- Email: [email protected]
Thermographic Analysis of Spark Location Distribution in Sinking EDM
Available online at www.sciencedirect.com
ScienceDirect Procedia CIRP 68 (2018) 280 – 285
19th CIRP Conference on Electro Physical and Chemical Machining, 23-27 April 2018, Bilbao, Spain
Thermographic Analysis of Spark Location Distribution in Sinking EDM F. Klockea, S. Schneidera,*, B. Frauenknechta, L. Hensgena, A. Klinka, K. Oßwaldb a
Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany b Institute for Materials and Material Technologies, Pforzheim University, Pforzheim, Germany
* Corresponding author. Tel.: +49-241-80-28243; fax: +49-241-80-22293. E-mail address: [email protected]
Abstract In order to have a good comprehension of the heat input and hence the temperature evolution in the EDM process it is crucial to gain an insight into the two-dimensional position distribution of consecutive discharges. This distribution affects the material removal from the workpiece and tool electrodes as well as the manufacturing precision. Consequently, an investigation of the discharge distribution across the electrode profile represents a very relevant research issue. Therefore, for this article the rear side of thin specimens was observed using a thermographic camera whilst the front was machined by a S-EDM process. Every discharge caused a heat input on the front, which was conducted through the specimen and generated a hot spot on its rear. The temperature field was recorded by a thermographic camera. The recorded data were analyzed by a specific image data processing software, which detected the hot spots and classified them according to their two-dimensional position. In a further step, the machining data was compared to the image data in order to assign the discharge energy to the discharge location. This comparison provides the time- and spatial-resolved energy distribution during the S-EDM process. ©2018 2018The The Authors. Published by Elsevier B.V. © Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of thec committee scientific of committee of Conference the 19th CIRP Conference on Electro and Chemical Machining. Peer-review under responsibility of the scientifi the 19th CIRP on Electro Physical and ChemicalPhysical Machining
Keywords: EDM; temperature measurement; energy distribution
1. Introduction and Literature Review In electrical discharge machining (EDM) material removal is achieved by high-frequency electrical discharges, that melt and evaporate workpiece material. The evaporation of material leads to ejection of molten material. Therefore, a local removal of material is attained at the location of discharge. Consequently, the two-dimensional distribution of discharges has considerable influence on the removal process and hence its detection has been approached by several research groups. The electrical discharges leads to thermal loads in the workpiece material which causes several rim zone modifications. The capability of a priori design of specific surface integrity properties prior to a manufacturing process represents a challenge in current research. One way to approach this challenge is the concept of process signatures which provides a link between the material loadings and the material modifications [1-3]. For a comprehensive description of thermal loadings, it is necessary to gain an understanding of the
temporally and spatially resolved energy input into the workpiece in the S-EDM process. Kunieda et al. [4] developed an approach of detecting the spark location by measuring a current signal in branched wires placed on the tool electrode. Due to the correlation between current, electrical resistance and spark location the ratio of the integrated signals allowed to calculate the discharge position. However, the accuracy of this method decreases when low resistance materials, such as steel, are used. A further improvement of this method was achieved by Han et al. [5] considering the potential difference between the branches instead of the current ratio. Therefore, the voltage originated by the current conducted through the branches was measured. As only the ending part of voltage signals was used for detection, influence of noise generated by the discharge could be minimized and detection accuracy could be refined to several millimeters. Qiang et al. [6] developed another method for the two dimensional EDM spark location, making use of the magnetic
2212-8271 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 19th CIRP Conference on Electro Physical and Chemical Machining doi:10.1016/j.procir.2017.12.064
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F. Klocke et al. / Procedia CIRP 68 (2018) 280 – 285
field generated by the discharge current. These signals were measured using two pairs of Hall sensors placed on opposite sides of a square-shaped workpiece electrode. The comparison of the sensor signals allowed to calculate the spark location. This method also reached an accuracy of several millimeters. Smith and Koshy [7] presented a method to detect the spark location by acoustic emission. For this purpose, acoustic emission caused by a discharge was measured at each end of a wire electrode. Analyzing the specific frequencies of electromagnetic interference and acoustic emission signals, the time lag between both measurements was determined and thereby the (one-dimensional) spark location. Acoustic emission is a highly precise way of spark location detection as it can minimize detection inaccuracy to 0.07 mm. Nevertheless, real-time and two-dimensional implementation of this method is still a challenge, as successive discharges lead to interference with the acoustic signals of earlier discharges. Kitamura et al. [8] observed the EDM machining gap through a SiC single crystal wafer as a workpiece electrode. SiC is a very suitable material for process observation, as it is electroconductive and sufficiently transparent to observe the process. As the thermal loading of a discharge damaged the crystal structure of SiC, every discharge led to an intransparent spot on the wafer. Hence, discharge distribution became apparent and it was found, that discharge locations were evenly distributed over the electrode surface. The same research group also used Ga2O3 single crystal as an electrode material [9], as it had similar characteristics as SiC but a higher and more uniform optical transmissivity which allowed observation of the arc plasma. The aim of this paper is to present a possibility of detecting the spatial-resolved distribution of discharges in S-EDM at high resolution for common discharge values and electrode materials. Therefore, the rears of thin specimens were observed during EDM processing, using a thermographic camera. The temperature evolution within the specimens was recorded, so spark locations could be observed by a local rise of temperature. This concept has several advantages compared to former approaches as is suitable for any conductive material and not limited to translucent materials, although this method is restricted by the record speed of the thermographic camera. In this investigation the pause duration had to be extended to ensure the recording of every discharge. The characteristic hot spots in the temperature field are analyzed by a specific image data processing software, which detects the spark locations and classifies them according to their two dimensional position. Also, if a discharge appears at the same position as a former discharge it can be observed. In a further step, the machining data is compared to the image data in order to assign the discharge energy to the discharge location. This comparison provides the time-resolved spatial energy distribution during the S-EDM process. This data of energy input is a requirement to calculate the entire temperature field in the workpiece and hence to apply the approach of process signatures [1,2].
2. Experimental Setup The experiments were conducted on a GFMS AgieCharmilles FORM 2000 sinking EDM machine. The erosion took place in an electrically insulated reservoir filled with oelheld IonoPlus IME-MH CH-based dielectric fluid (Fig. 1). A cylindrical copper electrode with frontal area diameter of dTL = 5 mm was used as a tool. Plates made of 42CrMo4 steel (1.7225) were used as specimens. For constant flushing conditions, dielectric fluid was pumped continuously into the erosion zone. Since the reservoir was insulated the specimen was electrically contacted to the machine table.
tool electrode (copper) specimen
5 mm Fig. 1. Experimental setup for measuring local temperatures and detecting discharge spots with thermographic camera.
For workpiece temperature measurement a high speed thermographic camera FLIR X6580sc was used. As the maximum sampling rate of the thermographic camera is frec = 1250 Hz the theoretical discharge frequency was adjusted to fe,th = 610.5 Hz in order to fulfill Nyquist-Shannon sampling theorem securely. This was reached by increasing the pause duration of the process. The measured averaged discharge frequency was fe,real ≈ 350 Hz. The lens had a focal length of 50 mm providing a field of view of approx. 12x12 mm which relates to an object resolution of 0.15 mm. The specimen was integrated in the reservoir wall. Thus, only one side was in touch with the dielectric fluid and the outer side was visible (cp. Fig. 1). The specimens were coated with a special heat resistant black lacquer. Thus, it can be assumed that the specimens had an emissivity of ߝ ൎ ͳ (and nearly emitted black body radiation). The erosion parameters for the results presented in chapter 3.2 were kept constant (Table 1). The increased pause duration compared to a standard erosion process is remarkable. This raise was necessary to decrease the discharge frequency to a level for which the thermographic camera can fulfill the sampling theorem. Table 1. Erosion parameters. Erosion parameter
Value
Unit
Discharge current ܫ
11.5
A
Open circuit voltage ݑො
100
V
Pulse duration ݐ
38
ߤݏ
Pause duration ݐ
1600
ߤݏ
Erosion time between flushing jumps ݐ௦
2
s
282
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To determine the electrical energy of the EDM process the current and voltage characteristic of each discharge was analyzed in real time with a process monitoring tool, based on a field-programmable gate array (FPGA) [10]. Consequently, it was possible to characterize each single discharge and evaluate the process afterwards. 3. Methods 3.1. Heat transfer The thermographic camera detects intensity distributions of wave length spectrums. With Planck’s law of black body radiation these intensity spectrums can be translated into the temperature of the radiating black body (emissivity H { 1). As mentioned before, for this investigation it can be assumed that the examined specimen is nearly a black body with an emissivity H | . The observed temperature changes at the outside workpiece originate from the heat dissipation of the discharges in the working gap (inside workpiece). The discharge energy transforms into heat which causes melting and evaporization and hence material removal. The residual heat dissipates through the workpiece and induces a temperature rise within the material. This temperature rise generates different material modifications like changes in grain size, phase changes or residual stresses [11]. Furthermore, it leads to heat radiation at the rear surface of the workpiece which can be detected by the thermographic camera. To estimate the time lag between the pulse initiation and the according temperature rise at the opposite surface a heat transfer FEM simulation based on the Fourier’s law ߩܿ
߲ܶ ൌ ߘሬԦ ڄ൫ߣߘሬԦܶ൯ σݍ ߲ݐ
Temperature T / K
ݍሶ ̶
320 d = 0.2 mm
310
313 K 300 Δt = 150 µs
290 0
1
In order to detect discharge locations, an image data processing algorithm implemented in MATLAB® was used for adapting the image data from the thermographic camera. The temperature field is depicted in a pixel matrix (80 × 80). With the used optics a pixel equates to APixel = 0.15 mm x 0.15 mm. Each element of the matrix represents the temperature of a certain area of the specimen’s surface. For further processing, the difference of two following frames is considered in order to obtain the evolution of the temperature field (Fig. 3a). In a following step the algorithm detects the location of the maximum of the matrix, since a maximum in the heat evolution matrix could probably indicate a discharge. If the maximum value, that represents the increase of temperature from one frame to the next, is sufficiently high the warming is traced back to a discharge. In this case location and time of the discharge are stored (Fig. 3b). (a)
3 mm
Interframe time tif = 0.8 ms
ΔTmax = 26.3 K
3.2. Image Data Processing
(b)
(c)
(1)
was performed to verify the camera settings. The results are shown in Fig. 2. The thickness of the plate at the erosion area was chosen to be d = 0.2 mm because it was the thickness of the plate specimen.
330
'Tmax = 26.3 K which is in good accordance to the results of thermographic analysis (Fig. 2 bottom right) considering the high temperature fluctuation and the comparably slow framerate. The time lag between heat input and temperature rise was determined to 't = 150 µs. Additionally, in Fig. 2 the interframe time of the recording is depicted (white/grey). Because all along the interframe time the temperature is high enough to be detected by the thermographic camera the record frequency is high enough to capture every discharge.
296 K 2 3 Time t / ms
4
5
Fig. 2. Simulated temperature profile on the outside workpiece after a single discharge (Ie = 11.5 A; Ue = 31.3 V; te = 38 μs; ξwp = 50 %).
A simulated single discharge (Ie = 11.5 A; Ue = 31.3 V; te = 38 µs; ξwp = 50 %) causes a maximum temperature rise of
3 mm
3 mm
Fig. 3. Image data processing sequence: (a) Difference of thermograpic recordings (b) identify the position (c) disregarded area for following discharge.
Since the spread of a discharge-caused hot spot is visible over a few frames, the algorithm needs to be able to differentiate new hotspots from older ones. For this purpose an eventual increase of a hot spot temperature in the following frame is taken into account, as the algorithm disregards a slightly larger area than the actual hot spot size (Fig. 3c). By this means the algorithm avoids to detect one discharge twice. These measurements allow a sustainable and determined detection of spark locations and thereby enable an insight in the spatial discharge distribution. These aspects are important to investigate the influence of consecutive discharges on the material temperature and hence the material loading which leads to material modifications [1-3]. 4. Results and Discussion For further analysis of the process, different stages of machining were considered, as shown in Fig. 4. Therefore, five
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thousand discharges two times during and at the end of machining were evaluated concerning their spatial distribution.
t2 = 74.8032 s – 92.7776 s Δt2 = 17.9744 s 15 5000 discharges in each picture 10 5
t3 = 153.120 s –170.7616 s Δt3 = 17.6416 s
discharges per pixel
t1 = 0.0024 s – 19.2856 s Δt1 = 19.2832 s
0
2.5 mm
There are a few discharges which occur not on the tool area (r = 2.5 mm), but in the side gap 'dsidegap (cp. Fig. 6). The increasing distance to the center leads to a growth of area by its square. Therefore, discharge frequencies were normalized with the related area (Fig. 5b), in order to gain a comparable unit for discharge distribution. The discharge frequency per area over the whole electrode area decreases slightly with increasing distance to center. This phenomenon will be investigated in the next section. A discrepancy was only found in the center. This regards to sensitivity of the detection and area calculation. The observed area in the center range is only one pixel in the investigated picture. So if the discharge is not detected exactly at the electrode center the discharge was counted in the next range. Additionally, the approximation to a circle leads to errors in area calculation. For the side gap discharges (r t 2.5 mm) the discharge frequency is nearly zero because of the increasing area.
tool r = 2.5 mm
Δdside gap | 0.1 mm workpiece
Fig. 4. Discharge distribution during the process (t1 and t2) and at the end (t3) of the process.
60 50 40 30 20 10 0 25 20
fe,overall = 350 Hz
(a)
(b)
15 10 5
0
0
0.5 1 1.5 2 2.5 Distance to center dc / mm
Fig. 6. Discharge in the sidegap.
Considering the discharge frequency per area for the three above mentioned time intervals (c.p. Fig. 4), Fig. 7 shows the evolution of discharge distribution for discrete time intervals. It can be seen that in early process stages ('t1 and 't2) the discharge frequency it nearly uniform. At the end of the process ('t3) an increase of the concentration at a distance of dc = 0.6 mm can be observed. This hot spot can also be seen in the evaluation of the image data. This phenomenon leads to the decrease of the discharge frequency per area with increasing distance to the center (cp. Fig. 5). Discharge frequency per area fcc c/ (Hz/mm2)
Discharge frequency Discharge frequency per area fcc c / (Hz/mm2) f / Hz
An analysis of measured discharge energies showed that due to choosing an iso-energetic process under stable machining conditions nearly every discharge had the same amount of energy. Hence, the energy distribution equals the discharge distribution. Therefore only the discharge distribution is taken into account for further considerations. However, the discharge distribution varies during the process. The discharges are distributed relatively uniformly, whereas a concentration of discharges can be seen in the end of the process. For the following results, the discharge frequency was evaluated in relation to the position on the electrode for the whole process. Absolute discharge frequency rises with the distance to the center as shown in Fig. 5a.
3
Fig. 5. (a) Absolute discharge frequency as a function of distance to the electrode center, (b) discharge frequency per area depending on distance to center.
30
't1 't2 cp. Fig 4 't3 whole process
25 20 15 10 r = 0.6 mm
5 0
0
0.5 1 1.5 2 Distance to center dc / mm
2.5
Fig. 7. Discharge frequency per area during the process, at the end and averaged over the whole process.
F. Klocke et al. / Procedia CIRP 68 (2018) 280 – 285
A local concentration of discharges can be the result of two reasons. Either the local electrical field increased or the local dielectric strength decreased. A decrease of dielectric strength can be explained with an agglomeration of removed particles which build a particle bridge between the tool and workpiece electrode. Because of a small erosion depth and hence good flushing condition this scenario is improbable. But it cannot be excluded that an eddy water was built in the end of the. A more probable scenario is that the electric field increased locally. For a constant open voltage a local increase of the electric field can only be explained by a decrease of gap width. The specimen was d = 0.2 mm thick at the start of the investigated erosion process. It was machined until the breakthrough was observed. This required about Tmachining = 318.34 s (less the flushing cycle Terosion = 220.72 s absolute erosion time). The cause of a decrease of gap width might be a deformation of the remaining workpiece at the very end of the machining. Due to the minimal thickness at this point of time the small cross-section connecting it to the rest of the material leads to a minimal heat dissipation and a temperature rise. As a consequence the material might be softened and deformed due to a spatial temperature gradient which leads to increasing discharge frequencies in areas being deformed towards the tool electrode. A 2D finite element simulation was built to prove this theory. Therefore, a temperature gradient on a thin bar regarding to the thin plate in the experiment was applied. The endings of the bar were fixed. The resulting deformation is depicted in Fig. 8. On the upper side the temperature field due to erosion process was set as a boundary condition. Because of the temperature raise the metal expanded due to its thermal expansion coefficient. This leads to compressive stress due to fixation of the bar endings. Since the temperature and hence the expansion on the working gap side is higher the bar bends into working gap direction to reduce the compressive stress.
T(x)
smax = 52.7 µm 0
25 50 displacement s / µm
probable distance between consecutive discharges is about the average bubble radius. The radii presented in the work of Kitumara et al. are comparatively smaller (dc ≈ 1 mm), but machining took place on a SiC single crystal workpiece electrode. Klink et al [13] identified a correlation between bubble size and thermal conductivity, by investigating acoustic emission signals in EDM. They found that bubble size increases for materials with a lower thermal conductivity as less thermal energy is conducted through the workpiece and hence more energy is available for bubble growth. Consequently, the higher average bubble radius in this paper can be explained, as SiC single crystal has a significantly higher thermal conductivity (λSi = 280 W/(mK)) as 42CrMo4 steel (λ42CrMo4 = 43 W/(mK)). 15 distribution in process i. i. d.
Probability P / %
284
10
5
0
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Distance between consecutive discharges dc / mm
Fig. 9. Probability for distances between consecutive discharges compared to an independent identical distribution (i. i. d.).
Determining the distance of consecutive discharges is fundamental to calculate the material loading in terms of process signatures for the workpiece. Knowing the correct energy dissipation radius the temperature field of one single discharge is well-known today [14-16]. But this temperature field interferes on the one hand with the macroscopic temperature field of the workpiece material [17,18] and on the other hand with the temperature field of the previous discharge. For a comprehensive model of material loading these three aspects have to be considered. Hence, it is important to know the location of consecutive discharges. 5. Summary
Fig. 8. Finite element simulation of displacement due to temperature raise.
Another phenomenon, that was investigated, is the probability of the distance between two consecutive discharges. In Fig. 9 the probability for the distance between two consecutive discharges is depicted. Additionally, the probability of a distance for randomly distributed discharges is shown (i. i. d.: independent identically distributed). The peak of the probability for random discharges is close to the electrode radius, whereas the probability for distance between discharges occuring in the process has a distinct peak at dc = 1.3 mm. Kitumara et al. [12] found, that discharge occurance is influenced by debris particles located at the boundary between bubble and working fluid. Hence the most
A new method for the determination of discharge locations was introduced that can be applied under realistic EDM conditions regarding common workpiece and tool electrode materials. Only the pause duration had to be extended. The discharge frequency per area was shown to be almost constant over the electrode area except for the very end of machining. However, the distance between consecutive discharges differs significantly from the expected independent identical distribution. This was found to be in accordance with bubble formation in the gap.
F. Klocke et al. / Procedia CIRP 68 (2018) 280 – 285
Acknowledgements The authors wish to thank the German Research Foundation (DFG) for funding the transregional Collaborative Research Center SFB/TRR 136 “Process Signatures” (Bremen, Aachen, Oklahoma), subproject F02. Moreover, they express their gratitude to Wolfgang Gohout from Pforzheim University for his scientific support.
References [1] Brinksmeier E, Gläbe R, Klocke F, Lucca D. Process Signatures – an Alternative Approach to Predicting Functional Workpiece Properties. Procedia Engineering 19. 2011. p. 44-52. [2] Brinksmeier E, Klocke F, Lucca D, Sölter J, Meyer D. Process Signatures – A New Approach to Solve the Inverse Surface Integrity Problem in Machining Processes. Procedia CIRP 13. 2014. p. 429-434. [3] Klink A. Process Signatures of EDM and ECM Processes – Overview from Part Functionality and Surface Modification Point of View. Procedia CIRP 42. 2016. p. 240-245. [4] Kunieda M, Kojima H. On-Line Detection of EDM Spark Locations by Multiple Connection of Branched Electric Wires. Annals of CIRP 39 (1). 1990. p. 171-174. [5] Han F, Grangure P, Kunieda M. Improvement in accuracy in potential method for detecting EDM spark locations. Journal of Materials Processing Technology 206 (1-3). 2008. p. 328-332. [6] Qiang H, Yong H, Wansheng Z. Research of two-dimension EDM spark locations detection using electromagnetic method. Measurement 31 (2). 2002. p. 117-122. [7] Smith C, Koshy P. Applications of acoustic mapping in electrical discharge machining. CIRP Annals - Manufacturing Technology 62 (1). 2013. p. 171-174.
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[8] Kitamura T, Kunieda M, Abe K. High-Speed Imaging of EDM Gap Phenomena using Transparent Electrodes. Procedia CIRP 6. 2013. p. 314319. [9] Kitamura T, Kunieda M, Abe K. Observation of Arc Plasma in EDM Using Transparent Electrodes. ASPEN. 2013. [10] Schwade M. Automatisierte Analyse hochfrequenter Prozesssignale bei der funkenerosiven Bearbeitung von Magnesium für die Medizintechnik. Apprimus Verlag. 2017. [11] Klocke F, Schneider S, Ehle L, Meyer H, Hensgen L, Klink A. Investigations on Surface Integrity of Heat Treated 42CrMo4 (AISI 4140) Processed by Sinking EDM. Procedia CIRP 42. 2016. p. 580-585. [12] Kitamura T, Kunieda M, Abe K. Observation of relationship between bubbles and discharge locations in EDM using transparent electrodes. Precision Engineering 40. 2015. p. 26-32. [13] Klink A, Holsten M, Schneider S, Koshy P. Acoustic emission signatures of electrical discharge machining. CIRP Annals - Manufacturing Technology 65 (1). 2016. p. 229-232. [14] Hinduja S, Kunieda M. Modeling of ECM and EDM processes. CIRP Annals - Manufacturing Technology 62 (2). 2013. p. 775-797. [15] Yeo S, Kurnia W, Tan P. Critical assessment and numerical comparison of electro-thermal models in EDM. Journal of Materials Processing Technology 203 (1-3). 2008. p. 241-251. [16] Klocke F, Schneider S, Harst S, Welling D, Klink A. Energy-based Approaches for Multi-scale Modelling of Material Loadings During Electric Discharge Machining. Proc. CIRP 31. 2015. p. 191-196. [17] Oßwald K, Schneider S, Hensgen L, Klink A, Klocke F. Experimental investigation of energy distribution in continuous sinking EDM. CIRP Journal of Manufacturing Science and Technology (in press). 2017. [18] Klocke F, Schneider S, Mohammadnejad M, Hensgen L, Klink A. Inverse Simulation of Heat Source in Electrical Discharge Machining (EDM). Procedia CIRP 58. 2017. p. 1-6.
ScienceDirect Procedia CIRP 68 (2018) 280 – 285
19th CIRP Conference on Electro Physical and Chemical Machining, 23-27 April 2018, Bilbao, Spain
Thermographic Analysis of Spark Location Distribution in Sinking EDM F. Klockea, S. Schneidera,*, B. Frauenknechta, L. Hensgena, A. Klinka, K. Oßwaldb a
Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany b Institute for Materials and Material Technologies, Pforzheim University, Pforzheim, Germany
* Corresponding author. Tel.: +49-241-80-28243; fax: +49-241-80-22293. E-mail address: [email protected]
Abstract In order to have a good comprehension of the heat input and hence the temperature evolution in the EDM process it is crucial to gain an insight into the two-dimensional position distribution of consecutive discharges. This distribution affects the material removal from the workpiece and tool electrodes as well as the manufacturing precision. Consequently, an investigation of the discharge distribution across the electrode profile represents a very relevant research issue. Therefore, for this article the rear side of thin specimens was observed using a thermographic camera whilst the front was machined by a S-EDM process. Every discharge caused a heat input on the front, which was conducted through the specimen and generated a hot spot on its rear. The temperature field was recorded by a thermographic camera. The recorded data were analyzed by a specific image data processing software, which detected the hot spots and classified them according to their two-dimensional position. In a further step, the machining data was compared to the image data in order to assign the discharge energy to the discharge location. This comparison provides the time- and spatial-resolved energy distribution during the S-EDM process. ©2018 2018The The Authors. Published by Elsevier B.V. © Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of thec committee scientific of committee of Conference the 19th CIRP Conference on Electro and Chemical Machining. Peer-review under responsibility of the scientifi the 19th CIRP on Electro Physical and ChemicalPhysical Machining
Keywords: EDM; temperature measurement; energy distribution
1. Introduction and Literature Review In electrical discharge machining (EDM) material removal is achieved by high-frequency electrical discharges, that melt and evaporate workpiece material. The evaporation of material leads to ejection of molten material. Therefore, a local removal of material is attained at the location of discharge. Consequently, the two-dimensional distribution of discharges has considerable influence on the removal process and hence its detection has been approached by several research groups. The electrical discharges leads to thermal loads in the workpiece material which causes several rim zone modifications. The capability of a priori design of specific surface integrity properties prior to a manufacturing process represents a challenge in current research. One way to approach this challenge is the concept of process signatures which provides a link between the material loadings and the material modifications [1-3]. For a comprehensive description of thermal loadings, it is necessary to gain an understanding of the
temporally and spatially resolved energy input into the workpiece in the S-EDM process. Kunieda et al. [4] developed an approach of detecting the spark location by measuring a current signal in branched wires placed on the tool electrode. Due to the correlation between current, electrical resistance and spark location the ratio of the integrated signals allowed to calculate the discharge position. However, the accuracy of this method decreases when low resistance materials, such as steel, are used. A further improvement of this method was achieved by Han et al. [5] considering the potential difference between the branches instead of the current ratio. Therefore, the voltage originated by the current conducted through the branches was measured. As only the ending part of voltage signals was used for detection, influence of noise generated by the discharge could be minimized and detection accuracy could be refined to several millimeters. Qiang et al. [6] developed another method for the two dimensional EDM spark location, making use of the magnetic
2212-8271 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 19th CIRP Conference on Electro Physical and Chemical Machining doi:10.1016/j.procir.2017.12.064
281
F. Klocke et al. / Procedia CIRP 68 (2018) 280 – 285
field generated by the discharge current. These signals were measured using two pairs of Hall sensors placed on opposite sides of a square-shaped workpiece electrode. The comparison of the sensor signals allowed to calculate the spark location. This method also reached an accuracy of several millimeters. Smith and Koshy [7] presented a method to detect the spark location by acoustic emission. For this purpose, acoustic emission caused by a discharge was measured at each end of a wire electrode. Analyzing the specific frequencies of electromagnetic interference and acoustic emission signals, the time lag between both measurements was determined and thereby the (one-dimensional) spark location. Acoustic emission is a highly precise way of spark location detection as it can minimize detection inaccuracy to 0.07 mm. Nevertheless, real-time and two-dimensional implementation of this method is still a challenge, as successive discharges lead to interference with the acoustic signals of earlier discharges. Kitamura et al. [8] observed the EDM machining gap through a SiC single crystal wafer as a workpiece electrode. SiC is a very suitable material for process observation, as it is electroconductive and sufficiently transparent to observe the process. As the thermal loading of a discharge damaged the crystal structure of SiC, every discharge led to an intransparent spot on the wafer. Hence, discharge distribution became apparent and it was found, that discharge locations were evenly distributed over the electrode surface. The same research group also used Ga2O3 single crystal as an electrode material [9], as it had similar characteristics as SiC but a higher and more uniform optical transmissivity which allowed observation of the arc plasma. The aim of this paper is to present a possibility of detecting the spatial-resolved distribution of discharges in S-EDM at high resolution for common discharge values and electrode materials. Therefore, the rears of thin specimens were observed during EDM processing, using a thermographic camera. The temperature evolution within the specimens was recorded, so spark locations could be observed by a local rise of temperature. This concept has several advantages compared to former approaches as is suitable for any conductive material and not limited to translucent materials, although this method is restricted by the record speed of the thermographic camera. In this investigation the pause duration had to be extended to ensure the recording of every discharge. The characteristic hot spots in the temperature field are analyzed by a specific image data processing software, which detects the spark locations and classifies them according to their two dimensional position. Also, if a discharge appears at the same position as a former discharge it can be observed. In a further step, the machining data is compared to the image data in order to assign the discharge energy to the discharge location. This comparison provides the time-resolved spatial energy distribution during the S-EDM process. This data of energy input is a requirement to calculate the entire temperature field in the workpiece and hence to apply the approach of process signatures [1,2].
2. Experimental Setup The experiments were conducted on a GFMS AgieCharmilles FORM 2000 sinking EDM machine. The erosion took place in an electrically insulated reservoir filled with oelheld IonoPlus IME-MH CH-based dielectric fluid (Fig. 1). A cylindrical copper electrode with frontal area diameter of dTL = 5 mm was used as a tool. Plates made of 42CrMo4 steel (1.7225) were used as specimens. For constant flushing conditions, dielectric fluid was pumped continuously into the erosion zone. Since the reservoir was insulated the specimen was electrically contacted to the machine table.
tool electrode (copper) specimen
5 mm Fig. 1. Experimental setup for measuring local temperatures and detecting discharge spots with thermographic camera.
For workpiece temperature measurement a high speed thermographic camera FLIR X6580sc was used. As the maximum sampling rate of the thermographic camera is frec = 1250 Hz the theoretical discharge frequency was adjusted to fe,th = 610.5 Hz in order to fulfill Nyquist-Shannon sampling theorem securely. This was reached by increasing the pause duration of the process. The measured averaged discharge frequency was fe,real ≈ 350 Hz. The lens had a focal length of 50 mm providing a field of view of approx. 12x12 mm which relates to an object resolution of 0.15 mm. The specimen was integrated in the reservoir wall. Thus, only one side was in touch with the dielectric fluid and the outer side was visible (cp. Fig. 1). The specimens were coated with a special heat resistant black lacquer. Thus, it can be assumed that the specimens had an emissivity of ߝ ൎ ͳ (and nearly emitted black body radiation). The erosion parameters for the results presented in chapter 3.2 were kept constant (Table 1). The increased pause duration compared to a standard erosion process is remarkable. This raise was necessary to decrease the discharge frequency to a level for which the thermographic camera can fulfill the sampling theorem. Table 1. Erosion parameters. Erosion parameter
Value
Unit
Discharge current ܫ
11.5
A
Open circuit voltage ݑො
100
V
Pulse duration ݐ
38
ߤݏ
Pause duration ݐ
1600
ߤݏ
Erosion time between flushing jumps ݐ௦
2
s
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To determine the electrical energy of the EDM process the current and voltage characteristic of each discharge was analyzed in real time with a process monitoring tool, based on a field-programmable gate array (FPGA) [10]. Consequently, it was possible to characterize each single discharge and evaluate the process afterwards. 3. Methods 3.1. Heat transfer The thermographic camera detects intensity distributions of wave length spectrums. With Planck’s law of black body radiation these intensity spectrums can be translated into the temperature of the radiating black body (emissivity H { 1). As mentioned before, for this investigation it can be assumed that the examined specimen is nearly a black body with an emissivity H | . The observed temperature changes at the outside workpiece originate from the heat dissipation of the discharges in the working gap (inside workpiece). The discharge energy transforms into heat which causes melting and evaporization and hence material removal. The residual heat dissipates through the workpiece and induces a temperature rise within the material. This temperature rise generates different material modifications like changes in grain size, phase changes or residual stresses [11]. Furthermore, it leads to heat radiation at the rear surface of the workpiece which can be detected by the thermographic camera. To estimate the time lag between the pulse initiation and the according temperature rise at the opposite surface a heat transfer FEM simulation based on the Fourier’s law ߩܿ
߲ܶ ൌ ߘሬԦ ڄ൫ߣߘሬԦܶ൯ σݍ ߲ݐ
Temperature T / K
ݍሶ ̶
320 d = 0.2 mm
310
313 K 300 Δt = 150 µs
290 0
1
In order to detect discharge locations, an image data processing algorithm implemented in MATLAB® was used for adapting the image data from the thermographic camera. The temperature field is depicted in a pixel matrix (80 × 80). With the used optics a pixel equates to APixel = 0.15 mm x 0.15 mm. Each element of the matrix represents the temperature of a certain area of the specimen’s surface. For further processing, the difference of two following frames is considered in order to obtain the evolution of the temperature field (Fig. 3a). In a following step the algorithm detects the location of the maximum of the matrix, since a maximum in the heat evolution matrix could probably indicate a discharge. If the maximum value, that represents the increase of temperature from one frame to the next, is sufficiently high the warming is traced back to a discharge. In this case location and time of the discharge are stored (Fig. 3b). (a)
3 mm
Interframe time tif = 0.8 ms
ΔTmax = 26.3 K
3.2. Image Data Processing
(b)
(c)
(1)
was performed to verify the camera settings. The results are shown in Fig. 2. The thickness of the plate at the erosion area was chosen to be d = 0.2 mm because it was the thickness of the plate specimen.
330
'Tmax = 26.3 K which is in good accordance to the results of thermographic analysis (Fig. 2 bottom right) considering the high temperature fluctuation and the comparably slow framerate. The time lag between heat input and temperature rise was determined to 't = 150 µs. Additionally, in Fig. 2 the interframe time of the recording is depicted (white/grey). Because all along the interframe time the temperature is high enough to be detected by the thermographic camera the record frequency is high enough to capture every discharge.
296 K 2 3 Time t / ms
4
5
Fig. 2. Simulated temperature profile on the outside workpiece after a single discharge (Ie = 11.5 A; Ue = 31.3 V; te = 38 μs; ξwp = 50 %).
A simulated single discharge (Ie = 11.5 A; Ue = 31.3 V; te = 38 µs; ξwp = 50 %) causes a maximum temperature rise of
3 mm
3 mm
Fig. 3. Image data processing sequence: (a) Difference of thermograpic recordings (b) identify the position (c) disregarded area for following discharge.
Since the spread of a discharge-caused hot spot is visible over a few frames, the algorithm needs to be able to differentiate new hotspots from older ones. For this purpose an eventual increase of a hot spot temperature in the following frame is taken into account, as the algorithm disregards a slightly larger area than the actual hot spot size (Fig. 3c). By this means the algorithm avoids to detect one discharge twice. These measurements allow a sustainable and determined detection of spark locations and thereby enable an insight in the spatial discharge distribution. These aspects are important to investigate the influence of consecutive discharges on the material temperature and hence the material loading which leads to material modifications [1-3]. 4. Results and Discussion For further analysis of the process, different stages of machining were considered, as shown in Fig. 4. Therefore, five
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thousand discharges two times during and at the end of machining were evaluated concerning their spatial distribution.
t2 = 74.8032 s – 92.7776 s Δt2 = 17.9744 s 15 5000 discharges in each picture 10 5
t3 = 153.120 s –170.7616 s Δt3 = 17.6416 s
discharges per pixel
t1 = 0.0024 s – 19.2856 s Δt1 = 19.2832 s
0
2.5 mm
There are a few discharges which occur not on the tool area (r = 2.5 mm), but in the side gap 'dsidegap (cp. Fig. 6). The increasing distance to the center leads to a growth of area by its square. Therefore, discharge frequencies were normalized with the related area (Fig. 5b), in order to gain a comparable unit for discharge distribution. The discharge frequency per area over the whole electrode area decreases slightly with increasing distance to center. This phenomenon will be investigated in the next section. A discrepancy was only found in the center. This regards to sensitivity of the detection and area calculation. The observed area in the center range is only one pixel in the investigated picture. So if the discharge is not detected exactly at the electrode center the discharge was counted in the next range. Additionally, the approximation to a circle leads to errors in area calculation. For the side gap discharges (r t 2.5 mm) the discharge frequency is nearly zero because of the increasing area.
tool r = 2.5 mm
Δdside gap | 0.1 mm workpiece
Fig. 4. Discharge distribution during the process (t1 and t2) and at the end (t3) of the process.
60 50 40 30 20 10 0 25 20
fe,overall = 350 Hz
(a)
(b)
15 10 5
0
0
0.5 1 1.5 2 2.5 Distance to center dc / mm
Fig. 6. Discharge in the sidegap.
Considering the discharge frequency per area for the three above mentioned time intervals (c.p. Fig. 4), Fig. 7 shows the evolution of discharge distribution for discrete time intervals. It can be seen that in early process stages ('t1 and 't2) the discharge frequency it nearly uniform. At the end of the process ('t3) an increase of the concentration at a distance of dc = 0.6 mm can be observed. This hot spot can also be seen in the evaluation of the image data. This phenomenon leads to the decrease of the discharge frequency per area with increasing distance to the center (cp. Fig. 5). Discharge frequency per area fcc c/ (Hz/mm2)
Discharge frequency Discharge frequency per area fcc c / (Hz/mm2) f / Hz
An analysis of measured discharge energies showed that due to choosing an iso-energetic process under stable machining conditions nearly every discharge had the same amount of energy. Hence, the energy distribution equals the discharge distribution. Therefore only the discharge distribution is taken into account for further considerations. However, the discharge distribution varies during the process. The discharges are distributed relatively uniformly, whereas a concentration of discharges can be seen in the end of the process. For the following results, the discharge frequency was evaluated in relation to the position on the electrode for the whole process. Absolute discharge frequency rises with the distance to the center as shown in Fig. 5a.
3
Fig. 5. (a) Absolute discharge frequency as a function of distance to the electrode center, (b) discharge frequency per area depending on distance to center.
30
't1 't2 cp. Fig 4 't3 whole process
25 20 15 10 r = 0.6 mm
5 0
0
0.5 1 1.5 2 Distance to center dc / mm
2.5
Fig. 7. Discharge frequency per area during the process, at the end and averaged over the whole process.
F. Klocke et al. / Procedia CIRP 68 (2018) 280 – 285
A local concentration of discharges can be the result of two reasons. Either the local electrical field increased or the local dielectric strength decreased. A decrease of dielectric strength can be explained with an agglomeration of removed particles which build a particle bridge between the tool and workpiece electrode. Because of a small erosion depth and hence good flushing condition this scenario is improbable. But it cannot be excluded that an eddy water was built in the end of the. A more probable scenario is that the electric field increased locally. For a constant open voltage a local increase of the electric field can only be explained by a decrease of gap width. The specimen was d = 0.2 mm thick at the start of the investigated erosion process. It was machined until the breakthrough was observed. This required about Tmachining = 318.34 s (less the flushing cycle Terosion = 220.72 s absolute erosion time). The cause of a decrease of gap width might be a deformation of the remaining workpiece at the very end of the machining. Due to the minimal thickness at this point of time the small cross-section connecting it to the rest of the material leads to a minimal heat dissipation and a temperature rise. As a consequence the material might be softened and deformed due to a spatial temperature gradient which leads to increasing discharge frequencies in areas being deformed towards the tool electrode. A 2D finite element simulation was built to prove this theory. Therefore, a temperature gradient on a thin bar regarding to the thin plate in the experiment was applied. The endings of the bar were fixed. The resulting deformation is depicted in Fig. 8. On the upper side the temperature field due to erosion process was set as a boundary condition. Because of the temperature raise the metal expanded due to its thermal expansion coefficient. This leads to compressive stress due to fixation of the bar endings. Since the temperature and hence the expansion on the working gap side is higher the bar bends into working gap direction to reduce the compressive stress.
T(x)
smax = 52.7 µm 0
25 50 displacement s / µm
probable distance between consecutive discharges is about the average bubble radius. The radii presented in the work of Kitumara et al. are comparatively smaller (dc ≈ 1 mm), but machining took place on a SiC single crystal workpiece electrode. Klink et al [13] identified a correlation between bubble size and thermal conductivity, by investigating acoustic emission signals in EDM. They found that bubble size increases for materials with a lower thermal conductivity as less thermal energy is conducted through the workpiece and hence more energy is available for bubble growth. Consequently, the higher average bubble radius in this paper can be explained, as SiC single crystal has a significantly higher thermal conductivity (λSi = 280 W/(mK)) as 42CrMo4 steel (λ42CrMo4 = 43 W/(mK)). 15 distribution in process i. i. d.
Probability P / %
284
10
5
0
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Distance between consecutive discharges dc / mm
Fig. 9. Probability for distances between consecutive discharges compared to an independent identical distribution (i. i. d.).
Determining the distance of consecutive discharges is fundamental to calculate the material loading in terms of process signatures for the workpiece. Knowing the correct energy dissipation radius the temperature field of one single discharge is well-known today [14-16]. But this temperature field interferes on the one hand with the macroscopic temperature field of the workpiece material [17,18] and on the other hand with the temperature field of the previous discharge. For a comprehensive model of material loading these three aspects have to be considered. Hence, it is important to know the location of consecutive discharges. 5. Summary
Fig. 8. Finite element simulation of displacement due to temperature raise.
Another phenomenon, that was investigated, is the probability of the distance between two consecutive discharges. In Fig. 9 the probability for the distance between two consecutive discharges is depicted. Additionally, the probability of a distance for randomly distributed discharges is shown (i. i. d.: independent identically distributed). The peak of the probability for random discharges is close to the electrode radius, whereas the probability for distance between discharges occuring in the process has a distinct peak at dc = 1.3 mm. Kitumara et al. [12] found, that discharge occurance is influenced by debris particles located at the boundary between bubble and working fluid. Hence the most
A new method for the determination of discharge locations was introduced that can be applied under realistic EDM conditions regarding common workpiece and tool electrode materials. Only the pause duration had to be extended. The discharge frequency per area was shown to be almost constant over the electrode area except for the very end of machining. However, the distance between consecutive discharges differs significantly from the expected independent identical distribution. This was found to be in accordance with bubble formation in the gap.
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Acknowledgements The authors wish to thank the German Research Foundation (DFG) for funding the transregional Collaborative Research Center SFB/TRR 136 “Process Signatures” (Bremen, Aachen, Oklahoma), subproject F02. Moreover, they express their gratitude to Wolfgang Gohout from Pforzheim University for his scientific support.
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