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Curvelet transform for estimation of machining performance
Optik 131 (2017) 615–625
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Optik journal homepage: www.elsevier.de/ijleo
Original research article
Curvelet transform for estimation of machining performance Umamaheswara Raju. R.S. a,∗ , V. Ramachandra Raju b , R. Ramesh a a b
Department of Mechanical Engineering, MVGRCE, Vizianagaram, AP, India Department of Mechanical Engineering, JNTU Kakinada, EG District, AP, India
a r t i c l e
i n f o
Article history: Received 26 May 2016 Accepted 28 November 2016 Keywords: Curvelet transforms SVM classification Intelligent model Front end software performance estimation
a b s t r a c t Automation of the processes from design to final component manufacturing poses numerous challenges. The problems that need to be addressed are tolerances for geometry, dimensional accuracy and surface quality of the component. The first two can be addressed by integrating the Computer aided design, computer aided manufacturing and machining systems. The real time monitoring and performance estimation systems for surface quality need to be developed. As such approach in this work, front end software is developed where the input to software will be the surface image of the machined workpiece taken by a camera. The image is further processed in image processing module in mat lab for the texture feature extraction namely the curvelet transforms data. The software in turn classifies the machining processes undergone according to the trend in the curvelet transform data and then sends it to the corresponding process model for the estimation of the machining performance. In this work, the software is tested with sample test data and it displayed the machining process accurately and estimated the machining performance. The results are encouraging and this method can be used in real time estimation of machining process and machining performance. © 2016 Elsevier GmbH. All rights reserved.
1. Introduction In order to raise the productivity and reliability of the machined components, machining industries are moving ahead to completely automate the processes to diminish the human intervention, effort and errors. The advent of computers in all the sectors of manufacturing, helped in reducing the idle times and enhancing the machining performance. This advancement is made possible due to the open architectural controlled machine tools where there is an enormous scope for developing and incorporating such intelligent based systems. Manufacturing industry expends quite a reasonable amount of time and resources in achieving the quality for the machined components. Surface quality is one of the prime factors to be achieved in machining to ensure the quality of the product. The conventional way of roughness measurement is of a destructive way as the diamond probe moves on the machined component leaving small traces or marks on it. The surface quality of the components hinges on numerous factors like the clamping of the work and tool, vibration of the machine tool, rigidity, tool geometry, cutting parameters selected, machining process selected, cutting fluids, work piece material and tool material which contribute to the secondary roughness. The lays intensity, width and depth basically contribute to the surface roughness. In this study the effect of lays or the impressions
∗ Corresponding author. E-mail addresses: [email protected], [email protected] (R. Ramesh). http://dx.doi.org/10.1016/j.ijleo.2016.11.181 0030-4026/© 2016 Elsevier GmbH. All rights reserved.
[email protected]
(R.S.
Umamaheswara
Raju.),
[email protected]
(V.R.
Raju),
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left due to machining on the surface roughness is investigated. A non tangible method is proposed where the roughness can be estimated directly by giving surface image of the machined component. Section 2 focuses on the review of various machine vision systems (MVS) for estimating the surface roughness. Section 3 the experimental procedure undergone for the development of the Front end software is in Section 4. The comparative study and model efficiency is discussed in Section 5. The conclusion is presented in Section 6. 2. Review of progress in machine vision systems Rapid CAM on standalone machine tools is developed in order to enhance the machining performances and integration of design to final component is done [1]. Conventional stylus probe instruments are used for the measurement of surface roughness. Physically, the probe moves on the workpiece and marks the workpiece mostly in a destructive way. In order to overcome such problems, vibration based evaluation technique for an open architectural machine tool is suggested to estimate the surface finish [2]. A non-contact method for estimation of surface roughness is proposed using optical microscope and image processing. The image characteristics namely the gradient factor of the image is extracted using image processing. The statistical data of the image is used for estimation of the roughness [3]. Turning operation is performed on AISI 1040 steel at diverse cutting parameters. Artificial neural networks [ANN] model and multi regression model is used for estimation of roughness using Cutting parameters. The models are compared with statistical model [4]. Machine vision system is used for estimation of performance. Images are blurred due to the relative motion between the camera and workpiece. Such images are processed in an algorithm called Richardson Lucy which is based on Bayes theorem. The performance estimation if machining processes is done using the statistical parameters like spatial frequency, arithmetic average of gray level and standard deviation. ANN model is developed for statistical parameters and Roughness [5]. In order to reduce the surface damage of the machined components, optical systems are used. Face milling operation is performed on AISI 1040 steel and Aluminium alloy 5083. Polarized microscope is used to take the images of the machined surfaces. Images are converted into binary images and that data is used to estimate the roughness and neural networks [NN] model is developed for estimation of roughness [6]. Optimization of cutting parameters is done using response surface methodology, artificial neural network and support vector machine in achieving required surface roughness from vibration signature data [7]. Machine vision system for end milling components is developed at diverse cutting parameters. Stylus probe instrument is used to measure the roughness of machined components. Images of the machined components are taken and grey level co-occurrence matrixes are taken as image texture features to map with roughness. ANN method is developed for mapping and estimation of roughness [8]. Human identification system the biometrics is used for characterization of machined surfaces. Euclidean and Hamming distances of the workpiece images are used for recognition. Data base with known surface roughness is established for estimation [9]. A differential evolution algorithm based ANN is used for estimation of roughness in turning operation. In order to develop model cutting parameters and grey level of the surface image are used for estimation of surface roughness [10]. An adaptive neuro fuzzy model was developed to establish the relation between surface roughness and texture features of the surface image. Spatial frequency, arithmetic mean value, and standard deviation of grey levels from the surface image were given as input parameters for estimating the surface roughness [11]. Surface roughness of end milled parts is estimated using machine vision system (MVS) and with help of ANN. Model is developed between major peaks, grey level and cutting parameters [12]. Fuzzy logic intelligent method is used for predicting the machining performance. The cutting parameters are used for estimation of surface roughness (SR). Various alloying elements are added and the influence of alloying elements on SR is studied [13]. A MVS is used for capturing the images of the tool tips after machining. The profile is simulated according to the tool tip image and simulated images of specimens in a range of machining condition were detected using the algorithm [14]. In order to estimate the roughness characteristics, a MVS is developed. The images taken are processed for image features namely the wavelet. The wavelet data is used for training ANN for estimation of roughness [15]. Images are captured for grinded components using a camera and processed in image processing for sharpness evaluation of the colored image. An algorithm is developed based on the difference on color feature namely the RGB. Sharpness is decreased with the increase in the roughness [16]. A microscopic vision system is developed for estimation of roughness of a deep hole. Surface topography images of R-surface were analyzed by the gray-level co-occurrence matrix (GLCM) method. A support vector machine method SVM is developed for GLCM and surface roughness [17]. An optical system is used for measuring the surface roughness in drilling operation. Taguchi method is used for development of the model [18]. A high sensitive noncontact type captive sensor is used for measurement of surface roughness. The surface roughness measured using conventional are compared with sensor method and found to be well collating with grinding and milling [19]. An unmanned visual quality inspection method is proposed for determining surface roughness. Images are taken for components machined at various cutting parameters and image texture features extraction namely the wavelet is derived using image processing. An ANN model is developed for estimation of roughness from texture feature [20]. A light interferometer is used to examine the machine samples and three different regression models are developed and compared to estimate the roughness from the cutting parameters. The random forest regression model was a superior choice over multiple regression models for prediction of surface roughness [21]. The cutting parameters, nose radius, cutting fluids and cutting forces are used for developing a model for estimation of roughness. A fuzzy logic approach was used for the prediction of surface roughness [22]. In order to estimate the roughness of surface machined in abrasive water
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Fig. 1. Taylor Hobson Talysurf.
jet machining, a method based on extreme learning machine (ELM) is developed. The cutting parameters, thickness of the workpiece, flow rate, cutting speed are trained to the model to estimate the roughness. The results of ELM model were compared with genetic programming (GP) and artificial neural networks (ANNs) models [23]. A MVS is developed for determining the surface roughness in turned components. A commercial digital single-lens-reflex camera is used to capture the images of the machined components. Edge detection is used to extract the roughness profile at sub-pixel accuracy from the captured images. The roughness values measured using the proposed machine vision systems were verified using the stylus profilometer [24]. The main aim of this work is to develop front end software for estimating the surface quality of the component without any human interface. The idea is to build software for open architectural machine tools for roughness estimation. Several researches as said above developed MVS for estimation of surface roughness but classification of the machining processes is a novel approach proposed in this work. Even the images taken are processed in image processing for texture feature extraction namely the binary code, Grey level co-occurrence matrix, speckle patterns [34,36,37], light scattering [35], RGB, biometric system and wavelet transforms for estimation of performance. The recent advancements in roughness estimation through MVS is by using wavelet in which the image is processed to find the number of lines in vertical, horizontal and diagonal direction. Basically the images taken on the machined surface consists of lays in curve forms rather than in lines, due to the circular rotating cutting tools. In order to find the curves present in the image, a unique curvelet approach is used to find the image texture features. The classifier is used to classify the machining process through curvet data and further processed through individual model for estimation of the machining performance/roughness.
3. Experimental procedure 3.1. Machining Face milling and shoulder milling operation are performed on a standard vertical machining centers having spindle power of 3.7/5 kw, spindle feed of 50–5000 rpm, spindle taper BT 40 and rapid traverse rate of 20 m/min. Mild steel workpieces are selected for machining as they are readily available and price. The cutting parameters selected for machining are taken form [25] like Speeds (S) 763, 1050 and 1464 rpm, feed (F) 180, 200, 220 mm/min, depth of cuts (DOC) are like 0.8, 1, 1.2 mm respectively and the total number of experiments conducted are 27. The cutting parameters selected for shoulder milling are selected from the cutting tools manufactures catalog (MITSUBISHI) the S are 650, 1200, 1600 rpm, F 350, 636, 954 mm/min and DOC 0.5,1, and 1.5 mm respectively and total 27 different experiments are conducted.
3.2. Surface roughness measurement Roughness of the machined components are usually measured using two different methods namely contact and noncontact type. Contact type uses stylus and diamond probe which measures the variation or peaks and valleys for a given sampling length. This instrument is versatile and can be used for enormous range of roughness. In this work, in order to develop an intelligent software model Taylor Hobson Talysurf as shown in Fig. 1 is used to measure the roughness Ra of each any every component at three different location and directions to have a better average roughness value as shown in Table 1a for various cutting conditions respectively.
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Table 1a Curvelet transforms texture features and respective surface roughness in microns for Face Milling. Face Milling Sl. No
STD
Median
Mean
Mode
Entropy
Ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
115.1559 136.353 137.4176 133.6168 141.0027 142.1448 143.7542 127.9918 154.2151 150.8131 128.2503 111.9945 126.3899 131.7884 112.1354 118.3082 116.5885 120.2003 112.7305 105.3672 119.8907 122.8688 119.4038 116.1562 112.7442 113.0315 109.2811
0.7113 0.8114 0.9707 1.1663 1.5102 1.3509 1.1196 1.0193 1.7907 1.3588 0.8721 0.9875 0.893 0.8925 1.1455 0.893 2.151 1.1849 1.7751 1.0925 2.0553 1.4224 0.9899 1.1226 1.2017 1.2637 1.0686
2.6633 3.0688 3.444 3.8086 5.4108 4.9858 3.3014 3.2957 5.8019 4.5757 3.336 3.947 3.3618 3.3827 4.6638 2.9764 6.5514 3.9162 5.5181 3.4541 6.6131 4.7993 3.2003 3.8588 4.0272 4.5685 3.8965
0.0059 0.0004 0.0001 0.015 0.0373 0.0166 0.0003 0.0045 0.0183 0.0005 0.018 0.0121 0.0003 0.0202 0.023 0.0261 0.0676 0.0245 0.0005 0.0101 0.0054 0.0073 0.0001 0.0002 0.0205 0.0003 0.0097
6.0412 5.7063 5.243 4.7905 3.8856 4.2405 5.0595 5.2587 3.2972 4.3155 5.5653 5.2859 5.5518 5.4871 4.8685 5.6326 3.1425 4.9056 3.6739 5.0689 3.1367 4.2203 5.2626 4.968 4.8189 4.385 5.0544
0.42 1.11 0.77 1.04 1.16 1.59 0.89 0.92 1.14 1.28 1.33 1.55 1.33 1.408 1.49 1.24 1.27 1.81 1.71 1.69 2.34 1.8575 2.2 1.858 1.751 2.163 2.018
3.3. CCD camera images In the noncontact type measurement, several researches used techniques such as microscopy, interferometry, phase detection, speckle light scattering, etc. as attractive alternative for stylus probe instruments. As stylus probe instruments physically move on the machined component for the given sampling length to measure the roughness, Diamond probes marks some scratches on the machined components which are not recommended. In order to overcome such problems, an alternative method is proposed. In the proposed method, the surface images of the machined components are taken using normal CCD cameras. The sample images of the face and shoulder milling machined components at various S, F and DOC are shown in Fig. 2 and Fig. 3 respectively. The images thus taken are proposed further for estimation of the roughness. 4. Development of front end software using curvelet data 4.1. Curvelet transform Candes and Donoho [26] developed curvelets transforms for denoising the discontinuous curve forms. While working with pixel arrays, one doesn’t know where the discontinuities are caused due to the noising, blurring and unnatural pixelization. Rectification for such curve forms can be done using curvelet. Curvelet provides several features from existing wavelet, ridgelet and steerable pyramids for multi resolution representation. Dettori et al. [27]. Developed image based system for medicinal application for classification and identification of tissues. Several techniques like wavelet, ridgelet and curvelet are used for texture descriptions. A two steps approach in which former is used for discretion of texture feature and the later one is used for classification of various tissues. The algorithms are tested and found that curvelet data interpreted models showed better results than the other two methods. Curvelet transforms are developed to overcome the limitations of wavelet transforms. Curvelet transforms have wider geometrical features than wavelet transforms. Curvelet transforms works in multiple directions and positons on each scale length and needle shaped elements, so being called a multi scale pyramid [28]. Scaling relations obeyed by curvelet are usually parabolic at a scale 2−J , enveloped elements are assigned a ridge with a length 2−J/2 and width 2−J . The three main and major problems that can be addressed by curvelet than wavelet are, firstly optimally spare representation of objects with edges, discontinuities in general curves bounded with curvatures are smoothed using curve-punctuated smoothness. Secondly curvelet has the capability of modelling the geometry of wave propagations. Using hamilitonian flows could translate the center of the curvelet in optimally sparse representations of wave propagators. Curvelets are mainly adapted for Missing data present in reconstruction of such data lastly in optimal image reconstruction in severally ill-posed problems.
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Fig. 2. Face milled components at various speeds (S), Feeds (F) and depth of cut (DOC).
Fig. 3. Shoulder milled components at various speeds (S), Feeds (F) and depth of cut (DOC).
4.1.1. Discrete curvelet transforms As mentioned earlier curvelets are effective in detecting the curved images, the discrete variant used in this work is ‘USFFT’ (Un-equally Spaced Fast Fourier Transforms) algorithm. This transform is simpler, quicker and less redundant. Along the direction of the curvelet uses a devastated rectangular grid. The methodology adapted for implementing curvelet for the proposed problem is mentioned in the form of block diagram as shown in Fig. 4. In the parabolic scaling the windows Uj,l as shown in Fig. 5 get smoothly localized near the sheared edges. The typical wedge is represented in the shaded portion of the image. The x and n scales are the inputs to the curvelet transforms. In
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Fig. 4. Block diagram of Implementation of curvelet.
Fig. 5. Basic digital tiling.
an image x is a matrix of n * n pixel array. The coarse wavelet values are also included in the array. The output coefficients obtained in the curvelet are integer scale that is varying from coarse to fine. In curvelet transform the total of resolutions and the angles at the coarse level are the two major parameters involved in digital implementation [29]. Several features are extracted from the coefficient of the curvelet transforms. The five features that are extracted are mean, median, standard deviation (STD), mode and entropy and are shown in the Tables 1a and 1b for face and shoulder milling respectively in the subsequent sections.
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Table 1b Curvelet transforms texture features and respective surface roughness in microns for Shoulder Milling. Shoulder Milling Sl. No
STD
Median
Mean
Mode
Entropy
Ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
153.7274 152.9286 145.0376 158.891 156.928 156.5532 165.0341 139.0445 128.6236 139.4039 150.7678 156.7016 154.6931 148.8149 134.3956 174.936 153.6259 157.938 143.332 146.9662 147.2557 160.0418 152.5868 146.2828 145.8345 152.8287 161.9155
2.1354 2.188 1.1051 1.2064 1.8026 1.9905 1.1238 1.1755 1.896 1.269 1.1237 1.8862 1.4416 1.3371 0.8746 1.6746 1.6053 1.5182 2.0423 0.6752 0.9274 0.6706 1.2026 0.9136 0.8207 1.2063 1.2774
6.5695 7.2662 3.8433 3.6367 6.2672 6.0911 3.995 4.1444 6.5071 4.0667 3.3399 6.2041 4.6717 5.0512 3.0197 6.0751 6.9874 6.9198 7.3308 2.4193 4.3183 2.7168 3.879 3.9418 3.084 4.7165 4.9973
0.0011 0.0574 0.0431 0.0001 0.0101 0.0492 0.003 0.0821 0.0234 0.0007 0.0197 0.0005 0.0004 0.0207 0.0003 0.0396 0.0105 0.0181 0.001 0.011 0.0002 0.0002 0.0155 0.0004 0.009 0.0132 0.0122
2.2777 2.1941 4.6083 4.5723 2.7124 2.7611 4.7194 4.5463 2.9221 4.4721 4.8723 2.8419 3.8566 3.9008 5.5126 3.2801 3.361 3.6581 2.636 6.2162 5.0116 6.0142 4.6046 5.4228 5.7428 4.5858 4.1461
3.58 3.72 3.69 3.13 2.62 3.04 2.15 1.73 2.42 3.136 2.556 3.46 3.373 3.183 3.347 2.623 2.93 2.48 3.45 5.69 4.313 4.905 3.677 4.69 3.97 3.886 2.81
4.2. Front end software Measurement of surface roughness is a trivial task and even today in high end automatic systems, the roughness measurement is done manually using the conventional stylus probe instruments. As discussed in the earlier sections, due to its disadvantages, machining industry is looking forward for intelligent systems which could estimate the roughness. Several researches as discussed earlier tried developing intelligent models using cutting parameters. The proposed system is unique as it estimates the roughness and the machining processes using the image data. Due to the advent of computers and open architectural controlled machine tools in the manufacturing industry, such systems are possible. Complete automation of the manufacturing plant is made feasible due to such advanced controllers [30–33]. In The present work here in developed an intelligent support system for estimating the machining process undergone and surface roughness estimation that could enhance the efficiency of the CNC machine. In order to implement such system in open architectural controlled machine tools several interfaces need to be developed. A Human machine interface HMI developed is shown in Fig. 6. The first module developed is front end software in Mat-Lab guide where the interface is developed to upload the image. Image uploaded is processed by another interface where the uploaded image is processed in image processing module for the texture feature extraction namely the curvelet data. The third interface the classification of the machining process is done using SVM Classifier and further processed in specific machining process model for the estimation of the roughness and the process flow chart is shown in Fig. 7. The final HMI displays the machining process underwent and estimated roughness values are displayed as shown in Fig. 6a and b. The front end software displayed in Fig. 6a clear shows how a shoulder milling image uploaded as input. Once the image is uploaded backend the code is processed for curvelet transforms data generation, then further to SVM Classifier classifying the process, individual model is run for the estimating the surface roughness and finally the machining process and estimated surface roughness values are displayed as shown in Fig. 6a. similarly Fig. 6b shows the output for face milling for image number 11 of face milling. The final architecture flow chart of the proposed model is displayed in Fig. 7. 5. Comparative study and model efficiency The aim of this work is to develop front end software for real time estimation of the machining process underwent and estimate the machining performance. Front end software’s being rare for open architectural machine tools are useful in reducing the human efforts for measurement of surface roughness. This has been the motivational factor for this work. In this work, two different type of machining processes namely the face milling and shoulder milling operation is performed on MS work pieces. Three cutting parameters with each speeds, feeds and depth of cuts are selected and totally 27 different experiments each are conducted. Surface roughness of the each and every workpiece is measured and is shown in the
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Fig. 6. a. Front end software displaying output machining process and surface roughness for the image number 11 in Shoulder milling, b. Front end software displaying output machining process and surface roughness for the image number 11 in Face milling.
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Fig. 7. The Front end software process flow chart. Table 2 Machining process classification using SVM classifier. Sl No
Machining Process
Correct Classification
Misclassification
1 2 Total
Face Milling Shoulder Milling
3 3 6
0 0 0
Table 3 The Actual and model estimated surface roughness. Set No
STD
Median
Mean
Mode
Entropy
Ra
Class
Predicted Ra
Predicted Class
11 11 26 26 18 18
128.26 150.77 113.04 152.83 120.21 157.94
0.8721 1.1237 1.2637 1.2063 1.1849 1.5182
3.34 3.34 4.57 4.72 3.92 6.92
0.018 0.0197 0.0003 0.0132 0.0245 0.0181
5.5653 4.8723 4.385 4.5858 4.9056 3.6581
1.33 2.556 2.163 3.886 1.81 2.48
Face Milling Shoulder Milling Face Milling Shoulder Milling Face Milling Shoulder Milling
1.35 2.52 1.83 2.56 1.49 2.06
Face Milling Shoulder Milling Face Milling Shoulder Milling Face Milling Shoulder Milling
Tables 1a and 1b for face and shoulder milling respectively. Images of the components are taken using CCD camera and processed further in image processing tool box for texture feature extraction namely Curvelet transforms. The texture features for the face and end milling are shown in the Table 1a and Table 1b respectively. In order to map the curvelet transform data, surface roughness models are developed. Initially the curvelet transform data is classified using the SVM Classifier. The model is tested for 6 consecutive times by giving the curvelet transform data from the two different machining processes. The efficiency of the classifier is shown in the Table 2. The classifier efficiency is very high and it could predict the machining process with 100% accuracy. In order to develop the SVM model, out of 27 different curvelet transform data 26 are used for training the model and 1 set in each process is taken out for testing the model and as such 6 different test are conducted and the classifier as shown above in Table 2. After classification of the machining process, the data is sent to the individual machining process model for estimation of the surface roughness Ra. The Table 3 shows the actual and estimated roughness values. 6. Conclusion In this work, Front end software capable of estimating the machining process and the machining performance namely the surface roughness is done. This technology can be established in the open architectural controlled machine tools for the real
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time online inspection of the workpieces for the surface roughness. Such systems can easily replace the conventional stylus probe instruments, reduces the human efforts, errors and interventions. This technology can be used in high end automation where automatic inspection is required. In this technology, directly the image of the work piece is used for estimation of the machining performance. Images taken are processed in image processing namely the curvelet transforms for texture feature, in turn used for classification of the model and estimating the machining process. The results are encouraging the model estimation accuracy is also high so this technology can be used for real time measurement of machining performance. Acknowledgments Authors thank Prof. Kalidindi Venkata Lakshmipathi Raju, Principal, M V G R College of Engineering-VZM for the support and the academic freedom he has given to pursue research and development. The authors are also great full for P. 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Contents lists available at ScienceDirect
Optik journal homepage: www.elsevier.de/ijleo
Original research article
Curvelet transform for estimation of machining performance Umamaheswara Raju. R.S. a,∗ , V. Ramachandra Raju b , R. Ramesh a a b
Department of Mechanical Engineering, MVGRCE, Vizianagaram, AP, India Department of Mechanical Engineering, JNTU Kakinada, EG District, AP, India
a r t i c l e
i n f o
Article history: Received 26 May 2016 Accepted 28 November 2016 Keywords: Curvelet transforms SVM classification Intelligent model Front end software performance estimation
a b s t r a c t Automation of the processes from design to final component manufacturing poses numerous challenges. The problems that need to be addressed are tolerances for geometry, dimensional accuracy and surface quality of the component. The first two can be addressed by integrating the Computer aided design, computer aided manufacturing and machining systems. The real time monitoring and performance estimation systems for surface quality need to be developed. As such approach in this work, front end software is developed where the input to software will be the surface image of the machined workpiece taken by a camera. The image is further processed in image processing module in mat lab for the texture feature extraction namely the curvelet transforms data. The software in turn classifies the machining processes undergone according to the trend in the curvelet transform data and then sends it to the corresponding process model for the estimation of the machining performance. In this work, the software is tested with sample test data and it displayed the machining process accurately and estimated the machining performance. The results are encouraging and this method can be used in real time estimation of machining process and machining performance. © 2016 Elsevier GmbH. All rights reserved.
1. Introduction In order to raise the productivity and reliability of the machined components, machining industries are moving ahead to completely automate the processes to diminish the human intervention, effort and errors. The advent of computers in all the sectors of manufacturing, helped in reducing the idle times and enhancing the machining performance. This advancement is made possible due to the open architectural controlled machine tools where there is an enormous scope for developing and incorporating such intelligent based systems. Manufacturing industry expends quite a reasonable amount of time and resources in achieving the quality for the machined components. Surface quality is one of the prime factors to be achieved in machining to ensure the quality of the product. The conventional way of roughness measurement is of a destructive way as the diamond probe moves on the machined component leaving small traces or marks on it. The surface quality of the components hinges on numerous factors like the clamping of the work and tool, vibration of the machine tool, rigidity, tool geometry, cutting parameters selected, machining process selected, cutting fluids, work piece material and tool material which contribute to the secondary roughness. The lays intensity, width and depth basically contribute to the surface roughness. In this study the effect of lays or the impressions
∗ Corresponding author. E-mail addresses: [email protected], [email protected] (R. Ramesh). http://dx.doi.org/10.1016/j.ijleo.2016.11.181 0030-4026/© 2016 Elsevier GmbH. All rights reserved.
[email protected]
(R.S.
Umamaheswara
Raju.),
[email protected]
(V.R.
Raju),
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left due to machining on the surface roughness is investigated. A non tangible method is proposed where the roughness can be estimated directly by giving surface image of the machined component. Section 2 focuses on the review of various machine vision systems (MVS) for estimating the surface roughness. Section 3 the experimental procedure undergone for the development of the Front end software is in Section 4. The comparative study and model efficiency is discussed in Section 5. The conclusion is presented in Section 6. 2. Review of progress in machine vision systems Rapid CAM on standalone machine tools is developed in order to enhance the machining performances and integration of design to final component is done [1]. Conventional stylus probe instruments are used for the measurement of surface roughness. Physically, the probe moves on the workpiece and marks the workpiece mostly in a destructive way. In order to overcome such problems, vibration based evaluation technique for an open architectural machine tool is suggested to estimate the surface finish [2]. A non-contact method for estimation of surface roughness is proposed using optical microscope and image processing. The image characteristics namely the gradient factor of the image is extracted using image processing. The statistical data of the image is used for estimation of the roughness [3]. Turning operation is performed on AISI 1040 steel at diverse cutting parameters. Artificial neural networks [ANN] model and multi regression model is used for estimation of roughness using Cutting parameters. The models are compared with statistical model [4]. Machine vision system is used for estimation of performance. Images are blurred due to the relative motion between the camera and workpiece. Such images are processed in an algorithm called Richardson Lucy which is based on Bayes theorem. The performance estimation if machining processes is done using the statistical parameters like spatial frequency, arithmetic average of gray level and standard deviation. ANN model is developed for statistical parameters and Roughness [5]. In order to reduce the surface damage of the machined components, optical systems are used. Face milling operation is performed on AISI 1040 steel and Aluminium alloy 5083. Polarized microscope is used to take the images of the machined surfaces. Images are converted into binary images and that data is used to estimate the roughness and neural networks [NN] model is developed for estimation of roughness [6]. Optimization of cutting parameters is done using response surface methodology, artificial neural network and support vector machine in achieving required surface roughness from vibration signature data [7]. Machine vision system for end milling components is developed at diverse cutting parameters. Stylus probe instrument is used to measure the roughness of machined components. Images of the machined components are taken and grey level co-occurrence matrixes are taken as image texture features to map with roughness. ANN method is developed for mapping and estimation of roughness [8]. Human identification system the biometrics is used for characterization of machined surfaces. Euclidean and Hamming distances of the workpiece images are used for recognition. Data base with known surface roughness is established for estimation [9]. A differential evolution algorithm based ANN is used for estimation of roughness in turning operation. In order to develop model cutting parameters and grey level of the surface image are used for estimation of surface roughness [10]. An adaptive neuro fuzzy model was developed to establish the relation between surface roughness and texture features of the surface image. Spatial frequency, arithmetic mean value, and standard deviation of grey levels from the surface image were given as input parameters for estimating the surface roughness [11]. Surface roughness of end milled parts is estimated using machine vision system (MVS) and with help of ANN. Model is developed between major peaks, grey level and cutting parameters [12]. Fuzzy logic intelligent method is used for predicting the machining performance. The cutting parameters are used for estimation of surface roughness (SR). Various alloying elements are added and the influence of alloying elements on SR is studied [13]. A MVS is used for capturing the images of the tool tips after machining. The profile is simulated according to the tool tip image and simulated images of specimens in a range of machining condition were detected using the algorithm [14]. In order to estimate the roughness characteristics, a MVS is developed. The images taken are processed for image features namely the wavelet. The wavelet data is used for training ANN for estimation of roughness [15]. Images are captured for grinded components using a camera and processed in image processing for sharpness evaluation of the colored image. An algorithm is developed based on the difference on color feature namely the RGB. Sharpness is decreased with the increase in the roughness [16]. A microscopic vision system is developed for estimation of roughness of a deep hole. Surface topography images of R-surface were analyzed by the gray-level co-occurrence matrix (GLCM) method. A support vector machine method SVM is developed for GLCM and surface roughness [17]. An optical system is used for measuring the surface roughness in drilling operation. Taguchi method is used for development of the model [18]. A high sensitive noncontact type captive sensor is used for measurement of surface roughness. The surface roughness measured using conventional are compared with sensor method and found to be well collating with grinding and milling [19]. An unmanned visual quality inspection method is proposed for determining surface roughness. Images are taken for components machined at various cutting parameters and image texture features extraction namely the wavelet is derived using image processing. An ANN model is developed for estimation of roughness from texture feature [20]. A light interferometer is used to examine the machine samples and three different regression models are developed and compared to estimate the roughness from the cutting parameters. The random forest regression model was a superior choice over multiple regression models for prediction of surface roughness [21]. The cutting parameters, nose radius, cutting fluids and cutting forces are used for developing a model for estimation of roughness. A fuzzy logic approach was used for the prediction of surface roughness [22]. In order to estimate the roughness of surface machined in abrasive water
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Fig. 1. Taylor Hobson Talysurf.
jet machining, a method based on extreme learning machine (ELM) is developed. The cutting parameters, thickness of the workpiece, flow rate, cutting speed are trained to the model to estimate the roughness. The results of ELM model were compared with genetic programming (GP) and artificial neural networks (ANNs) models [23]. A MVS is developed for determining the surface roughness in turned components. A commercial digital single-lens-reflex camera is used to capture the images of the machined components. Edge detection is used to extract the roughness profile at sub-pixel accuracy from the captured images. The roughness values measured using the proposed machine vision systems were verified using the stylus profilometer [24]. The main aim of this work is to develop front end software for estimating the surface quality of the component without any human interface. The idea is to build software for open architectural machine tools for roughness estimation. Several researches as said above developed MVS for estimation of surface roughness but classification of the machining processes is a novel approach proposed in this work. Even the images taken are processed in image processing for texture feature extraction namely the binary code, Grey level co-occurrence matrix, speckle patterns [34,36,37], light scattering [35], RGB, biometric system and wavelet transforms for estimation of performance. The recent advancements in roughness estimation through MVS is by using wavelet in which the image is processed to find the number of lines in vertical, horizontal and diagonal direction. Basically the images taken on the machined surface consists of lays in curve forms rather than in lines, due to the circular rotating cutting tools. In order to find the curves present in the image, a unique curvelet approach is used to find the image texture features. The classifier is used to classify the machining process through curvet data and further processed through individual model for estimation of the machining performance/roughness.
3. Experimental procedure 3.1. Machining Face milling and shoulder milling operation are performed on a standard vertical machining centers having spindle power of 3.7/5 kw, spindle feed of 50–5000 rpm, spindle taper BT 40 and rapid traverse rate of 20 m/min. Mild steel workpieces are selected for machining as they are readily available and price. The cutting parameters selected for machining are taken form [25] like Speeds (S) 763, 1050 and 1464 rpm, feed (F) 180, 200, 220 mm/min, depth of cuts (DOC) are like 0.8, 1, 1.2 mm respectively and the total number of experiments conducted are 27. The cutting parameters selected for shoulder milling are selected from the cutting tools manufactures catalog (MITSUBISHI) the S are 650, 1200, 1600 rpm, F 350, 636, 954 mm/min and DOC 0.5,1, and 1.5 mm respectively and total 27 different experiments are conducted.
3.2. Surface roughness measurement Roughness of the machined components are usually measured using two different methods namely contact and noncontact type. Contact type uses stylus and diamond probe which measures the variation or peaks and valleys for a given sampling length. This instrument is versatile and can be used for enormous range of roughness. In this work, in order to develop an intelligent software model Taylor Hobson Talysurf as shown in Fig. 1 is used to measure the roughness Ra of each any every component at three different location and directions to have a better average roughness value as shown in Table 1a for various cutting conditions respectively.
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Table 1a Curvelet transforms texture features and respective surface roughness in microns for Face Milling. Face Milling Sl. No
STD
Median
Mean
Mode
Entropy
Ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
115.1559 136.353 137.4176 133.6168 141.0027 142.1448 143.7542 127.9918 154.2151 150.8131 128.2503 111.9945 126.3899 131.7884 112.1354 118.3082 116.5885 120.2003 112.7305 105.3672 119.8907 122.8688 119.4038 116.1562 112.7442 113.0315 109.2811
0.7113 0.8114 0.9707 1.1663 1.5102 1.3509 1.1196 1.0193 1.7907 1.3588 0.8721 0.9875 0.893 0.8925 1.1455 0.893 2.151 1.1849 1.7751 1.0925 2.0553 1.4224 0.9899 1.1226 1.2017 1.2637 1.0686
2.6633 3.0688 3.444 3.8086 5.4108 4.9858 3.3014 3.2957 5.8019 4.5757 3.336 3.947 3.3618 3.3827 4.6638 2.9764 6.5514 3.9162 5.5181 3.4541 6.6131 4.7993 3.2003 3.8588 4.0272 4.5685 3.8965
0.0059 0.0004 0.0001 0.015 0.0373 0.0166 0.0003 0.0045 0.0183 0.0005 0.018 0.0121 0.0003 0.0202 0.023 0.0261 0.0676 0.0245 0.0005 0.0101 0.0054 0.0073 0.0001 0.0002 0.0205 0.0003 0.0097
6.0412 5.7063 5.243 4.7905 3.8856 4.2405 5.0595 5.2587 3.2972 4.3155 5.5653 5.2859 5.5518 5.4871 4.8685 5.6326 3.1425 4.9056 3.6739 5.0689 3.1367 4.2203 5.2626 4.968 4.8189 4.385 5.0544
0.42 1.11 0.77 1.04 1.16 1.59 0.89 0.92 1.14 1.28 1.33 1.55 1.33 1.408 1.49 1.24 1.27 1.81 1.71 1.69 2.34 1.8575 2.2 1.858 1.751 2.163 2.018
3.3. CCD camera images In the noncontact type measurement, several researches used techniques such as microscopy, interferometry, phase detection, speckle light scattering, etc. as attractive alternative for stylus probe instruments. As stylus probe instruments physically move on the machined component for the given sampling length to measure the roughness, Diamond probes marks some scratches on the machined components which are not recommended. In order to overcome such problems, an alternative method is proposed. In the proposed method, the surface images of the machined components are taken using normal CCD cameras. The sample images of the face and shoulder milling machined components at various S, F and DOC are shown in Fig. 2 and Fig. 3 respectively. The images thus taken are proposed further for estimation of the roughness. 4. Development of front end software using curvelet data 4.1. Curvelet transform Candes and Donoho [26] developed curvelets transforms for denoising the discontinuous curve forms. While working with pixel arrays, one doesn’t know where the discontinuities are caused due to the noising, blurring and unnatural pixelization. Rectification for such curve forms can be done using curvelet. Curvelet provides several features from existing wavelet, ridgelet and steerable pyramids for multi resolution representation. Dettori et al. [27]. Developed image based system for medicinal application for classification and identification of tissues. Several techniques like wavelet, ridgelet and curvelet are used for texture descriptions. A two steps approach in which former is used for discretion of texture feature and the later one is used for classification of various tissues. The algorithms are tested and found that curvelet data interpreted models showed better results than the other two methods. Curvelet transforms are developed to overcome the limitations of wavelet transforms. Curvelet transforms have wider geometrical features than wavelet transforms. Curvelet transforms works in multiple directions and positons on each scale length and needle shaped elements, so being called a multi scale pyramid [28]. Scaling relations obeyed by curvelet are usually parabolic at a scale 2−J , enveloped elements are assigned a ridge with a length 2−J/2 and width 2−J . The three main and major problems that can be addressed by curvelet than wavelet are, firstly optimally spare representation of objects with edges, discontinuities in general curves bounded with curvatures are smoothed using curve-punctuated smoothness. Secondly curvelet has the capability of modelling the geometry of wave propagations. Using hamilitonian flows could translate the center of the curvelet in optimally sparse representations of wave propagators. Curvelets are mainly adapted for Missing data present in reconstruction of such data lastly in optimal image reconstruction in severally ill-posed problems.
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Fig. 2. Face milled components at various speeds (S), Feeds (F) and depth of cut (DOC).
Fig. 3. Shoulder milled components at various speeds (S), Feeds (F) and depth of cut (DOC).
4.1.1. Discrete curvelet transforms As mentioned earlier curvelets are effective in detecting the curved images, the discrete variant used in this work is ‘USFFT’ (Un-equally Spaced Fast Fourier Transforms) algorithm. This transform is simpler, quicker and less redundant. Along the direction of the curvelet uses a devastated rectangular grid. The methodology adapted for implementing curvelet for the proposed problem is mentioned in the form of block diagram as shown in Fig. 4. In the parabolic scaling the windows Uj,l as shown in Fig. 5 get smoothly localized near the sheared edges. The typical wedge is represented in the shaded portion of the image. The x and n scales are the inputs to the curvelet transforms. In
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Fig. 4. Block diagram of Implementation of curvelet.
Fig. 5. Basic digital tiling.
an image x is a matrix of n * n pixel array. The coarse wavelet values are also included in the array. The output coefficients obtained in the curvelet are integer scale that is varying from coarse to fine. In curvelet transform the total of resolutions and the angles at the coarse level are the two major parameters involved in digital implementation [29]. Several features are extracted from the coefficient of the curvelet transforms. The five features that are extracted are mean, median, standard deviation (STD), mode and entropy and are shown in the Tables 1a and 1b for face and shoulder milling respectively in the subsequent sections.
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Table 1b Curvelet transforms texture features and respective surface roughness in microns for Shoulder Milling. Shoulder Milling Sl. No
STD
Median
Mean
Mode
Entropy
Ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
153.7274 152.9286 145.0376 158.891 156.928 156.5532 165.0341 139.0445 128.6236 139.4039 150.7678 156.7016 154.6931 148.8149 134.3956 174.936 153.6259 157.938 143.332 146.9662 147.2557 160.0418 152.5868 146.2828 145.8345 152.8287 161.9155
2.1354 2.188 1.1051 1.2064 1.8026 1.9905 1.1238 1.1755 1.896 1.269 1.1237 1.8862 1.4416 1.3371 0.8746 1.6746 1.6053 1.5182 2.0423 0.6752 0.9274 0.6706 1.2026 0.9136 0.8207 1.2063 1.2774
6.5695 7.2662 3.8433 3.6367 6.2672 6.0911 3.995 4.1444 6.5071 4.0667 3.3399 6.2041 4.6717 5.0512 3.0197 6.0751 6.9874 6.9198 7.3308 2.4193 4.3183 2.7168 3.879 3.9418 3.084 4.7165 4.9973
0.0011 0.0574 0.0431 0.0001 0.0101 0.0492 0.003 0.0821 0.0234 0.0007 0.0197 0.0005 0.0004 0.0207 0.0003 0.0396 0.0105 0.0181 0.001 0.011 0.0002 0.0002 0.0155 0.0004 0.009 0.0132 0.0122
2.2777 2.1941 4.6083 4.5723 2.7124 2.7611 4.7194 4.5463 2.9221 4.4721 4.8723 2.8419 3.8566 3.9008 5.5126 3.2801 3.361 3.6581 2.636 6.2162 5.0116 6.0142 4.6046 5.4228 5.7428 4.5858 4.1461
3.58 3.72 3.69 3.13 2.62 3.04 2.15 1.73 2.42 3.136 2.556 3.46 3.373 3.183 3.347 2.623 2.93 2.48 3.45 5.69 4.313 4.905 3.677 4.69 3.97 3.886 2.81
4.2. Front end software Measurement of surface roughness is a trivial task and even today in high end automatic systems, the roughness measurement is done manually using the conventional stylus probe instruments. As discussed in the earlier sections, due to its disadvantages, machining industry is looking forward for intelligent systems which could estimate the roughness. Several researches as discussed earlier tried developing intelligent models using cutting parameters. The proposed system is unique as it estimates the roughness and the machining processes using the image data. Due to the advent of computers and open architectural controlled machine tools in the manufacturing industry, such systems are possible. Complete automation of the manufacturing plant is made feasible due to such advanced controllers [30–33]. In The present work here in developed an intelligent support system for estimating the machining process undergone and surface roughness estimation that could enhance the efficiency of the CNC machine. In order to implement such system in open architectural controlled machine tools several interfaces need to be developed. A Human machine interface HMI developed is shown in Fig. 6. The first module developed is front end software in Mat-Lab guide where the interface is developed to upload the image. Image uploaded is processed by another interface where the uploaded image is processed in image processing module for the texture feature extraction namely the curvelet data. The third interface the classification of the machining process is done using SVM Classifier and further processed in specific machining process model for the estimation of the roughness and the process flow chart is shown in Fig. 7. The final HMI displays the machining process underwent and estimated roughness values are displayed as shown in Fig. 6a and b. The front end software displayed in Fig. 6a clear shows how a shoulder milling image uploaded as input. Once the image is uploaded backend the code is processed for curvelet transforms data generation, then further to SVM Classifier classifying the process, individual model is run for the estimating the surface roughness and finally the machining process and estimated surface roughness values are displayed as shown in Fig. 6a. similarly Fig. 6b shows the output for face milling for image number 11 of face milling. The final architecture flow chart of the proposed model is displayed in Fig. 7. 5. Comparative study and model efficiency The aim of this work is to develop front end software for real time estimation of the machining process underwent and estimate the machining performance. Front end software’s being rare for open architectural machine tools are useful in reducing the human efforts for measurement of surface roughness. This has been the motivational factor for this work. In this work, two different type of machining processes namely the face milling and shoulder milling operation is performed on MS work pieces. Three cutting parameters with each speeds, feeds and depth of cuts are selected and totally 27 different experiments each are conducted. Surface roughness of the each and every workpiece is measured and is shown in the
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Fig. 6. a. Front end software displaying output machining process and surface roughness for the image number 11 in Shoulder milling, b. Front end software displaying output machining process and surface roughness for the image number 11 in Face milling.
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Fig. 7. The Front end software process flow chart. Table 2 Machining process classification using SVM classifier. Sl No
Machining Process
Correct Classification
Misclassification
1 2 Total
Face Milling Shoulder Milling
3 3 6
0 0 0
Table 3 The Actual and model estimated surface roughness. Set No
STD
Median
Mean
Mode
Entropy
Ra
Class
Predicted Ra
Predicted Class
11 11 26 26 18 18
128.26 150.77 113.04 152.83 120.21 157.94
0.8721 1.1237 1.2637 1.2063 1.1849 1.5182
3.34 3.34 4.57 4.72 3.92 6.92
0.018 0.0197 0.0003 0.0132 0.0245 0.0181
5.5653 4.8723 4.385 4.5858 4.9056 3.6581
1.33 2.556 2.163 3.886 1.81 2.48
Face Milling Shoulder Milling Face Milling Shoulder Milling Face Milling Shoulder Milling
1.35 2.52 1.83 2.56 1.49 2.06
Face Milling Shoulder Milling Face Milling Shoulder Milling Face Milling Shoulder Milling
Tables 1a and 1b for face and shoulder milling respectively. Images of the components are taken using CCD camera and processed further in image processing tool box for texture feature extraction namely Curvelet transforms. The texture features for the face and end milling are shown in the Table 1a and Table 1b respectively. In order to map the curvelet transform data, surface roughness models are developed. Initially the curvelet transform data is classified using the SVM Classifier. The model is tested for 6 consecutive times by giving the curvelet transform data from the two different machining processes. The efficiency of the classifier is shown in the Table 2. The classifier efficiency is very high and it could predict the machining process with 100% accuracy. In order to develop the SVM model, out of 27 different curvelet transform data 26 are used for training the model and 1 set in each process is taken out for testing the model and as such 6 different test are conducted and the classifier as shown above in Table 2. After classification of the machining process, the data is sent to the individual machining process model for estimation of the surface roughness Ra. The Table 3 shows the actual and estimated roughness values. 6. Conclusion In this work, Front end software capable of estimating the machining process and the machining performance namely the surface roughness is done. This technology can be established in the open architectural controlled machine tools for the real
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time online inspection of the workpieces for the surface roughness. Such systems can easily replace the conventional stylus probe instruments, reduces the human efforts, errors and interventions. This technology can be used in high end automation where automatic inspection is required. In this technology, directly the image of the work piece is used for estimation of the machining performance. Images taken are processed in image processing namely the curvelet transforms for texture feature, in turn used for classification of the model and estimating the machining process. The results are encouraging the model estimation accuracy is also high so this technology can be used for real time measurement of machining performance. Acknowledgments Authors thank Prof. Kalidindi Venkata Lakshmipathi Raju, Principal, M V G R College of Engineering-VZM for the support and the academic freedom he has given to pursue research and development. The authors are also great full for P. 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