Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks

The paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions 30 × 350 mm, and the sample material was GJS 500 - 7. Six cutting inserts were used for the designed experiment as well as variations of cutting speed, feed and depth of cut on CNC lathe DMG Moriseiki-CTX 310 Ecoline. After the conducted experiment, surface roughness of each sample was measured and a data set of 750 instances was formed. For data analysis, the Back-Propagation Neural Network (BPNN) algorithm was used. In modelling different BPNN architectures with characteristic features the results of RMS (Root Mean Square) error were controlled. Specially analysed were the RMS errors realised by different number of neurons in hidden layers. For the BPNN architecture with one hidden layer the architecture (4 – 8 - 1) was adopted with RMS error of 3,37%. In modelling the BPNN architecture with two hidden layers, a considerable amount of architectures was investigated. The adopted architecture with two hidden layers (4 - 2 - 10 - 1) generated the RMS error of 2,26%. The investigation was also directed at the size of the data set and controlling the level of RMS error

MATHEMATICAL MODELING OF THE INFLUENCE PARAMETERS DURING FORMATION AND PROPAGATION OF THE LÜDERS BANDS

In this study, an analysis of the influence parameters measured by the static tensile test, thermography and digital image correlation was performed during formation and propagation of the Lüders bands. A new approach to the prediction of stresses, maximum temperature changes and strains during the Lüders band formation and propagation is proposed in this paper. Application of the obtained mathematical models of influence parameters gives a clear insight into the behavior of niobium microalloyed steel at the beginning of the plastic flow, which can improve product quality and reduce costs during the forming of microalloyed steels with the appearance of the Lüders bands. The obtained models of influential parameters during formation and propagation of the Lüders bands have been developed by the regression analysis method. The proposed mathematical models showed low deviations of calculated results ranging from 1.34% to 12.37%.The local stress amounts, important in the forming of microalloyed steels since indicating surface roughness and plastic flow possibilities during the Lüders band propagation, are obtained by the mathematical model. It was found that stress amounts increase during the Lüders band propagation in the area behind the Lüders band front. The difference in stress amount between the start of the Lüders band propagation and advanced Lüders band propagation is 25.53 MPa

A neural network approach for chatter prediction in turning

[EN] Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit self-excited vibration. In this paper, an artificial neural network (ANN) is applied to model turning stability. The analytical stability limit is used to generate a data set that trains the ANN. It is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.The authors gratefully acknowledge financial support from the UNC ROI program. Elena Perez-Bernabeu and Miguel Selles also acknowledge support from Universitat Politenica de Valencia (PAID-00-17). Additionally, some of the neural net figures and the 10-fold cross validation figures are based on the TikZ codes provided on StackExchange-TeX by various users. Harish Cherukuri would like to thank them for their valuable advice.Cherukuri, H.; Pérez Bernabeu, E.; Sellés, M.; Schmitz, TL. (2019). A neural network approach for chatter prediction in turning. Procedia Manufacturing. 34:885-892. https://doi.org/10.1016/j.promfg.2019.06.1598858923

Emissivity prediction of functionalized surfaces using artificial intelligence

Tuning surface emissivity has been of great interest in thermal radiation applications, such as thermophotovoltaics and passive radiative cooling. As a low-cost and scalable technique for manufacturing surfaces with desired emissivities, femtosecond laser surface processing (FLSP) has recently drawn enormous attention. Despite the versatility offered by FLSP, there is a knowledge gap in accurately predicting the outcome emissivity prior to fabrication. In this work, we demonstrate the immense advantage of employing artificial intelligence (AI) techniques to predict the emissivity of complex surfaces. For this aim, we used FLSP to fabricate 116 different aluminum samples. A comprehensive dataset was established by collecting surface characteristics, laser operating parameters, and the measured emissivities for all samples. We demonstrate the successful application of AI in two distinct scenarios: (1) effective emissivity classification solely based on 3D surface morphology images, and (2) emissivity prediction based on surface characteristics and FLSP parameters. These findings open new pathways towards extended implementation of AI to predict various surface properties in functionalized samples or extract the required fabrication parameters via reverse engineering

Predictive models based on RSM and ANN for roughness and wettability achieved by laser texturing of S275 carbon steel alloy

Laser texturing is increasingly gaining attention in the field of metal alloys due to its ability to improve surface properties, particularly in steel alloys. However, the input parameters of the technology must be carefully controlled to achieve the desired surface roughness. Roughness is critical to the activation of the surface before further bonding operations, and it is often assessed using several parameters such as Ra, Rt, Rz, and Rv. This surface activation affects the properties of the metal alloy in terms of wettability, which has been evaluated by the deposition of ethylene glycol droplets through a contact angle. This allowed a direct relationship to be established between the final roughness, the wettability of the surface and the texturing parameters of the alloy. This raises the interest of being able to predict the behaviour in terms of roughness and wettability for future applications in improving the behaviour of metallic alloys. In this research, a comparative analysis between Response Surface Models (RSM) and predictive models based on Artificial Neural Networks (ANN) has been conducted. The model based on neural networks was able to predict all the output variables with a fit greater than 90%., improving that obtained by RSM. The model obtained by ANN allows a greater adaptability to the variation of results obtained, reaching deviations close to 0.2 μm. The influence of input parameters, in particular power and scanning speed, on the achieved roughness and surface wettability has been figured out by contact angle measurements. This increases its surface activation in terms of wettability. Superhydrophilic surfaces were achieved by setting the power to 20 W and scanning speed to ten mm/s. In contrast, a power of 5 W and a scanning speed of 100 mm/s reduced the roughness values.Funding for open access charge: Universidad de Málaga / CBU

Artificial Intelligence and Industry 4.0

Cílem této práce je poskytnout přehled aplikací metod umělé inteligence v kontextu průmyslu 4.0. První kapitola je věnována definici konceptu průmyslu 4.0, předchozímu vývoji průmyslu a zařazení vědního oboru umělé inteligence do tohoto konceptu. Druhá kapitola je zaměřena na rešerši aplikací metod umělé inteligence v oblasti obrábění, výrobního průmyslu, automatizace a energetiky. Závěr práce je věnován zhodnocení metod, jejich výhod a úskalí z pohledu jednotlivých praktických aplikací a zmiňuje možné směry budoucího vývoje.The aim of this work is to provide an overview of the application of artificial intelligence methods in the context of Industry 4.0. The first chapter defines the concept of industry 4.0, previous development of the industry and inclusion of the scientific field of artificial intelligence in this concept. The second chapter is focused on the applications of artificial intelligence methods in the field of machining, manufacturing industry, automation and energetics. The work concludes with evaluation of methods, their advantages and disadvantages from the point of view of individual practical applications and mentions possible directions of future development.

Estimativa de tempos de maquinagem com base em redes neuronais

Um dos fatores mais importantes na produção de moldes para injeção de plástico é a estimativa do custo dos serviços de maquinagem, que representam uma parte significativa do preço final do molde. O custo destes serviços é habitualmente determinado em função do tempo de maquinagem, cujo cálculo é geralmente longo e dispendioso. Se for considerado que as peças dos moldes de injeção são todas diferentes, compreende-se que a correta e célere estimativa de tempos de maquinagem é de grande importância para o sucesso de uma empresa. Esta dissertação apresenta uma proposta de aplicação de redes neuronais artificiais na estimativa de tempos de maquinagem de peças standard de moldes de injeção de plástico. Para o efeito, foram simuladas peças e calculados os tempos de maquinagem para recolher dados suficientes para o treino das redes neuronais. Foi estudada a influência da arquitetura de rede, da quantidade de dados de entrada e das variáveis utilizadas no treino da rede, de forma a encontrar a rede neuronal com maior precisão. A aplicação de redes neuronais neste trabalho revelou-se uma forma célere e eficaz de calcular os tempos de corte, podendo dar um forte contributo a empresas do setor.One of the most important factors in the production of plastic injection molds is the cost estimation of machining services which represents a significant part of the final mold price. The cost of these services is commonly determined as a function of the machining time, which is usually long and expensive to calculate. If it is considered that the injection mold parts are all different, it is understood that the correct and quick estimation of machining times is of great importance for the success of a company. This dissertation presents a proposal for the application of artificial neural networks in machining time estimation for standard injection molds parts. For this purpose, parts were simulated and machining times were calculated to collect enough data for training the neural networks. The influence of the network architecture, the amount of input data and the variables used in the training of the network were studied in order to find the neural network with greater precision. The application of neural networks in this work proved to be a quick and efficient way to calculate cutting times, which can give a strong contribution to companies in the sector