Tool life predictions in milling using spindle power with the neural network technique (original) (raw)
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Journal of Manufacturing Systems, 2017
Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, spindle power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate spindle power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle power data were collected from the power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the spindle power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8-18.4% with power data as compared to that between 0.4-17.9% with force data. Findings suggest that spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.
Research article, 2019
In turning operation, tool wear has to be controlled and should be kept within the desired limits for any machining process. Besides, in order to maximize gains from a manufacturing process, an accurate process model is required. This research work was carried out to compare the ability of Response Surface Methodology and Artificial Neural Network in the prediction of tool wear rate in a turning operation, using the machining parameters of spindle speed, feed rate and depth of cut. A CNC lathe machine was used to carry out the turning operations for 20 experimental runs, as generated by the design matrix. The experimental results (data) were recorded and analysed with Response Surface Methodology of Design Expert software, version 7.0 and Artificial Neural Network of MATLAB 2013a. The rule of “the higher the better” was employed to select the better model for predicting the tool wear rate. The results obtained revealed that with a coefficient of determination (R2) of 0.9956, Artificial Neural Network was acclaimed a better model for predicting tool wear rate ahead of Response Surface Methodology which has a coefficient of determination of 0.9894.
Scientia Iranica, 2017
Cutting tool wear in machining processes reduces the product surface quality, affects on the dimensional and geometrical tolerances and causes tool breakage during the metal cutting. Therefore, online tool wear monitoring is needed to prevent reduction in machining quality.An artificial neural network (ANN) model was developed in this study to predict and simulate the tool flank wear. To reach to this aim, an experiment array was provided using of full factorial method and the tests were conducted on a CNC lathe machine tool. Vibration amplitude of the cutting tool and cutting forces were considered as criterion variables in monitoring the tool flank wear. For designing the model, the cutting parameters, cutting forces and vibration amplitude were defined as model input and tool flank wear was selected as output. The model was also introduced as a simulation block diagram to be used as a useful model in online and automated manufacturing systems. The estimated and measured results were then compared with each other. Based on the comparison results, maximum squared error values are under and the R2 is 1 which it means that the designed model can predict the results with a high and reliable accuracy.
Procedia Manufacturing, 2016
Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze spindle power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the power meter (also called load meter) of the machine spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the spindle power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.
Sains Malaysiana, 2013
Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality of machined parts. The topic of tool life prediction has been an interesting and important research topic attracting the attention of a wide number of researchers in this particular area. In terms of the suitable methods used in this research topic, it is stated that both statistical and artificial intelligence (AI) approaches can be employed to model tool life. For further justifying the capability of the ANN model in predicting tool life, the current study was based on conducting experimental work for collecting the experimental data. After carrying out the experiment, 17 data sets were collected and they were divided into two subsets; the first one for training and the second for testing. Since the data sets seemed to be lower than the number of data sets used in previous studies, we attempted to make verification of the ability of the ANN model in learning and ...
An artificial-neural-networks-based in-process tool wear prediction system in milling operations
The International Journal of Advanced Manufacturing Technology, 2005
An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation ANN model. The input variables for the proposed ANN-ITWP system were feed rate, depth of cut from the cutting parameters, and the average peak force in the y-direction collected online using a dynamometer. After the proposed ANN-ITWP system had been established, nine experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm. Experiments have shown that the ANN-ITWP system is able to detect tool wear in 3-insert milling operations online, approaching a real-time basis.
Design of neural network-based estimator for tool wear modeling in hard turning
Journal of Intelligent Manufacturing, 2008
Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.
Applicability of ANN models and Taguchi method for the determination of tool life in turning
MATEC web of conferences, 2017
Tool life is an important parameter in machining processes, affecting directly the quality of machined components and the process cost. It is already shown that various parameters can affect tool life such as process parameters, i.e. depth of cut, cutting speed and feed, or material properties of cutting tool and workpiece. The determination of the effect of each parameter on tool life is of crucial importance when designing the manufacturing process of a product in order to select suitable process parameter values and tool types. Several empirical formulas for the determination of tool life exist in the relevant literature; especially in the case of CBN cutting tools for turning, a cubic polynomial formula was proposed to model the relationship between tool life and cutting speed. The determination of the polynomial parameters was performed by conducting cutting experiments for several cutting speeds, without the aid of a design of experiments (DoE) method in order to model properly this non-linear relationship. In this paper, the feasibility of determining this non-linear relationship by conducting experiments designed by Taguchi method and using artificial neural networks (ANN) is investigated for several cases and conclusions on the applicability of this approach are presented.
Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy
Advances in Mechanical Engineering
An imperative requirement of a modern machining system is to detect tool wear while machining to maintain the surface quality of the product. Vibration signatures emanating during machining with a single point cutting tool have proven to be good indicators for the tool’s health. The current research undertaken utilizes vibration signatures while turning EN9 and EN24 steel alloy to predict tool life using Artificial Neural Network (ANN). During initial meager experimentation, tool acceleration during machining was recorded, and the width of the flank wear at the end of each run was measured using Tool Makers Microscope. The recorded experimental data is utilized to develop the neural network with the variation of operating parameters and corresponding tool vibration with measured tool flank wear. The endeavor undertaken for the development of ANN flank wear prediction model was effective with a regression coefficient of 0.9964. The proposed methodology of indirect measurement of tool...