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► Taguchi’s method provides a powerful and effective approach to optimize cutting conditions in turning. ► Experiments have been conducted using the L9 orthogonal array in a CNC ...turning machine. ► According to the ANOVA analysis, feed rate has an effect on Ra and Rz at a reliability level of 95.
This study focuses on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz). Experiments have been conducted using the L9 orthogonal array in a CNC turning machine. Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide cutting tools. Each experiment is repeated three times and each test uses a new cutting insert to ensure accurate readings of the surface roughness. The statistical methods of signal to noise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. Results of this study indicate that the feed rate has the most significant effect on Ra and Rz. In addition, the effects of two factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appear to be important. The developed model can be used in the metal machining industries in order to determine the optimum cutting parameters for minimum surface roughness.
► The surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut by full factorial experimental design. ► Artificial neural networks (ANN) and ...multiple regression approaches are used to model the surface roughness of AISI 1040 steel. ► The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model.
Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model.
In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized ...using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (
R
a
) and total (
R
t
) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding
R
a
and
R
t
values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.
This study includes comparison with experimental results of models and modelling with fuzzy logic of the effect on surface roughness of cutting parameters (rotational speed (n), feed rate (f), depth ...of cut (a) and tool tip radius (r)) in CNC turning of Co28Cr6Mo wrought steels. Fuzzy logic modelswere established that can determine the optimum rotational speed, feed rate, depth of cut and tool tip radius for surface roughness (Ra) according to the hardness of material and type of cutting tool. In the model created using fuzzy logic, membership functions and foot widths of input parameters and output parameter were utilized. In the rule base, triangular (trim-f) membership functions were selected by the Mamdani approach. The results obtained using this fuzzymodel and experimental results were interpreted and compared with 2dimensional graphics.
► Mathematic models are developed to determine multi-objective optimal cutting conditions. ► Surface roughness, as a function of cutting parameters, is obtained using the response surface methodology ...(RSM). ► The feed rate is the dominant factor affecting surface roughness in CNC turning of AISI 304 austenitic stainless steel.
This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.
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•Mathematic models are developed to determine multiobjective optimal cutting conditions.•Surface roughness, as a function, is obtained using the response surface methodology.•The tool ...tip radius is the dominant factor affecting surface roughness of medical material.
This study involves modelling of experimental data of surface roughness of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (spindle rotational speed, feed rate, depth of cut and tool tip radius). In order to determine critical states of the cutting parameters variance analysis (ANOVA) was applied while optimisation of the parameters affecting the surface roughness was achieved with the Response Surface Methodology (RSM) that is based on the Taguchi orthogonal test design. The validity of the developed models necessary for estimation of the surface roughness values (Ra, Rz), was approximately 92%. It was found that for Ra 38% of the most effective parameters is on the tool tip radius, followed by 33% on the feed rate whereas for Rz tool tip radius occupied 43% with the feed being at 33% rate. To achieve the minimum surface roughness, the optimum values obtained for spindle rpm, feed rate, depth of cut and tool tip radius were respectively, 318rpm, 0.1mm/rev, 0.7mm and 0.8mm.
This paper presents of the influence on vibration of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (rotational speed, feed rate, depth of cut and tool tip radius). The ...influences of cutting parameters have been presented in graphical form for understanding. To achieve the minimum vibration, the optimum values obtained for rpm, feed rate, depth of cut and tool tip radius were respectively, 318 rpm, 0.25 mm/rev, 0.9 mm and 0.8 mm. Maximum vibration has been revealed the values obtained for rpm, feed rate, depth of cut and tool tip radius were respectively, 636 rpm, 0.1 mm/rev, 0,5 mm and 0.8 mm.
In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropriate feeding force in order to perform an efficient cutting operation. This can be accomplished by ...controlling the feed rate and thrust force by accurately detecting the cutting resistance against the bandsaw blade during cutting operation. In this study, a neural-fuzzy-based force model for controlling band sawing process was established. Cutting parameters were continuously updated by a secondary neural network, to compensate the effect of environmental disturbances. Required feed rate and cutting speed were adjusted by developed fuzzy logic controller. Results of cutting experiments using several steel specimens show that the developed neural-fuzzy system performs well in real time in controlling cutting speed and feed rate during band sawing. A material identification system was developed by using the measured cutting forces. Materials were identified at the beginning of the cutting operation and cutting force model was updated by using the detected material type. Consequently, cutting speed and feed rate were adjusted by using the updated model. The new methodology is found to be easily integrable to existing production systems.
Industrial materials are materials used in the manufacture of products such as durable machines and equipment. For this reason, industrial materials have importance in many aspects of human life, ...including social, environmental, and technological elements, and require further attention during the production process. Optimization and modeling play an important role in achieving better results in machining operations, according to common knowledge. As a widely preferred material in the automotive sector, hardened AISI 4140 is a significant base material for shaft, gear, and bearing parts, thanks to its remarkable features such as hardness and toughness. However, such properties adversely affect the machining performance of this material system, due to vibrations inducing quick tool wear and poor surface quality during cutting operations. The main focus of this study is to determine the effect of parameter levels (three levels of cutting speed, feed, and cutting depth) on vibrations, surface roughness, and acoustic emissions during dry turning operation. A fuzzy inference system-based machine learning approach was utilized to predict the responses. According to the obtained findings, fuzzy logic predicts surface roughness (88%), vibration (86%), and acoustic emission (87%) values with high accuracy. The outcome of this study is expected to make a contribution to the literature showing the impact of turning conditions on the machining characteristics of industrially important materials.
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