BZ120掘进机铸造型回转台的设计含5张CAD图
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Geotechnical EngineeringpISSN 1226-7988, eISSN 1976-3808 /12205 KSCE Journal of Civil Engineering (2017) 21(1):168-177Copyright 2017 Korean Society of Civil Engineers DOI 10.1007/s12205-016-0433-5 New Model for Predicting Instantaneous Cutting Rate of Axial-type RoadheadersQianqian Zhang*, Zhennan Han*, Mengqi Zhang*, and Jianguang Zhang*Received June 2, 2015/Revised November 30, 2015/Accepted Janaury 19, 2016/Published Online February 29, 2016AbstractRoadheaders are mechanical excavators which have been extensively used in tunneling, mining and civil engineering works. Performance prediction of the roadheader is major factor in determining the economy of an underground excavation project. In this study, a new model for predicting the Instantaneous Cutting Rate (ICR) has been developed. The performance of EBZ260W axialtype roadheader has been evaluated in the laboratory artificial rock cutting tests and in the tunneling field tests. Using the model, the processes of the artificial rock cutting in the laboratory tests have also been simulated. The predicted average ICRs in the laboratory tests, and in the simulations, together with the measured average ICRs in the laboratory tests and in the field tests are presented in this paper. The comparison of the predicted and measured ICRs shows that the relative error is within 5%. There are also reasonable agreements and strong correlations between the model and tests results. The new ICR prediction model is also supported by the empirical prediction formulas developed by other researchers. Hence, the model can be used to reliably predict the ICR for axial-type roadheaders. Keywords: roadheader, performance prediction, instantaneous cutting rate, cutting power, cutting depth1. IntroductionPerformance prediction is an important issue on the application of roadheader in tunnel excavation. It includes machine selection, productivity and bit consumption. A model is presented in this paper in which one aspect of the performance prediction is covered. It is the Instantaneous Cutting Rate (ICR), which is the production rate during the period of cutting. The roadheader production rate is controlled by several parameters including (Copur et al., 1998): (a) rock parameters, such as rock compressive and tensile strength. (b) Ground conditions, such as jointing, stratification, ground water. (c) Machine specification, including cutterhead power, machine weight, cutterhead type, pick type, number and allocation of picks on the cutterhead. (d) Operational parameters, such as shape and size of opening, inclination, quality of labor. A combination of these parameters determines the production rate of a given machine under certain ground conditions. For the past research on the performance prediction methods of roadheaders (Fowell et al., 1994; Balci et al., 2004; Copur et al., 2001; Bilgin et al., 2004; Avunduk et al., 2014; Comakli et al., 2014; Ebrahimabadi et al., 2015), they can be summarizedinto three groups. The first group focuses on the use of smallscale and full-scale laboratory rock cutting tests. The second group developed empirical models which are based a large amount of field data. The third group concentrates on the use of Artificial Neural Networks (ANN).The first group for predicting the ICR of a roadheader is to use the Specific Energy (SE). The studies made by Fowell and Mcfeat-Smith (1976), Fowell et al. (1994) find a good relationship between SE obtained from small-scale rock cutting test and insitu ICR for the medium and heavy weight roadheaders. The commonly used method generally use the cutting power, optimum SE obtained from full-scale rock cutting test and energy transfer ratio from the cutterhead to rock (Rostami et al., 1994). Based on the results of massive full-scale linear rock cutting tests, Balci et al. (2004) refined Rostamis ICR prediction equation for the axial and transverse-type roadheaders. Further, Copur et al. (2001) and Balci et al. (2004) showed that the optimum SE was highly correlated to the product of the uniaxial compressive strength and the brazilian tensile strength of the rocks. For the empirical performance prediction models, they are generally based on practical experience and the statistical analysis of massive fielddata. Based on the in-situ observations in many tunneling and mining projects, Bilgin et al. (1988; 1990; 1997; 2004) performed experimental studies to investigate the effect of rock compressive strength and rock quality designation on the ICR of a roadheader. By considering the rock discontinuities, he also developed a performance prediction model for the axial-type roadheaders. By studying the empirical relationships between the uniaxial compressive strength and the ICR of an axial-type roadheader with cutterhead power of 230 kW and an Alpine Miner AM 100 transverse-type roadheader with cutterhead power of 250 kW, Gehring (1989) developed performance prediction formulas for axial-type and transverse-type roadheaders respectively. By means of the measured excavation rates, Thuro and Plinninger (1999; 2003) carried out a research on the correlations between the specific rock properties and the geological factors. They then developed an empirical relationship between uniaxial compressive strength and ICR for a transverse-type roadheader with 132 kW cutterhead power. By examining the available field performance data of different roadheaders at various geological conditions, Copur et al. (1997; 1998) showed that the predictions of the cutting rate can be more accurate if the cutterhead power and the machine weight are considered together in addition to the rock compressive strength. He also developed prediction equations for the ICR of transverse-type roadheaders. Based on the detailed field data including machine performance and geomechanical parameters for 62 cutting cases in tunnels, Ebrahimabadi et al. (2011) showed the relationship between the ICR and the rock mass brittleness index. This index is related to the compressive strength, tensile strength and rock quality designation of the rock mass. He then developed a new predictive model for the ICR with respect to the SE, and the angle between the tunnel axis and the planes of weakness (Ebrahimabadi et al., 2011). He then presented a universal performance prediction model for roadheaders (Ebrahimabadi et al., 2012). Based on rock mass properties, Yazdani-Chamzini and Siamak-Haji (2013) developed an empirical equation for predicting the roadheader performance. Recently, ANN have emerged as a new method for analyzing geotechnical engineering problems, and have been used for predicting the ICR of roadheaders (Salsani et al., 2013; Avunduk et al., 2014; Ebrahimabadi et al., 2015).For the above cited works, research on prediction of ICR based on the simulation of cutting power has not been reported. In this study, based on the formulas from earlier studies for single pick forces under different cutting conditions, mathematical formulas for forces acting on the cutterhead have been derived. Further, based on the relationship between the cutting torque and the cutting power, a prediction model for the ICR has been developed. Then, by incorporating the design parameters of EBZ260W axial-type roadheader cutterhead, the procedure has been programmed, which can simulate various operating modes. The reliability of the simulation results has been verified with data from the laboratory artificial rock cutting tests and the field tests of EBZ260W axial-type roadheader.2. Development of New Prediction ModelThe cutterhead of a roadheader operates with a composite motion. It includes rotating and swinging, and also, through the cemented carbide tip of picks to break up the rocks. Due to the complex geological conditions in tunneling, it is very difficult to measure the cutting force acting on the cutterhead. As such, the data obtained from the laboratory rock cutting tests with a single pick are usually used as the basis for calculating the forces of each cutterhead pick.2.1 Determination of Pick ForcesThe prediction formulas of the pick forces obtained from the laboratory tests are more reasonable and reliable than those obtained in the theoretical and numerical simulation methods. In order to research the effects of dominant rock properties on pick performance, Bilgin et al. (2006) conducted a large number of experiments on 22 different rock specimens having uniaxial compressive strength values varied between 10-170 MPa. The entire test was carried out with an S-35/80H conical pick manufactured by Sandvik which tip angle 80o . The prediction formulas of cutting force and normal force were established in unrelieved cutting mode and relieved cutting mode. The results show that the mean cutting and normal forces are highly correlated with the uniaxial compressive strength. Since Eqs. (1) and (2) are fitted to a large amount of experimental data, they can therefore predict the pick forces accurately. As such, they can be used in practical engineering calculations.In unrelieved cutting mode:FC=(0.826C+21.76)d9.8FN=(1.217C1.014d9.8 (1)In relieved cutting mode:FC=(2.374C0.785)d9.8FN=(0.752C1.051)d9.8 (2)where, FC is the mean cutting force in N, FN is the mean normal force in N, C is the uniaxial compressive strength of rock in MPa, d is the cutting depth in mm.The pick forces are divided into three orthogonal forces: normal, cutting and sideway forces, as shown in Fig. 1(a). During the rock breaking process of a roadheader, the picks do not only rotate with the cutterhead, but also swing around the gyration center of the cutting arm. Hence, the actual cutting track is a space cycloid (Huang, 1996). According to the projection principle of Gauss-Krueger, the moving trajectory of the pick is on a two-dimensional plane, as shown in Fig. 1(a) (Zhang, 2012). In this figure, the shaded area represents the variation of cutting depth when the ith pick rotates in a circle. The maximum cutting depth is given by Eq. (3). Then, the cutting depth as shown in Fig. 1(b) can be expressed as a function of time t, which is given by Eq. (4).dmax=vt0=v2w=60vn （3）d=60vnsin（n30t+1） （4）where, v is the swing speed in m/s, n is the rotational speed in rpm, w is the angular velocity in rad/s, t0 is the time taken of pick rotates a period in s, is the initial phase of picks on the cutterhead.2.2 Prediction Model for ICRThe cutting mechanism of a roadheader comprises a driving motor, a coupling, gear box and a cutterhead. The cutting power is the power consumed by the cutterhead during the interaction between the picks and the rock. This is an important index which shows the cutting ability of a roadheader. The power loss on the gear box and other transmission parts has little impact on the test results. It is therefore ignored in the model simulation.Assuming that there are K picks installed on the cutterhead, and at any one time, there are m picks interacting with the rock simultaneously. If the attack angle is , the installation radius of pick is Rg, as illustrated in Fig. 1(a). For any single pick, the torque Ti acting on the cutterhead due the ith pick can be calculated by multiplying the pick cutting force with its corresponding installation radius, as shown in Eq. (5). The total power P required for the rock cutting process with m picks can then be calculated, as shown in Eq. (6).Ti=FCiRgi （5）P=i=1mTin9550=i=1m（FCiRgi）n9550 （6）After substituting Eqs. (1-4) and (5) into Eq. (6), the prediction formulas of cutting power can be written as follows:PUt=i=1m0.826C+21.7660vnsinn30t+iRgin974.5 （7）PRt=i=1m2.347C0.78560vnsinn30t+iRgin974.5 （8）where, PU(t) and PR(t) are the cutting power at any time in unrelieved and relieved cutting modes respectively, other notations are as given in Eqs. (1), (4) and (5).Specific energy (SE) is one of the most important factors in determining the efficiency of the cutting systems, it is defined as the work consumed to excavate a unit volume of rock as formulated below in Eq. (9).SE=0tP（t）dtV （9）where, SE is specific energy in kWh/m3 , P(t) is the cutting power of the cutterhead at any time in kW, t is cutting time in s and V is debris volume in m3 .The widely recognized method of predicting ICR of roadheader is to use cutting power, SE and energy transfer ratio from the cutterhead to rock, as shown in Eq. (10). Rostami et al. (1996) recommended using optimum SE values obtained from full-scale cutting tests in order to have a reliable prediction.ICR=kPSE （10）where, ICR is the instantaneous cutting rate in m3 /h, k is a constant related to total system efficiency and is usually assumed as 0.8 for roadheaders, P is the cutting power of the mechanical miner in kW, other notation is given in Eq. (9)Furthermore, substituting Eq. (9) into Eq. (10), a new ICR prediction model has been developed, as shown in Eq. (11). Using the simulation program, the cutting power data under various working conditions can be obtained. Using the cutting power data and cutting time t, the amount of work done can also be computed. Assuming that the cutterhead has been drilled into the rock mass, and only the swing cutting process was carried out, the debris volume can then be computed by multiplying the longitudinal section area along the centerline of cutterhead and the cutting distance.ICR=kPV0tP（t）dt （11）where, notations are as given in Eqs. (9) and (10).2.3 Simulation of Rock Cutting ProcessRock breaking processes can be divided into two different modes such as relieved and unrelieved cutting mode. In relieved cutting mode, the interaction between the adjacent picks is put into account. Nevertheless, there is no interaction in unrelieved cutting mode. With the advance of the cutterhead, the cutting depth of picks, which are arranged in spiral lines, gradually increase. The process of rock breaking is then transitioned from unrelieved to relieved cutting mode. The condition for the cutting mode to transition has been incorporated into the simulation program.Roadheader excavate the rock using picks that are mounted on a rotating cutterhead which is supported by a boom. An axial cutterhead with three spirals has been used in the simulation of rock cutting, as shown in Fig. 2. Triangles represent picks that are active in the rock breaking process, which are referred to as the activated picks. Circles represent picks that are not active. The number of the activated picks is dependent on the cutting thickness H and drilling depth W of the cutterhead. The rock cutting process has been simulated by a cutterhead with swing speed v and rotational speed n, and the radius of gyration Rgi of the ith activated pick. For a given H and W, the longitudinal sectional area of the cutterhead can be calculated, and it is represented by S(H, W). The prediction formulas of the ICR in unrelieved and relieved cutting modes are given by Eqs. (12) and (13) respectively.ICRU=kPS(H,W)vt0ti=1m（0.826C+21.76）60vnsin（n30t+i）Rgin974.5dt （12）ICRR=kPS(H,W)vt0ti=1m（2.347C0.785）60vnsin（n30t+i）Rgin974.5dt （13） where ICRU and ICRR are the instantaneous cutting rate in unrelieved and relieved cutting modes respectively in m3 /h, S(H, W) is the longitudinal section area of the cutterhead calculated by the cutting thickness H and drilling depth W, other notations are as given in Eqs. (7-8) and (11).3. Tests on Artificial and In-situ Rocks3.1 Artificial Rock Cutting Tests in LaboratoryIn the National Coal Mining Machinery Equipment Engineering Laboratory, a masonry wall was constructed using natural siltstone and concrete, with a compressive strength varying between 60-100 MPa. For the tests on EBZ260W axial-type roadheader, the concrete had a compressive strength of 74 8 MPa, which was determined using the standard method recommended by the International Society for Rock Mechanics (ISRM) for the unconfined compressive strength. Using FLUKE435 power quality analyzer during the cutting processes, the cutting power of the cutterhead was measured.The EBZ260W axial-type roadheader has a rated cutting power of 260 kW, a rated rotation rate of 32.5 rpm and a total weight of more than 80 tons for the entire machine. It has been designed to work economically in rock tunnel with a uniaxial compressive strength of less than 80 MPa, and a local rock mass of less than 110 MPa. The equipped cutterhead has a length of 1000 mm, and an average diameter of 760 mm. It has three spiral lines and 51 picks. All the picks have 25 mm diameter cemented carbide heads. Fig. 3 shows the pick arrangements on the cutterhead.There were seven operating modes throughout the test, corresponding to the seven motion directions marked by the black arrows, as shown in Fig. 4. At the beginning of the test, thecutting arm of the roadheader was in a horizontal midline position. In Operating Mode 1, the cutterhead drilled into the rocks at a depth of 740 20 mm. Thereafter, in Operating Modes 2 and 3, it took a horizontal swing cutting into the rocks in the right and left directions respectively. In Operating Mode 4, the cutterhead moved upwards to cut the rocks. In Operating Mode 5, it carried out up milling in the right direction with 52% lateral projected area of cutterhead. In Operating Mode 6, the cutterhead moved down and continued to cut the rocks. In Operating Mode 7, it carried out down milling in the right direction with 46% lateral projected area of cutterhead. During the test, the drilling power was provided by the tracked type walk mechanism, and the swing power was provided by the rotating and lifting cylinder.Fig.3.Pick Arrangements on Cutterhead of EBZ260W Roadheader3.2 Field Tests in TunnelingThe field tests of EBZ260W axial-type roadheader were carried out in a rock roadway tunnel of Daichiba Coal Mine. The Mine is located in Guangyuan, Sichuan Province of China. The tests lasted four months. As shown in Fig. 5, the cross-section of the roadway tunnel is an arch with an area of 9.1 m2 . Its maximum horizontal and vertical dimensions are 3.5 m and 2.9 m, respectively. During tunneling, the operators recorded the geological conditions of the rocks surrounding the tunnel, and also collected many rock samples. The rocks have a monoclinic layered structure, tilting to the south with a joint occurrence of 189o 37o . Their thickness is about 65.0 m, which includes quartz sandstone, siltstone and mudstone of medium thickness that are interbedded with black chert layers. There are oblique bedding layers in the entire rock formation, and x-type cracking in local areas. By performing the uniaxial compression tests on the trimmed core samples, the average uniaxial compressive strength was 87 MPa. As shown in Table 1, for the field tests, the total driving distance is 605.7 m, corresponding to an average monthly driving distance of 151.4 m. The maximum driving distance in one month is 175 m. Based on the daily recorded driving distance, driving time, pick consumption and operational aspects of the machine, the corresponding daily productivity was determined. Fig. 4. Tests for Artificial Rock Cutting4. Results, Validation and Discussions4.1 Results of Artificial Rock Cutting Tests in LaboratoryAcquired from the data acquisition system during the artificialrock cutting tests, Fig. 6 shows the curves of the cutting power. As there was a lag between the data acquisition time and the start time of Operating Mode 2, the cutting power was not recorded during the initial period of the test. Furthermore, due to a fault in the loose wiring between the power quality analyzer and the cutterhead, the cutting power was also not recorded in Operating Mode 3.Fig. 5. Diagrammatic Sketch of the Roadway SectionIn Operating Modes 5, 6 and 7, the results show that there is a slope linear increase in the cutting power during the first 20 s. This caused the rapid increase in the number of the activated picks as the cutterhead started to cut into the rocks. After the first 20 s, the number of activated picks continued to increase but at a slower rate until the cutting power reached its maximum. Then, the power dropped to 60.1-84% of its maximum and kept in a periodic steady state thereafter.As the adhesion coefficient of the concrete surface of the roadheader crawler and the laboratory concrete floor was only 0.44, this small coefficient could not prevent the lateral displacement of the roadheader in Operating Modes 2, 3, 5 and 7. As a consequence, significant lateral scratches were formed on the floor. The data show that the maximum lateral displacement and the minimum average chip thickness both occurred in Operating Modes 2 and 3, as shown in Table 2.A comparison of the maximum lateral displacements in Table 2 and the ICR in Table 3, shows that when there was a lateral displacement in the cutting process, the picks were sliding and with a slower swing speed while cutting the rock mass. This resulted in a smaller average chip thickness and a smaller ICR. When the cutterhead moved up and down in Operating Modes 4 and 6, especially in Mode 4, the cutting power was greater. The peak cutting power was 254.1 kW which was close to the rated cutting power. At this power, the average chip thickness was about 12 mm. This was due to no lateral displacement between the crawler and the floor when the cutterhead was moving up. Further, in Operating Mode 4, the SE was 11.1 kWh/m3 and the ICR was 11.69 m3 /h. The ratio of cutter spacing to cutting depth was two. This is consistent with that reported in the literature (Bilgin et al., 2006) from the rock cutting test with a single pick.表1.行车距离统计数据Table 1. Statistical Data of the Driving Distance实验周期每月行驶距离/m平均每日行驶距离每日最大行驶距离第一个月175.05.88.8第二个月第三个月第四个月总计605.7 / /隧道横截面积9.1m3表2.各工况掘进机试验数据 Table 2. Experiment Data of Roadheader for Each Operating Mode操作模式平均摆动速度（m/s）切削距离（m）平均切削厚度（mm）最大横向位移（m）20.9810-30.441.810.5331.3510-30.562.500.5346.2510-30.511.54051.4910-30.62.740.3662.3310-30.384.300.0272.4310-30364.480.414.2 Simulation Results of Cutting PowerIn accordance to the design parameters of the cutterhead of EBZ260W axial-type roadheader, the sequence of the picks in contact with the rocks has been derived. Based on Eqs. (7) and (8), the calculation procedure has been programmed. The simulation of each operating mode was carried out in accordance with the laboratory test conditions. In the simulation, the rocks have been assumed to be homogeneous with a uniaxial compressive strength of 74 MPa. The effects of joints, bedding, and fracture surface have been ignored. For each operating mode, the measured average moving speed in the test was used as the moving speed of the simulated cutterhead. This resulted in a cutting depth that is close to that obtained from the test. When the roadheader was under a steady cutting state, the simulation results show a periodic feature. Based on this feature, the simulation time of 100 s was selected.The results of the simulated cutting power were mainly affected by the cutting depth and the number of activated picks, as shown in Fig. 7. During the initial stage of the rock cutting process, as the cutterhead has not cut into the rock completely, the cutting depth was smaller than the cutter spacing. As such, the activated picks were all in unrelieved cutting mode in which the macrocracks formed by the adjacent picks were not intersected. As the rock cutting process proceeded to the next stage, the roadheader entered a steady working condition. During this stage, the rock breaking mode of the activated picks transitioned from unrelieved cutting mode to relieved cutting mode. The macrocracks formed by the adjacent picks have gradually expended and intersected. The corresponding cutting power quickly reduced to a steady state, and the average cutting power was 76.9-84.7% of the maximum. These are in good agreements with the test results. In Operating Modes 4 and 6, the activated picks were all in relieved cutting mode as the cutting depths were greater, which corresponds to the greater cutting power and the greater ICR. On the other hand, in Operating Mode 2, 3 and 5, the activated picks were in unrelieved cutting mode as the cutting depths were smaller, which corresponds to the smaller cutting power and the smaller ICR. Although the average chip thicknesses produced in Operating Modes 6 and 7 are close to each other, the contact area between the rocks and the cutterhead in Operating Mode 7 is only about half of the lateral projected area of the cutterhead. As such, there were less activated picks, which resulted in a smaller ICR as compared to that in Operating Mode 6.表3.人工岩石切削试验试验结果与数值结果的比较 Table 3. Comparison of Experimental and Numerical Results of the Artificial Rock Cutting Tests操作模式试验结果模拟结果实际ICRPmaxLabPmaxLabSELabICRCalLabPmaxSimPavgSimSESimICRCalSimICRAct(kw)(kw)(Kwh/m3)(m3/h(kw)(kw)(kwh/m3)(m3/h)(m3/h)270.749.524.281.6438.631.315.351.632.03377.75343.735.34254.1162.511.1211.69219.0158.510.3512.2611.5592.551.620.072.0625.521.112.072.081.96117.399.622.623.5279.764.614.473.574.15786.350.620.541.9748.034.413.692.012.4 Fig. 6. Test Results of Cutting Power Fig. 7. Simulation Results of Cutting Power4.3 Verification of Simulation Results Using Artificial Rock TestsA comparison of the experimental and simulation results shows that in Table 3, the cutting power, the SE and the ICR agree well with each other in Operating Mode 4. These good agreements can be attributed to zero lateral displacement of roadheader in Operating Mode 4. Further, the prediction formulas of pick forces in the ICR prediction model were developed from the full-scale linear cutting tests under stable conditions. Hence, as the roadheader has significant lateral displacements in the other operating modes, the simulation results in these modes were smaller than the experimental results. In addition, there are differences between the results in the artificial rock cutting test and in the full-scale linear cutting test. The prediction model has been developed based on the full-scale linear cutting test data with a single pick at a cutting speed of 0.13 m/s, while the cutting speed of the cutterhead was between 0.21-1.43 m/s in the laboratory test. Eqs. (1) and (2) have been developed under the following conditions: the primary tip angle of 80o , attack angle of 55o , skew and tilt angles of 0o . However, the conditions in this test were primary tip angle of 85o , attack angle of 49o , skew and tilt angles nonzero. In addition, Roepke et al. (1983) pointed out that the cutting force acting on the pick with tip angle of 90o is significantly higher than that acting on a pick with a tip angle of 60o in the rock cutting test.Due to the connecting fault between the analyzer and the roadheader, the cutting power was not recorded in Operating Mode 3. However, the correlation analyses of the average swing speed and the cutting power show that the correlation coefficients of average swing speed and the maximum and average cutting powers are 0.976 and 0.927 respectively, as shown in Fig. 8. In line with the results of the correlation analyses, Eq. (14) have been developed. By applying Eq. (14) to the average swing speed of 0.00135 m/s, the maximum and average cutting powers of Operating Mode 3 are 77.07 kW and 53 kW, respectively. The calculated cutting powers are closed to those measured in Operating Mode 2. This is because the working conditions of the cutterhead are similar to the right and left swings in the two operating modes. These results show that the cutting power can be determined by Eq. (14) accurately in this study.Fig. 8. Relation between Cutting Power and Average Swing Speedfor Experimental StudiesFig.9. New Model for Predicting Instantaneous Cutting PowerPmaxLab=35040v+29.762PavgLab=22075v+23.201 （14）where v is average swing speed in m/s.For the simulated and experimental cutting powers, the correlation analyses show that they are in close agreements. The correlation coefficients for the maximum and average cutting powers are 0.985 and 0.982, respectively, as shown in Fig. 9. The validation of the relationships between numerical and experimental studies of cutting power was also checked by regression analyses in SPSS statistic program at a confidence level of 0.95. In this analysis, the average and maximum cutting power obtained from the simulation method were selected as independent variables. In contrast, the average and maximum cutting power obtained from the experimental studies were selected as dependent variables. The results are listed in Table 4. Since P-values derived from Ftests are less than 0.05, it can be said that the relationships between numerical calculation and experimental studies are reliable. In the light of the results of the statistical analyses, Eq. (15) can be proposed:PmaxLab=0.9415PmaxSim+45.115PavgLab=0.8716PavgSim+27.651 （15）To verify the ICR prediction model developed in this study, the predicted ICR of each operational mode have been calculated using Eqs. (12) and (13) for both the experiment and simulation. The corresponding measured ICRs have been obtained based on the time taken and the material cut volume, as shown in Table 3. In engineering applications, the cutting efficiency of a roadheader usually refers to the average cutting rate of all operational modes. Based on the ICR of each operational mode in Table 3, the predicted average ICRs in both the experiment and simulation, and the measured average ICR are 4.18, 4.3 and 4.4 m3 /h, respectively. This shows that the predicted ICRs are close to the measured ICR, and the relative errors among the three average ICRs are less than 5%. Hence, the predicted ICR by the new prediction model can be considered reasonable and accurate.4.4 Verification of Simulation Results Using Field Tests and Empirical MethodsAffected by certain factors such as equipment inspection, replacement picks and laborers quality and so on, the ICR of EBZ260W axial-type roadheader was fluctuating during the field tests, as shown in Fig. 10. The average daily productivity was calculated based on the cumulative driving distance, the working period and the cross-sectional area of tunnel. The maximum ICR was 4.42 m3 /h (point A in Fig. 10). In the laboratory artificial rock cutting test, the cutting time was shorter and the cutting area smaller, which can be considered as an ideal cutting state. Hence, the predicted average ICRs in the laboratory tests, and in the simulations, together with the measured average ICR in the field test were compared. The results show that the predicted ICR agree well with the measured ICR, which further validates the new ICR prediction model as a reliable tool for the performance prediction of a roadheader.Moreover, in order to verify the applicability of the new ICR prediction model, the ICR were estimated using Eqs. (12) and (13) and the empirical formulas in Table 5. For these estimations, the uniaxial compressive strength varied between 20-150 MPa, rock quality designation (RQD) and cutterhead power (P) were assumed to be 100% and 260 kW. For making a reasonable comparison, assuming the cutterhead power is directly proportional to ICR (Balci et al., 2004, Comakli et al., 2014) and all empirical equations for axial roadheader are normalized for a cutterhead power of 260 kW. For example, the instantaneous cutting rate found by using the model developed by Gehring is divided by 2.3, since that model was developed for a roadheader with 230 kW cutterhead power. The simulation tests for the rock cutting were carried out with the design parameters of the cutterhead model of EBZ260W axial-type roadheader. The average cutter spacing was 19.5 mm, assuming the ratio of cutter spacing to cutting depth was 3, and then the maximum cutting depth was about 6.5 mm. Throughout the simulation test, the drilling depth of 900 mm, the rotating speed of 32.5 rpm, and the swing speed of 0.0035 m/s were kept constant. The graphical representation of comparison is shown in Fig. 11.Table4.The F Test Results of the Cutting Power Obtained from Numerical and Experimental Studies变量来源平方和df均方F值P值PmaxLab-PmaxSim回归23258.048123258.048121.2330.000剩余767.4464191.861总计24025.4935PavgLab-PavgSim回归10096.690110096.690102.2840.001剩余394.850498.712总计10491.5405The results from the new prediction model and from empirical formulas, all show an exponential distribution of ICR and they are consistent with each other. Especially, the results from the new prediction model turn out highly in accordance with the calculated results of Gehrings empirical formula. Gehring (1989) presented an empirical formula of the ICR for an axial roadheader with 230 kW cutterhead power, and the corresponding uniaxial compressive strength of rock varied between 38- 94 MPa, while the full-scale linear cutting tests were performed on 22 rock samples which had the compressive strength values varied between 10-170 MPa (Bilgin et al., 2006). As shown in Fig. 11, when the uniaxial compressive strength of rock was greater than 38 MPa, the simulation results of the ICR were slightly larger than those calculated by Gehrings empirical formula. Due to the applied range of empirical models depend on the extent of the available data, then the reliability of the predicted ICR was verified by the calculated ICR of Gehrings empirical formula within a certain compressive strength range. The new ICR prediction model can be applied to rock formations ranging from medium hard to hard rocks.In addition, for the simulations and analyses carried out in this study, even through the effects of discontinuities in rock masses, such as jointing, bedding and foliation, have not been included, the performance of the new ICR prediction model for EBZ260W axial-type roadheader is still good. Further, the model is relatively easy to use, and usually takes a short time to get a solution for axial-type roadheader. Moreover, if the design parameters of a cutterhead model of a given machine are known, the new ICR model can also be applied to these excavation equipment sets, such as transversal-type roadheader, continuous miner, shears and surface miner.5. ConclusionsA new ICR prediction model has been developed in this study. Based on the simulation method, this model can predict the ICR of axial roadheaders with different pick arrangement and swing speed of the cutterhead. The test and simulation results show that during the cutterhead moved upwards, the ICR was faster as compared to that under other operating modes. By the cutting route of moving up and down, this can make the roadheader more stable thereby improving the ICR in mining engineering. The laboratory artificial rock cutting tests and the field tests in tunneling for EBZ260W axial-type roadheader were conducted. The comparison between the predicted and the measured ICRs show good agreement. 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(2012). “Study on cutting head moving speed of mine roadheader determined based on pick tracing.” Coal Science and Technology, Vol. 40, No. 8, pp. 71-74, DOI: 10.13199/j.cst.2012. 08.74.zhangmq.024 (in Chinese).KSCE土木工程学报(2017)21(1)：168-177 岩土工程版权所有2017韩国土木工程师学会 PISSN 1226-7988，eISSN 1976-3808DOI 10.1007/s 12205-016-0433-5 Www.S/12205 技术说明轴向式掘进机预测瞬时切削速率的新模型Qianqian Zhang*, Zhennan Han*, Mengqi Zhang*, and Jianguang Zhang*2015年6月2日/2015年11月30日修订/2016年1月19日接受/2016年2月29日在线发布*文摘掘进机是一种机械挖掘机，广泛应用于隧道掘进、采矿和土木工程中，掘进机的性能预测是决定地下开挖工程经济性的主要因素。本文提出了一种新的瞬时切削速率预测模型(ICR)。对EBZ260W型轴向掘进机进行了室内人工岩石切削试验和掘进现场试验，并对其性能进行了评价。利用该模型，对人工岩石在实验室试验中的切削过程进行了模拟。本文给出了实验室试验和模拟试验中的预测平均ICRs，以及实验室试验和现场试验的实测平均ICRs。预测与实测集成电路的比较表明，相对误差在5%以内。模型与试验结果之间也有合理的一致性和很强的相关性。新的ICR预测模型也得到了其他研究者提出的经验预测公式的支持。因此，该模型可以可靠地预测轴向型掘进机的ICR。关键词：掘进机、性能预测、瞬时切削速度、切削功率、切削深度。*1.导言掘进机性能预测是掘进机在隧道开挖中应用的一个重要问题。它包括机器选择、生产率和位消耗。本文提出了一个模型，该模型涵盖了性能预测的一个要素。即瞬时切削速率(ICR)，即切削过程中的生产率。掘进机生产率由几个参数控制，包括(Copur等人，1998年)：(1)岩石参数，如岩石压缩和抗拉强度。(2)地面条件，如接缝、分层、地下水。(3)机器规格，包括刀盘功率、机器重量、刀盘类型、截齿类型、刀盘上截齿的数目和分配。(4)操作参数，如开口的形状和大小、倾角、劳动质量。这些参数的组合决定了机器在一定地面条件下的生产率。过去对掘进机性能预测方法的研究(Fowell团队，1994年；Balci团队，2004年；Copur Etal，2001年；Bilgin团队，2004年；Avunduk团队，2014年；Comakli Etal，2014年；Ebrahimabadi团队，2015年)，可归纳为三类。第一类的重点是使用小规模和全面的实验室岩石切削试验.第二类建立了基于大量实地数据的经验模型。第三类主要研究人工神经网络(ANN)的应用。第一类用于掘进机ICR的预测是利用比能量(SE)。主要是Fowell和Mcfeat-Smith(1976)的研究。Fowell团队(1994)发现，中、重型掘进机小型岩石切削试验获得的SE与现场ICR之间存在良好的关系。这些方法一般使用切削力，从充分的岩石切削试验中得到的最优解，以及从刀盘到岩石的能量传递比(Rostami团队，1994年)。根据大量的大规模线性岩石切削试验结果，Balci Etal.(2004)改进的罗斯塔米的ICR预测方程的轴向型和横向型掘进机。此外，Copur团队(2001年)和Balci团队(2004)发现，最佳SE值与岩石的单轴抗压强度和巴西抗拉强度的乘积高度相关。对于实证绩效预测模型，一般是基于实际经验和海量场的统计分析。数据。根据许多掘进和采矿项目的现场观测，Bilgin团队(1988；1990；1997；2004)研究了岩石压缩强度和岩石质量指标对掘进机ICR的影响，并通过考虑岩石不连续性，建立了轴向式掘进机的性能预测模型。Gehring(1989)通过对230 kW轴向掘进机和250 kW高寒矿车AM 100横向掘进机单轴抗压强度与ICR关系的研究，分别建立了轴向式掘进机和横向式掘进机的性能预测公式。Thuro和Plinninger (1999；2003)利用实测的开挖速率，对特定岩石性质与地质因素之间的相关性进行了研究。然后，他们建立了一个经验关系的单轴压缩强度与ICR的横向式掘进机的132 kW截齿功率。Copur团队(1997；1998)通过检查不同地质条件下不同掘进机的可用现场性能数据，表明，如果将刀盘功率和机器重量与热块抗压强度一起考虑，则对切削速率的预测可以更准确。他还建立了横向式掘进机ICR的预测方程.根据62个隧道切削实例的详细现场数据，包括机械性能和地质力学参数，Ebrahimabadi团队(2011)发现了ICR与岩体脆性指数之间的关系。该指标与岩体的抗压强度、抗拉强度和岩体质量指标有关。然后，他为ICR开发了一个关于SE的新的预测模型，以及隧道轴线与薄弱面之间的角度(Ebrahimabadi团队，2011年)。然后，他介绍了道路掘进机的通用性能预测模型(Ebrahimabadiet 团队，2012年)。基于岩体特性，Yazdani-Chamzini 和 Siamak-Haji(2013年)建立了预测掘进机性能的经验公式。最近，ANN已成为分析岩土工程问题的一种新方法，并已被用于预测掘进机的ICR (Salsani团队，2013年；Avunduk团队，2014年；Ebrahimabadi团队，2015年)。就上述工作而言，基于切削功率仿真的ICR预测研究尚未见报道。本文在前人对不同切削条件下单次切削力计算公式的基础上，推导出了刀盘力的数学表达式。在此基础上，根据切削扭矩与切削功率的关系，建立了ICR的预测模型，并结合EBZ260W型掘进机刀盘的设计参数，编制了模拟各种工作方式的程序。通过实验室人工岩石切削试验和EBZ260W轴向掘进机的现场试验，验证了模拟结果的可靠性。2.新预测模型的发展掘进机的截齿与复合材料一起工作动议。它包括旋转和摆动，也包括，通过硬质合金尖的截齿，以打破岩石。由于掘进过程中复杂的地质条件，刀盘上的切削力很难确定。因此，用单截刀进行室内岩石切削试验所得的数据通常作为计算刀盘截齿力的依据。2.1拾取力的确定本文给出的截齿力的预测公式。与理论和数值模拟方法相比，实验室试验更加合理可靠。为了研究优势岩石性质对酸洗性能的影响，Bilgin团队(2006)在22个单轴抗压强度在10-170 MPa之间的岩石试件上进行了大量试验。整个试验用Sandvik公司制造的，顶锥角为80的S-35/80H锥截头进行。预测建立了切削力和法向力的计算公式。结果表明，平均切削力和法向力与单轴抗压强度高度相关。从Eqs开始。(1)和(2)对大量实验数据进行拟合，可以准确地预测截齿力。因此，它们可用于实际工程计算中。在无卸式切削模式下：FC=(0.826C+21.76)d9.8FN=(1.217C1.014d9.8 (1)在卸式切削模式下： FC=(2.374C0.785)d9.8FN=(0.752C1.051)d9.8 (2)其中，FC是切削力，单位为N。FN是正态力，单位为N，C是岩石的单轴抗压强度，单位Mpa，d是切削深度，以mm为单位的。拾取力分为三种正交力：如图1(a)所示，在掘进机的破岩过程中，采煤机不仅不与刀盘旋转，而且围绕切削臂的回转中心摆动。因此，实际的切削轨迹是一个空间摆线(Huang，1996)。根据Gauss-Krueger的投影原理，如图1(a)所示，截齿的运动轨迹是二维平面(张，2012)。在这个图中，阴影区域表示当第一截齿在一个圆中旋转时，切削深度的变化。最大切削深度由方程给出(3)。然后，如图1(b)所示，切削深度可以表示为时间t的函数，这个函数由等式给出(4)。 图1.切削深度的计算模型：（a）切削过程的模拟；（b）切削深度的变化dmax=vt0=v2w=60vn （3）d=60vnsin（n30t+1） （4）其中，v是以m/s为单位的摆动速度，n是转速，w是以rad/s为单位的角速度。t0是截齿旋转所需的时间，是刀盘上截齿的初始阶段。2.2 ICR预测模型掘进机的截割机构包括驱动机构、电机，联轴器，齿轮箱和刀盘。切削力是刀盘与岩石相互作用时所消耗的能量。这是一个显示掘进机截割能力的重要指标。齿轮箱等传动部件的功率损耗对试验结果影响不大。因此，它在模型模拟中被忽略了。假设刀盘上安装了K个截齿，而且在任何时候，都会有M个参数同时与岩石相互作用。如果攻角为，则截齿的安装半径为Rg，如图1(a)所示。对于任何一个截齿，由于第1截齿作用在刀盘上的扭矩Ti可通过将截齿切削力与其相应的安装半径相乘而得到，如方程(5)所示。用m截齿切削岩石所需的总功率P可以计算，如方程(6)所示。Ti=FCiRgi （5）P=i=1mTin9550=i=1m（FCiRgi）n9550 （6）把方程(5)(6)代入方程(1-4)和(5)可列出等式(6)。预测切削功率公式可写如下：PUt=i=1m0.826C+21.7660vnsinn30t+iRgin974.5 （7）PRt=i=1m2.347C0.78560vnsinn30t+iRgin974.5 （8）其中，PU(t)和PR(t)分别是在任何时候非相关和相关的切削方式，其它量是方程 (1)、(4)、(5)中给出的。比能(SE)是影响切削系统效率的最重要因素之一，它被定义为挖掘单位体积的岩石所消耗的能量，如下公式（9）所示。SE=0tP（t）dtV （9）其中，SE是以kWh/m3为单位的比能量，P(t)是刀盘在任何时候的切削功率（kw），t是切削时间，以s为单位，V是以m3为单位的碎屑体积。公认的掘进机ICR预测方法是使用切削功率、SE和从刀头到岩石的能量传动比，如方程（10）所示。罗斯塔米等人(1996)建议使用从全尺度试验中获得的最佳SE值，以便有可靠的预测。ICR=kPSE （10）其中，ICR是以m3/h为单位的瞬时切削率。三3/h，k是与系统总效率相关的常数，通常为0.8，P是机械采煤机的切削功率(千瓦)，其他符号由方程（9）所得。此外，将方程（9）代入方程(10)，一个新的ICR如方程所示，建立了预测模型(11)。利用仿真程序，可以获得不同工况下的切削功率数据。利用切削功率数据和切削时间t，可以计算出所完成的工作量。假设刀盘已钻入岩体内，且只进行摆动切削过程，则可通过沿刀盘中心线的纵向截面面积与切削距离的乘积来计算碎片体积。ICR=kPV0tP（t）dt （11） 其中所有量都是方程（9）（10）中给出的。2.3岩石切削过程的模拟破岩过程可分为两种不同的过程模式，相关和非相关截割模式。在相关截割模式下，需要考虑相邻截齿之间的相互作用。而在非相关的切削模式中不存在交互作用。随着刀盘的推进，螺旋线排列的截齿的切削深度逐渐增大。岩石破碎过程将非相关切削模式过渡为相关切削模式。过渡模式的条件已经被纳入了仿真程序中。掘进机使用安装在由臂架支撑的旋转刀盘上面的截齿来挖掘岩石。如图2所示，三螺旋轴刀盘已被用于模拟岩石切削。三角形表示岩石破碎过程中活跃的截齿，称为活化截齿。圆圈代表不活动的截齿，活化截齿的数量取决于刀盘的切削厚度H和钻深W。岩石切削过程以一个摆动速度为v、转速为n、活化截齿回转半径为Rgi的刀盘模拟。对于给定的H和W，可以用S(H，W)表示刀盘的纵截面积。方程（12）（13）分别是非相关和相关切削模式的预测公式。ICRU=kPS(H,W)vt0ti=1m（0.826C+21.76）60vnsin（n30t+i）Rgin974.5dt （12）ICRR=kPS(H,W)vt0ti=1m（2.347C0.785）60vnsin（n30t+i）Rgin974.5dt （13） 其中，ICRU和ICRR分别是非相关切削模式和相关切削模式下的瞬时切削速度，单位是m3/h，S(H，W)是用切削厚度H和钻孔深度W计算出的刀盘纵向截面面积，其他量由方程 (7-8)和(11)得到。Fig. 2. Schematic Diagram of Rock Cutting Process with Cutterhead图2.刀盘切削岩石的原理图3.人造及原位岩石的试验3.1实验室人工岩石切削试验在国家煤矿机械设备工程中室内采用天然粉砂岩和混凝土建造砌体墙，抗压强度在60100 MPa之间。在EBZ260W轴压集箱试验中，混凝土抗压强度为748 MPa，采用国际岩石力学学会推荐的无侧限抗压强度标准方法测定。利用FLUKE 435型电能质量分析仪对切削过程中刀具的切削功率进行了测量。EBZ260W型轴向掘进机有额定切削量。功率260 kW，额定转速32.5转，整机总重量超过80吨。设计工作环境在单轴抗压强度小于80 MPa，局部岩体质量小于110 MPa的岩石隧道中。所装备的刀盘长度为1000毫米，平均直径为760毫米。它有三个螺旋线和51个截齿。所有的截齿都有直径为25毫米的硬质合金头。图3表示出了刀盘上的截齿布置。Fig.3.Pick Arrangements on Cutterhead of EBZ260W Roadheader图3.EBZ260W 掘进机的刀盘截齿布置在整个测试过程中有七种操作模式，对应于用黑色箭头标记的七个运动方向，如图4所示。在测试开始时，掘进机的截割臂处于水平中线位置。在工作方式1中，刀盘在深度为74020 mm的范围内钻入。此后，在操作模式2和3中，分别向右和左方向对岩石进行水平摆动切削。在操作模式4中，刀盘向上移动以切削岩石。在操作模式5中，对刀盘横向投影面积为52%的刀具进行了正确方向的铣削加工。在操作模式6中，刀盘向下移动，并继续切削岩石。在操作模式7中，采用了正确的方向铣削，刀盘的横向投影面积为46%。在试验过程中，钻杆动力由履带式行走机构提供，摆动功率由旋转升缸提供。3.2隧道现场试验本文对EBZ260W型轴向掘进机在大柴矿岩巷巷道进行了现场试验。该矿位于中国四川省广元。测试持续了四个月。如图5所示，这条公路隧道的横截面是一个面积为9.1平方米的拱形。它的最大水平尺寸和垂直尺寸分别为3.5米和2.9米。在隧道施工过程中，施工人员记录了隧道周围岩石的地质条件，并采集了大量的岩石样品。这些岩石具有单斜层状结构，向南倾斜，其联合产状为18937，共有189处。厚度约为65.0m，包括石英砂岩、粉砂岩与黑色硅质岩相间的中厚度泥岩。整个岩层存在斜层理层，局部地区存在x型裂缝。通过对修剪后的堆芯进行单轴压缩试验可得，平均单轴抗压强度为87 MPa，如表1所示。现场试验的总行驶距离为605.7 m，平均月行驶距离为151.4 m，一个月内的最大行驶距离为175 m，根据设备的日记录行驶距离、行车时间、酸洗消耗和运行情况，确定了相应的日生产率。 Fig. 4. Tests for Artificial Rock Cutting 图4.人工岩石切削实验Fig. 5. Diagrammatic Sketch of the Roadway Section图5.巷道断面图解表1.行车距离统计数据Table 1. Statistical Data of the Driving Distance实验周期每月行驶距离/m平均每日行驶距离每日最大行驶距离第一个月175.05.88.8第二个月第三个月第四个月总计605.7 / /隧道横截面积9.1m34.结果、验证和讨论4.1实验室人工岩石切削试验结果从人工采集岩石切削试验采集的数据，图6显示了切削功率的曲线，由于数据采集时间与运行模式2的启动时间之间存在一定的滞后性，在试验的初始阶段没有记录切削功率。此外，由于电能质量分析器与机头之间连接松散的故障，切削功率也没有记录在操作模式3中。在操作模式5、6和7中，结果表明在前20s，切削功率呈线性增长，这导致了刀盘开始切入岩石时工作截齿的数量迅速增加。在前20s，工作截齿的数量继续增加，但速度较慢，直到切削功率达到最大值。然后，功率降至最大功率的60.184%，此后保持非周期稳定状态。掘进机混凝土表面与实验室混凝土地面的粘着系数仅为0.44，该小系数不能防止掘进机在2、3、5、7工况下的侧向位移。数据表明，最大横向位移和最小平均切屑厚度都发生在操作模式2和3中，如表2所示。比较表2和表3中最大侧向位移的ICR值表明，当切削过程中存在侧向位移时，截齿在切削岩体时是以较慢的摆动速度滑动的。当刀盘在工作模式4和6中上下移动时，尤其是在模式4时，切削功率更大。这个最大切削功率为254.1 kW，与切削功率接近。在这种功率下，平均芯片厚度约为12毫米。这是因为当刀盘向上移动时，履带机和地面之间没有横向位移。此外，在运行方式4中，SE为11.1kWh/m3。而ICR为11.69m3/h。刀具间距与切削深度之比是2。这与文献(Bilgin等人，2006年)的报道是一致的。 表2.各工况掘进机试验数据 Table 2. Experiment Data of Roadheader for Each Operating Mode操作模式平均摆动速度（m/s）切削距离（m）平均切削厚度（mm）最大横向位移（m）20.9810-30.441.810.5331.3510-30.562.500.5346.2510-30.511.54051.4910-30.62.740.3662.3310-30.384.300.0272.4310-30364.480.41 表3.人工岩石切削试验试验结果与数值结果的比较 Table 3. Comparison of Experimental and Numerical Results of the Artificial Rock Cutting Tests操作模式试验结果模拟结果实际ICRPmaxLabPmaxLabSELabICRCalLabPmaxSimPavgSimSESimICRCalSimICRAct(kw)(kw)(Kwh/m3)(m3/h(kw)(kw)(kwh/m3)(m3/h)(m3/h)270.749.524.281.6438.631.315.351.632.03377.75343.735.34254.1162.511.1211.69219.0158.510.3512.2611.5592.551.620.072.0625.521.112.072.081.96117.399.622.623.5279.764.614.473.574.15786.350.620.541.9748.034.413.692.012.44.2切削功率仿真结果的刀具头的设计参数。本文推导了EBZ260W轴向掘进机与岩石接触的截齿顺序。基于方程(7)和(8)编制了计算程序。根据实验室试验条件，对各工况进行了模拟。在模拟中，假定岩石是均匀的，单轴抗压强度为74 MPa。没有考虑节理、层理和断裂面的影响。对于每一种工作模式，试验中所测得的平均运动速度作为模拟刀盘的运动速度。这使得切削深度接近于从试验中得到的深度。当掘进机处于稳定的截割状态时，模拟结果具有周期性。在此基础上，选择了100 s的仿真时间。模拟切削功率的结果主要有：受切削深度和活化截齿的数量，如图7所示。在岩石切削过程的初始阶段，由于刀盘没有完全切削到岩石中，切削深度小于刀具间距。因此，活化截齿都处于不相关的切削模式，相邻截齿形成的宏观断口不相交。当岩石切削过程进入下一阶段时，掘进机进入了稳定的工作状态。在此阶段，活化截齿的破岩方式由非相关切削模式向相关切削模式转变。相邻截齿形成的宏观架已逐渐扩展和相交。相应的切削力迅速下降到稳定状态，平均扦插力为最大值的76.9%84.7%。这些结果与测试结果一致。在工作模式4和6中，随着切削深度的增大，活化截齿均处于相关截割模式，这与切削功率越大、ICR越大相对应。另一方面，在操作模式2、3和5中，当切削深度较小时，活化截齿处于非相关切削模式，这对应于切削功率越小，ICR越小。虽然在工作模式6和7中产生的平均切屑厚度彼此接近，但在操作方式7中，岩石与刀盘之间的接触面积仅为刀盘外侧投影面积的一半左右。因此，使用较少的活化截齿，从而使ICR比在运作模式6中的ICR更小。 Fig. 6. Test Results of Cutting Power 图6.切削功率试验结果 Fig. 7. Simulation Results of Cutting Power 图7. 切削功率的仿真结果4.3用人工岩石验证仿真结果试验表3中的实验与仿真结果的比较结果表明：工作方式4的截割力、SE和ICR之间有很好的一致性，这些良好的性能归因于掘进机在工作方式4中的侧向位移为零。在此基础上，从稳定条件下的全尺度线性切削试验出发，建立了icr预测模型中截齿力的预测公式。由于掘进机在其他工况下存在明显的侧向位移，所以在这些模式下的模拟结果均小于试验结果。此外，人工岩石切削试验结果与全尺寸线性试验结果存在差异。切削试验建立了基于单截齿切削速度为0.13m/s的全尺寸线性切削试验数据的预测模型，而在实验室试验中，刀盘的切削速度在0.21-1.43m/s之间。方程(1)和(2)是在以下条件下展开的：初尖角为80，攻角为55，倾斜和倾斜角度为0。然而，试验的条件是85的初尖角。攻角为49，倾斜和倾斜角度非零。此外,R oepke等人(1983)指出，在岩石切削中，尖角为90的截齿所受的切削力明显高于顶角为60的截齿上的切削力。由于分析仪和热标头之间的连接故障，切削功率没有记录在操作模式3中。通过对掘进机平均摆动速度和截割功率的相关分析表明，平均摆动速度与最大切削功率和平均切削功率的相关系数分别为0.976和0.927，如图8所示。与相关分析的结果一致，可得方程(14)。通过方程(14)可得，在平均摆动速度为0.00135 m/s时，工作方式3的最大切削功率为77.07kW，平均切削功率为53 kW。计算出的切削功率与在操作模式2中测量到的切削功率相近，这是因为在两种工作模式下，掘进机的工作条件类似于左右摆动。这些结果表明，切削功率可以用当量来确定。(14)在本研究中准确。Fig. 8. Relation between Cutting Power and Average Swing Speedfor Experimental Studies图8. 实验研究中切削功率与平均摆动速度的关系PmaxLab=35040v+29.762PavgLab=22075v+23.201 （14）其中v是以m/s为单位的平均摆动速度。对于模拟和实验切削功率，相关分析表明，它们之间存在着密切的相关性。最大切削力和平均切削力的相关系数分别为0.985和0.982，如图9所示。在置信度为0.95的条件下，用SPSS统计程序进行回归分析，验证了切削力数值研究与实验研究之间的关系。在此分析中，选取模拟得到的平均切削功率和最大切削功率作为自变量。而实验研究得到的平均切削功率和最大切削功率作为相关变量，结果列于表4。由于F-检验得出的P-值小于0.05，可以说数值计算与实验研究之间的关系是可行的。根据统计分析的结果，可以提出公式(15)：PmaxLab=0.9415PmaxSim+45.115PavgLab=0.8716PavgSim+27.651 （15）为了验证在本研究中建立的ICR预测模型，用方程(12)和(13)计算了各工作模式的预测ICR，分别用于实验和仿真。如表3所示，在工程应用中，巷道掘进机的截割效率通常是指所有作业模式的平均截割率，根据表3中每种作业模式的ICR，预测的试验和模拟的平均ICR分别为4.18、4.3和4.4m3/h，分别。这表明，预测的ICR与已有的ICR接近，三种平均ICR的相对误差均小于5%。因此，新的预测模型所预测的ICR是合理的、准确的。Fig.9. New Model for Predicting Instantaneous Cutting Power图9. 切削功率数值与实验研究的关系 表4.实验研究得出的切削功率和F值的检验结果Table4.The F Test Results of the Cutting Power Obtained from Numerical and Experimental Studies变量来源平方和df均方F值P值PmaxLab-PmaxSim回归23258.048123258.048121.2330.000剩余767.4464191.861总计24025.4935PavgLab-PavgSim回归10096.690110096.690102.2840.001剩余394.850498.712总计10491.54054.4用现场试验和经验方法验证仿真结果受设备检查等因素影响，在现场试验中，EBZ260W轴向掘进机的ICR在更换采煤机和工人时存在波动，如图10所示。根据隧道的累计掘进距离、作业周期和断面面积计算了掘进机的日平均生产率。最大ICR为4.42m3/h(图10 A点)。室内人工岩石切削试验，切削时间短，切削面积较小，可视为一种理想切削状态。因此，将实验室试验和模拟试验中预测的平均ICR与现场试验测得的平均ICR进行了比较。结果表明，预测的ICR与实测的ICR吻合较好，进一步验证了新的ICR预测模型是掘进机性能预测的可靠工具。此外，为了验证新的ICR的适用性预测模型，利用方程 (12)和(13)以及表5中的经验公式对ICR进行了估计。对于这些估计， 单轴抗压强度在20150 MPa之间，岩石质量指标(RQD)和刀盘功率(P)分别为100%和260 kW。为了合理起见，假设刀盘功率与ICR成正比(Balci等人，2004年；Comakli等人，2014年)，轴向掘进机的所有经验方程都归一化为260 kW。例如，利用Gehring建立的模型，将瞬时切削率除以2.3，这是因为该模型是针对230 kW刀盘功率的掘进机而开发的。利用EBZ260W型轴向掘进机的设计参数，对掘进机的截石过程进行了模拟试验。在刀具间距与切削深度之比为3的情况下，平均切削间距为19.5mm，最大切削深度约为6.5mm。在模拟试验中，钻井深度为900 mm，转速为32.5rpm，机翼转速为0.0035 m/s。比较的图示如图11所示。新的预测模型和经验公式的结果都证明ICR的指数分布是一致的。特别是新的预测模型的结果与Gehring经验公式的计算结果非常吻合。Gehring(1989)给出了230 kW刀盘功率的轴向掘进机ICR的经验公式，相应的岩石单轴抗压强度在3894 MPa之间，同时进行了全尺寸的线性切削试验，22个岩石样品的抗压强度值变化在10-170 MPa之间(Bilgin等人，2006年)。如图11所示，当岩石的单轴抗压强度大于38 MPa时，ICR的模拟结果略大于Gehring经验公式计算的结果。由于经验模型的适用范围取决于现有数据的范围，因此用Gehring经验公式计算的ICR在一定的抗压强度范围内验证了预测ICR的可靠性。新的ICR预测模型可应用于中硬岩石地层。另外，对于本文所进行的模拟和分析，即使通过节理、顺层、叶面等岩体不连续效应的研究，EBZ260W轴式掘进机新的ICR预测模型仍有较好的性能。此外，该模型相对容易使用，通常需要较短的时间就能得到轴向式掘进机的解算。此外，如果已知给定机器的刀盘模型的设计参数，则新的ICR模型也可应用于横截式掘进机、连续掘进机、剪切机和露天采煤机等挖掘设备。 图10.掘进机在现场实验中的ICR变化 表5. 轴向掘进机经验性能预测模型Table5. Emprical Performance Prediction Models Prevously Developed for Axal Roadheader参考ICR预测方程Gehring（1989）ICR=1739/C1.13Bligin et al.（1997；2004）ICR=0.28P(0.974)RMCIRMCI=C(RQD100)2/3Balci et al.（2004）ICR=kP0.41C0.67 图11. 不同实证方法与本实验结果比较5.结论本文提出了一种新的ICR预测模型。基于仿真方法，该模型可以预测不同截齿布置和刀盘摆动速度的轴向掘进机的ICR。试验和仿真结果表明，在刀盘向上移动时，ICR比其他工作方式下的ICR更快。通过上下移动的截割路线，改进采掘工程中的ICR，使掘进机更加稳定。对EBZ260W型掘进机进行了室内人工岩切削试验和掘进现场试验。预测与实测的ICR的比较表明了较好的一致性。此外，在不同单轴抗压强度的岩石切削过程中，用新的ICR预测模型确定了ICR，并与其他经验公式的计算结
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