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

Cavalcante, Esley Silva; Vasconcelos, Luís Gonzaga Sales; Brito, Karoline Dantas; Brito, Romildo Pereira

doi:10.1016/j.porgcoat.2023.107773

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Furthermore, the painting technologies employed in automotive paint shops play a central role in the environmental equation. With this perspective, Cavalcante et al [13] introduce a neural network predictive control (NNPC) system for optimizing temperature control in an automotive oven's drying and curing processes, effectively reducing costs while maintaining painting quality.…”

Section: Environmental Impact and Technological Innovations In Automo...mentioning

confidence: 99%

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

Carpitella

2024

Sustainability

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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.

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Section: Environmental Impact and Technological Innovations In Automo...mentioning

confidence: 99%

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

Carpitella

2024

Sustainability

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“…Scientific Novelty Directions for Future Research [36] Developing and implement a neural network predictive control (NNPC) system for the drying and curing process in automotive paint, for temperature prediction, and control of specific BIW (Body-in-White) parts.…”

Section: Referencementioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

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Optimization of CED dry film thickness using artificial neural network for automobile industries

Prasad,

Singh,

Prasad

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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To optimize the cathodic electro deposition paint film on automobile body for corrosion protection, AI driven solutions are generated. Cathodic electro deposition technique has been adopted for coating process by controlling process variables such as surface area, non-volatile matter, processing time, bath temperature, and electrode voltage. An artificial neural network model has been developed using the experimental dataset to train and enable it to predict dry film thickness. The proposed ANN model has been evaluated with an error rate of 6% which is well within the accepted limit of 10%. The study also shows that mild steel’s dry film thickness was more responsive to electrode voltage and bath temperature than other metals. The study suggests that to achieve the most economical output, it is recommended to maintain the bath chemistry while adjusting the physical characteristics such as voltage and temperature, as indicated by the findings.

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

Cited by 3 publications

References 5 publications

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

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

Review of Energy-Related Machine Learning Applications in Drying Processes

Optimization of CED dry film thickness using artificial neural network for automobile industries

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