Incremental Latin Hypercube Additive Design for LOLIMOT

Voigt, Tim; Kohlhase, Martin; Nelles, Oliver

doi:10.1109/etfa46521.2020.9212173

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 2020

DOI: 10.1109/etfa46521.2020.9212173

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Incremental Latin Hypercube Additive Design for LOLIMOT

Tim Voigt

Martin Kohlhase

Oliver Nelles

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Cited by 2 publications

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Latin hypercubes for constrained design of experiments for data-driven models

Schneider,

Hellmig,

Nelles

2023

At - Automatisierungstechnik

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The quality of data used for data-driven modeling affects the model performance significantly. Thus, design of experiments (DoE) is an important part during model development. The design space is constrained in many applications. In this work, the constrained case is investigated. An Latin hypercube based approach is applied and analyzed for strongly constrained design spaces. Contrary to commonly used optimization techniques, an incremental procedure is proposed. In every step, new data are added to the design. Each new point is selected by a distance-based criterion. The performance of the created designs is evaluated by the quality of the trained models. For different constraints, artificial data sets are created with a function generator. The performance of local model networks and Gaussian process regression models trained with those designs is evaluated and compared to models trained on data sets based on Sobol’ sequences.

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Latin hypercubes for constrained design of experiments for data-driven models

Schneider,

Hellmig,

Nelles

2023

At - Automatisierungstechnik

View full text Add to dashboard Cite

show abstract

Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge

2021

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The use of data-based models is a favorable way to optimize existing industrial processes. Estimation of these models requires data with sufficient information content. However, data from regular process operation are typically limited to single operating points, so industrially applicable design of experiments (DoE) methods are needed. This paper presents a stepwise DoE and modeling methodology, using Gaussian process regression that incorporates expert knowledge. This expert knowledge regarding an appropriate operating point and the importance of various process inputs is exploited in both the model construction and the experimental design. An incremental modeling scheme is used in which a model is additively extended by another submodel in a stepwise fashion, each estimated on a suitable experimental design. Starting with the most important process input for the first submodel, the number of considered inputs is incremented in each step. The strengths and weaknesses of the methodology are investigated, using synthetic data in different scenarios. The results show that a high overall model quality is reached, especially for processes with few interactions between the inputs and low noise levels. Furthermore, advantages in the interpretability and applicability for industrial processes are discussed and demonstrated, using a real industrial use case as an example.

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Incremental Latin Hypercube Additive Design for LOLIMOT

Cited by 2 publications

References 11 publications

Latin hypercubes for constrained design of experiments for data-driven models

Latin hypercubes for constrained design of experiments for data-driven models

Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge

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