An adaptive fuzzy control system to maximize rough turning ...

Int J Adv Manuf Technol (2011) 53:71–79 DOI 10.1007/s00170-010-2816-y

ORIGINAL ARTICLE

An adaptive fuzzy control system to maximize rough turning productivity and avoid the onset of instability Juho Ratava & Mikko Rikkonen & Ville Ryynänen & Johanna Leppänen & Tuomo Lindh & Juha Varis & Inga Sihvo

Received: 11 February 2010 / Accepted: 23 June 2010 / Published online: 5 September 2010 # Springer-Verlag London Limited 2010

Abstract This paper presents a new method to improve cutting efficiency for steel rough turning. To date, most efforts aimed at improving productivity during cutting operations have concentrated on optimizing material handling to and from the machinery. Here, the focus is on improving the efficiency of the turning operation itself. The approach is to control feed rate to raise machine power to a maximum safe level while avoiding the onset of cutting instability. The measure of machine power comes directly from the spindle motor and is held below the cutting machine’s power capacity. Detecting the onset of instability relies on interpreting data that come from installed instrumentation. A fuzzy inference system processes the inputs and makes the final control decisions. The prototype system was tuned using data collected in a variety of cutting situations. Subsequent testing of the tuned control system showed that it was capable of successfully maximizing power usage while still avoiding the onset of instability. Keywords Rough turning . Unstable cutting . Cutting instability . Fuzzy control . Fuzzy inference system

1 Introduction In today’s increasingly competitive manufacturing environment, processes must become more and more productive. Finding ways to improve is a critical and ongoing activity. Thus far, the metal cutting industry has improved productivity J. Ratava (*) : M. Rikkonen : V. Ryynänen : J. Leppänen : T. Lindh : J. Varis : I. Sihvo Lappeenranta University of Technology, Skinnarilankatu 34, P. O. Box 20, 53851 Lappeenranta, Finland e-mail: [email protected]

mainly by automating material handling to and from the cutting machinery. To achieve even greater productivity gains, the focus is shifting away from automated material handling and onto the cutting machinery, and the speed and overall throughput of the cutting processes. Maximum throughput results when a machine is cutting at the highest speed allowed by its structural design and available power. However, quality and safety considerations often limit this speed, and machinery often operates at well below design capacity. CNC machinists normally set cutting parameters (i.e., depth of cut, cutting speed, and feed rate) conservatively to avoid the onset of instability and the safety hazards and poor quality results that follow. These conservative settings result in cutting operations that do not use all the available power of the machine, a condition referred to as power capacity underutilization. To address power capacity underutilization for turning operations, real-time adaptive control systems have been developed that enable continual adjustment of cutting parameters [1, 2]. Masory et al. [1] described a control system that compared measured cutting force with programmed cutting force (predetermined allowable reference force). The difference between the two values provided corrections to the feed rate. When the cutting force was measured below the programmed force, feed rate increased. When it was measured higher, feed rate decreased. Whether or not this resulted in better optimization of the feed rate depended on the validity of the programmed force value. Lundholm and Lindström [2] evaluated a more complex realtime adaptive control system that consisted of several control loops to monitor and respond to measured variables such as motor current, tool condition, tool wear, and chatter. Different implementations of adaptive control have taken different approaches to processing control input data. When monitoring cutting processes, fuzzy logic has proven useful

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for interpreting and acting on sensor measurements. To predict and account for tool condition, neuro-fuzzy systems have sufficed (e.g., Mesina and Langari [3] for milling operations). Zheng et al. [4] developed a web-based system for turning machinery to select tools, cutters, and cutting parameters online. The system provided cutting performance feedback to the machinist, enabling him to continue playing a key role by observing turning force, power consumption, vibration status, and workpiece distortion. These and other control systems developed to address power capacity underutilization have provided benefits, but they have not realized the full potential of real-time adaptive control. There are still more gains available in cutting speed and throughput, and more general approaches can be developed that will apply more broadly. To avoid power capacity underutilization for a turning machine, a real-time adaptive control system can monitor and respond to spindle power level. This approach has been the subject of several studies (e.g., Gutierrez and Calderon [5] Optimization of CNC cutting parameters by the electrical power method: Turning case). Continually adjusting the variables that control the machining process enables a turning machine to operate safely near its power capacity and still avoid the onset of instability. For a turning operation, spindle power level varies largely with the material being cut, depth of cut, cutting speed, and feed rate. Of these three variables, the depth of cut usually cannot be changed from its preset values for technical reasons. Both cutting speed (a function of spindle rotational speed and workpiece diameter) and feed rate can be changed. Today, it is most common to vary feed rate to optimize spindle power level. Increasing feed rate achieves the objective of raising spindle power level, but depending on the workpiece material, cutting can become unstable before reaching the power capacity of the turning machine. To maximize productivity, feed rate must reach a maximum that avoids the onset of instability. Turning machine instability is readily apparent to an observer. For the cutting conditions studied in this article, during a normal smooth and stable cutting operation, a continual stream of metal chips flows off the cutter in a specific direction influenced by the lead angle of the cutting tool. With the onset of instability, chips begin flying out in random anomalous directions (mostly upwards). An unpleasant high-pitched sound frequently accompanies this change in chip behavior. There have been studies published on the effects of feed rate on machining stability. Stoic et al. [6] examined how variations in cutting depth, unfavorable force ratios, cutting tool size, and non-uniform contact stress distributions can affect stability during turning at high feed rates. To gage instability, they monitored cutting forces, vibrations, and sounds. Local cutting process instabilities can also induce

Int J Adv Manuf Technol (2011) 53:71–79

dynamic instabilities in the machine structure leading to selfexcited vibrations commonly referred to as chatter [7–9]. Building on this body of work, a real-time adaptive control system to maximize rough turning productivity and avoid the onset of instability was developed and is presented here. The controller monitors cutting conditions and continually adjusts feed rate to safely maximize power usage. Monitoring is carried out by a variety of sensors, and the control input data are processed via a fuzzy inference system that responds to sub-optimal conditions.

2 Setup for control system development To develop real-time adaptive control of the rough-cutting process, a CNC turning center installed at the Lappeenranta University of Technology (LUT) was fitted with the appropriate functional performance monitoring instrumentation and signal processing equipment. A PC with internal data acquisition capability provided the computing needed to receive and process data and issue commands back to the CNC. An experienced CNC machinist carried out all lathe operations. 2.1 Turning center and stock material selection A Daewoo Puma 2500Y CNC lathe with FANUC 18i-TB CNC served as the development turning machine and test bed. The cutting tools and tool holders were from Sandvik Coromant. The cutter inserts were Sandvik’s high-speed machining grade GC 4015. Most operations used the SNMM 12 04 12-PR square insert for general roughing with the DSBNL 2525M 12 holder. The lead cutting angle was 75°. Some cutting operations used the Sandvik CNMM 12 04 12-PR rhombic 80° insert with the lead angle set at 95°. For the majority of the cutting operations, the selected stock material was 34CrNiMo6 steel (AISI 4337/4340), quenched and tempered to a hardness of 320 HB. This steel is popular, versatile, and often encountered by the metal cutting industry. It is one of the more challenging steels to machine. To ensure that results were not material specific, a second steel with significantly different material properties was used in its place for a smaller percentage of the cutting operations. This was P355NH (EN 10028-3), a fine-grain structural steel formulated for pressure vessel use. 2.2 Deciding the important control parameters Two of the parameters that would feature in the real-time adaptive control of cutting operations are fundamental. These were spindle motor power and feed rate. The planned real-time adaptive control would vary feed rate to effect


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spindle motor power level. Less obvious were the parameters that would signal the onset of instability in the cutting process. Observation and the body of work regarding the onset of instability in turning operations indicated that noise and vibration would be the appropriate parameters. Accelerometers, an acoustic emissions transducer, and a microphone installed in the Daewoo turning center monitored these parameters. Unstable machining was expected to increase the magnitude or power (RMS) of one or more signals.

an electronic module that processes the sensor signals, collects voltage information, and provides the power reading. Sitting in the lathe’s electric cabinet near the motor controller, the WLM read three-phase motor voltage directly from the motor controller output. A reading of electrical current came from one of the WLM current sensors encircling one of the three phases. The Shure Prologue 14L dynamic microphone has a frequency response of 40–13,000 Hz (±6 dB). It sat in the machine cabinet about 700 mm from the spindle.

2.3 Sensors and transducers

2.4 Data acquisition and management and feed rate control

Cutter acoustic emission was captured using one SEA acoustic emissions transducer and an SEP acoustic emissions processor, both manufactured by Nordmann GmbH. To sense the cutter vibrations, two Kistler 5114 Piezotron® couplers processed signals coming from a pair of PCB Piezotronics 353B03 ICP accelerometers. One WLM active-power metering module also from Nordmann GmbH provided an accurate measurement of spindle motor power. Finally, a Shure Prologue 14L dynamic microphone recorded general noise level. The microphone signal passed through a Boss GE-7 boosting equalizer for amplification (see Table 1). According to Nordmann, the SEA piezoelectric acoustic emission sensor is capable of measuring frequencies from 5 kHz to 1 MHz. The typical frequency produced by the cutter during an instability event is more than 100 kHz. The SEP processor modulates this frequency downwards for recording. The SEA sensor is positioned as close to the cutting tool as possible without compromising safety and the usability of the lathe. It is attached with screws directly to the tool holder of the turning machine turret (see Fig. 1). The published measurement range for the PCB Piezotronics model 353B03 quartz shear ICP® accelerometers is ±500 g peak (±4,905 m/s² peak). Their nominal sensitivity is 10 mV/g with a nominal frequency range of 0.7–11,000 Hz (±10%). The accelerometers came with their own male threads for mounting and screwed directly into tapped holes in the tool holder. Again, both were located as close as possible to the cutting tool. One stood vertically in the plane of the cutting tool's main force vector, best positioned to respond to vibrations normal to the feed axis. The other lay horizontally in the plane of the feed, best positioned to respond to vibrations along the feed axis. Again, see Fig. 1. During machining, a metal cover over the turret protected the three sensors mounted to the lathe’s tool holder. Steel tubing conduits protected sensor electrical wiring. The Nordmann active-power measurement meter (WLM) can measure voltage and current up to 200 kHz. This makes it suitable for use with a frequency-converted motor drive, such as the spindle motor of the Daewoo Puma lathe. The meter comprises Hall effect current sensors and

In addition to the instrumentation, the prototype adaptive control system comprised a PC running Windows XP, an internal PC-based data acquisition system, software for data management and analysis, and software for adaptive control. A National Instruments PCI-6251 multichannel data acquisition circuit board installed in the PC collected the processed sensor signals. The PCI-6251 board has an A/D conversion resolution of 16 bits and a multichannel composite maximum sampling rate of 1 MS/s. National Instruments’ LabVIEW software served to provide a graphical data acquisition interface with realtime monitoring and data management capability. For testing done during this project, the data-sampling rate was set normally to 20 kS/s (20 kHz). For real-time adaptive control, the control system used the Data Acquisition Toolbox™ and Simulink®, both marketed by The MathWorks™. Communications were via the Fanuc OpenFactory API Specifications (FOCAS). The Data Acquisition Toolbox™ directly accessed the instrumentation, and a Simulink® model handled the logic.

3 Developing the fuzzy adaptive control Two prime objectives guided the design and development of the real-time fuzzy adaptive control for turning operations. First, it had to maximize feed rate during rough cutting to use as much as possible of the machine’s power capacity and, second, do this while avoiding the onset of machining instabilities. To accomplish these, the control system would need to increase feed rate from a preset level, recognize the onset of instability, and respond by adjusting feed rate to establish optimized cutting conditions. 3.1 Identifying the characteristics of instability The first step forward was to use sensor data to identify the characteristics that would signal the onset of unstable machining. A series of turning operations using 34CrNiMo6

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Table 1 Installed sensors and signal conditioners listed by identification label ID label

Parameter

Sensor

Signal Conditioner

AE ACC1 ACC2 WLM MIC

Acoustic emission Cutter vibration, normal to stock Cutter vibration, direction of feed Spindle power Noise level

Nordmann SEA PCB Piezotronics 353B03 ICP PCB Piezotronics 353B03 ICP Nordmann WLM Shure Prologue 14L

Nordmann SEP Kistler 5114 Piezotron® coupler Kistler 5114 Piezotron® coupler Nordmann WLM Boss GE-7

steel stock was designed and carried out to capture sensor data both during normal cutting and during unstable cutting. By observing these cutting operations and noting when stable or unstable, a later study of recorded sensor data would reveal what characteristics might signal the onset of instability. All tests follow the same pattern, though in some tests there were minor differences, for example when starting with a bar with a different diameter. The experiments were machined with a cutting speed of 150 m/min, with feed rate varying between 0.4, 0.5, 0.6, and 0.7 mm/rev and depth of cut varying between 2, 3, 4, and 4.5 mm. The NC code used was the same in all the experiments as well as the material supplied. Throughout these turning operations, an experienced machinist observed, judged, and noted suspect changes in cutting performance. He scored each event based on severity using a scale from 1 to 10. Higher scores corresponded to worse levels of cutting instability. If the severity was sufficient to require adjusting the cutting parameters, the machinist noted this as well. Generally, adjustments were only required for values from 6 to 10. The collected expert knowledge is presented in Table 2. The recorded observations were used to identify how the cutter acoustic emissions and vibrations changed during each event in the corresponding sensor data. The characteristics in the sensor data that signaled the onset and severity of instability became apparent. Spindle motor power levels remained within the power capacity of the lathe throughout,

though momentarily entering levels that would not be sustainable for long periods. For a depth of cut greater than 3 mm, instability began to appear when the feed rate exceeded 0.6 mm/rev. Machining remained stable for lower feed rates when machining 34CrNiMo6. As expected, the microphone and the sensors attached to the lathe turret clearly responded to cutting instability. The power (RMS) of the cutting sound (MIC) increased, especially near 6. Vibration of the cutter jumped, especially along the direction of feed (ACC2). Acoustic emission (AE) did increase, as well. Change in all monitored variables is shown in Figs. 2, 3, 4, 5, 6, and 7. The relationship between power consumption and cutting instability is displayed in Fig. 8. It is noteworthy that main servo power (RMS) seemed to correlate rather well with cutting instability (Fig. 9). However, in this specific case, this was not useful, as one the goals of the experimentation was to see how much the power consumption could be increased before the cutting turned unstable and as such, setting a target power level to be kept to any lower than the maximum sustainable motor power was counterproductive. While it appeared that moderate success could be achieved by using only the calculated power (RMS) of the measured signals, using simple signal power is not the best measure, as increasing machining power causes increasing signal power. Thus, increasing power usage, the main function of the control system, might set off false alarms. In order to deal with the situations where using plain signal power triggered false alarms, the spectra of measured signals were studied using Welch’s method [10], which Table 2 Expert knowledge about cutting instability in initial experiments, each value is a separate sample

Fig. 1 Position of the sensors on the tool holder of the turret

f=0.4 f=0.5 f=0.6 f=0.7

a=2

a=3

a=4

a=4.5

1 1 2 3

2 2 4 8

1, 1 3 6 8

2, 6, 6, 7,

3 6 8 9


Fig. 2 Increase of tangential tool holder vibrations as cutting instability increases; initial test shown with circles, later tests with crosses, relationship between measured value and instability as the line

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Fig. 4 Increase of acoustic emission as cutting instability increases; markings as in Figs. 2 and 3

revealed a resonating frequency clearly seen in tangential acceleration. The resonance produced a spike within the two octaves above 2 kHz in the power spectral density. A comparison between spectral component magnitude and cutting instability is displayed in Fig. 9. The exact frequency of the indicating spectral component depends on cutting parameters and prevalent conditions. Most importantly, the height of the peak in decibels remains relatively constant when signal power increases of other reasons than cutting instability when using the acceleration signal power as a reference level. Therefore, the measure scales with increasing machining power. In order to compare the results obtained with different methods, the typical values detected for samples where the

expert (i.e., the machinist) had considered the cutting unstable were normalized into a scale from 1 to 10. The method using axial acceleration, acoustic emission, and microphone signal power (RMS) seemed to work very well, but as expected, produced some false positives in the second series of tests (Fig. 10). For comparison, the method using only tangential acceleration spectral component magnitude seemed to lack detection capability at high levels of instability (Fig. 11). The final measure of instability used in this study is the average of the value of these two estimates (Fig. 12). The automated estimates were considered to be successful if the system would not lower feed on samples where the machinist would not lower feed or the system would lower feed where machinist would lower feed, shown in the lower left and upper right corners of Figs. 10, 11, and 12. Two cases of

Fig. 3 Increase of axial tool holder vibrations as cutting instability increases; markings as in Fig. 2

Fig. 5 Increase in noise level as cutting instability increases; markings as in Figs. 2, 3, and 4

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Fig. 6 Audio signal variance as cutting instability increases; markings as in Figs. 2, 3, 4, and 5


Fig. 8 Main servo power consumption compared with cutting instability; markings as in Figs. 2, 3, 4, 5, 6, and 7

The real-time adaptive fuzzy control must adjust feed rate when necessary to establish optimized cutting conditions. It must evaluate incoming data and decide whether and how to adjust. To make these decisions, the fuzzy control mechanism includes the fuzzyfier, inference system, and defuzzyfier.

The fuzzyfier transforms incoming measurement data (crisp input) into fuzzy quantifiers, by matching them to corresponding levels of membership in a fuzzy set [11], essentially real values in the unit distance. The inference system uses these values to calculate the needed fuzzy adjustment. Finally, the defuzzyfier transforms the fuzzy adjustment back into feed rate (crisp output). Like many fuzzy logic applications [12], this is a rule-based expert system approach. Spindle motor power and cutting “unstableness” are the fuzzyfier inputs. Using the fuzzyfier to transform them to real “truth” or set membership values in the unit distance enables the fuzzy adaptive control to return discrete triggered responses to the continuous data input.

Fig. 7 Change in audio signal power at 6 kHz as cutting instability increases; markings as in Figs. 2, 3, 4, 5, and 6

Fig. 9 Change in the peak component of the acceleration signal spectrum at 2–8 kHz as the cutting instability increases; note that the vertical axis is logarithmic; markings otherwise as in Figs. 2, 3, 4, 5, 6, 7, and 8

failure were considered false positives (detecting instability when the cutting process was stable, upper left in the figures) and failure to detect instability (lower right in the figures). Borderline cases were judged favorably, as fuzzy sets are able to handle uncertainty. 3.2 Fuzzy control mechanism


Fig. 10 Estimate based on acceleration, AE, and audio signal power (RMS); crosses are estimates; clear failures circled; solid line shows a relationship between expert and automated estimates; dashed lines show control threshold

The sets used by the control mechanism that relate to instability (UM) are “slightly unstable” (UMS), “moderately unstable” (UMM), and “very unstable” (UMB). Sets related to maximizing performance by maximizing the usage of the lathe’s full capacity or detecting low power usage (LP) are “very low power usage” (LPB), “moderately low power usage” (LPM), and “slightly low power usage” (LPS). Additional related sets controlling the power usage are “slightly too high power usage” (HPS), “moderately too high power usage” (HPM), and “very much too high power usage” (HPB). The “too high power usage” sets are a failsafe in case a change in feed rate or cutting conditions results in an unsafe or damaging increase in spindle motor power level. Additionally, the system has an auxiliary set of

Fig. 11 Estimate based on acceleration spectral component (peak) magnitude; markings as in Fig. 10

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Fig. 12 Average of estimates shown in Figs. 10 and 11; markings the same as in those figures

“everything okay” (OK) designed to stabilize the cutting parameters when there are no major issues with the cutting conditions. The set membership functions for power usage were selected according to the lathe manufacturer’s specifications relating to maximum continual power output and maximum peak power output. The maximum continuous power is 15 kW. All power-related sets are trapezoidal. LPS has a support from 13 to 15 kW and a core from 13.5 to 14 kW. LPM has a support from 12 to 13.5 and core from 12.5 to 13. LPB has a support from 0 to 12.5 and a core from 0 to 12. Similarly, HPS has a support from 14.5 to 16 and core from 15 to 15.5, HPM support from 15.5 to 17 and core from 16 to 16.5, HPB support from 16.5 to infinity, and core from 17 to infinity. This also means that the input space for power is complete, i.e., all possible inputs can be fuzzified so that the sum of the relevant fuzzy set is 1. If there are no other inputs, the control has a steady state consuming 14.75 kW, just below the maximum continuous power consumption threshold. The set membership functions for cutting instability were selected such that the measure described in Section 3.1 matches as closely as possible with the machinist’s judgment of cutting process status. This is achieved by modeling the relationship between expert knowledge and projecting the machinist’s estimates on the automated estimate space. The value analogous to 5.5 on the expert’s estimate is the threshold for control. This value is matched to the 0.5 level of the UMS set. UMS and UMM are symmetric triangular sets with the peak at 1 and a support two units wide. UMB completes the subsection of the space from the end of the core of UMM to 10 (input maximum value).

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Based on the levels of membership in the introduced fuzzy sets, the inference system decides the needed feed rate adjustment, again as fuzzy sets. The sets that relate to feed rate adjustment (f) are “negative big” (FNB), “negative medium” (FNM), “negative small” (FNS), “zero” (FZE), “positive small” (FPS), “positive medium” (FPM), and “positive big” (FPB). These are symmetric triangular sets, with the peak at 5%, 10%, and 15% adjustment for S, M, and B, respectively, and the support ±5% from the peak. The inference mechanism decides the membership values for these sets based on logical rules set up in the fuzzy control system’s rule base. The following principles were the basis;

the development of this control system. There is room to improve in several ways, including optimizing the software architecture and implementation. Partial reason for the sluggishness may be the extensive logging a recording functions in use, as well as the use of an architecture designed to house multiple analysis modules in addition to the system described in this study. In addition, follow-on systems could interface more directly with the machine controller of the turning center, giving more flexibility and responsiveness.

1. If “cutting” is unstable, then feed rate will be “decreased.” 2. If “power usage” is low, then feed rate will be “increased.” 3. If “power usage” is too high, then feed rate will be “decreased.” 4. If everything is “okay”, then feed rate will remain “constant.”

To validate the real-time fuzzy adaptive control of the turning system, the machinist carried out a second set of turning operations designed to explore optimization performance. These experiments were designed to learn how well the control system recognized the onset of instability and maximized feed rate within the power capacity of the lathe while keeping the cutting process stable. As in previous tests, the judgment of the machinist was the standard of comparison. The same experimental design was used for both sets of experiments, though in the validation experiments the system was allowed to control feed rate. Only the first samples of the series of samples recorded by the control system during its working period were used in this comparison, due to the machinist finding it difficult to judge the machining condition very quickly between the control actions beyond “improving” and “worsening”. Instability in the first samples is described in Table 3. In Figs. 2, 3, 4, 5, 6, 7, 8, and 9, these samples are represented with crosses. Turning operations used both the square and rhomboid cutting tool inserts. For the square insert, the tool angle was 75°. For the rhomboid insert, the angle was 95°. There was no discernable difference in adaptive control performance between these setups; however, testing was not very extensive. When compared against the judgments of the machinist, the fuzzy control system demonstrated an 81% success rate correctly, identifying the onset of instability during the turning operations performed during validation testing. After adjusting the parameters for detecting cutting instability,

In addition, cutting may become unstable before reaching the lathe’s power capacity, a conflict between principles 1 and 2. The fuzzy control also must be able to resolve this conflict. Taking all into account, using a fuzzy (infinitevalue) generalization of the Łukasiewicz algebra (Ł∞), seven logical rules form the basis of the inference mechanism. Rule Rule Rule Rule Rule Rule Rule

1: 2: 3: 4: 5: 6: 7:

UM ¼ UMS UMM UMB LP ¼ LPS LPM LPB HP ¼ HPS HPM HPB OK ¼ :ðUM _ LP _ HPÞ LPX ^ :UM ! FPX, where X=S, M, B HPX _ UMX ! FNX, where X=S, M, B OK ! FZE.

The actual implementation of the inference system offers some flexibility. Rules can be added to account for other cutting issues that may arise. The defuzzyfier transforms the fuzzy output from the inference system back into a “crisp” feed rate value. It decides this value based on the seven rule sets and the lathe’s current feed rate using the centroid defuzzyfication method. The crisp value results by projecting the centroid (geometric center point) of the union of the fuzzy sets onto the feed rate axis. On the less than desirable side of the implemented system’s properties, the reaction time for the system was long. This reaction time should be as short as possible from the first detection of the problem to the adjustment of feed rate correcting the problem. The long reaction time demonstrated during validation testing (5 to 10 s) precludes effective use of the system for shorter turning operations. There is insufficient time to respond and converge on the optimal cutting values. Computational efficiency was not of primary importance in

4 Validating the fuzzy adaptive control system

Table 3 Expert knowledge on instability in validation experiments, sorted by cutting parameters a=2 f=0.4 f=0.5 f=0.6 f=0.7

2, 1, 1, 2,

2, 2, 2, 2,

2, 2 2, 2 3 2, 3

a=3

a=4

2, 2, 4, 6,

3, 4, 5, 6,

3 3 5 6

3, 5, 6, 7,

a=4.5 5, 6, 7, 7,

6 6 8 10

3, 4, 7, 7,

5, 6, 8, 8,

5, 8 6, 8 8 10, 10


the system could identify up to 86.7% of the recorded measurements (Fig. 11). The system made 10 incorrect analyses of 75 cases. Of these, four were false positives, and in six cases, the system failed to detect the cutting instability. In some cases, the work piece started to emit a high-pitched cutting noise while not unstable. The phenomenon caused an increase in overall vibration level and was considered as a possible reason false positives. Overall, the increase in microphone power (RMS) observed in the initial tests was not detected in validation testing with the same magnitude. The fuzzy control system was supposed to decrease feed rate if it detected instability and increase feed rate when operating below power capacity. Additionally, feed rate was supposed to settle into a steady state without oscillating up and down. This all worked as designed in the cases where the system was capable of identifying instability or measured underutilization of power. The exact gains for control varied depending on the depth of cut and feed rate initial value, with the feed rate generally reaching steady state around 0.6 to 0.65 mm/rev after possibly visiting the maximum allowed value of 0.7 mm/rev.

5 Conclusions The real-time adaptive fuzzy control recognized power capacity underutilization and increased feed rate to compensate. At the onset of instability, it lowered the feed, finding the appropriate balance between fast rough turning and safe and good quality rough turning. The adaptive control demonstrated an 86.7% success rate, identifying the onset of unstable machining. With the combination of cutting tool Sandvik Coromant SNMM 12 04 12-PR and 34CrNiMo6 steel mainly examined here, the system worked well. However, the system is rather slow, requiring

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special attention on computational efficiency during further development. Acknowledgement The writers would like to thank senior laboratory technician Juha Turku from LUT Metal Technology for lending his experience in turning for this study.

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