A probabilistic neural network for uncertainty prediction with applications to manufacturing process monitoring

Zhang, Bin; Shin, Yung C.

doi:10.1016/j.asoc.2022.108995

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…Furthermore, model complexity and severe non-linearity in ML can hinder the evaluation of uncertainty [182]. Although there are promising approaches, e.g., Gaussian mixture models for NN [183,184] and Probabilistic Neural Network (PNN) [184], or the use of Baysian Networks [180], there are several limitations limiting potential applications, such as high computational cost and simplified assumptions [184]. Therefore, future research needs to make progress on the general theory of integrating uncertainty into ML methods to allow manufacturing in order to ensure high quality and stability in production.…”

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confidence: 99%

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

et al. 2023

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While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

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