MLP and ANFIS Applied to the Prediction of Hole Diameters in the Drilling Process

Gerônimo, Thiago Matheus; Cruz, Carlos E. D.; Campos, F. de; Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos

doi:10.5772/51629

Cited by 2 publications

(4 citation statements)

References 16 publications

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Section: Artificial Intelligence-based Modeling For Ti6al4v Machining...mentioning

confidence: 99%

“…Eduardo et al and Geronimo et al used ANN, namely MLP and MLP and ANFIS to modeling a Ti6Al4V drilling operation. 98,99 The signals from several sensors, including acoustic emission sensors, three-dimensional dynamometer, and Hall effect sensor were collected during a drilling operation. The in-process data along with the hole diameter and the surface roughness were used to develop and train the networks.…”

Section: Fuzzy Logic Theory To Predict Ti6al4v Machining Responsementioning

confidence: 99%

“…The authors also stated that the methodology could be viable for control the cutting parameters in the drilling process accordingly with the output requirement, but also, that the implementation of such systems in the industry does not present much investment or changes in the equipment to be monitored. 99 Artificial intelligence models for Ti6Al4V machining: strategies, goals & challenges From Table 6 it was possible to understand that authors explore AI-based models to study aspects regarding the machined surface integrity, such as surface roughness and geometrical accuracy. While others, create models to predict aspects regarding the tool wear and life.…”

Section: Fuzzy Logic Theory To Predict Ti6al4v Machining Responsementioning

confidence: 99%

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Intelligent machining methods for Ti6Al4V: A review

Carvalho

Pereira

Horovistiz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision-making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence-based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.

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Section: Artificial Intelligence-based Modeling For Ti6al4v Machining...mentioning

confidence: 99%

Section: Fuzzy Logic Theory To Predict Ti6al4v Machining Responsementioning

confidence: 99%

Section: Fuzzy Logic Theory To Predict Ti6al4v Machining Responsementioning

confidence: 99%

See 1 more Smart Citation

Intelligent machining methods for Ti6Al4V: A review

Carvalho

Pereira

Horovistiz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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show abstract

“…As seen from Figure 2, RFB networks have only three layers, which makes them superior to the MLP for easy design [29]. More details about the structure and specifications of MLP and RBF can be found in [27][28][29][30][31][32][33].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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MLP and ANFIS Applied to the Prediction of Hole Diameters in the Drilling Process

Cited by 2 publications

References 16 publications

Intelligent machining methods for Ti6Al4V: A review

Intelligent machining methods for Ti6Al4V: A review

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

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