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Hybrid Monitoring of Chip Formation and Straightness in CNC Turning by Utilizing Daubechies Wavelet Transform
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Procedia Manufacturing 25 (2018) 279–286 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden 8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden
Hybrid Monitoring of Chip Formation and Straightness in CNC Hybrid Monitoring of Chip Formation and Straightness in CNC Turning by Utilizing Daubechies Wavelet Transform Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Turning by Utilizing Daubechies Wavelet Transform 2017, Vigo (Pontevedra), Spain Somkiat Tangjitsitcharoen*, Haruetai Lohasiriwat Somkiat Tangjitsitcharoen*, Haruetai Lohasiriwat
Department Industrial Engineering, Faculty of Engineering,in Chulalongkorn University Costing models forof capacity optimization Industry 4.0: Trade-off Phayathai Road, Patumwan, 10330, Chulalongkorn Thailand Department of Industrial Engineering, FacultyBangkok, of Engineering, University Phayathai Road, Patumwan, Bangkok, 10330, Thailand efficiency between used capacity and operational
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb University of Minho, 4800-058 Guimarães, Portugal Abstract b Unochapecó, 89809-000 Chapecó, SC, Brazil Abstract The relations of straightness, chip formation, and cutting forces are investigated during the CNC turning process. Utilizing the Daubechies transform,chip the formation, dynamic cutting forcesforces are decomposed to classify andprocess. broken Utilizing chip signals The relationswavelet of straightness, and cutting are investigated duringthe thestraightness CNC turning the according towavelet the frequency ranges. The straightness frequency occurs at the on the and higher levelchip of wavelet Daubechies transform, the dynamic cutting forces are decomposed to lower classifyfrequency the straightness broken signals Abstract transform. to Thethe chip breakingranges. frequency at the frequency higher frequency of wavelet transform according to the according frequency Theappears straightness occurs on at the lower lower level frequency on the higher level of wavelet chip length.The The results supported the use of the feed forces to estimate theofstraightness error during the cutting transform. chip breaking frequency appears at decomposed the higher frequency on the lower level wavelet transform according to the Under the concept of supported "Industrythe4.0", processes will be pushed the to straightness be increasingly interconnected, process. chip length. The results use ofproduction the decomposed feed forces to estimate error during the cutting information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization process. goes beyond the traditional aim capacity © 2018 The Authors. Published by of Elsevier B.V.maximization, contributing also for organization’s profitability and value. © 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Publishedand by Elsevier B.V. committee Peer-review under responsibility of the scientific the8th 8th SwedishProduction Production Symposium. Indeed, lean management continuous improvement approaches suggest Symposium. capacity optimization instead of Peer-review under responsibility of the scientific committee ofofthe Swedish Peer-review under responsibility of the scientific committee the 8th Swedish Symposium. maximization. The study of capacity optimization andofcosting models Production is an important research topic that deserves a
Keywords: Turning; Decomposed forces; Straightness; Chip; Wavelet transform contributions from both the cutting practical and theoretical perspectives. This paper presents and discusses a mathematical Keywords: Turning; Decomposed cutting forces; Straightness; Chip; Wavelet transform model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity 1. Introduction optimization might hide operational inefficiency. The The geometric of Elsevier the workpiece is an important indication of machining performance. Workpiece © 2017 Authors. accuracy Published by B.V. The geometric accuracy of the workpiece issuch an of important indication of machining Workpiece acceptance usually depends on various measures asthe surface roughness and straightness which can be measured Peer-review under responsibility of the scientific committee Manufacturing Engineering Societyperformance. International Conference acceptance depends on various measures suchmeasuring as surfaceinstrument. roughness and straightness which can betomeasured and obtainedusually simultaneously using the surface profile However, it is rather difficult perform 2017. and obtained usingthe theactual surface profile measuring instrument. However,research it is rather to perform direct measuresimultaneously continually during machining process. As a result, extensive hasdifficult been conducted to direct measure continually duringCapacity the actual machining As a result, extensive research has been conducted to Keywords: Cost Models; ABC; TDABC; Management; Idle process. Capacity; Operational Efficiency
1. Introduction * Corresponding author. Tel.: 662-218-6814.
address:author. [email protected] * E-mail Corresponding Tel.: 662-218-6814. The cost of idle capacity is a fundamental information for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 2018 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured Peer-review of the scientific committee of the 8th Production Symposium. 2351-9789 2018responsibility The Authors. Published by Elsevier B.V.hours in several©under ways: tons of production, available of Swedish manufacturing, etc. The management of the idle capacity Peer-review underTel.: responsibility the761; scientific committee the 8th Swedish Production Symposium. * Paulo Afonso. +351 253 of 510 fax: +351 253 604of741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 8th Swedish Production Symposium. 10.1016/j.promfg.2018.06.084
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come up with the valid in-process prediction methods. Seung and Sung [1] proposed a multi-step straightness control system to minimize the straightness error of the machined shafts using the fuzzy self-learning method. Many other researchers investigated relationship among technical properties of the CNC machine such as the cutting conditions, cutting parameters [2,3], the real time measurement of dynamic cutting forces [4,5,6,7], and the workpiece quality. Recently, the dynamic cutting forces have been proposed to monitor the straightness in CNC turning [8]. The study employed the Fast Fourier Transform (FFT) and proved that the cutting force ratio together with some cutting parameters (e.g., cutting speed, feed rate, rake angle) can be used in straightness error prediction. However, when the cutting forces received from the direct measurement system were mixed with noise signals such as broken chips, the prediction could be weak. Therefore, to gain more accurate prediction the cutting forces have to firstly get decomposed in order to classify the straightness signal out from the noise signals. Though the FFT method was able to detect the chatter vibration in frequency domain [9], in-process detection requires time domain analysis in order to monitor the corresponding force signals and identify the chatter vibration robustly. It was found earlier that the Daubechies wavelet transform could be used to decompose the dynamic cutting forces to identify the chatter signal in both time and frequency domains simultaneously [10]. The wavelet transform method was successfully utilized to classify the roundness and straightness frequency, which occurred in the 8th level of the transformation [11,12]. Hence, the aim of this research is to propose the use of Daubechies wavelet transform to decompose the dynamic cutting forces in CNC turning process and recruit the straightness signal from the other noise signals. The straightness frequency is expected to appear at the high level of the 8th level of the decomposed cutting force while the chip breaking and noise signals will happen at the lower levels due to the higher frequency of itself. 2. Investigation of Dynamic Cutting Forces and Straightness The preliminary experiments were conducted to investigate relations between signals of the dynamic cutting forces and straightness. The carbon steel S45C and coated carbide tools were used in all testing conditions whereas a range of cutting conditions were varied by cutting speed, feed rate, depth of cut, tool nose radius, and rake angle. The dynamic cutting forces were monitored using a dynamometer which was installed on the CNC turret with the use of sampling rate of 10 kHz and amplified through the cut-off frequency of 5 kHz before digitization and calculation in the computer program. On the other hand, the workpiece straightness is measured off-line using the straightness tester. The measuring direction of the straightness is parallel to the workpiece axis on the cylindrical parts [13,14]. Fig. 1 and Fig.2 illustrate the results when there were continuous chips and broken chips, respectively. The obtained results include three components of the dynamic cutting forces which are the radial force (F x ), the feed force (F y ), the main force (F z ), and the corresponding straightness measured in time domain.
Fig. 1. Example of dynamic cutting forces (F x ,F y ,F z ) (middle) and straightness profile in time domain (right) when continuous chip occurs (left).
Fig. 2. Example of dynamic cutting forces (F x ,F y ,F z ) (middle) and straightness profile in time domain (right) when broken chip occurs (left).
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After that, utilizing the FFT method, their power spectrum densities (PSD) are demonstrated in frequency domain (Fig. 3 and Fig. 4). From the preliminary results, two types of signal conditions are found. Firstly, when there are only continuous chips, the frequency of the straightness profile is correspondent with the frequency of the dynamic cutting forces (e.g., 33 Hz in Fig. 3). The plot has no sign of noise signals. Secondly, when the chips are broken and hit the cutting tool and/or the workpiece, the dynamic cutting forces vary and result in the relatively larger amplitudes (Fig. 2) as compared to the ones from the continuous chip condition (Fig. 1). The PSD plot in this condition shows two distinct signals; straightness frequency which is found in lower frequency range, and chip breaking frequency which is found in higher frequency range (900 Hz to 1 kHz in Fig.4). Generally, the shorter chip length leads to the higher chip breaking frequency. It is difficult to differentiate between both signals when straightness and broken chip frequency are on the same PSD of the dynamic cutting forces (Fig. 4). Thus, the Daubechies wavelet transform is proposed to pull the straightness signal out from the noise signals (Fig. 5).
Fig. 3. Example of PSD of dynamic cutting forces (left) and PSD of straightness in frequency domain (right).
Fig. 4. Example of PSD of dynamic cutting forces (left) and PSD of straightness in frequency domain (right).
Straightness frequency
Chip breaking frequency
Straightness frequency Fig. 5. Illustration of decomposed feed force (Fy) in time domain (left) and its PSD in frequency domain (right) using speed of 200 m/min, feed rate of 0.25 mm/rev, depth of cut 0.8 mm, tool nose radius of 0.8 and rake angle of 11°.
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Only F y or the feed force is selected to be transformed and utilized to model the in-process prediction of straightness error as the feed force is most sensitive to the straightness on account of the measuring direction [8,11,12]. In the other word, since straightness referred to whether the workpiece is machined into appropriate alignment with the horizontal level, the feed force which also applies horizontally is the major contribution. In Fig. 5, it is clear that the PSD of the decomposed feed force effectively differentiate straightness frequency from chip breaking frequency on the 8th level. Hence, the decomposed feed force from the 8th level will be monitored and utilized to estimate the straightness error during the CNC turning. 3. In-Process Prediction of Straightness Model The proposed model consists of two main components (Fig. 6). Firstly, the set up component, this component consists of cutting conditions and tool related factors previously found to have great effect on the cutting force which eventually affects the straightness. Secondly, as the cutting process continues, the on-process component of the model will take into consideration the additional dynamic variances which result in better prediction. The on-process component utilized the measured feed force continually detected via dynamometer during actual cutting operation. Major reason is that the cutting forces have been repeatedly proved to correspond with the roughness and straightness on the surface of the machined workpiece [8-12]. Note that the dynamic response of dynamometer has been checked by employing the hammer test, and the natural frequency of the dynamometer is 2.7 kHz. When analyzed, the measured feed force (F y ) is considered to be consist of two portions; the static feed force (F y(static) or F y(s) ) and the dynamic feed force (F y(dynamic) ). As illustrated in Fig.6, the average force required for cutting process is the static portion and hence calculated from the difference between F y average and F o . The variation of measured force is the dynamic portion and hence calculated from the difference between the maximum and the minimum forces. Normally, the static feed force varies according to the workpiece hardness whereas the dynamic feed force mainly depends on the cutting conditions. Therefore, the ratio of both forces is proposed to be used in the model. However, since the dynamic feed force may also include the noise and broken chip signals as mentioned previously. It is necessary to decompose the dynamic feed force before taking the ratio. The equation (1) illustrates the cutting force ratio of decomposed feed force to its static feed force. The decomposed feed force is calculated from the amplitude of the signals in dynamic portion, which is obtained and correspondent with the machining time record of straightness. 𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑑𝑑𝑑𝑑𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶)) � 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑)
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = �
𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑚𝑚𝑚𝑚)−𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑)
=�
Straightness Prediction Model
𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑)
(1)
�
Static feed force (Y Axis)
Dynamic feed force (Y Axis)
150
Cutting conditions - Cutting speed - Feed rate - Depth of cut
Measured feed force - Fy (static) - Fy (dynamic)
Tool factors - Tool nose radius - Rake angle
8th level Decomposed Fy (dynamic) Cutting force ratio
120
120
90
90
60
Fy(static) = Fy(s) 30
0 22000 -30
Feed force (N)
On-process component
Fy(max)
Fy(avg)
Feed force (N)
Set up component
150
60
Fy(min)
Fy(dynamic)
30
F0
26000
Time (s)
30000
0 25000 -30
30000
Time (s)
Fig. 6. Prediction model concept and example of measured feed force in detail obtained from cutting speed of 200 m/min, feed rate of 0.25 mm/rev, depth of cut of 0.6 mm, tool nose radius of 0.8 and rake angle of 11°.
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Finally, the in-process straightness error prediction model which consisted of both set up and on-process components is developed in a form of exponential function as shown in equation (2). 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚) −𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚)
𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡 = 𝐶𝐶𝐶𝐶1 ∙ (𝑉𝑉𝑉𝑉)𝑎𝑎𝑎𝑎1 ∙ (𝑓𝑓𝑓𝑓)𝑎𝑎𝑎𝑎2 ∙ (𝐷𝐷𝐷𝐷)𝑎𝑎𝑎𝑎3 ∙ (𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛 )𝑎𝑎𝑎𝑎4 ∙ (𝐹𝐹𝐹𝐹)𝑎𝑎𝑎𝑎5 𝛾𝛾𝛾𝛾 ∙ �
𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑠𝑠𝑠𝑠)
𝑎𝑎𝑎𝑎6
(2)
�
Where S t is the straightness error in µm, V is the cutting speed in m/min, f is the feed rate in mm/rev, D is the depth of cut in mm, R n is the tool nose radius in mm, e is the exponential value of 2.718, γ is the rake angle in degree, F y (max) and F y (min) are the maximum and minimum decomposed dynamic feed force in N, F y (s) is the static feed force in N, a 1 , a 2 , a 3 , a 4 , a 5 , a 6 and C 1 are the powers and constants of the model. The multiple regression analysis is adopted and the least square method is used to model the prediction. 4. Experimental Setup and Procedure
Data collection
The experiments are conducted on 4-axis CNC turning machine. Dynamometer is installed to measure feed force. Collected data is processed in the same manner with preliminary experiments. The experimental procedures and predefined cutting conditions are summarized in Fig. 7. 1. Change new cutting tool and start turning processes according to each predefined condition
Cutting conditions
2. Start cutting, record the force signal data and measure the straightness using the straightness tester. 3. By using wavelet transform, decompose the dynamic Fy in both time and frequency domains and check the relations with the straightness.
Repeat steps 1-5 for all cutting conditions
Data Analysis
Workpiece
4. Select the 8th level of the decomposed signals that straightness signals occur without noise and calculate the cutting force ratio. 5. Plot the graphs between the straightness error and the cutting force ratio.
Verification
6. Formulate straightness error prediction model at 95% confident level. 7. Verify the performance of the obtained prediction model with the new cutting condition trails.
Fig. 7. Experimental procedures and predefined cutting conditions.
5. Results and Discussions Results from all testing conditions confirmed that the Daubechies wavelet transform is an effective method to screen out the noise signals from the straightness signals which otherwise are mixed together in the measured feed force data. As expected, the frequency of the decomposed feed force which corresponds to the straightness frequency
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is found in the 8th level of the transformation whereas noise signals which having frequency exceed 100 Hz appears at the lower level of the transformation. As an example, Fig. 8 demonstrates the decomposed feed force in time and frequency domains for a broken chip condition. All noises found in higher frequency range are screened out since 1st to 6th transformations. Hence, a spike at 18 Hz is found in the 8th transformation level. This frequency of 18 Hz corresponds to the straightness signal since the same 18 Hz frequency is found in the PSD of the workpiece straightness as shown in Fig. 9. In conclusion, the decomposed feed force on the 8th level of wavelet transformation can be used to monitor and predict the in-process workpiece straightness without effects from the chip conditions which likely to occur in the actual machining process.
Broken chip frequency
Straightness Frequency Fig. 8. Example of decomposed feed force of broken chip in time domain (left) and frequency domain (right) using cutting speed of 100 m/min, feed rate of 0.25 mm/rev, depth of cut of 0.8 mm, tool nose radius of 0.8 and rake angle of -6°.
Straightness frequency
Fig. 9. Illustration of the straightness in time domain and its PSD in frequency domain using cutting speed of 100 m/min, feed rate of 0.25 mm/rev, depth of cut of 0.8 mm, tool nose radius of 0.8 and rake angle of -6°.
6. Straightness Model and Accuracy The experimentally obtained in-process prediction of straightness error model at 95% confident level is expressed in equation (3) below. According to equation (3), positive and negative powers represent effect on straightness in opposite directions. Hence, the predictor variables can be categorized in two separate groups, the predictors with positive and negative power.
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𝑭𝑭𝑭𝑭𝒚𝒚𝒚𝒚(𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎) −𝑭𝑭𝑭𝑭𝒚𝒚𝒚𝒚(𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒏𝒏𝒏𝒏)
∙ 𝒆𝒆𝒆𝒆−𝟎𝟎𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟐𝟐𝟐𝟐𝟎𝟎𝟎𝟎 ∙ � 𝑺𝑺𝑺𝑺𝒕𝒕𝒕𝒕 = 𝒆𝒆𝒆𝒆𝟔𝟔𝟔𝟔.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 ∙ 𝑽𝑽𝑽𝑽−𝟎𝟎𝟎𝟎.𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟎𝟎𝟎𝟎𝟑𝟑𝟑𝟑 ∙ 𝒇𝒇𝒇𝒇𝟏𝟏𝟏𝟏.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑 ∙ 𝑫𝑫𝑫𝑫𝟎𝟎𝟎𝟎.𝟐𝟐𝟐𝟐𝟑𝟑𝟑𝟑𝟐𝟐𝟐𝟐𝟎𝟎𝟎𝟎 ∙ 𝑹𝑹𝑹𝑹−𝟎𝟎𝟎𝟎.𝟔𝟔𝟔𝟔𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐 𝒏𝒏𝒏𝒏
𝑭𝑭𝑭𝑭𝒚𝒚𝒚𝒚(𝒔𝒔𝒔𝒔)
𝟎𝟎𝟎𝟎.𝟏𝟏𝟏𝟏𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟏𝟏𝟏𝟏
�
7 285
(3)
• Predictors with positive power include the force ratio, the feed rate (f), and the depth of cut (D). - The relation between the straightness error and the cutting force ratio shows the same trend even though the cutting conditions have been changed (Fig. 10). In general, the straightness error increases with the increase in the cutting force ratio [11].
Fig. 10. Example of experimentally obtained relation between straightness and cutting force ratio.
-
Effects from the feed rate and the depth of cut are similar. Higher feed rate and larger depth of cut lead to larger cutting area which further causes more vibration during cutting [15]. As a result, the straightness becomes poorer. In the other word, larger value of S t or more straightness error is expected. • Predictors with negative power include the cutting speed (V), the rake angle (R n ), and the tool nose radius (γ). - The faster cutting speed causes the higher cutting temperature which results in material softening and easier to cut [16]. Hence, lower value of S t or decreasing in the straightness error is expected. - The bigger rake angle results in better heat dissipation and finer chip flowability as a consequence of the lower tool wear rate and the lower cutting force. Therefore, the straightness error is also reduced. - The larger tool nose radius will reduce the feed marks on the machined surface [17]. The straightness error is reduced as a consequence. After knowing the in-process straightness error prediction model, the new cutting experiment conditions had been conducted in order to verify the accuracy of the model. The directly measured straightness errors versus ones from the prediction from all testing trials are shown in Fig. 11. Mostly, the prediction fell between the ±10% of the measured value. The prediction accuracy is about 92.14% which is slightly higher than the previous research which reported 91.85% accuracy [8]. The underlying reason may be the continuous chips found in the previous study and hence, the measured feed force may already consist of low noise signals. However, more noise signals due to broken chips can be assumed under actual machining process. Therefore, the straightness error prediction based on wavelet transformed data is expected to be a better prediction.
Fig. 11. Verification of in-process prediction of straightness error model.
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7. Conclusion The hybrid monitoring and prediction of chip formation and straightness error has been investigated under various cutting conditions. The results can be concluded as follow; 1. The Daubechies wavelet transform can be utilized to decompose the measured feed force in both time and frequency domains simultaneously. The 8th level of wavelet transformation is recommended to monitor and predict the in-process workpiece straightness without effects from the chip conditions. 2. The cutting force ratio can eliminate the effects of cutting conditions. The ratio is calculated by taking the ratio of the highest peak-to-valley amplitude of decomposed dynamic feed force to its static feed force. 3. The exponential function can be employed to model the relation of the straightness error, the cutting force ratio, and the cutting parameters. The multiple regression analysis is utilized to calculate the powers and constant of the model with the use of least square method at 95% confidence level. The major contribution of this research is that the straightness error can be predicted and checked during the inprocess turning without machine stoppage, which leads to the higher productivity and better quality of workpiece. Acknowledgments This work was performed by the partial funding of The Thailand Research Fund (TRF). References [1] S.C. Kim, S.C. Chung, Synthesis of the multi-step straightness control system for shaft straightening processes. Mechatronics, 12 (2002) 139156. [2] X. Bi, Y. Liu and Y. Liu, Analysis and control of dimensional precision in turning process. In Proceedings of the 21th Chinese Control and Decision Conference, 2009. [3] T. Somkiat, Development of intelligent identification of cutting states by spectrum analysis. Journal of Advanced mechanical Design, System, and Manufacturing, 2 (2008) 366-377. [4] G. Jialiang, H. Rongdi, A united model of diametral error in slender bar turning with a follower rest. International Journal of Machine Tools & Manufacture, 46 (2006) 1002-1012.. [5] B. Kilic, Juan A, A.C. and S. Raman, Inspection of the cylindrical surface feature after turning using coordinate metrology. International Journal of Machine Tool & Manufacture, 47 (2007) 1893-1903. [6] A.M. Shawky, M.A. Elbestawi, In-process evaluation of workpiece geometrical tolerances in bar turning. Int. J. Mach. Tools Manufact, 36 (1996) 33-46. [7] Z. Hessainia, A. Belbah, M.A. Yallese, T. Mabrouki, J.F. Rigal, On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations, Measurement, 46 (2013) 1671–1681. [8] T. Shansungnoen, S. Tangjitsitcharoen, Investigation of relation between Straightness and Cutting Force in CNC Turning Process, Appl Mech Mater. 789-790 (2015) 812-820. [9] S. Tangjitsitcharoen, N. Pongsathonwiwat, Development of chatter detection in milling processes, Int J Adv Manuf Tech, 65 (2013) 919-927. [10] S. Tangjitsitcharoen, S. Tanintorn, S. Ratanakuakangwan, Advance in chatter detection in ball end milling process by utilizing wavelet transform, J Intell Manuf, 26(3), (2013) 485-499. [11] S. Tangjitsitcharoen and M. Sassantiwong, In-Process Prediction of Straightness in CNC Turning by Using Wavelet Transform, In Proceedings of the 2nd International Conference on Green Materials and Environmental Engineering, (2015) 199-203. [12] S. Tangjitsitcharoen, K. Samanmit, Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces, Journal of Engineering Manufacture, (2016) 1-16. [13] G. Liotto, C. Wang, Straightness measurement of a long guide way A comparison of dual-beam laser technique and optical collimator. The 2nd International Symposium on Precision Mechanical Measurements, (2004). [14] S.H.R. Ali, H.H Mohamed, M.K. Bedewy, Identifying Cylinder Liner Wear using Precise Coordinate Measurements. International Journal of Precision Engineering and Manufacturing, 10(5), (2009) 19-25. [15] S. Tangjitsitcharoen, S. Ratanakuakangwan, Monitoring of cutting conditions with dry cutting on CNC turning machine. Journal of Key Engineering Materials, 443 (2010) 382-387. [16] L. An, Turning parameter optimization for minimum production cost by integer programming. International conference on System Science, Engineering Design and Manufacturing Informatization, (2010). [17] P. Venkataramaiah, K.DharmaReddy, P. Meramma, Analysis on influence of feed rate and tool geometry on cutting forces in turning using Taguchi method and Fuzzy logic. Procedia Materials Science. 5 (2014) 1692-1701.
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8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden 8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden
Hybrid Monitoring of Chip Formation and Straightness in CNC Hybrid Monitoring of Chip Formation and Straightness in CNC Turning by Utilizing Daubechies Wavelet Transform Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Turning by Utilizing Daubechies Wavelet Transform 2017, Vigo (Pontevedra), Spain Somkiat Tangjitsitcharoen*, Haruetai Lohasiriwat Somkiat Tangjitsitcharoen*, Haruetai Lohasiriwat
Department Industrial Engineering, Faculty of Engineering,in Chulalongkorn University Costing models forof capacity optimization Industry 4.0: Trade-off Phayathai Road, Patumwan, 10330, Chulalongkorn Thailand Department of Industrial Engineering, FacultyBangkok, of Engineering, University Phayathai Road, Patumwan, Bangkok, 10330, Thailand efficiency between used capacity and operational
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb University of Minho, 4800-058 Guimarães, Portugal Abstract b Unochapecó, 89809-000 Chapecó, SC, Brazil Abstract The relations of straightness, chip formation, and cutting forces are investigated during the CNC turning process. Utilizing the Daubechies transform,chip the formation, dynamic cutting forcesforces are decomposed to classify andprocess. broken Utilizing chip signals The relationswavelet of straightness, and cutting are investigated duringthe thestraightness CNC turning the according towavelet the frequency ranges. The straightness frequency occurs at the on the and higher levelchip of wavelet Daubechies transform, the dynamic cutting forces are decomposed to lower classifyfrequency the straightness broken signals Abstract transform. to Thethe chip breakingranges. frequency at the frequency higher frequency of wavelet transform according to the according frequency Theappears straightness occurs on at the lower lower level frequency on the higher level of wavelet chip length.The The results supported the use of the feed forces to estimate theofstraightness error during the cutting transform. chip breaking frequency appears at decomposed the higher frequency on the lower level wavelet transform according to the Under the concept of supported "Industrythe4.0", processes will be pushed the to straightness be increasingly interconnected, process. chip length. The results use ofproduction the decomposed feed forces to estimate error during the cutting information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization process. goes beyond the traditional aim capacity © 2018 The Authors. Published by of Elsevier B.V.maximization, contributing also for organization’s profitability and value. © 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Publishedand by Elsevier B.V. committee Peer-review under responsibility of the scientific the8th 8th SwedishProduction Production Symposium. Indeed, lean management continuous improvement approaches suggest Symposium. capacity optimization instead of Peer-review under responsibility of the scientific committee ofofthe Swedish Peer-review under responsibility of the scientific committee the 8th Swedish Symposium. maximization. The study of capacity optimization andofcosting models Production is an important research topic that deserves a
Keywords: Turning; Decomposed forces; Straightness; Chip; Wavelet transform contributions from both the cutting practical and theoretical perspectives. This paper presents and discusses a mathematical Keywords: Turning; Decomposed cutting forces; Straightness; Chip; Wavelet transform model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity 1. Introduction optimization might hide operational inefficiency. The The geometric of Elsevier the workpiece is an important indication of machining performance. Workpiece © 2017 Authors. accuracy Published by B.V. The geometric accuracy of the workpiece issuch an of important indication of machining Workpiece acceptance usually depends on various measures asthe surface roughness and straightness which can be measured Peer-review under responsibility of the scientific committee Manufacturing Engineering Societyperformance. International Conference acceptance depends on various measures suchmeasuring as surfaceinstrument. roughness and straightness which can betomeasured and obtainedusually simultaneously using the surface profile However, it is rather difficult perform 2017. and obtained usingthe theactual surface profile measuring instrument. However,research it is rather to perform direct measuresimultaneously continually during machining process. As a result, extensive hasdifficult been conducted to direct measure continually duringCapacity the actual machining As a result, extensive research has been conducted to Keywords: Cost Models; ABC; TDABC; Management; Idle process. Capacity; Operational Efficiency
1. Introduction * Corresponding author. Tel.: 662-218-6814.
address:author. [email protected] * E-mail Corresponding Tel.: 662-218-6814. The cost of idle capacity is a fundamental information for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 2018 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured Peer-review of the scientific committee of the 8th Production Symposium. 2351-9789 2018responsibility The Authors. Published by Elsevier B.V.hours in several©under ways: tons of production, available of Swedish manufacturing, etc. The management of the idle capacity Peer-review underTel.: responsibility the761; scientific committee the 8th Swedish Production Symposium. * Paulo Afonso. +351 253 of 510 fax: +351 253 604of741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 8th Swedish Production Symposium. 10.1016/j.promfg.2018.06.084
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come up with the valid in-process prediction methods. Seung and Sung [1] proposed a multi-step straightness control system to minimize the straightness error of the machined shafts using the fuzzy self-learning method. Many other researchers investigated relationship among technical properties of the CNC machine such as the cutting conditions, cutting parameters [2,3], the real time measurement of dynamic cutting forces [4,5,6,7], and the workpiece quality. Recently, the dynamic cutting forces have been proposed to monitor the straightness in CNC turning [8]. The study employed the Fast Fourier Transform (FFT) and proved that the cutting force ratio together with some cutting parameters (e.g., cutting speed, feed rate, rake angle) can be used in straightness error prediction. However, when the cutting forces received from the direct measurement system were mixed with noise signals such as broken chips, the prediction could be weak. Therefore, to gain more accurate prediction the cutting forces have to firstly get decomposed in order to classify the straightness signal out from the noise signals. Though the FFT method was able to detect the chatter vibration in frequency domain [9], in-process detection requires time domain analysis in order to monitor the corresponding force signals and identify the chatter vibration robustly. It was found earlier that the Daubechies wavelet transform could be used to decompose the dynamic cutting forces to identify the chatter signal in both time and frequency domains simultaneously [10]. The wavelet transform method was successfully utilized to classify the roundness and straightness frequency, which occurred in the 8th level of the transformation [11,12]. Hence, the aim of this research is to propose the use of Daubechies wavelet transform to decompose the dynamic cutting forces in CNC turning process and recruit the straightness signal from the other noise signals. The straightness frequency is expected to appear at the high level of the 8th level of the decomposed cutting force while the chip breaking and noise signals will happen at the lower levels due to the higher frequency of itself. 2. Investigation of Dynamic Cutting Forces and Straightness The preliminary experiments were conducted to investigate relations between signals of the dynamic cutting forces and straightness. The carbon steel S45C and coated carbide tools were used in all testing conditions whereas a range of cutting conditions were varied by cutting speed, feed rate, depth of cut, tool nose radius, and rake angle. The dynamic cutting forces were monitored using a dynamometer which was installed on the CNC turret with the use of sampling rate of 10 kHz and amplified through the cut-off frequency of 5 kHz before digitization and calculation in the computer program. On the other hand, the workpiece straightness is measured off-line using the straightness tester. The measuring direction of the straightness is parallel to the workpiece axis on the cylindrical parts [13,14]. Fig. 1 and Fig.2 illustrate the results when there were continuous chips and broken chips, respectively. The obtained results include three components of the dynamic cutting forces which are the radial force (F x ), the feed force (F y ), the main force (F z ), and the corresponding straightness measured in time domain.
Fig. 1. Example of dynamic cutting forces (F x ,F y ,F z ) (middle) and straightness profile in time domain (right) when continuous chip occurs (left).
Fig. 2. Example of dynamic cutting forces (F x ,F y ,F z ) (middle) and straightness profile in time domain (right) when broken chip occurs (left).
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After that, utilizing the FFT method, their power spectrum densities (PSD) are demonstrated in frequency domain (Fig. 3 and Fig. 4). From the preliminary results, two types of signal conditions are found. Firstly, when there are only continuous chips, the frequency of the straightness profile is correspondent with the frequency of the dynamic cutting forces (e.g., 33 Hz in Fig. 3). The plot has no sign of noise signals. Secondly, when the chips are broken and hit the cutting tool and/or the workpiece, the dynamic cutting forces vary and result in the relatively larger amplitudes (Fig. 2) as compared to the ones from the continuous chip condition (Fig. 1). The PSD plot in this condition shows two distinct signals; straightness frequency which is found in lower frequency range, and chip breaking frequency which is found in higher frequency range (900 Hz to 1 kHz in Fig.4). Generally, the shorter chip length leads to the higher chip breaking frequency. It is difficult to differentiate between both signals when straightness and broken chip frequency are on the same PSD of the dynamic cutting forces (Fig. 4). Thus, the Daubechies wavelet transform is proposed to pull the straightness signal out from the noise signals (Fig. 5).
Fig. 3. Example of PSD of dynamic cutting forces (left) and PSD of straightness in frequency domain (right).
Fig. 4. Example of PSD of dynamic cutting forces (left) and PSD of straightness in frequency domain (right).
Straightness frequency
Chip breaking frequency
Straightness frequency Fig. 5. Illustration of decomposed feed force (Fy) in time domain (left) and its PSD in frequency domain (right) using speed of 200 m/min, feed rate of 0.25 mm/rev, depth of cut 0.8 mm, tool nose radius of 0.8 and rake angle of 11°.
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Only F y or the feed force is selected to be transformed and utilized to model the in-process prediction of straightness error as the feed force is most sensitive to the straightness on account of the measuring direction [8,11,12]. In the other word, since straightness referred to whether the workpiece is machined into appropriate alignment with the horizontal level, the feed force which also applies horizontally is the major contribution. In Fig. 5, it is clear that the PSD of the decomposed feed force effectively differentiate straightness frequency from chip breaking frequency on the 8th level. Hence, the decomposed feed force from the 8th level will be monitored and utilized to estimate the straightness error during the CNC turning. 3. In-Process Prediction of Straightness Model The proposed model consists of two main components (Fig. 6). Firstly, the set up component, this component consists of cutting conditions and tool related factors previously found to have great effect on the cutting force which eventually affects the straightness. Secondly, as the cutting process continues, the on-process component of the model will take into consideration the additional dynamic variances which result in better prediction. The on-process component utilized the measured feed force continually detected via dynamometer during actual cutting operation. Major reason is that the cutting forces have been repeatedly proved to correspond with the roughness and straightness on the surface of the machined workpiece [8-12]. Note that the dynamic response of dynamometer has been checked by employing the hammer test, and the natural frequency of the dynamometer is 2.7 kHz. When analyzed, the measured feed force (F y ) is considered to be consist of two portions; the static feed force (F y(static) or F y(s) ) and the dynamic feed force (F y(dynamic) ). As illustrated in Fig.6, the average force required for cutting process is the static portion and hence calculated from the difference between F y average and F o . The variation of measured force is the dynamic portion and hence calculated from the difference between the maximum and the minimum forces. Normally, the static feed force varies according to the workpiece hardness whereas the dynamic feed force mainly depends on the cutting conditions. Therefore, the ratio of both forces is proposed to be used in the model. However, since the dynamic feed force may also include the noise and broken chip signals as mentioned previously. It is necessary to decompose the dynamic feed force before taking the ratio. The equation (1) illustrates the cutting force ratio of decomposed feed force to its static feed force. The decomposed feed force is calculated from the amplitude of the signals in dynamic portion, which is obtained and correspondent with the machining time record of straightness. 𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑑𝑑𝑑𝑑 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑑𝑑𝑑𝑑𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶)) � 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑)
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = �
𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑚𝑚𝑚𝑚)−𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑)
=�
Straightness Prediction Model
𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑑𝑑𝑑𝑑)
(1)
�
Static feed force (Y Axis)
Dynamic feed force (Y Axis)
150
Cutting conditions - Cutting speed - Feed rate - Depth of cut
Measured feed force - Fy (static) - Fy (dynamic)
Tool factors - Tool nose radius - Rake angle
8th level Decomposed Fy (dynamic) Cutting force ratio
120
120
90
90
60
Fy(static) = Fy(s) 30
0 22000 -30
Feed force (N)
On-process component
Fy(max)
Fy(avg)
Feed force (N)
Set up component
150
60
Fy(min)
Fy(dynamic)
30
F0
26000
Time (s)
30000
0 25000 -30
30000
Time (s)
Fig. 6. Prediction model concept and example of measured feed force in detail obtained from cutting speed of 200 m/min, feed rate of 0.25 mm/rev, depth of cut of 0.6 mm, tool nose radius of 0.8 and rake angle of 11°.
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Finally, the in-process straightness error prediction model which consisted of both set up and on-process components is developed in a form of exponential function as shown in equation (2). 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚) −𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚)
𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡 = 𝐶𝐶𝐶𝐶1 ∙ (𝑉𝑉𝑉𝑉)𝑎𝑎𝑎𝑎1 ∙ (𝑓𝑓𝑓𝑓)𝑎𝑎𝑎𝑎2 ∙ (𝐷𝐷𝐷𝐷)𝑎𝑎𝑎𝑎3 ∙ (𝑅𝑅𝑅𝑅𝑛𝑛𝑛𝑛 )𝑎𝑎𝑎𝑎4 ∙ (𝐹𝐹𝐹𝐹)𝑎𝑎𝑎𝑎5 𝛾𝛾𝛾𝛾 ∙ �
𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦(𝑠𝑠𝑠𝑠)
𝑎𝑎𝑎𝑎6
(2)
�
Where S t is the straightness error in µm, V is the cutting speed in m/min, f is the feed rate in mm/rev, D is the depth of cut in mm, R n is the tool nose radius in mm, e is the exponential value of 2.718, γ is the rake angle in degree, F y (max) and F y (min) are the maximum and minimum decomposed dynamic feed force in N, F y (s) is the static feed force in N, a 1 , a 2 , a 3 , a 4 , a 5 , a 6 and C 1 are the powers and constants of the model. The multiple regression analysis is adopted and the least square method is used to model the prediction. 4. Experimental Setup and Procedure
Data collection
The experiments are conducted on 4-axis CNC turning machine. Dynamometer is installed to measure feed force. Collected data is processed in the same manner with preliminary experiments. The experimental procedures and predefined cutting conditions are summarized in Fig. 7. 1. Change new cutting tool and start turning processes according to each predefined condition
Cutting conditions
2. Start cutting, record the force signal data and measure the straightness using the straightness tester. 3. By using wavelet transform, decompose the dynamic Fy in both time and frequency domains and check the relations with the straightness.
Repeat steps 1-5 for all cutting conditions
Data Analysis
Workpiece
4. Select the 8th level of the decomposed signals that straightness signals occur without noise and calculate the cutting force ratio. 5. Plot the graphs between the straightness error and the cutting force ratio.
Verification
6. Formulate straightness error prediction model at 95% confident level. 7. Verify the performance of the obtained prediction model with the new cutting condition trails.
Fig. 7. Experimental procedures and predefined cutting conditions.
5. Results and Discussions Results from all testing conditions confirmed that the Daubechies wavelet transform is an effective method to screen out the noise signals from the straightness signals which otherwise are mixed together in the measured feed force data. As expected, the frequency of the decomposed feed force which corresponds to the straightness frequency
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is found in the 8th level of the transformation whereas noise signals which having frequency exceed 100 Hz appears at the lower level of the transformation. As an example, Fig. 8 demonstrates the decomposed feed force in time and frequency domains for a broken chip condition. All noises found in higher frequency range are screened out since 1st to 6th transformations. Hence, a spike at 18 Hz is found in the 8th transformation level. This frequency of 18 Hz corresponds to the straightness signal since the same 18 Hz frequency is found in the PSD of the workpiece straightness as shown in Fig. 9. In conclusion, the decomposed feed force on the 8th level of wavelet transformation can be used to monitor and predict the in-process workpiece straightness without effects from the chip conditions which likely to occur in the actual machining process.
Broken chip frequency
Straightness Frequency Fig. 8. Example of decomposed feed force of broken chip in time domain (left) and frequency domain (right) using cutting speed of 100 m/min, feed rate of 0.25 mm/rev, depth of cut of 0.8 mm, tool nose radius of 0.8 and rake angle of -6°.
Straightness frequency
Fig. 9. Illustration of the straightness in time domain and its PSD in frequency domain using cutting speed of 100 m/min, feed rate of 0.25 mm/rev, depth of cut of 0.8 mm, tool nose radius of 0.8 and rake angle of -6°.
6. Straightness Model and Accuracy The experimentally obtained in-process prediction of straightness error model at 95% confident level is expressed in equation (3) below. According to equation (3), positive and negative powers represent effect on straightness in opposite directions. Hence, the predictor variables can be categorized in two separate groups, the predictors with positive and negative power.
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𝑭𝑭𝑭𝑭𝒚𝒚𝒚𝒚(𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎) −𝑭𝑭𝑭𝑭𝒚𝒚𝒚𝒚(𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒏𝒏𝒏𝒏)
∙ 𝒆𝒆𝒆𝒆−𝟎𝟎𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟐𝟐𝟐𝟐𝟎𝟎𝟎𝟎 ∙ � 𝑺𝑺𝑺𝑺𝒕𝒕𝒕𝒕 = 𝒆𝒆𝒆𝒆𝟔𝟔𝟔𝟔.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 ∙ 𝑽𝑽𝑽𝑽−𝟎𝟎𝟎𝟎.𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟎𝟎𝟎𝟎𝟑𝟑𝟑𝟑 ∙ 𝒇𝒇𝒇𝒇𝟏𝟏𝟏𝟏.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑 ∙ 𝑫𝑫𝑫𝑫𝟎𝟎𝟎𝟎.𝟐𝟐𝟐𝟐𝟑𝟑𝟑𝟑𝟐𝟐𝟐𝟐𝟎𝟎𝟎𝟎 ∙ 𝑹𝑹𝑹𝑹−𝟎𝟎𝟎𝟎.𝟔𝟔𝟔𝟔𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐 𝒏𝒏𝒏𝒏
𝑭𝑭𝑭𝑭𝒚𝒚𝒚𝒚(𝒔𝒔𝒔𝒔)
𝟎𝟎𝟎𝟎.𝟏𝟏𝟏𝟏𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟏𝟏𝟏𝟏
�
7 285
(3)
• Predictors with positive power include the force ratio, the feed rate (f), and the depth of cut (D). - The relation between the straightness error and the cutting force ratio shows the same trend even though the cutting conditions have been changed (Fig. 10). In general, the straightness error increases with the increase in the cutting force ratio [11].
Fig. 10. Example of experimentally obtained relation between straightness and cutting force ratio.
-
Effects from the feed rate and the depth of cut are similar. Higher feed rate and larger depth of cut lead to larger cutting area which further causes more vibration during cutting [15]. As a result, the straightness becomes poorer. In the other word, larger value of S t or more straightness error is expected. • Predictors with negative power include the cutting speed (V), the rake angle (R n ), and the tool nose radius (γ). - The faster cutting speed causes the higher cutting temperature which results in material softening and easier to cut [16]. Hence, lower value of S t or decreasing in the straightness error is expected. - The bigger rake angle results in better heat dissipation and finer chip flowability as a consequence of the lower tool wear rate and the lower cutting force. Therefore, the straightness error is also reduced. - The larger tool nose radius will reduce the feed marks on the machined surface [17]. The straightness error is reduced as a consequence. After knowing the in-process straightness error prediction model, the new cutting experiment conditions had been conducted in order to verify the accuracy of the model. The directly measured straightness errors versus ones from the prediction from all testing trials are shown in Fig. 11. Mostly, the prediction fell between the ±10% of the measured value. The prediction accuracy is about 92.14% which is slightly higher than the previous research which reported 91.85% accuracy [8]. The underlying reason may be the continuous chips found in the previous study and hence, the measured feed force may already consist of low noise signals. However, more noise signals due to broken chips can be assumed under actual machining process. Therefore, the straightness error prediction based on wavelet transformed data is expected to be a better prediction.
Fig. 11. Verification of in-process prediction of straightness error model.
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Somkiat Tangjitsitcharoen et al. / Procedia Manufacturing 25 (2018) 279–286 Somkiat Tangjitsitcharoen / Procedia Manufacturing 00 (2018) 000–000
7. Conclusion The hybrid monitoring and prediction of chip formation and straightness error has been investigated under various cutting conditions. The results can be concluded as follow; 1. The Daubechies wavelet transform can be utilized to decompose the measured feed force in both time and frequency domains simultaneously. The 8th level of wavelet transformation is recommended to monitor and predict the in-process workpiece straightness without effects from the chip conditions. 2. The cutting force ratio can eliminate the effects of cutting conditions. The ratio is calculated by taking the ratio of the highest peak-to-valley amplitude of decomposed dynamic feed force to its static feed force. 3. The exponential function can be employed to model the relation of the straightness error, the cutting force ratio, and the cutting parameters. The multiple regression analysis is utilized to calculate the powers and constant of the model with the use of least square method at 95% confidence level. The major contribution of this research is that the straightness error can be predicted and checked during the inprocess turning without machine stoppage, which leads to the higher productivity and better quality of workpiece. Acknowledgments This work was performed by the partial funding of The Thailand Research Fund (TRF). References [1] S.C. Kim, S.C. Chung, Synthesis of the multi-step straightness control system for shaft straightening processes. Mechatronics, 12 (2002) 139156. [2] X. Bi, Y. Liu and Y. Liu, Analysis and control of dimensional precision in turning process. In Proceedings of the 21th Chinese Control and Decision Conference, 2009. [3] T. Somkiat, Development of intelligent identification of cutting states by spectrum analysis. Journal of Advanced mechanical Design, System, and Manufacturing, 2 (2008) 366-377. [4] G. Jialiang, H. Rongdi, A united model of diametral error in slender bar turning with a follower rest. International Journal of Machine Tools & Manufacture, 46 (2006) 1002-1012.. [5] B. Kilic, Juan A, A.C. and S. Raman, Inspection of the cylindrical surface feature after turning using coordinate metrology. International Journal of Machine Tool & Manufacture, 47 (2007) 1893-1903. [6] A.M. Shawky, M.A. Elbestawi, In-process evaluation of workpiece geometrical tolerances in bar turning. Int. J. Mach. Tools Manufact, 36 (1996) 33-46. [7] Z. Hessainia, A. Belbah, M.A. Yallese, T. Mabrouki, J.F. Rigal, On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations, Measurement, 46 (2013) 1671–1681. [8] T. Shansungnoen, S. Tangjitsitcharoen, Investigation of relation between Straightness and Cutting Force in CNC Turning Process, Appl Mech Mater. 789-790 (2015) 812-820. [9] S. Tangjitsitcharoen, N. Pongsathonwiwat, Development of chatter detection in milling processes, Int J Adv Manuf Tech, 65 (2013) 919-927. [10] S. Tangjitsitcharoen, S. Tanintorn, S. Ratanakuakangwan, Advance in chatter detection in ball end milling process by utilizing wavelet transform, J Intell Manuf, 26(3), (2013) 485-499. [11] S. Tangjitsitcharoen and M. Sassantiwong, In-Process Prediction of Straightness in CNC Turning by Using Wavelet Transform, In Proceedings of the 2nd International Conference on Green Materials and Environmental Engineering, (2015) 199-203. [12] S. Tangjitsitcharoen, K. Samanmit, Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces, Journal of Engineering Manufacture, (2016) 1-16. [13] G. Liotto, C. Wang, Straightness measurement of a long guide way A comparison of dual-beam laser technique and optical collimator. The 2nd International Symposium on Precision Mechanical Measurements, (2004). [14] S.H.R. Ali, H.H Mohamed, M.K. Bedewy, Identifying Cylinder Liner Wear using Precise Coordinate Measurements. International Journal of Precision Engineering and Manufacturing, 10(5), (2009) 19-25. [15] S. Tangjitsitcharoen, S. Ratanakuakangwan, Monitoring of cutting conditions with dry cutting on CNC turning machine. Journal of Key Engineering Materials, 443 (2010) 382-387. [16] L. An, Turning parameter optimization for minimum production cost by integer programming. International conference on System Science, Engineering Design and Manufacturing Informatization, (2010). [17] P. Venkataramaiah, K.DharmaReddy, P. Meramma, Analysis on influence of feed rate and tool geometry on cutting forces in turning using Taguchi method and Fuzzy logic. Procedia Materials Science. 5 (2014) 1692-1701.