Experimental Investigation of Surface Roughness of Cutting Parameters in T6 Aluminum Alloy Milling Process

Özsoy, Neslihan

doi:10.22399/ijcesen.590692

Cited by 5 publications

(1 citation statement)

References 16 publications

(20 reference statements)

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“…It determines the optimal conditions, in order to obtain a minimum of the cost function. The Taguchi optimization method is often used to obtain low surface roughness in various cutting operations [23][24][25][26][27].…”

Section: Taguchi Methods For Optimization Of Cutting Parametersmentioning

confidence: 99%

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

et al. 2022

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Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training.

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Section: Taguchi Methods For Optimization Of Cutting Parametersmentioning

confidence: 99%

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

et al. 2022

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Novel methods for optimizing CNC aluminum alloy machining parameters in polymer mold cavities

Ikhries,

Al-Shawabkeh

2024

International Journal of Lightweight Materials and Manufacture

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Surface roughness and material removal rate analyses of hard copper alloy in wire electrical discharge machining

EvranSavas

2020

Emerging Materials Research

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This experimental and statistical study focuses on investigation of the effects of machining parameters on surface roughness (SR) and material removal rate (MRR) in wire electrical discharge machining (WEDM) of hard copper (Cu) alloy. Experimental design was carried out using the L18 (21 × 32) orthogonal array according to the Taguchi method. The process parameters considered were wire type, pulse time and duration between two pulses. Copper-coated wire and zinc (Zn)-coated wires were used as cutting tools. Analysis of the signal-to-noise (S/N) ratio was utilized to investigate the effects of process parameters on the responses. Analysis of variance was employed to determine the percent contributions and significance levels of process parameters on responses. Analysis of the surface integrity and characteristics of the machined specimens was observed using scanning electron microscopy, and energy-dispersive X-ray spectroscopy analysis was employed. This study may be used as a reference to investigate SR and MRR in WEDM of hard copper alloy.

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Experimental Investigation of Surface Roughness of Cutting Parameters in T6 Aluminum Alloy Milling Process

Cited by 5 publications

References 16 publications

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Novel methods for optimizing CNC aluminum alloy machining parameters in polymer mold cavities

Surface roughness and material removal rate analyses of hard copper alloy in wire electrical discharge machining

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