Advancements in Computational Intelligence for Enhanced Big Data Analytics in Industry 4.0 and 5.0

Abstract

Deep learning (DL) architectures are extensively used in robotics and industrial automation, emphasizing the importance, difficulties, and potential applications. DL has transformed industrial processes by enabling automation, efficiency gains, and autonomous operation. It is capable of understanding complex patterns and representations from data. This chapter explores different DL architectures, such as transformers, generative adversarial networks (GANs), autoencoders, and recurrent neural networks (RNNs). It discusses the roles of these models in tasks ranging from autonomous navigation in robotic systems to defect detection in manufacturing. Case studies provide concrete advantages and results from the applications of DL in industrial defect detection, predictive maintenance, warehouse automation, and robotic assembly lines. Important issues such as worker displacement, safety, dependability, data quality, and model interpretability are addressed. Future research topics such as enhanced architectures and integration with cutting-edge technologies like edge computing and the Internet of Things are recommended. Finally, possible prospects for innovation, improved safety, and increased efficiency in our discussion of the consequences for industry are highlighted. To fully utilize DL in industrial automation and robotics and promote sustainable growth and innovation in the industrial sector, cooperation among researchers, policymakers, and industry stakeholders is necessary.