Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Abstract

Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure and interpret data, is a methodological approach to solve complicated optimization problems based on abundant data. ML is recently gaining momentum as algorithmic applications, computing potency, and available data sets increased manifold over the past two decades, providing an information-rich environment in which human reasoning can partially be replaced by computer reasoning. In this paper, we want to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield. We specifically want to assess the future role and importance of DoE: Will it remain unaltered by ML, will it be made obsolete, or will it be reinforced? With this, we want to

A review on machine learning algorithms for the ionic liquid chemical space

“…155,156 On the other hand, ML could substantially help the aim of DoE by detecting non-obvious factor effects and interactions (falsely considering interrelated factors as independent is a common problem in DoE approaches). 157 ML algorithms could completely replace the DoE approaches, as theoretically they are able to create correlations by taking into account all the possible factors inuencing a process. However, in reality we are signicantly restricted by the lack of enough computational power (and sometimes data) to create and run such complex models.…”

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

“…Therefore, revealing all the information encrypted over the large amount of experimental results derived from this type of multifactorial processes becomes a highly challenging task. In such cases, machine learning (ML) offers a cutting-edge computer-based methodology with the ability of handling very complex multivariate datasets, in which there are unknown patterns between inputs and outputs or large amount of uncategorized or different kind of data relating with complex processes, being able to transform data into useful information and knowledge (Gago et al, 2010c;Ertel, 2017;Bini, 2018;Freiesleben et al, 2020). On this purpose, different ML algorithms such as artificial neural networks (ANNs); deep neural networks (DNNs); convolutional neural networks (CNNs); support vector machines (SVMs) or random forest (RF) has been used in plant biotechnology (Niazian and Niedbala, 2020) and, particularly, in PTC (Gago et al, 2010a).…”

Harnessing artificial intelligence for the next generation of 3D printed medicines

“…Numerous industrial sectors have come to rely on traditional optimisation techniques (such as DoE, mechanistic modelling, pharmacokinetics (PK) modelling, and FEA), so are ML techniques really favourable for adoption in pharmaceutical 3DP? In short, ML is the future of process optimisation, and will likely combine with elements of traditional tools or supersede them entirely [206,256,257]. Whereas traditional techniques are often limited by their scope of use (e.g., PK modelling focuses on in vivo drug behaviour), ML can cover the breadth of existing non-AI tools combined.…”