Prediction of Tool Wear Using Artificial Neural Networks during Turning of Hardened Steel

Twardowski, Paweł; Wiciak-Pikuła, Martyna

doi:10.3390/ma12193091

Cited by 57 publications

(31 citation statements)

References 14 publications

(14 reference statements)

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“…The literature survey and our experience obtained during the creation of neural models allowed to identify several characteristic stages occurring during this process [20,25,37,[39][40][41][42]44,45,52]. The creation of an ANN model proceeds in five stages:…”

Section: Methodsmentioning

confidence: 99%

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The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

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It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

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Section: Methodsmentioning

confidence: 99%

“…The validation using neural networks gave better results than the one using regression models. The developed forecasting system was able to accurately predict surface wear and roughness [52]. In the fault prognosis context, ANNs are important tools as they enable the implementation of the prediction task easily and accurately [37].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

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“…It was found that teaching-learning-based optimization (TLBO) was found to be superior to the bacteria foraging optimization (BFO) in terms of better convergence and a shorter time of computation-hence, the TLBO is recommended during the optimization of hard turning processes. The hardened steel optimization problems were also investigated by Twardowski and Wiciak-Pikuła [11]. They predicted the tool wear during turning of hardened 100Cr6 steel with the application of multilayer perceptron (MLP)-based artificial neural networks.…”

Section: Ofmentioning

confidence: 99%

Advances in Hard–to–Cut Materials: Manufacturing, Properties, Process Mechanics and Evaluation of Surface Integrity

2020

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The rapid growth of a modern industry results in a growing demand for construction materials with excellent operational properties. However, the improved features of these materials can significantly hinder their manufacturing, therefore they can be defined as hard–to–cut. The main difficulties during the manufacturing/processing of hard–to–cut materials are attributed to their high hardness and abrasion resistance, high strength at room or elevated temperatures, increased thermal conductivity, as well as their resistance to oxidation and corrosion. Nowadays the group of hard–to–cut materials includes the metallic materials, composites, as well as ceramics. This special issue, “Advances in Hard–to–Cut Materials: Manufacturing, Properties, Process Mechanics and Evaluation of Surface Integrity” provides a collection of research papers regarding the various problems correlated with hard–to–cut materials. The analysis of these studies reveals primary directions regarding the developments in manufacturing methods, and the characterization and optimization of hard–to–cut materials.

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“…They are based on calibration procedures. This involves process parameters measurement that correlate with wear (cutting force, vibration, sound, acoustic emission, temperature, energy consumption, or surface roughness) [16]. They are easy-to-apply methods, but their reliability is limited regarding direct methods [17].…”

Section: Introductionmentioning

confidence: 99%

Simple Discriminatory Methodology for Wear Analysis of Cutting Tools: Impact on Work Piece Surface Morphology in Case of Differently Milled Kinetics Steel H13

et al. 2020

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Recently, there is growing interest in optimisation of finishing process thanks to the technologies to follow online the wear of cutting tools. In the present paper, one of the cheapest and simplest non-contact methodologies is described in detail and investigated with robustness evaluation. To simulate the finishing operation of a die, in this study, two cavities were designed in AISI H13 steel. Different inserts corresponding to PVD-(Ti,Al)N coated cemented carbide tool were tested. The described methodology is easy to be applied in manufacturing cutting machining with the opportunity to be implemented on machining processes to follow reasonably wear process of cutting tools.

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Prediction of Tool Wear Using Artificial Neural Networks during Turning of Hardened Steel

Cited by 57 publications

References 14 publications

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Advances in Hard–to–Cut Materials: Manufacturing, Properties, Process Mechanics and Evaluation of Surface Integrity

Simple Discriminatory Methodology for Wear Analysis of Cutting Tools: Impact on Work Piece Surface Morphology in Case of Differently Milled Kinetics Steel H13

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