of these, a cyber-physical system to deburr a weld beam between two parts of complex geometry, has been successfully working in-line for the last ten years. This represents an improvement of the ergonomics of the operators and their working conditions, as well as a reduction of costs. This shows that the automation of finishing process means an opportunity to increase productivity, quality and efficiency in the factory.

Pulsed direct current magnetic energy harvesting by robotic spot-welding in smart automotive factory

A magneto-mechano-electric (MME) generator is considered as a promising candidate for power supplying to IoT sensors in a smart factory by harvesting noise magnetic field. However, most studies have carried out only with alternating current (AC) magnetic field from laboratory and home surroundings. Here, we demonstrate a pulsed direct current (DC) magnetic field-driven MME generator to harvest energy from spot-welding process in a manufacturing facility of Hyundai Motor Company. By one-spot-welding process, the MME generator generated a open-circuit peak voltage of 7.4 V as well as a peak power of 0.8 mW. The generated electricity was utilized to charge the 10 µF capacitor from 0 to 4.8 V with five times spot-welding, meaning a lot of electricity could be harvested by thousands of spot-welding during vehicle assembly process. Finally, we demonstrated self-powered IoT system by charging 3.3 mF capacitor using MME generator to power multifunctional IoT sensor.

A cross-sectional study determining the prevalence of musculoskeletal diseases in automotive factory workers

Background/Aim: Work-related musculoskeletal disorders (WMSDs) develop due to exposure to physical and psycho-social factors in the work environment. The diagnostic criteria and prevalence of WMSDs are not well established. In this study, we aimed to determine which WMSDs occur in automotive industry workers and what the underlying risk factors may be.
 Methods: A cross-sectional study was designed that included 200 automotive industry workers who were diagnosed with a WMSD by physical and radiological examination in the physical therapy and rehabilitation outpatient clinic in the past year. We recorded demographic data, risk factors and WMSD diagnosis names for the patients.
 Results: In our study, the most common diagnosis was low back disorder (66.5%), followed by neck and shoulder (58%) and upper extremity (23%) disorders. There was a statistically significant increase in the occurrences of shoulder-neck diseases, upper extremity, and low back-lower extremity diseases in workers with inappropriate posture (P < 0.001). Workers who exerted heavy effort or performed repetitive motions were statistically more likely to experience upper extremity disorders (P < 0.001) as well as low back-lower extremity disorders (P = 0.020 and P < 0.001, respectively); there was no statistically significant change in the incidence of shoulder and neck diseases with heavy effort (P = 0.538).
 Conclusion: WMSD is a serious health and economic problem. In our study, we found that low back and lower extremities and neck and shoulder problems are the most common types of WMSDs in individuals working in the automotive sector. Not using proper posture while working causes health problems in the shoulders, neck, upper extremities and low back and lower extremities. Employees in this sector should be encouraged to use good work ergonomics.

Evaluation of failure risks for manual tightening operations in automotive assembly lines

PurposeThe tightening operations are one of the most critical operations in automotive assembly lines because of its direct impact on customer safety. This study aims to evaluate the major complexity drivers for manual tightening operations, correlate with real tightening failure data and propose mitigations to improve the complexity.Design/methodology/approachIn the first stage, the complexity drivers for manual tightening operations were identified. Then, the relative importance of the risk attributes was defined by using pairwise comparisons questionnaire. Further, failure mode effect analysis–analytic hierarchy process (FMEA–AHP) and AHP ratings methods were applied to 20 manual tightening operations in automotive assembly lines. Finally, the similarities between the revealed results and the real failure rates of a Turkish automotive factory were examined and a sensitivity analysis was conducted.FindingsThe correlation between the proposed methods and manual tightening failure data was calculated as 83%–86%. On the other hand, the correlation between FMEA–AHP and AHP ratings was found as 92%. Poor ergonomics, operator competency and training, operator concentration-loose attention fatigue, manual mouthing before the tightening operation, frequent task changes, critical tightening sequence, positioning of the part and/or directional assembly were found relatively critical for the selected 20 tightening operations.Originality/valueThis is a unique study for the evaluation of the attributes for manual tightening complexity in automotive assembly lines. The output of this study can be used to improve manual tightening failures in manual assembly lines and to create low complexity assembly lines in new model launches.

Optimizing automotive inbound logistics: A mixed-integer linear programming approach

The optimization of inbound logistics from first-tier suppliers at the aggregation level of a factory is a challenging coordination problem. In practice, different transport modes and multiple constraints exist and need to be taken into account: the available number of storage places, capacity of goods-entry, delivery profile and transportation pattern constraints, truck utilization, demand satisfaction, and the reverse flow of empty load carriers. A scalable solution method that is tailored to an automotive factory is currently not available. Optimization is usually carried out manually, e.g. experience-based or by using decision trees that follow an iterative optimization approach. Most of the mathematical models in the literature only deal with parts of the requirements and do not validate results in the required complexity. We present a mixed-integer linear programming approach to solve this complex problem using Gurobi. The solution capability and quality are enhanced by the introduction of a cyclic inventory constraint and valid inequalities. The evaluation of the approach is based on a case study of a German automotive factory with 570 suppliers and 3927 stock keeping units and reduces the as-is total cost by 33.86%. Additionally, CO2 emissions are reduced by 74.66%. We further show that variable order quantities, along with a courier and express service as an additional transport mode, are of high economic relevance. As part of a sensitivity analysis, we show how the total costs change, when (a) goods-entry handling constraints, (b) truck utilization, (c) storage constraints, or (d) transportation costs vary, and (e) the effect of a cyclic inventory constraint.

EVALUATION OF DRAINAGE NETWORK ON GAYA MOTOR ROAD OF SUNGAI BAMBU SUB-DISTRICT TANJUNG PRIOK

<p>Drainage is a way to dispose of excess water that is not in an area. In other words, this drainage also works in controlling checked areas, puddles, and also flooding. Flooding is an event when excessive water flows overland. North Jakarta is an example of the city area in Indonesia that often experiences such flooding events with a repeated frequency every year. With low topographic conditions and also located in the northern coastal area of Java Island, resulting in a large flow of water through North Jakarta. One of the areas that always experiences flooding is Jalan Gaya Motor, Sungai Bambu Village Tanjung Priok sub-district. Flooding in this area caused major losses in economic mobility in North Jakarta because the road is the main access to an s considerable automotive factory area in the Jakarta area. Drainage systems in the area need to be designed to accommodate water discharge especially when it is raining. Drainage capacity needs to be accounted for to accommodate existing water discharge. From the results of the analysis obtained the amount of channel capacity value of 0.46 m<sup>3</sup>/s, then bulk discharge flood plan in the period of-2 Years 22.95 m<sup>3</sup>/s, for 5 Years 34.26 m<sup>3</sup>/s, for 10 Years 41.75 m<sup>3</sup>/s. With the flooded value of the plan, it was concluded that the capacity of the existing channel could not accommodate rain at the peak discharge of the plan, so the area always experienced flood events. Also, drainage channels in the area were affected by the high rise in water levels due to the influence of tides with an average increase of 0.6 m with an average elevation of water level of 0.607.</p>

Conformity Assessment of Informative Labels in Car Engine Compartment with Deep Learning Models

Industry 4.0 has been changing and improving the manufacturing processes. To embrace these changes, factories must keep up to date with all the new emerging technologies. In the automotive industry, the growing demand for customization and constant car model changes leads to an inevitable grow of complexity of the final product quality inspection process. In the project INDTECH 4.0, smart technologies are being explored in an automotive factory assembly line to automate the vehicle quality control, which still relies on human inspection based on paper conformity checklists. This paper proposes an automated inspection process based on computer vision to assist operators in the conformity assessment of informative labels affixed inside the engine compartment of the car. Two of the most recent object detection algorithms: YOLOv5 and YOLOX are evaluated for the identification of labels in the images. Our results show high mean average precision on both algorithms (98%), which overall, tells us that both algorithms showed good performances and have potential to be implemented in the shop floor to support the vehicle quality control.

Reducing Flow Time in an Automotive Asynchronous Assembly Line – An application from an automotive factory

Reducing Flow Time in an Automotive Asynchronous Assembly Line – An application from an automotive factory

Simulation for Decision Support in Process Reengineering in the Automotive Industry

With globalization and the high competitiveness faced in a business environment, adopting technological solutions to satisfactory respond to an increasingly demanding customer is the watchword for organizations. With technological advances and an increasingly volatile society, it is mandatory for industries to ensure agile production processes capable of responding quickly and assertively to consumer expectations. Industry 4.0 with its technological pillars comes up to boost company to have more flexible and agile processes. This work results of a project developed in an automotive factory that will be producing a new mechanical part. The definition of the best layout of the production line was carried in an instable environment characterized by budget limitations, security and space restrictions that forced the company to study and discuss four possible layouts to define the future line. The goal was to ensure that the line was able to produce 3625 parts/week with an Operational Income of 87%. Moreover, it was also important to consider the cost of implementing a new line in the company. With this study it is proven that in an automotive industry where flexibility is required in an unpredictable environment during a decision-making process, analyzing the impact and possible consequences of every change in a layout definition phase is a great asset, where alteration like adding a conveyor, Automated Guided Vehicle or Robots can be made easy and quickly, and important conclusions can be taken from this analyze with less effort. Finally, 2 criteria were created to examine every variant and a decision was made.

Agent-Based Distributed Data Analysis in Industrial Cyber-Physical Systems

Cyber-Physical System (CPS) is a key concept in the fourth industrial revolution, acting as the backbone infrastructure to develop Industry 4.0 compliant solutions based on decentralized networks of cyber-physical entities. In such systems, the distribution of intelligence and data analytics capabilities by different computational layers assumes a crucial relevance to support monitoring, diagnosis, prediction, and optimization tasks. Multiagent Systems (MAS) is a suitable approach to support this distribution through vertical and horizontal dimensions. In this context, this article introduces a modular agent-based architecture to balance the distribution of data analysis capabilities in industrial CPS. The proposed MAS solution was implemented in an industrial automotive factory plant, which applicability, novelty, and benefits were recognized, e.g., through its inclusion in the Innovation Radar of the Great EU-funded Innovations due to its contribution for the implementation of Zero Defects Manufacturing strategies in multistage production systems.