Assembly Process Modeling Through Long Short-Term Memory

Precup, Stefan-Alexandru; Gellért, Árpád; Dorobanțiu, Alexandru; Zamfirescu, Constantin-Bala

doi:10.1007/978-981-16-1685-3_3

Cited by 9 publications

(13 citation statements)

References 17 publications

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“…The utilization of such a mixed dataset in the evaluation process is a new evaluation approach in our research. The collected data was processed as in [ 16 ] to maintain the same approach and obtain results that can be compared between the algorithms. Following the testing procedures from [ 16 ], the results were measured on the dataset that was split into 75% for training and 25% for testing, by using the same metrics: the prediction rate, the coverage and the prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the stochastic methods [ 14 , 15 ] and even the LSTM [ 16 ] yield an inferior prediction rate compared to the prediction techniques based on decision trees explored in this paper, as the training data did not contain all the possible assembly sequences, thus making those algorithms unsuitable in scenarios with new data. In contrast, the methods based on decision trees have a prediction rate of 100% since for any input they are able to produce an outcome.…”

Section: Discussionmentioning

confidence: 99%

“…The collected data was processed as in [16] to maintain the same approach and obtain results that can be compared between the algorithms. Following the testing procedures from [16], the re sults were measured on the dataset that was split into 75% for training and 25% for test ing, by using the same metrics: the prediction rate, the coverage and the prediction ac curacy. The prediction rate is the percentage of predictions done out of all the assembly steps from the testing set.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Otherwise, in the case where the current Markov model cannot predict, the order is decremented until a prediction can be done or all the models were evaluated without success and, in that case, the PPM itself cannot predict. In [ 16 ], Precup et al predicted the next assembly step with an optimally configured LSTM neural network. Among all of the above mentioned assembly modelling techniques, the PPM [ 15 ] proved to be the most accurate.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 13 , 14 ], Markov predictors were investigated, whereas in [ 15 ] the Prediction by Partial Matching (PPM) algorithm was analyzed. In [ 16 ], a Long Short-Term Memory (LSTM) was involved to predict the next assembly steps. All these predictors will be further detailed in the next section.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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Collaborative exploitation of various AI methods in adaptive assembly assistance systems

Precup¹,

Matei²,

Walunj³

et al. 2023

Procedia Computer Science

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Anomaly Detection in Smart Industrial Machinery Through Hidden Markov Models and Autoencoders

Sorostinean,

Burghelea,

Gellert

2024

IEEE Access

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This study addresses the need to develop a sustainable manufacturing process in industrial factories, as the industry desires to remain competitive while it is challenged to adopt eco-friendly practices. A Machine Learning-based software is proposed to deal with the environmental issues, aiming to facilitate the monitoring and analysis of industrial machinery, more exactly of CNC woodworking machines. The focus is on two aspects that determine the environmental impact: energy consumption and toxic emissions, which are used to determine the operating modes of the machines and to detect potential working anomalies. This software consists of a pipeline with two main components: the first one aims to categorize the operating modes of the used machines through time series clustering methods, such as Hidden Markov Models. The second component employs Hidden Markov Models again alongside deep learning based Autoencoders, in order to identify huge deviations within the environmental data. For evaluation, a dataset was collected as a time series from a CNC woodworking machine and then the preprocessed data was further analyzed using the implemented software. The experiments have shown that for anomaly detection in machine operating modes, the Hidden Markov Model outperforms the Autoencoder and state-of-the-art models in terms of efficiency and robustness.

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Assembly Process Modeling Through Long Short-Term Memory

Cited by 9 publications

References 17 publications

Assembly Assistance System with Decision Trees and Ensemble Learning

Assembly Assistance System with Decision Trees and Ensemble Learning

Collaborative exploitation of various AI methods in adaptive assembly assistance systems

Anomaly Detection in Smart Industrial Machinery Through Hidden Markov Models and Autoencoders

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