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Journal ArticleDOI

Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

TL;DR: In this article, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling using the Taguchi design of experiments (DoE) method.
Abstract: In this paper, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling The data used for the training and checking of the networks’ performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force Using feedforward artificial neural networks (ANNs) trained with the Levenberg–Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5×3×1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 186% and to be consistent throughout the entire range of values
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors present the various methodologies and practices that are being employed for the prediction of surface roughness, including machining theory, experimental investigation, designed experiments and artificial intelligence (AI).
Abstract: The general manufacturing problem can be described as the achievement of a predefined product quality with given equipment, cost and time constraints. Unfortunately, for some quality characteristics of a product such as surface roughness it is hard to ensure that these requirements will be met. This paper aims at presenting the various methodologies and practices that are being employed for the prediction of surface roughness. The resulting benefits allow for the manufacturing process to become more productive and competitive and at the same time to reduce any re-processing of the machined workpiece so as to satisfy the technical specifications. Each approach with its advantages and disadvantages is outlined and the present and future trends are discussed. The approaches are classified into those based on machining theory, experimental investigation, designed experiments and artificial intelligence (AI).

903 citations


Cites methods from "Prediction of surface roughness in ..."

  • ...ANN modeling was also used along with designed experiments by Benardos and Vosniakos [47] in face milling....

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  • ...E-mail address: vosniak@central.ntua.gr (G.-C. Vosniakos)....

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Journal ArticleDOI
TL;DR: In this paper, the authors applied the Taguchi optimization methodology to optimize cutting parameters in end milling when machining hardened steel AISI H13 with TiN coated P10 carbide insert tool under semi-finishing and finishing conditions of high speed cutting.

746 citations

Journal ArticleDOI
TL;DR: The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks, and can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Abstract: Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.

663 citations


Cites methods from "Prediction of surface roughness in ..."

  • ...These approaches can be generally categorized as follows: (i) Empirical or statistical methods that are used to study the effect of internal parameters and choose appropriate values for them based on the performance of model (Benardos & Vosniakos, 2002; Ma & Khorasani, 2003)....

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Journal ArticleDOI
TL;DR: This tutorial review offers protocols, tips, insight, and considerations for practitioners interested in using micromilling to create microfluidic devices to provide a potential user with information to guide them on whethermicromilling would fill a specific need within their overall fabrication strategy.
Abstract: This tutorial review offers protocols, tips, insight, and considerations for practitioners interested in using micromilling to create microfluidic devices. The objective is to provide a potential user with information to guide them on whether micromilling would fill a specific need within their overall fabrication strategy. Comparisons are made between micromilling and other common fabrication methods for plastics in terms of technical capabilities and cost. The main discussion focuses on “how-to” aspects of micromilling, to enable a user to select proper equipment and tools, and obtain usable microfluidic parts with minimal start-up time and effort. The supplementary information provides more extensive discussion on CNC mill setup, alignment, and programming. We aim to reach an audience with minimal prior experience in milling, but with strong interests in fabrication of microfluidic devices.

409 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a generic view of machining monitoring systems and facilitate their implementation, and present six key issues involved in the development of intelligent machining systems: (1) the different sensor systems applied to monitor machining processes, (2) the most effective signal processing techniques, (3) most frequent sensory features applied in modelling machining process, (4) the sensory feature selection and extraction methods for using relevant sensory information, (5) the design of experiments required to model a machining operation with the minimum amount of experimental data and (6) the
Abstract: Many machining monitoring systems based on artificial intelligence (AI) process models have been successfully developed in the past for optimising, predicting or controlling machining processes. In general, these monitoring systems present important differences among them, and there are no clear guidelines for their implementation. In order to present a generic view of machining monitoring systems and facilitate their implementation, this paper reviews six key issues involved in the development of intelligent machining systems: (1) the different sensor systems applied to monitor machining processes, (2) the most effective signal processing techniques, (3) the most frequent sensory features applied in modelling machining processes, (4) the sensory feature selection and extraction methods for using relevant sensory information, (5) the design of experiments required to model a machining operation with the minimum amount of experimental data and (6) the main characteristics of several artificial intelligence techniques to facilitate their application/selection.

343 citations


Cites background or methods from "Prediction of surface roughness in ..."

  • ...The table shows research works based on full factorial designs [29, 54, 62, 68, 69], Taguchi’s orthogonal arrays [ 8 , 70] and response surface designs [71]....

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  • ...Research issues in monitoring machining systems based on artificial intelligence (AI) process models cover several topics, such as sensor system selection [1, 2], multi-sensor and sensor-fusion systems [1, 3, 4], signal processing and sensory feature selection/extraction [5, 6], design of experiments [7, 8 ] and AI techniques to model the process [1, 9]. In spite of the intensive research being carried out in this field, there is still no ......

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  • ...Hence, cutting-force monitoring is frequently used to diagnose/predict both tool condition [5, 16–18] and part accuracy [7, 8 , 19, 20]....

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  • ...Tool breakage detection [18] Surface roughness prediction [7, 8 , 19] Dimensional part accuracy prediction [19, 20]...

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  • ...Benardos [ 8 ] also reported similar conclusions, and it was confirmed experimentally that the X components of cutting forces were the most significant descriptors for surface roughness modelling....

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References
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Book
01 Aug 1995
TL;DR: Taguchi as discussed by the authors presented Taguchi Techniques for Quality Engineering (TQE), a technique for quality engineering in the field of high-level geometry. Technometrics: Vol. 31, No. 2, pp. 253-255.
Abstract: (1989). Taguchi Techniques for Quality Engineering. Technometrics: Vol. 31, No. 2, pp. 253-255.

2,685 citations

Journal ArticleDOI
TL;DR: In this paper, an in-process surface recognition system was developed to predict the surface roughness of machined parts in the end milling process to assure product quality and increase production rate by predicting the surface finish parameters in real time.
Abstract: An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness Ra values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness Ra values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.

195 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the feasibility of an intelligent sensor fusion technique to estimate on-line surface finish (Ra) and dimensional deviations (DD) during machining and presented a systematic method for sensor selection and fusion using neural networks.
Abstract: This paper examines the feasibility for an intelligent sensor fusion technique to estimate on-line surface finish (Ra) and dimensional deviations (DD) during machining. It first presents a systematic method for sensor selection and fusion using neural networks. Specifically, the turning of free-machining and low carbon steel is considered. The relationships of the readily sensed variables in machining to Ra and DD, and their sensitivity to process conditions are established. Based on this experimental data and using statistical tools, the sensor selection and fusion method assists the experimenter in determining the average effect of each candidate sensor on the performance of the measuring system. In the case studied, it appeared that the cutting feed, depth of cut and two components of the cutting force (the feed and radial force components) provided the best combination to build a fusion model for on-line estimation of Ra and DD in turning. Surface finish was assessed with an error varying from 2 to 25% under different process conditions, while errors ranging between 2 and 20 μm were observed for the prediction of dimensional deviations.

195 citations