Optimization of Cutting Speed and Feed Rate on Surface Roughness and Vibration using Taguchi Method: A Review

Abstract

<span lang="EN-ID">The result of a turning process is strongly influenced by the process parameters that could result in the product to be unacceptable. The cutting parameters may be determined according to the material hardness and roughness of the workpiece surface. The purpose of this paper is to investigate the effects of cutting speed and feed rate on surface roughness and vibration. In Taguchi method, the number of experiments is reduced by orthogonal arrays while the effects of uncontrollable factors are also also reduced. The Taguchi method is used to reduce track, experimental time and production cost. Simple and precise are the most benefits of this method. Unstable vibrations in machining operations, known as chats, can cause damage to tools, workpieces, and machine tools. Cutting force is found to be the most dominant factor affecting surface roughness.</span>

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

“…Various methodologies, including computational neural networks, genetic algorithms, the Taguchi method, response surface methodology, can be applied in this research. These diverse approaches offer insights into the influence of individual factors and interactions between them on the functional objective [8]. The study conducted predictive modeling for optimizing micro-Friction Stir Spot Welding in tensile shear tests and other output, leveraging the capabilities of the RapidMiner data science platform.…”

Experimental results on the quality of the surface on turning with inclined tangential cutting line

“…The study takes into consideration four parameters, insert radius, depth of cut, feed and cutting speed, and for the design of the experiments, the Taguchi method with L 27 orthogonal matrix is used. Because disparate results are hard to be interpreted, several other research papers are identified referring to the optimization of cutting parameters for surface roughness on turning [15][16][17][18]. It is also useful to predict the roughness of the turned surface based on the values of cutting parameters and vibration signals measured during machining [19,20].…”