Machine learning-based metamodels for sawing simulation

Morin, Michael; Paradis, F.; Rolland, Amélie; Wery, Jean; Gaudreault, Jonathan; Laviolette, François

doi:10.1109/wsc.2015.7408329

Cited by 19 publications

(18 citation statements)

References 19 publications

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“…In the industry, a combination of lumber products resulting from a log processing is called the product basket. Previous works state the lumber production prediction problem in terms of a supervised learning problem [1,10]. That is, pairs of feature vectors and product baskets are given to a supervised learning algorithm, such as Random Forest, and the algorithm builds a model from the feature space to the basket space.…”

Section: Prediction Problem Formulationmentioning

confidence: 99%

Neural Network Architectures and Feature Extraction for Lumber Production Prediction

Martineau

Morin

Gaudreault

et al. 2021

Proceedings of the Canadian Conference on Artificial Intelligence

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We tackle the problem of predicting the lumber products resulting from the break down of the logs at a given sawmill. Although previous studies have shown that supervised learning is well suited for that prediction problem, to our knowledge, there exists only one approach using the 3D log scans as inputs and it is based on the iterative closest-point algorithm. In this paper, we evaluate the combination of neural network architectures (multilayer perceptron, residual network and PointNet) and log representation as input (industry know-how-based features, 2D projections, and 3D point clouds) in the context of lumber production prediction. Our study not only shows that it is possible to predict the output of a sawmill using neural networks, but also that there is value in combining industry know-how-based features and 3D point clouds in various network architectures.

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Section: Prediction Problem Formulationmentioning

confidence: 99%

Neural Network Architectures and Feature Extraction for Lumber Production Prediction

Martineau

Morin

Gaudreault

et al. 2021

Proceedings of the Canadian Conference on Artificial Intelligence

Self Cite

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“…For example, depending on the simulation setting and log scan, computing the resulting basket of products for one log can take from a few seconds to 3 hours and more using Optitek. Considering that fact, [10] proposed to approximate these simulators with AI metamodels. In particular, several Machine Learning (ML) classifiers are trained on results from past simulations.…”

Section: Previous Work On Sawmill Simulation Metamodelingmentioning

confidence: 99%

“…However, it might appear desirable for the cost of making a false prediction to vary depending on the true class label y and false predictionŷ. The predictionproduction score, s pre×pro , was specifically introduced in [10] for the problem of sawmill simulator metamodelling. Let y andŷ be once again the real and predicted baskets of products associated with a log x.…”

Section: Evaluation Scoresmentioning

confidence: 99%

Dissimilarity to Class Medoids as Features for 3D Point Cloud Classification

Chabanet

Chazelle

Thomas

et al. 2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Several sawmill simulators exist in the forest-product industry. They are able to simulate the sawing of a log to generate the set of lumbers that would be obtained by transforming a log at a sawmill.In particular, such simulators are able to use a 3D scan of the exterior shape of the logs as input for the simulation. However, it was observed that they can be computationally intensive. Therefore, several authors have proposed to use Artificial Intelligence metamodel, which, in general, can make predictions extremely fast once trained. Such models can approximate the results of a simulator using a vector of descriptive features representing a log, or, alternatively, the full 3D log scans. This paper proposes to use dissimilarity to representative log scans as features to train a Machine Learning classifier. The concept of class Medoids as representative elements of a class will be presented, and a Simlarity Discrimant Analysis was chosen as a good candidate ML classier. This classifier will be compared with two others models studied by the authors.

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“…neural network for developing metamodel to be used for tooth root stress analysis of microgear. Morin et al [35] used machine learning to generate metamodels for sawing simulation in wood industry. Altogether, metamodels transform the implicitly stochastic response of the simulation as an explicit deterministic functional form [24,27,29].…”

Section: Plos Onementioning

confidence: 99%

Simulation metamodeling approach to complex design of garment assembly lines

et al. 2020

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The today's competitive advantage of ready-made garment industry depends on the ability to improve the efficiency and effectiveness of resource utilization. Ready-made garment industry has long historically adopted fewer technological and process advancement as compared to automotive, electronics and semiconductor industries. Simulation modeling of garment assembly line has attracted a number of researchers as one way for insightful analysis of the system behaviour and improving its performance. However, most of simulation studies have considered ill-defined experimental design which cannot fully explore the assembly line design alternatives and does not uncover the interaction effects of the input variables. Simulation metamodeling is an approach to assembly line design which has recently been of interest to researchers. However, its application in garment assembly line design has never been well explored. In this paper, simulation metamodeling of trouser assembly line with 72 operations was demonstrated. The linear regression metamodel technique with resolution-V design was used. The effects of five factors: bundle size, job release policy, task assignment pattern, machine number and helper number on throughput of the trouser assembly line were studied. An increase of the production throughput by 28.63% was achieved for the best factors' setting of the metamodel.

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Machine learning-based metamodels for sawing simulation

Cited by 19 publications

References 19 publications

Neural Network Architectures and Feature Extraction for Lumber Production Prediction

Neural Network Architectures and Feature Extraction for Lumber Production Prediction

Dissimilarity to Class Medoids as Features for 3D Point Cloud Classification

Simulation metamodeling approach to complex design of garment assembly lines

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