Machine learning and data mining in manufacturing

Dogan, Alican; Birant, Derya

doi:10.1016/j.eswa.2020.114060

Cited by 351 publications

(105 citation statements)

References 118 publications

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“…On the other hand, the growing mass of data has made data mining methods centered on machine learning and deep learning more attention [8]. And it is used in manufacturing production scheduling [9], equipment monitoring [10], quality control [11], and other aspects.…”

Section: Related Workmentioning

confidence: 99%

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN-ELM

Wang

Chen

2021

Journal of Sensors

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With the continuous development of the manufacturing industry, the requirement for strip steel quality is becoming higher and higher in automobile manufacturing, mechanical processing, and electronic and electrical industries. The precise control of strip quality depends on the accurate prediction of strip quality to a certain extent. However, the data collected by a large number of sensors on the complex strip production line and generated by the computer control system presents the characteristics of high dimensionality, high coupling, and nonlinearity, which brings difficulties to the prediction of strip quality. The continuous production of massive data in the production line also forces steel enterprises to seek new data mining methods, mining the relationship between sensor data to predict and control strip quality. To solve these problems, this paper proposes a GBDBN-ELM model, which is more efficient and more accurate than other algorithms. In this model, the RBM in DBN is replaced with GBRBM, so that RBM no longer depends on the binary distribution, can handle continuity values, and retain more data features. In order to solve the problem of too long DBN training time, this article replaces the BP network in DBN with an ELM regression model. The ELM model predicts the strip quality based on the extracted data abstract features, thereby improving the model’s prediction accuracy and shortening the training time. In this paper, the GBDBN-ELM model is compared with the BP neural network, ELM, and DBN, and root mean square error, R square coefficient of determination, and training time are selected as evaluation indexes of the models. The experimental results show that the improved GBDBN-ELM model can not only improve the accuracy of strip steel quality prediction but also shorten the time of model training. The model proposed in this paper has achieved good results in prediction accuracy and performance.

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Section: Related Workmentioning

confidence: 99%

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN-ELM

Wang

Chen

2021

Journal of Sensors

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“…22 ML methods are now being applied to a variety of problems in materials science, including fields such as the development of battery materials and the testing of composite materials, 23,24 where enormous amounts of raw data are generated on a daily basis that can be used to train the model for effective decision-making. 25 The artificial neural network (ANN) is a computational ML method, inspired by the architecture of biological neural networks, 26 that has been applied to many problems related to composite materials [27][28][29][30] and other materials for defect detection. 31,32 ML methods are being applied to materials design and characterization fields.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Framework for Detection of Defects in 3D-Printed Fiber Reinforcement Composites

Chen

Yanamandra

Gupta

2021

JOM

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One of the major challenges in applying tomography methods for detecting defects in composite materials is the large image datasets generated during imaging, which require significant effort for the detection of damage. Machinelearning (ML) methods require a large training dataset and can be efficient in processing tomography datasets for defect detection. Methods need to be developed for processing images to train the ML algorithms, which is the focus of the present work. An additive manufactured fiber reinforced composite material is imaged using a micro-CT scan to generate an image set for defect detection. The microstructures are processed using the binarized statistical image features (BSIF) method for compression without compromising the desired information about defects. The result shows that the convolutional neural network model has a mean square error of 0.001 in fiber orientation prediction, and a scheme has been developed for defect detection based on the predictions obtained from the ML models.

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“…ML is an aid to computer by modeling past experiences for forecasting the future considering as a major topic in artificial intelligence (AI) [5]. It has been an incredible discover in identifying the connection among the information, processing big data size and able to achieve the same performance as machine operation [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

Zainuddin

Hasan

2021

Bulletin EEI

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Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

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Machine learning and data mining in manufacturing

Cited by 351 publications

References 118 publications

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN-ELM

Quality Prediction of Strip in Finishing Rolling Process Based on GBDBN-ELM

Artificial Neural Networks Framework for Detection of Defects in 3D-Printed Fiber Reinforcement Composites

Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

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