Automotive Painting Research Articles

Investigation of Treatability of Automotive Industry Wastewaters by Coagulation-Flocculation, Fenton and UV/H2O2

In the automotive industry, painting operations have emerged as a major source of environmental concerns. In this study, the treatability of wastewater obtained from the automotive industry painting process was investigated by using coagulation-flocculation, ultraviolet/hydrogen peroxide (UV/H2O2), and Fenton processes. The effects of pH, coagulant, and anionic polymer doses were examined for three different coagulants in the coagulation-flocculation process. Optimal conditions were determined based on chemical oxygen demand (COD) and turbidity removal efficiency. As a result of the study, the optimum conditions for the coagulant identified as ferric chloride, which achieved the highest COD removal of 49.6% and turbidity removal of 74.4%, were determined as pH 8, coagulant dose of 400 mg L-1, and anionic polymer dose of 1 mg L-1. In the second stage of the study, Fenton and UV/H2O2 processes were applied to the effluent of the coagulation-flocculation process to increase the removal efficiency. When the results were evaluated, it was observed that the maximum COD removal efficiency of 63.4% was attained under the conditions of pH 3 and 400 mg L-1 H2O2 concentration applied in the UV/H2O2 process. However, in the Fenton process, a maximum COD removal efficiency of only 51% was achieved. From this study, it can be concluded that the UV/ H2O2 process applied after coagulation-flocculation increases the removal efficiency and can be an alternative and effective treatment process for wastewater from the automotive painting process.

Treatment of Automotive Paint Wastewater: Photocatalytic degradation of methylene blue using semi-conductive ZrO2

Addressing water pollution, particularly in the automotive industry's painting processes, is vital due to its significant environmental impact, and the use of photocatalysis, an environmentally friendly and energy-efficient advanced oxidation method, holds promise for removing non-biodegradable organic dyes from wastewater. In this study, the use of semiconductor ZrO2 nanoparticles in the photocatalytic degradation of pollutants in wastewater under UV light was investigated. Zeta potential, Brunauer–Emmett–Teller (BET) surface area and UV-Vis absorption spectroscopy analyses were performed on the ZrO2 nanoparticle synthesized under optimized experimental conditions. ZrO2 nanoparticles synthesized under the optimized experimental conditions exhibited a high specific surface area (51.793 m2/g). ZrO2 nanoparticles had strong absorption in the visible light region, and the energy band gap was estimated to be approximately 3.062 eV. The photocatalytic activity was evaluated by the degradation of methylene blue under UV light (366 nm). The effects of parameters such as the amount of catalyst, concentration and pH of the dye solution, the wavelength of the UV light source used (366 and 254 nm) and the type of test environment on the removal efficiency of methylene blue were investigated. ZrO2 nanoparticles showed a high degradation efficiency of 91% in a strong alkaline environment, which may be the result of the facilitated formation of –OH radicals due to the increased concentration of hydroxyl ions.

Design and Application of Process for Energy-saving and Environmentally Friendly Automotive Painting

Design and Application of Process for Energy-saving and Environmentally Friendly Automotive Painting

Oven Pintar Berbasis Kontrol Elektonik Modul untuk Pengeringan Bahan Praktek di Laboratorium Pengecatan

Extreme climate change makes the situation and environmental conditions uncertain, causing the painting practice lecture process to be less than optimal. Weather that tends to be cloudy has the potential to damage the paint from student practicum results. Referring to these problems, in this research an LPG-assisted automatic paint dryer was made as the solution offered. The purpose of this research is to make an oven to support the acceleration of drying of student paint. This research is a design type, which is carried out to support effective and efficient learning in accordance with the planned learning series. The instruments used in this study were: (1) paint drying oven machine assessment sheet; and (2) the target audience's response questionnaire. Indicators of research success are based on process and results. This smart oven based on electronic control module is very suitable to be used as a tool in improving the quality of painting results, especially in automotive painting. Making a smart oven based on electronic control of this module does not require too much money and is very easy to understand in operating the system because of the simplicity of how the oven machine system works. Questionnaire responses of students who have used smart ovens show results that agree and strongly agree with the existence of this smart oven.

Neural network predictive control applied to automotive paint drying and curing processes

In the automotive painting process, maintaining optimal operating conditions in drying and curing ovens is crucial to ensure high-quality paint finishes, particularly for specific car body-in-white (BIW) parts' temperature profiles. This paper presents a methodology for developing and implementing a Neural Network Predictive Control (NNPC) system for painting drying and curing processes in an automotive oven used in the electrodeposition stage (Elpo oven). The objective is to enhance temperature control for BIW parts. To train the Artificial Neural Networks (ANN) in the various zones of the Elpo oven, a dataset was generated using a phenomenological model based on first principles. Random disturbances were applied to the input variables (conveyor speed and zone temperatures) to capture the dynamic response of the output variables (temperature in specific BIW parts). The dataset was split into training (80 %), validation (10 %), and test (10 %) sets, and the ANN training was performed using the backpropagation algorithm. The implemented NNPC utilizes the trained ANNs to predict future temperature values in specific BIW parts (controlled variables). To determine the optimal control signals (manipulated variables), an optimizer based on the Generalized Predictive Control (GPC) model was employed, and the objective function was minimized using the Ant Colony Optimization Algorithm (ACO). Through simulations of four operational scenarios with applied disturbances, the results demonstrate satisfactory performance of the NNPC, effectively maintaining controlled temperatures of BIW parts close to predefined setpoints. The utilization of NNPC offers improved temperature control for BIW parts, mitigating painting issues, reducing rework and operating costs, while ensuring painting quality is uncompromised.

An Analysis of Optimization for Car PBS Scheduling Based on Greedy Strategy State Transition Algorithm

The differences in constraints between an automotive painting workshop and a final assembly workshop can lead to production scheduling disruptions, and using the Painted Body Store (PBS) in automotive manufacturing can optimize production scheduling to meet the production requirements of the final assembly workshop. This is achieved by establishing a multi-objective mixed-integer optimization scheduling model for PBS and solving the model using a state transition algorithm based on the greedy strategy; finally, a comparison was made with the classical genetic algorithm. The results show that the optimal objective function is achieved when the body sequence movement iterations for data sets N and R are 34 and 54, respectively. At this point, the highest objective function scores are 23.46 and 49.79, and vehicles entering the PBS scheduling system from the painting-to-assembly line require 31 and 38 sequence adjustments, respectively. Compared with the classical genetic algorithm, the simulation results show that the state transition algorithm based on the greedy strategy is significantly better; it can also perform well in discrete sequence movement operations, demonstrating strong global search capabilities and fast convergence. It provides a new approach for optimizing vehicle scheduling in the automotive painting-to-assembly line.

Automotive Paint Sludge: A Review of Pretreatments and Recovery Options

Automotive paint sludge (PS) is the waste product generated in the painting process of vehicle bodies. Although automotive spray painting is an automated operation, its efficiency is still quite low, since approximately 40–50% of the employed paint does not reach the target and, after being mixed with the collecting water, becomes PS. PS is a very complex material that contains several organic and inorganic components, in addition to 90% water immediately after production. Italian automotive factories produce from 2.5 to 5.0 kg of PS per painted car. If that figure is related to the number of vehicles produced worldwide every year, in the order of 100 million, it determines an annual PS amount in the order of 200,000–500,000 t. Consequently, a proper final destination for PS must be found. The waste management hierarchy and the principles of the circular economy require that we privilege solutions that foresee the recovery of valuable products or energy. This paper first reviews the processes and the machines that have been recently developed to obtain an enhanced mechanical dewatering of PS. The pretreatment of PS dewatering is often crucial in order to obtain high efficiency in the subsequent recovery process. Afterwards, the paper presents and discusses the recovery options that have been proposed and tested, at different scales, by several authors in the last thirty years. The processes for PS management can be grouped as follows: (i) direct employment of physically/chemically treated PS in the production of primers and sealants; (ii) utilization of PS for the production of building materials, as supplementary components of cement concrete, mortar, or bituminous mixtures; (iii) extraction of valuable organic and inorganic products by using thermal processes (pyrolysis, gasification); (iv) biological processes for PS detoxification, metal recovery, and stabilization before landfilling.

Plasma electrolytic oxidation treatments for bimetallic substrates enabling sustainable procedures for automotive painting

Magnesium- or Aluminium based lightweight alloys are increasingly utilized in the automotive sector. To ensure corrosion protection of these lightweight alloys, plasma electrolytic oxidation (PEO) can be utilized to from protective layers in an efficient manner on those lightweight alloy materials. In real vehicles often a blend of joined materials is used and it must be evaluated if the joined material combinations can be subjected directly to corrosion protection treatments or if separated treatments are necessary for different materials, which would significantly enhance production costs. In this study we investigate the feasibility of the PEO process for already joined Al-alloy and Mg-alloy materials. Special focus is put on the compatibility with subsequent cathodic dip coating (CDC) processes and the corrosion resistance of the coated materials was evaluated, which is highly relevant for applications in vehicles. We provide characterization of the formed PEO layers and could demonstrate different layer formation behaviour and composition, depending on the alloy material. It could be also shown that Mg-alloy and Al-alloy materials joined by gluing and riveting can be subjected to the PEO process, resulting in effective, simultaneous protective layer formation on both materials. Finally, it is demonstrated that the joined and PEO treated materials exhibit excellent compatibility with the cathodic dip coating process and that previous PEO treatment results in better corrosion resistance also for cathodic dip coated samples.

ESTRATEGIA DE SELECCIÓN DE HARDWARE Y ALGORITMOS DE INTELIGENCIA ARTIFICIAL EN ENTORNOS 'EDGE COMPUTING'

Currently most of the data collected in companies and industrial manufacturing environments through IoT devices is processed in the cloud [1]. Given the large volume of data that each company manages due to the emergence of IoT, cloud computing is not the best option for certain sectors such as automotive [3]. Within this sector, the quality perceived by the end customer is closely linked to the assembly line. These assembly lines collect a high number of variables (temperatures, pressures, pumps, etc.) and a real-time prediction by means of small digital twins in this process would avoid both material and labor costs. Today, to perform such anticipation through artificial intelligence models in real time is unfeasible due to the latency that exists with cloud processing. Therefore, there is an imminent need to develop applications that are deployed at the edge of the network for the automotive manufacturing and painting process that enable the generation of digital twins. In this regard, the proposal of the 'Edge Computing' concept [2] seeks to mitigate in part this situation. For this reason, this paper has focused not only on demonstrating that splitting the data of a large model by generating small 'Edge' models maintains the predictive capacity of each algorithm, but also stress tests (limits, constraints, etc.) have been performed on the 'Edge Computing' hardware technology currently available on the market that allows generating and processing this type of models. Key Words: Edge Computing, Internet of things, Machine Learning, Industry 4.0

Telomere length and chromosomal fragility increase in car painters exposed to organic solvents

ObjectiveTo comparing the chromosome fragility ratio and relative telomere length, of a group of car painters occupationally exposure to organic solvents with a group of non-exposed individuals in the city of Bogotá (Colombia). MethodsThis was a retrospective study of two cohorts (33 men each), matched by age (±2 years) at 1:1 ratio. The differences in the fragility ratio and relative telomere length distributions were determined for both groups. Case group was composed for adult males working in artisanal shops located in Bogotá (Colombia), who were occupationally exposed to organic solvents through automotive painting for a minimum of two years. Blood samples were analyzed. The chromosomal fragile sites were detected using cytogenetics techniques and DNA was extracted to determine the length of telomeres using qPCR (Cawthon’s method). Chromosomal fragility ratio and relative telomeric length were compared between car painters exposed to organic solvents and no-exposed individuals. ResultsStatistically significant differences in Chromosomal fragility ratio were found between exposed (0.645 ± 0.440, med = 0.520) and non-exposed (0.414 ± 0.217, med = 0.400) (p = 0.037, Wilcoxon one-tailed test). Also, in relative telomere length, in exposed (2.728 ± 5.581, med = 1.668) and non-exposed (1.835 ± 4.727, med = 0.732) individuals (p = 0.002, Wilcoxon one-tailed test). Low degree correlation for the two parameters, using Spearman's Rho, was found (0.21, p = 0.419). ConclusionsExposure to organic solvents increase both the telomere length and the rate of chromosomal sites fragile in the exposed group compared to the unexposed group. This study is a pioneer in the evaluation of the genotoxicity of exposure to organic solvents obtained the chromosomal fragility ratio and measuring the relative length of telomeres. We suggest applying this methodology to workers in other types of industries, for example carpentry shops, paint factories and stores where organic solvents are mixed and there is occupational exposure due to the inadequate use of protection measures or space conditioning.

Viscoelastic modelling of fibre-reinforced thermoplastics in hygrothermal circumstances

Thermoplastics are favourable to the automotive industry due to their recycling possibility. Carbon fiber reinforced thermoplastics (CFRTP) are passed through the automotive paint shop. The imposed thermal loading presents a challenge to implementing economically feasible CFRTP in body structures. The present study provides a simulation approach to analyse the anisotropic viscoelastic deformation behaviour to assess this scenario. Validation experiments were conducted by optically measuring the out-of-plane displacement of dry and moisture-saturated specimens subjected to a simulated cathodic dip painting-dryer. Preliminary lay-up assessment for the automotive painting process is deemed possible due to the good agreement between simulation and experiments.

Emission characteristics and ozone formation potentials of VOCs from ultra-low-emission waterborne automotive painting

Automotive painting plants are important emission sources of volatile organic compounds (VOCs) that contribute significantly to ground-level ozone (O₃) pollution in atmosphere. Here, we investigated process-specified emission characteristics of VOCs, without or with advanced adsorption/incineration after-treatments, from an ultra-low-emission (ULE) waterborne painting process in a modernized automotive plant. Overall, more than 80 VOCs species were identified and sorted into seven main categories. In the stack emissions without after-treatments, oxygenated VOCs (alcohols, esters, ketones, ethers, etc.) were found to be the most abundant components (48.8%), followed by aromatic (30.9%), alkanes (16.9%) and alkenes (1.2%). Among the different VOCs species discharged to atmosphere (i.e. after adsorption/incineration after-treatments), aromatics demonstrated a predominant contribution (by 60.6%) to the total O₃ formation potentials (OFPs) despite their relatively lower abundance. Trimethylbenzene was identified to have the highest OFPs, and thus should be controlled with peculiar priority. As compared to traditional organic solvent-based painting process, the ULE waterborne process implemented in the target plant allows to reduce the OFPs from 10.7 mg m−3 to 3 mg m−3 (or by 72%). Additional monitoring by unmanned aerial vehicle (over more than 3000 sampling points in the plant) confirmed that the instantaneous concentrations of fugitive VOCs were well below the regulated limit value during typical working and non-working days. These findings may provide important reference for reduction of VOCs emissions and O3 pollution from automotive painting processes.

Comprehensive Review of Nanoparticles Dispersion Technology for Automotive Surfaces

Many innovations arose from the continual and thorough monitoring of overlooked characteristics of materials found in the environment. Automotive paints are always constantly exposed to a broad range of ambient temperature conditions, which reduces their longevity and encourages algae development. Through the effective incorporation of nanotechnology with this lotus effect, it has become possible to provide self-cleaning ability along with air purification and antibacterial performance to automotive surfaces like paint and coating. The addition of nanoparticles such as Titanium dioxide (TiO2) and Silicon dioxide (SiO2) helps to improve functionalities like water or stain resistance, ultra-violet protection, and scratch resistance. When the nanoparticles were added into paint, they degraded the polluting compounds on the material's surface by photo catalysis. Multiple photocatalytic functions and self-cleaning properties were observed in nanoparticles added to polyester acrylic paint. Therefore, this paper discussed the history of automotive painting, nanopaint technology, previous research on the method preparation, development, and current progress, the environmental health aspects of nanotechnology, as well as the performance in terms of automotive surfaces. The study discovered the requirements for nanoparticle dispersion and coating uniformity and appearance on automotive surfaces, which will serve as a benchmark for dispersion and coating methods for automotive surfaces.

A techno-economic evaluation of low-grade excess heat recovery and liquid desiccant-based temperature and humidity control in automotive paint shops

The paint shop is the most energy-intensive process in an automotive manufacturing plant, with air management systems that supply air to paint booths consuming the most energy. These systems are crucial for temperature and humidity control, in which they ensure the quality of the final product by preventing paint defects and thus avoid the additional cost of reworking. This is especially true for water-based paints, in which evaporation and film formation processes are influenced by the temperature and humidity of the surrounding air. This study aims to investigate the incorporation of liquid desiccant technology into a conventional air management system for paint shops operating in different climates, which presents the novelty of the study. The technology is promising because it can regulate humidity, act as a dehumidifier or humidifier depending on the demand and stores energy in a thermo-chemical form. In addition, waste heat sources available in the paint shop can be used for the regeneration of the liquid desiccant solution. The techno-economic evaluation of this novel process indicates that the proposed system can control the temperature and humidity of the supply air within the range required for optimal painting and achieve significant energy savings in both cold and hot/humid climates, with a reduction of 44.4% and 33.6% of the energy cost compared to the conventional operation and a payback period of 6.15 and 5.74 years respectively, using calcium chloride as the desiccant solution. The sensitivity analysis investigates the effect of the energy and carbon price on the performance of the system. It is concluded that the integration of liquid desiccant technology into conventional air management systems for paint booths has a huge potential to increase the energy-efficiency of automotive painting.

Control Method for Improving Voltage Response and Ripple of Isolated DC-DC Converter

Affected by the abrupt changes of load and unregulated first-stage rectifiers, it is difficult for the output voltage of the isolated DC-DC converter with traditional proportional-integral (PI) control to meet the needs of industrial applications such as automotive electrophoretic painting. A voltage response improving the method based on a radial basis function neural network (RBFNN) is proposed in this study for the output voltage mutation of DC converter caused by load mutation. By detecting the output voltage deviation and load current value, the RBF model gives the compensation amount of the duty ratio to enhance the PI controller to speed up the voltage regulation and improve voltage response. For the six-pulse voltage ripple introduced by the unregulated rectifier in the previous stage, a delay link is adopted, where the compensation amount of the duty ratio and its delay time are reasonably designed to reduce the ripple value. The experimental test platform is built, and the proposed method is verified. The results show that the proposed method can effectively improve the output voltage response and ripple as well as improve the output voltage quality of the DC converter, which is of certain engineering application value.

Minimizing deviations of car sequencing in a JIT production painting line

PurposeThe first in first out (FIFO) rule is an indispensable rule in the automotive industry. However, during production, the order of coming to welding operation and exiting through the painting operation is broken. Consequently, the rule of FIFO also gets broken. The purpose of this paper is to eliminate the deviations of the automotive painting process in a just in time (JIT) environment.Design/methodology/approachThe paper has been solved using observations and expert’s opinions and recorded data in the real problem area. Bypass technique and express line have been used as a real problem for the minimization of these deviations. The current process and the bypass process have been compared with simulation optimization.FindingsThis paper provides analytical insights about how to prevent delays during the JIT production painting process. Delays have been prevented by using the bypass technique and express line together. The number of delays reduced with an improvement of 129. The ratio of effect (for four months) has been founded to be 0.12. The output rate has been increased from 80% to 89%.Research limitations/implicationsThis paper focused on the number of vehicles in the cooling area of the painting process in the study; there may occur bottleneck processes or products. It does not contain station-based bottleneck processes.Practical implicationsFrom the real-case problem, one will see that the bypass technique and the express line can be applied and generalized to many similar problems and help companies conduct their assembly lines more efficiently.Originality/valueThis paper is the only example that has been used to bypass technique and express line together on the paint/cooling process in a JIT production system.

REVIEW ON PHOTOGRAMMETRIC SURFACE INSPECTION IN AUTOMOTIVE PRODUCTION

Abstract. When purchasing a premium car for a substantial sum, first impressions count. Key to that first impression is a flawless exterior appearance, something self-explanatory for the customer, but a far greater challenge for production than one might initially assume. Fortunately, photogrammetric technologies and evaluation methods are enabling an ever greater degree of oversight in the form of comprehensive quality data at different automotive production stages, namely stamping, welding, painting and finishing. A drawback lies in the challenging production environment, which complicates inline integratability of certain technologies. In recent years, machine vision and deep learning have been applied to photogrammetric surface inspection with ever increasing success. Given comprehensive surface quality information throughout the entire production chain, production parameters can be dialed in ever tighter in a data-driven fashion, leading to a sustainable increase in quality. This paper provides a review of current and potential contributions of photogrammetry to this end, discussing several recent advances in research along the way. Particular emphasis will be placed on early production stages, as well as the application of machine vision and deep learning to this challenging task. An outline for further research conducted by the authors will conclude this paper.

Bilevel interactive optimisation for rebatching scheduling problem with selectivity banks in high variety flow line production

Mass customization enables the integration of traditional flow line production with product platforms to accommodate abundant product-process varieties. These platform-based flow lines explore common process routes while highlighting rebatching scheduling with selectivity banks (RBS) to handle large process varieties across production stages at minimum setup cost. Given the inherent coupling between decision making in job diverging and retrieval quality, an interactive optimization approach is necessary for the RBS problem. This study proposes a bilevel interactive optimization (BIO) model for RBS to accommodate high variety flow line production. The model addresses the conflicting goals of lane occupancy cost, process setup cost, and job divergence and retrieval efficiency. Regarding job divergence at the leader-level, a vehicle routeing problem with precedence constraints is formulated and solved by a constructed genetic algorithm (GA). Concerning job retrieval at the follower-level and the ongoing characteristic of selectivity banks, a dispatching problem with various batch size preference and dynamic time window is established and dealt with a restricted dynamic programming (RDP) algorithm after balancing search efficiency and accuracy. Thus, to solve the BIO, a hybrid GA-RDP is developed and implemented. A practical application to an automotive painting shop illustrates the operational benefits of the BIO model for the RBS problem.

Development and adaptation of the technology of air biotreatment in trickle-bed bioreactor to the automotive painting industry

The automotive painting industry is a source of environmental pollution caused by Volatile Organic Compounds (VOCs) present in the discharged ventilation air. To contribute to the mitigation of this type of air pollution, Ekoinwentyka Ltd. developed – from pilot scale to full scale – and adapted the technology of Compact Trickle Bed Bioreactor (CTBB) whose operating principle builds upon co-current downflow of the gas phase (polluted air) and liquid phase (solution of mineral salts) through a packed bed where active microorganisms are immobilized in the biofilm on the surfaces of packing elements. Pilot-scale bioreactor 0.32 m in diameter and 1.50 m total height had its packed bed inoculated with a consortium of microorganisms dominated by Pseudomonas fluorescens bacteria. During the experimental programme that lasted several months, the flow rate of air drawn from the ventilation system of the painting shop was changing between 1.0 and 10.0 m3/h and the inlet concentration of VOCs ranged from 10 to 200 ppm. By measuring VOC concentration in the purified air, the factor of VOC biodegradation was found to range between 85 and 99%. Based on pilot-scale experiments, full-scale CTBB has been developed 2.8 m in diameter and 10 m total height and installed as an add-on component of the ventilation system of the painting shop. Test operation at gas flow rates up to 6000 m3/h, confirmed VOC biodegradation factor at the level of 85–99% thus proving a positive result of CTBB technology adaptation to the conditions of the automotive painting industry.

Characteristics of the VOCs emitted from automotive painting workshop and control facilities

Characteristics of the VOCs emitted from automotive painting workshop and control facilities