Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms

Ti–6Al–4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti–6Al–4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost- and time-effective way.

Section: Literature Reviewmentioning

confidence: 99%

Cutting force prediction in ultrasonic-assisted milling of Ti–6Al–4V with different machining conditions using artificial neural network

Namlu

Turhan

Sadigh

et al. 2020

“…PSO (Kennedy & Eberhart, 1995) is a metaheuristic search algorithm, mimicking the movement of organisms in a bird flock or fish school. Due to its simple concept and fast convergence, PSO has attracted much attention and wide applications in various fields, including systems identification problems (e.g., Modares et al, 2010; Tang et al, 2010; Majhi & Panda, 2011; Cornoiu et al, 2013; Hu et al, 2014; Zeng et al, 2014; Kramar et al, 2015). PSO combines self and social experience for directing search to an optimal solution.…”

Section: Stage 1: Obtaining the Number Of Submodels And The Initial Pmentioning

confidence: 99%

A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems

Adeniran

El-Férik

2016

Several industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

“…Several researchers have been devoted to the application of machine learning methods in the field of machining. Examples could be the low-carbon machining process planning (Chen et al, 2022), prediction of surface roughness in high-pressure jet-assisted turning (Kramar et al, 2016), modeling of charge geometry and parameters on the depth of penetration in explosive cutting (Nariman–Zadeh et al, 2003), optimization of machining process parameters (Famili, 1994; Pourmostaghimi et al, 2020), development of support systems for the proper selection of machine tools and machining process parameters (Rojek, 2017), selection of the proper cutting fluids based on the machining process such as milling, grinding, honing, and lapping (Mogush et al, 1988), prediction of the micro-end mill and micro-drills failure (Sevil and Ozdemir, 2011), and development of processing resource allocations for smart workshops in cloud manufacturing and its optimization (Hui et al, 2021). However, based on the authors’ knowledge, the use of machine-learning to predict the onset of shear localization has not been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the onset of shear localization based on machine learning

Akar

Ayli

Ulucak

et al. 2023

Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors’ knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.