Automotive Paint Shop Research Articles

Overcoming Barriers to Digital Transformation towards Greener Supply Chains in Automotive Paint Shop Operations

Given the resource-intensive nature of automotive manufacturing processes and their potential to substantially contribute to ecological footprints, the integration of sustainable logistic practices in the context of digital transformation becomes imperative. This paper focuses on the implementation of green supply chain strategies within the automotive sector, targeting significant risks associated with environmental impact, specifically in the critical domain of automotive paint shops. Automotive paint shops indeed play a significant part in determining the overall sustainability of automotive production. Recognized for their role in vehicle esthetics and corrosion protection, the sustainable integration of these facilities is crucial in the pursuit of a greener automotive future. A comprehensive multi-criteria decision-making framework is herein proposed as a valuable tool in pinpointing the most critical barriers to digital transformation and simultaneously prioritizing suitable green logistic strategies in the context of automotive paint shop risk-management procedures. The practical utility of the model extends to practitioners in the automotive paint shop supply chain, particularly those engaged in digitalizing critical operations, facilitating well-informed decision-making aligned with environmental sustainability goals. The findings of this research highlight the critical importance of implementing tailored strategies, including crisis preparedness, transparent communication, proactive outreach, and strategic investments in technology and partnerships, to address barriers and enhance sustainability practices within automotive paint shop operations, thereby contributing to the overall resilience and long-term viability of automotive supply chains.

Combining material flow simulation and optimization for sustainable manufacturing – application in automotive paint shops

The necessity for sophisticated decision support in industrial manufacturing has been described manifoldly and simulation approaches as well as optimization methods are continuously developed. This can be especially promising when it comes to decision-making with multidimensional requirements of economic and ecological efficiency. However, due to the required expert knowledge, there is still a gap between theoretically applicable and industrially applied methods. The paper therefore presents results of two use cases as part of the European project ECOFACT where different approaches for the combination of simulation and (heuristic) optimization were applied with industry partners. An overview of recent works and systematic literature reviews of sustainable manufacturing and the approaches for combining simulation and optimization is given. Correspondingly, the investigated use cases of automobile paint shops refer to the reduction of environmental impacts by optimized heating processes and an improved batching process to minimize paint waste. In the first case, setup times for heating color pools and ovens were shifted to avoid load peaks in a calculation time of less than one minute. In the second use case batch size was increased by around 25%.

A simulation-based multi-objective optimisation for critical parameters design of an automotive paint shop using design of experiments

A simulation-based multi-objective optimisation for critical parameters design of an automotive paint shop using design of experiments

Quadratic Unconstrained Binary Optimization for the Automotive Paint Shop Problem

Quadratic Unconstrained Binary Optimization for the Automotive Paint Shop Problem

The Relationship of Individual Characteristics, Benzene Concentration with DNA Degeneration in Car Painting Workshop Workers

Background: A car paint shop is one of the works that use paint as the main working material containing benzene. Benzene is a colorless liquid aromatic compound that is carcinogenic to humans. This compound is classified as a group 1 human carcinogen by the IARC (International Agency for Research on Cancer).
 Objectives: This study aimed to analyze the correlation between individual characteristics and workplace benzene concentrations with DNA degeneration of workers in an automotive paint shop in Kalijudan, Surabaya.
 Methods: This study is an observational study with a cross-sectional design and a quantitative approach. Secondary data from the previous study were collected from 20 respondents, all of whom were paint workers in an auto paint shop. The dependent variable in this study is DNA degeneration and the independent variables are age, body mass index (BMI), number of years working, smoking behavior, and benzene concentration. To analyze the correlation of independent and dependent variables, the chi-square test was used. And then the phi test and Cramer's v are used to analyze the strength of the correlation between the two variables. 
 Results: Measurements of benzene concentrations in the workplace showed higher mean concentrations than TLV-TWA. As a result of laboratory tests, 13 out of 20 workers showed DNA degeneration. At the same time, the results showed a moderate correlation between BMI and DNA degeneration, and a very weak correlation between benzene concentration at work, individual characteristics (worker age, years of work, and smoking behavior) and DNA degeneration.
 Conclusions: This study found no correlation between individual characteristics and workplace benzene concentration with DNA degeneration in Surabaya auto paint shop workers.
 
 Keywords: benzene, car painting shop, DNA, individual characteristics

Review of coating and curing processes: Evaluation in automotive industry

The paint shop plant, as the largest energy consumer in the automotive manufacturing process, requires special attention to optimize energy efficiency and thermal management, reduce environmental impact and diminish the total costs to improve marketability. The current review covers the description of the automotive paint shop, recognition of the paint film layers, characterization of the paint spray applicators, evolution of their technology's advancement, explanation of the coating processes development, and investigation of the curing ovens thermal procedure in an automotive production stage. After systematically recalling pioneering studies dealing with the coating techniques and opening strategies, this paper focuses on reviewing recent findings conducted with improved modern methods in this subject area for higher sustainability and efficiency. Critical performance factors in developing surface durability, corrosion protectivity, paint material consumption, and environmental pollution are discussed. Focus is brought to the electrostatic spray-painting procedure flow physics understanding, particularly the complicated three-dimensional behaviors of paint droplets formation, distribution, control, and deposition on the body in constructed spray plums. Then, the baking and drying process of the produced film layer in the curing oven is precisely reviewed. The review additionally aids in the identification of knowledge gaps in the literature that should be addressed in future research.

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.

A Case Study on First Time Quality Feature Investigation for an Automotive Paint Shop

In today’s highly automated automotive paint shops, production equipment such as robots and conveyors controlled by various sensors and programmable logic controllers (PLCs) are widely used to coat vehicle bodies-in-white. A high-volume automotive paint line typically consists of miles of conveyor lines and dozens of sealing and painting robots equipped with hundreds of sensors and PLCs. It takes a body-in-white several hours to travel on conveyor tracks to complete the painting process. Throughout this process, thousands of process variables are monitored and collected from sensors and devices that control the automatic equipment. When a vehicle is painted, process variables and associated outcomes are collected in a centralized database called the Painted Surface Performance Management (PSPM) system. This database contains a complete history of each painted vehicle and its corresponding quality inspection results. In this work, machine learning (ML) models are used to select a list of the most important features that may affect the outcomes of the painted vehicles. However, this list needs to go through additional steps of feature investigations to validate true causal relationships between the selected features and the target variables. Once this step is completed, the selected features can then be used to target operational first time quality (FTQ) of products through use of such ML models to predict FTQ outcomes. An automotive paint shop data from 2020 to early 2021 was used to conduct a case study to demonstrate the process of feature investigation. Observations and recommendations from the ML model analysis are summarized for improvement.

A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems

This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs.

Efficient Simulation of Convective Ovens in Automotive Paintshops

Abstract Heat transfer modeling of large industrial ovens such as those used in automotive paintshops is difficult due to the large and multiple scales and long curing times. The flow inside a convective oven is turbulent, and the process includes large temperature gradients. An efficient simulation requires a simplified and localized model of the heat transfer coupling. We present a novel method with three ingredients: localization using local Nusselt numbers of the oven nozzles, projection of Nusselt number profiles onto the target, and efficient conduction modeling on a coarse background mesh. The approach, which was developed in a research project together with the Swedish automotive industry, makes it possible to accurately simulate a curing oven with close to real-time performance. The simulation results are demonstrated to be in close agreement with measurements from automotive production.

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.

Waste Heat Recovery in Automotive Paint Shop via Organic Rankine Cycle and Thermal Energy Storage System—Selected Thermodynamic Issues

Baking ovens are necessary to be installed in a paint shop of assembly automotive manufacturers for drying the paint of automotive bodies (i.e., in the coating process). In this process, a large amount of heat is provided by burning the natural gas in the gas burner. Practically, the design of the heat confinement in the oven is often poor, which results in considerable heat losses (i.e., waste heat) which are released during the drying process and significantly raise the temperature of a working environment thereby lowering the thermal comfort of the factory staff. To address this issue and limit the waste heat transfer to the surroundings, the application of a waste heat recovery system of a specific design employing the organic Rankine cycle (ORC) may be a viable alternative solution. A combined design of such a system utilizing an evaporator and thermal energy storage (TES) device in a simple ORC layout will be discussed in this article. The obtained simulation result was computed using MATLAB coupled with thermophysical properties libraries, i.e., CoolProp. The obtained results indicate that the sustainability of the studied system scheme appears to be favorably implemented in the selected paint shop and may benefit to lower the temperature of the working area, improve the thermal comfort of factory staff and at the same time produce electricity since some car/automotive manufacturers likely run the production for over 20 hours per day.

A simulation-based multi-objective optimization for critical parameters design of an automotive paint shop using Design Of Experiments

A simulation-based multi-objective optimization for critical parameters design of an automotive paint shop using Design Of Experiments

Optimizing Painting Sequence Scheduling Based on Adaptive Partheno-Genetic Algorithm

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints, and solved this problem by Python. The proposed method solves the optimal solution, consuming 575 s.

A finite state method in improvement and design of lean Bernoulli serial production lines

Production systems have a significant impact on national and global economies. It is, therefore, of particular interest to develop, master, and apply new methodologies improving the performance of the production systems. A systematic approach to the improvability and design for lean production in the case of the serial Bernoulli production lines is presented in this research using the finite state method and application of the Markovian modeling approach. A new concept of the bottleneck identification procedure is presented based on the analytical relationship between line properties and performance measures. Also, a new design methodology combining features of the finite state method and genetic evolution algorithm is developed to enable the efficient lean design of the serial Bernoulli production lines. The developed procedures are applied in the case of an automotive paint shop system including bottleneck identification concerning the production rate, the work-in-process, and the probabilities of blockage and starvation. Finally, the lean automotive paint shop system is obtained in an efficient and designer-friendly way.

Solution Approaches for an Automotive Paint Shop Scheduling Problem

In the paint shops of the automotive supply industry, a large number of synthetic material pieces need to be painted every day to provide the large variety of items required for car manufacturing. Because of the sophisticated automated production process and the tight due dates requested by car manufacturers, finding an optimized production schedule becomes a challenging task that is at the present time performed by multiple human planners.In this paper, we formulate and solve a novel real-life paint shop scheduling problem from the automotive supply industry which introduces unique constraints and objectives that do not appear in the existing literature. Additionally, we provide a new collection of benchmark instances based on real-life planning scenarios that can be used to evaluate solution techniques for the problem.An exact approach based on constraint programming is able to provide optimal solutions for smaller instances, but many larger instances could not be solved yet. Therefore, we propose a metaheuristic method based on local search that uses novel neighborhood relations and various ways to escape local optima. Our approach is able to provide feasible solutions for all instances within reasonable running time.

BiDrac Industry 4.0 framework: Application to an Automotive Paint Shop Process

To implement an Industry 4.0 Framework in an ongoing industrial manufacturing process to prepare it for complex Artificial Intelligence use cases is not an easy task. The automotive industry is undergoing a large transformation due to a variety of disruptive factors through the introduction of CASE (Connected, Autonomous, Shared, Electrified) technologies. Production improvements will enable future development of CASE capabilities. This paper presents an Industry 4.0 framework, named BiDrac, that integrates computing, communication and control under an Industrial Cyber–Physical​ System (ICPS) ecosystem. It combines Artificial Intelligence and Industrial Internet of Things (IIoT) inside the Industry 4.0 paradigm for the Predictive Maintenance of an Automotive Paint Shop Process inside an Industrial Cloud (IC) as an open platform that provides community of cloud-based solutions to enable the development of new digital solutions. Several real use cases of the proposed platform have been included in the results section to illustrate the potential of the proposed framework.

Decomposition-based real-time control of multi-stage transfer lines with residence time constraints

It is commonly observed in the food industry, battery production, automotive paint shop, and semiconductor manufacturing that an intermediate product’s residence time in the buffer within a production line is controlled by a time window to guarantee product quality. There is typically a minimum time limit reflected by a part’s travel time or process requirement. Meanwhile, these intermediate parts are prevented from staying in the buffer for too long by an upper time limit, exceeding which a part will be scrapped or need additional treatment. To increase production throughput and reduce scrap, one needs to control machines’ working mode according to real-time system information in the stochastic production environment, which is a difficult problem to solve, due to the system’s complexity. In this article, we propose a novel decomposition-based control approach by decomposing a production system into small-scale subsystems based on domain knowledge and their structural relationship. An iterative aggregation procedure is then used to generate a production control policy with convergence guarantee. Numerical studies suggest that the decomposition-based control approach outperforms general-purpose reinforcement learning method by delivering significant system performance improvement and substantial reduction on computation overhead.

Application of Semi-Analytical Methods in Production Systems Engineering

Production lines can be designed by an analytical, semi-analytical, or numerical approach. This paper gives a brief introduction to the analytical approach of a single buffer line, the aggregation method, and the analytical approach of a multi-buffer line. An automotive paint shop production system will be used as a figurative example to compare the aggregation method and the recently developed analytical approach for a multi-buffer line. A discussion at the end will show the advantages and disadvantages of the analytical approach.

Deep reinforcement learning for a color-batching resequencing problem

In automotive paint shops, changes of colors between consecutive production orders cause costs for cleaning the painting robots. It is a significant task to re-sequence orders and group orders with identical color as a color batch to minimize the color changeover costs. In this paper, a Color-batching Resequencing Problem (CRP) with mix bank buffer systems is considered. We propose a Color-Histogram (CH) model to describe the CRP as a Markov decision process and a Deep Q-Network (DQN) algorithm to solve the CRP integrated with the virtual car resequencing technique. The CH model significantly reduces the number of possible actions of the DQN agent, so that the DQN algorithm can be applied to the CRP at a practical scale. A DQN agent is trained in a deep reinforcement learning environment to minimize the costs of color changeovers for the CRP. Two experiments with different assumptions on the order attribute distributions and cost metrics were conducted and evaluated. Experimental results show that the proposed approach outperformed conventional algorithms under both conditions. The proposed agent can run in real time on a regular personal computer with a GPU. Hence, the proposed approach can be readily applied in the production control of automotive paint shops to resolve order-resequencing problems.