Industrial materials are used in the manufacture of products such as durable machines and equipment. For this reason, industrial have importance many aspects human life, including social, environmental, technological elements, require further attention during production process. Optimization modeling play an important role achieving better results machining operations, according to common knowledge. As a widely preferred material automotive sector, hardened AISI 4140 is significant base for...

This work aims to develop an adaptive network-based fuzzy inference system (ANFIS) for surface roughness and vibration prediction in cylindrical grinding. The uses a piezoelectric accelerometer generate signal related grinding features roughness. To accomplish such goal, experimental study was carried out consisted of 27 runs machine operating with aluminium oxide wheel AISI 8620 steel workpiece. workpiece speed, feed rate depth cut were used as input ANFIS, which turn outputs (Ra) (az )....

In the present study, prediction of cutting forces and surface roughness was carried out using neural networks support vector regression (SVR) with six inputs, namely, three axis vibrations tool holder speed, feedrate depth cut. The data obtained by experimentation are used to construct predictive models. A feedforward backpropagation network SVR have been selected for modelling. coefficient determination ( R 2 ), mean absolute error root square were calculated each method, these values...

Grinding is a widely used manufacturing method in state of art industry. By realizing needs manufacturers, grinding parameters must be carefully selected order to maintain an optimum point for sustainable process. Surface roughness generally accepted as important indicator parameters. In this study, effects surface were experimentally and statistically investigated. A complete factorial experimental flow was designed three level variable. 62 HRC AISI 8620 cementation steel process with...

This paper presents of the influence on vibration Co28Cr6Mo medical alloy machined a CNC lathe based cutting parameters (rotational speed, feed rate, depth cut and tool tip radius). The influences have been presented in graphical form for understanding. To achieve minimum vibration, optimum values obtained rpm, radius were respectively, 318 0.25 mm/rev, 0.9 mm 0.8 mm. Maximum has revealed 636 0.1 0,5

This study presents a new method for modeling an adaptive neuro-fuzzy inference system (ANFIS) based on vibration predicting surface roughness in the CNC turning process. The input parameters of model are insert nose radius, cutting speed, feed rate, depth cut and amplitude, which determine output parameter roughness. A Gauss type membership function was used to train ANFIS. predicted values derived from ANFIS were compared with experimental data. obtained prediction accuracy 97.52%...

Surface roughness measurement is one of the basic that determines quality and performance final product. After machined operations, tracer end tools are commonly used in industry order to measure surface occurred on surface. This technique has disadvantages such as user errors because it requires calibration device occurring during measurement. In this study, measuring evaluation techniques were conducted by using display devices over image which processed surfaces. performed getting makes...

In this paper, we propose that a neuro-fuzzy based adaptive controller for control of band sawing process. The system composed two different kinds back propagation networks and fuzzy logic controller. first network accomplishes the reference forces are compared with measured cutting force values in real time. required feed rate speed adjusted by proposed parameters continuously updated secondary neural model to compensate disturbances. provides possibility identification material be cut....

The aim of this study is to provide insights into the performance copper-based brake pads used in high-speed trains and contribute a more predictable braking system by leveraging mathematical artificial intelligence (AI) models. wear behavior Cu-based was investigated using pin-on-disc test setup under different speeds, temperatures, loads with constant sliding distance. Additionally, AI models were developed predict friction coefficient rate values obtained from experiments. This innovative...

In this work, a technique is proposed to predict surface roughness by using neural network. Surface could be predicted within reasonable degree of accuracy taking feed rate, cutting speed, depth cut and three orthogonal axis (x, y, z) signals vibrations tool holder as input parameters. 27 experiments were performed CNC lathe with carbide tool. Experimental data obtained from turning process used for training testing network architecture based prediction system. When experimental results...

This study includes comparison with experimental results of models and modelling fuzzy logic the effect on surface roughness cutting parameters (rotational speed (n), feed rate (f), depth cut (a) tool tip radius (r)) in CNC turning Co28Cr6Mo wrought steels. Fuzzy modelswere established that can determine optimum rotational speed, rate, for (Ra) according to hardness material type tool. In model created using logic, membership functions foot widths input output parameter were utilized. rule...

This study includes fuzzy logic modeling of surface roughness experimental values obtained as a result machining Co28Cr6Mo medical alloy in CNC turning (rotational speed (n), feed rate (f), depth cut (a) and tool tip radius (r)) depending on cutting parameters. According to the hardness material be type used, solution models that can determine most suitable for (Ra) were created. In model created using logic, studies rule base by membership functions input parameters output parameters,...