Surface roughness modeling of CBN hard steel turning

Study in the paper investigate the influence of the cutting conditions parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting tool insert. For the modeling of surface roughness parameters was used central compositional design of experiment and artificial neural network as well. The values of surface roughness parameters Average mean arithmetic surface roughness (Ra) and Maximal surface roughness (Rmax) were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness parameters of hard steel and determined significantly influential cutting conditions parameters

Neural network modelling of Abbott-Firestone roughness parameters in honing processes

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.Peer ReviewedPostprint (author's final draft

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Using Multiple Linear Regression and Artificial Neural Network to Predict Surface Roughness in Turning Operations

Quality of surface roughness has a great impact on machine parts during their useful life. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this paper, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed rate, and depth of cut. Two mathematical models are developed to predict the surface roughness and to select the required surface roughness by using the Multi-regression model and Artificial Neural Networks (ANN). To test the developed models, 27 pieces of steel alloy HRC15 were operated and the roughness of their surfaces measured. The results showed that the ANN model estimates the surface roughness with high accuracy compared to the multiple regression model with the average deviation from the real values of about 1%

Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

AbstractThis paper develops a predictive and optimization model by coupling the two artificial intelligence approaches – artificial neural network and genetic algorithm – as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters

Intelligent machining methods for Ti6Al4V: a review

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.publishe

Pokročilé modelování povrchové drsnosti pomocí neuronových sítí, Taguchiho metody a genetického algoritmu

Modern manufacturing requires reliable and accurate models for the prediction of machining performance. Predicting surface roughness before actual machining plays a very important role in machining practice. This paper presents the modeling methodology for predicting the surface roughness in turning of unreinforced polyamide based on artificial neural networks (ANNs), Taguchi method and genetic algorithm (GA). The machining experiment was conducted based on Taguchi’s experimental design using L27 orthogonal array. Input variables consisted of cutting speed, feed rate, depth of cut and tool nose radius, while surface roughness (Ra) was considered as output variable. To systematically identify optimum settings of ANN design and training parameters, Taguchi method was applied. Furthermore, a simple procedure based on GA for enhancing the ANN model prediction accuracy was applied. Statistically assessed as an accurate model, ANN model equation was graphically presented in the form of contour plots to study the effect of the cutting parameters on the surface roughness.Moderní výroba vyžaduje spolehlivé a přesné modely pro predikci výkonu zpracování. Předvídání drsnost povrchu před vlastním zpracováním hraje velmi důležitou roli v obráběcí praxi. Tato práce představuje modelovou metodiku pro odhad drsnosti povrchu při soustružení z prostého polyamidu na bázi umělé inteligence neuronových sítí (ANNs), Taguchiho metodě a genetických algoritmů (GA). Obráběcí experiment byl proveden na základě experimentálního Taguchiho návrhu pomocí L27 ortogonální pole. Vstupními proměnnými jsou řezná rychlost, posuv, hloubka řezu a poloměru břitu, zatímco drsnost povrchu (Ra) je považována za výstupní proměnnou. Pro systematickou identifikaci optimálního nastavení ANN návrhu a odborné přípravy parametrů byla použita metoda Taguchi. Dále byl použit jednoduchý postup, založený na GA pro zvýšení přesnosti modelu ANN predikce. Statisticky vyhodnocený přesný model ANN rovnice byl graficky prezentován ve formě obrysů pro studium vlivu řezných parametrů na drsnost povrchu