This book aims at addressing the challenges of contemporary manufacturing in Industry 4.0 environment and future manufacturing (aka Industry 5.0) by implementing soft computing as one of the major sub-fields of artificial intelligence (AI). Learning and predicting capabilities of soft computing techniques (e.g. machine learning) as well as optimization capabilities (e.g. evolutionary algorithms) and other features of AI should be exploited to respond to issues of smart manufacturing. Raising demand for product personalization introduces a high level of uncertainty and requires highly adaptive, agile processes and systems. Increased efficiency and predictive quality are requested from manufacturing processes and systems to accomplish the first time right production in a business environment led by dynamicity, individualization, and mass customization. Therefore, robust and resilient solutions are needed in terms of integrating state-of-the-art soft computing techniques and developing new ones to tackle the above challenges. These solutions should rely on learning, prognostic, and optimization features to successfully address unforeseen events and take into account the availability and status of manufacturing system resources and the need for real-time feedback and (re)action, as well as specific concerns for security and effective human-system collaboration.
Areas of interest include but are not limited to applications of soft computing to address the following:
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