Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Khorasani, AmirMahyar; Yazdi, Mohammad Reza Soleymani

doi:10.1007/s00170-015-7922-4

Cited by 92 publications

(50 citation statements)

References 32 publications

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“…[Saadallah 2018] trained an ensemble of deep learning (DL)-algorithms to predict the stability of a milling process. Furthermore, random forests with 10,000 trees were applied for a condition monitoring system in order to predict the tool wear [Khorasani 2015, Saadallah 2018Wu 2018].…”

Section: Ml-based Process Parameter Predictionmentioning

confidence: 99%

Adaptive Scheduling Through Machine Learning-Based Process Parameter Prediction

Frye¹,

Schmitt²,

Bergmann³

et al. 2019

MM SJ

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Detailed manufacturing process data and sensor signals are typically disregarded in production scheduling. However, they have strong relations since a longer processing time triggers a change in schedule. Although promising approaches already exist for mapping the influence of manufacturing processes on production scheduling, the variability of the production environment, including changing process conditions, technological parameters and the status of current orders, is usually ignored. For this reason, this paper presents a novel, data-driven approach that adaptively refines the production schedule by applying Machine Learning (ML)-models during the manufacturing process in order to predict the process-dependent parameters that influence the schedule. With the proper prediction of these parameters based on the process conditions, the production schedule is proactively adjusted to changing conditions not only to ensure the sufficient product quality but also to reduce the negative effects and losses that delayed rescheduling would cause. The proposed approach aims on minimizing the overall lateness by utilizing an active data exchange between the scheduling system and the predictive MLmodels on the process level. The efficiency of the solution is demonstrated by a realistic case study using discrete event simulation.

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Section: Ml-based Process Parameter Predictionmentioning

confidence: 99%

Adaptive Scheduling Through Machine Learning-Based Process Parameter Prediction

Frye¹,

Schmitt²,

Bergmann³

et al. 2019

MM SJ

View full text Add to dashboard Cite

show abstract

“…ANN is a logical software developed by imitating the working mechanism of the human brain, to perform basic functions such as brain learning the new information, recalling the learned information (Silva et al, 2017). ANN also provides estimates of intermediate values that cannot be performed in experiments and is frequently used in scientifi c work areas such as engineering (Khorasani and Yazdi, 2017;Gurgen et al, 2018), health sciences (Beauchet et al, 2018), etc. Recently, ANN has received considerable attention in the fi eld of wood products industry.…”

Section: Sažetak • U Istraživanju Je Modelirana Upojnost Vode I Debljmentioning

confidence: 99%

“…A total of 128 data obtained from experimental study were used to set an ANN model. The number of different hidden neurons (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and two different training algorithms (LM and SCG) were used to achieve the optimal network with the best performance. As a result, the optimal network was obtained in 16 hidden neurons and the LM algorithm.…”

Section: Zaključakmentioning

confidence: 99%

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

2019

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In this study, water absorption and thickness swelling values of medium density fiberboard (MDF) were modelled by artificial neural networks (ANN). MDF panels were produced with different rates of paraffin (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specific intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffin addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coefficients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

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“…Masood and Hassan [8] used feature-based ANNs to realize the pattern recognition for bivariate process mean shifts. Khorasani and Yazdi [9] developed a dynamic surface roughness monitoring system based on an ANN in a milling operation that uses cutting conditions as input and surface roughness as output. Armaghan and Renaud [10] proposed using knowledge acquisition as a basis for seeking solutions from non-compensatory multi-criteria decision aids.…”

Section: Related Workmentioning

confidence: 99%

Thinking process rules extraction for manufacturing process design

Zhou

Wang

et al. 2017

Adv. Manuf.

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To realize the reuse of process design knowledge and improve the efficiency and quality of process design, a method for extracting thinking process rules for process design is proposed. An instance representation model of the process planning reflecting the thinking process of technicians is established to achieve an effective representation of the process documents. The related process attributes are extracted from the model to form the related events. The manifold learning algorithm and clustering analysis are used to preprocess the process instance data. A rule extraction mechanism of process design is introduced, which is based on the related events after dimension reduction and clustering, and uses the association rule mining algorithm to realize the similar process information extraction in the same cluster. Through the vectorization description of the related events, the final process design rules are formed. Finally, an example is given to evaluate the method of process design rules extraction.

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Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Cited by 92 publications

References 32 publications

Adaptive Scheduling Through Machine Learning-Based Process Parameter Prediction

Adaptive Scheduling Through Machine Learning-Based Process Parameter Prediction

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

Thinking process rules extraction for manufacturing process design

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