Claims:1. A method for real-time adaptive thermal stabilization of a CNC machine (7) or part thereof; the method comprising steps of:
i) measuring the real time changes of predefined temperature dependent machining component of the CNC machine [7] to assess heat load on the machine, involving a internet of things (IOT) device [10]; said assessment involving-
a. measuring heat generated by the machining component; and
b. measuring the heat dissipated from the CNC machine [7] or part thereof using plurality of temperature sensors [T15, T16, T17] mounted on the CNC machine [7] using industrially programmable logic control module [8] through internet of things (IOT) device (10);
ii) communicating the heat generatedand dissipated by the machine to IoT device [10] via serial communicationfor calculating the heat load.
iii) changing load of a variable frequency drive of compressor [2] in the chiller unit [1] as per the heat load through the internet of things (IOT) device (10) to thermally stabilize the machine tool [7]; and
iv) adopting the temperature regulated coolant [4] for the CNC machine [7]for its thermal stabilization.

2. The method as claimed in claim 1, wherein the temperature dependent parameter is measured using plurality of sensors selected from a group comprising run time, machine load, spindle speed, physical changes or chemical changes.

3. The method as claimed in claim 1, wherein the sensor is selectedfrom a group comprising thermal sensor, photo-electricsensors, thermo-electricsensors, electro-chemicalsensors, electro-magneticsensors, thermo-opticsensorsand combination thereof.

4. The method as claimed in claim 1, wherein the coolant is selected from a group comprising water, oil, polymer, emulsions and mixture thereof.

5. The method as claimed in claim 1, wherein chiller unit [1] and the CNC machine [7] are in two-way communication and industrially programmable logic control module [8] is in one-way communication with the IOT device [10].

6. The method as claimed in claim 1, wherein heat generation is measured byreal-time CNC machine data and empirical or physics-based heat generation models adopted inIoT device [10].

7. The method for real-time adaptive control of temperature in a CNC machine and/or its components wherein thermal error reduces to less than 50%.

8. A method for real-time adaptive thermal stabilization of a spindle of CNC machine; the method comprising steps of:
i) measuring the real time changes of predefined parameter controlling temperature of spindle [5] to assess heat load on the machine, involving a internet of things (IOT) device [10]; said assessment involving-
a. measuring the heat generation;
b. measuring the heat dissipated by the part of CNC machine [7], the spindle [5] using plurality of temperature sensors [T15,16, T17] mounted on the spindle [5] and communicating to the IoT device [10] via an industrially programmable logic control module [8];
ii) communicating the heat generated and dissipated by the spindle [5] to IoT device [10] via serial communication for calculating the heat load;
iii) changing load of a variable frequency drive of compressor [2] as per the heat load through the internet of things (IOT) device (10) to regulate the coolant temperature of chiller unit [1]; and
iv) adopting the temperature regulated coolant [4]for the spindle [5] to control the temperature of the CNC machine (7).

9. The method as claimed in claim 8, wherein the temperature dependent parameters are selected from a group comprising run time, machine load, spindle speed, chemical changes and physical changes.

10. The method as claimed in claim 8, wherein the temperature dependent parameters are measured using plurality of temperature sensors.
11. The method as claimed in claim 8,heat generating component is selected from a group of sub-systems comprising motor of the spindle [11], front bearings [12f], and rear bearings [12b].

12. A system for thermal stabilization for a CNC machine; said system comprising a real-time adaptive control of temperature involving method claimed in claims 1 and 8; said system comprising: a chiller unit [1]; a chiller compressor with variable frequency drive [2]; a chiller pump [3]; a coolant [4]; spindle [5]; plurality of coolant pipes along with temperature sensors [6]; a machine tool[7];CNC controller[9]; a programmable logic module [8]; a IoT based device[10]; plurality of temperature sensors [T15,T16, T17].
, Description:TECHINICAL FIELD
The present disclosure is in relation to a thermal stabilization system for a machine tool. Particularly, the present invention provides thermal stabilization of a Computer Numerical Control (CNC) machine tool to improve its thermal characteristics. More particularly, the disclosure provides a method for thermal stabilization of motorised spindle of a CNC machine tool based on real-time estimation of heat generation from the motor and bearings using physics-based and/or empirical models.

BACKGROUND AND PRIOR ART
Thermal error accounts for about 75% of the overall geometrical errors in work-pieces manufactured by machines. Hence, thermal characteristics of machine tools have been considered as key element to enhance the performance of high-speed machine tools (Mayr, J. et al. Thermal Issues in Machine Tools. CIRP Ann. Manuf. Technol. 2012, 61, 771–791.).
The motorized spindle unit is an indispensable component of high-performance computer numerical control (CNC) machine tools and a key element to guarantee the precision of machines. It incurs mechanical and electromagnetic losses because of its high speed and non-sinusoidal power supply. Most of these losses are transformed into heat and transferred to the surrounding air, cooling fluid, and machine parts by methods of thermal conduction, convection and radiation. Another major heat source within the motorized spindle is the friction at the bearing contact points. This results in uneven deformation of the parts of the machine, which directly affects the accuracy of the spindle and the bearing preload. Apart from these two internal heat sources, another heat source for the motorized spindle is ambient temperature. Due to these factors, motorized spindles are highly prone to thermal disturbances leading to thermal errors.
Conventional methods employed to deal with thermal issue includes thermal design measures such as spindle structure optimization (Xiaoleiet al.Thermal design of cooling structure of CNC machine tool spindle system based on insect wing flow bionic flow path, Chinese Journal of Engineering Design, 2018, 25(5): 583-589), material design optimization (Spuret. al. Thermal behaviour optimization of machine tools, CIRP Ann. - Manuf. Technol. 37 (1) (1988) 401–405, T. Moriwaki, K. Yokoyama, C. Zhao),improving machining accuracy in turning with use of tool holder made of super-invar (International Mechanical Engineering Conference, Sydney, 1991, pp. 88–92 ) and cooling system designing (Optics and Precision Engineering ISSN:1004-924X, CN: 22-1198/TH Vol 26, No. 06, Pages 1415-1429 June 2018) to obtain superior thermal characteristics of spindle.
However, these methods require high cost and even then,thermal displacement cannot be completely avoided since the external and internal heat sources under varying operating conditions cannot be accurately predicted at the design stage.
One of the most commonly used techniques to take out the generated heat is by employing a re-circulating type chiller unit for different CNC machine components. The conventionally employed cooling strategy for these units involves following the coolant temperature with the ambient temperature. More specifically, the technique, Ambient Temperature Strategy(ATS)works by switching the chiller unit ON/OFF through a manual setting in chiller unit known as chiller differential temperature (CDT), which is an integral number from +10? C to -10? C. Although this cooling system allows for the variation in trigger frequency of compressor depending upon the temperature variations due to fluctuating cutting load, the dissipation rate will however not match well with the heat generation rate causing the spindle to distort. The two-position control system leads to large thermal errors especially when high-speed machining is performed. PID-based (US 5197537, US 54761377) systems control the cooling rate so as to achieve the objective of maintaining precise temperature levels of the machine or the cooling fluid. However, this system is not only expensive, it does not ensure the lowest possible thermal distortion of the spindle which in turn leads to degradation of machining accuracy and precision, thereby affecting the performance of CNC machine tool.
Considering the requirements of quality of machining accuracy and precision of machined components, it is necessary to develop a low cost and effective method to manage thermal issues appropriately and overcome the disadvantages associated with the available methods.
SUMMARY OF THE INVENTION
Accordingly, the invention provides a method for real-time adaptive thermal stabilization of a CNC machine (7) or part thereof; the method comprising steps of
measuring the real time changes of predefined temperature dependent machining component of the CNC machine [7] to assess heat load on the machine, involving a internet of things (IOT) device [10]; said assessment involving-
measuring heat generated by the machining component; and
measuring the heat dissipated from the CNC machine [7] or part thereof using plurality of temperature sensors [T15, T16, T17] mounted on the CNC machine [7] using industrially programmable logic control module [8] through internet of things (IOT) device (10);
communicating the heat generatedand dissipated by the machine to IoT device [10] via serial communicationfor calculating the heat load.
changing load of a variable frequency drive of compressor [2] in the chiller unit [1] as per the heat load through the internet of things (IOT) device (10) to thermally stabilize the machine tool [7]; and
adopting the temperature regulated coolant [4] for the thermal stabilization of CNC machine [7].
A method for real-time adaptive thermal stabilization of a spindle of CNC machine; the method comprising steps of:
measuring the real time changes of predefined parameter controlling temperature of spindle [5] to assess heat load on the machine, involving a internet of things (IOT) device [10]; said assessment involving-
measuring the heat generation;
measuring the heat dissipated by the part of CNC machine [7], the spindle [5] using plurality of temperature sensors [T15,16, T17] mounted on the spindle [5] and communicating to the IoT device [10] via an industrially programmable logic control module [8];
communicating the heat generated and dissipated by the spindle [5] to IoT device [10] via serial communication for calculating the heat load;
changing load of a variable frequency drive of compressor [2] as per the heat load through the internet of things (IOT) device (10) to regulate the coolant temperature of chiller unit [1]; and
adopting the temperature regulated coolant [4]for the thermal stabilization of spindle [5] of CNC machine (7).
A system for thermal stabilization for a CNC machine; said system comprising a real-time adaptive control of temperature involving method of present invention; said system comprising: a chiller unit [1]; a chiller compressor with variable frequency drive [2]; a chiller pump [3]; a coolant [4]; spindle [5]; plurality of coolant pipes along with temperature sensors [6]; a machine tool [7]; CNC controller [9]; a programmable logic module [8]; a IoT based device [10]; plurality of temperature sensors [T15,T16, T17].

BRIEF DESCRIPTION OF FIGURES.
The features of the present invention can be understood in detail with the aid of the appended figures. It is to be noted however, that the appended figures illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope for the invention.
Figure 1: Illustrates the chiller unit, the CNC machine tool and the integration of the data acquisition system, the CNC controller through FOCAS (FANUC Open CNC API Specification) dynamic linked library files with the model-based chiller trigger unit software developed in Python platform.
Figure 2: Locations of all temperature and displacement sensors along with the precision ground disk fixed on the spindle shaft to measure spindle distortion.
Figure 3: Spindle distortion is measured through three capacitive displacement sensors, Sa, Sb and Sc on the precision ground disk.
Figure 4: Experimental profile wherein the spindle speed is varied in a step manner from 2500 rpm to 15000 rpm and then back to 2500 rpm.
Figure 5: Comparison of temperature evolution for various points in spindle and ambient for the experimental profile shown in Fig. 4 for both ATS and CTM strategies.
Figure 6: The thermal distortion observed when compressor is operated through CTM method (right) is much lower than the one during ATS method (left).
Figure 7: Spindle speed experimental profile- wherein speed is varied in a random manner from 0 rpm till 15000 rpm.
Figure 8: Comparison of temperature profiles for the experimental profile shown in Fig. 7for both ATS and CTM strategies. The heat generated and dissipated is also plotted to illustrate further.
Figure 9: Comparison of thermal distortion, pitch error and yaw angle observed for CTM method (right) and ATS method (left).

DETAILED DESCRIPTION OF INVENTION:
The present invention relates to the development of a cost-effective, high-performance cooling method for a CNC machine, so that thermal errors are reduced at the source. The cooling methodology is effective because of the fact that the amount of cooling is adaptive to the real-time heat generation and not on the temperature variations; implying that as the heat generation changes so does the cooling. The method is adoptable for various parts of a CNC machine tool, including motorized spindle. The cooling method is termed as Cooler Trigger Model (CTM).
The foregoing description of the embodiments of the invention has been presented for the purpose of illustration. It is not intended to be exhaustive or to limit the invention to the precise form disclosed as many modifications and variations are possible in light of this disclosure for a person skilled in the art in view of the figures, description and claims. It may further be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural reference unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by person skilled in the art.

The CTM control method is based on minimizing the deviation between the heat generation rate and the heat extraction rate. For instance, when the load on the machine tool changes, so does the heat generation and concurrently the employed control method is such that the heat dissipation will also change to ensure less build-up of residual heat and in turn reduce thermal distortion of the spindle.
The invention provides a method for real-time adaptive thermal stabilization system for a CNC machine (7) or part thereof; and a system for said process; the system comprising: a chiller unit [1]; a chiller compressor with variable frequency drive [2]; a chiller pump [3]; a coolant [4]; plurality of coolant pipes [6]; a CNC machine tool[7] comprising of motorized spindle [5];CNC controller[9]; a programmable logic module[8]; a IoT based device[10]; plurality of sensors[T15,T16,T17].
The extent of control is related to real-time estimation of heat generation from the machine components using physics-based and/or empirical models and balancing the heat dissipation with the heat generation. The process flow of the thermal stabilization system is depicted in Scheme1.

Scheme 1
The Figure 1 provides the schematic of the CTM method comprising, the chiller unit [1], the CNC machine tool [7] and the integration of the data acquisition system, the CNC controller [9] through FOCAS (FANUC Open CNC API Specification) dynamic linked library files with the model-based chiller unit trigger software developed in Python platform. The thermal stabilization is done by measuring the real-time load or the heat generation through an in-house developed Internet of Things (IoT) device named IRIS [10] and thereby communicating the corresponding heat generation load to the Variable frequency Drive (VFD) [2] of the compressor unit (2) through IRIS.
The process flow includes measuring critical parameters, monitoring them, setting controlling parameters and communicating the required information to the corresponding physical systems. IRIS is used as the platform to set-up communication between different physical systems. It reads the real-time information from CNC control unit [9]. The communication setup of IRIS with CNC is independent of the controller and hence, thermal stabilization technique works for all type of controllers and consequently for all machines. The real-time changes in the machining parameters, like spindle speed and load are captured using IRIS and subsequently, the real-time heat generation is calculated using the model-based main logic program executed in IOT device IRIS[10] using Python software.Chiller unit [1] with the machine tool is integrated with the Python-based logic [10] for controlling the load of the chiller compressor [2]. The real-time heat dissipation is monitored using temperature sensors mounted on the corresponding inlet and outlet of the spindle[6a, 6b] respectively. The temperature values are measured using a temperature module affixed to an industrial Programmable Logic Control (PLC) module [8]. PLC [8] module is also communicating the data to IRIS via serial communication and the amount of heat dissipated is calculated using the main logic. Now, in order to equate the amount of heat dissipated with the heat generated, the load of the chiller unit compressor is controlled by establishing communication with the chiller unit [1] and changing the load on the VFD compressor [2] as per the calculations in the main logic. While the CNC system [7] and chiller unit [1] are in two-way communication with IRIS, PLC is only communicating to IOT device IRIS [10]. Specifically, in the case of chiller unit [1], the load percentage calculated from the main logic is communicated to the chiller unit [1] and from the chiller unit [1], the actual compressor load at which it is running is communicated back as a feedback to IRIS[10]. Due to the real-time computation of aforementioned parameters, this method is able to take care of large realistic fluctuations in the load/spindle speed and dead-time component during any machining process and therefore is readily deployable on a practical machine shop.
One aspect of the invention is that the novel control method can be implemented on any chiller unit used for cooling spindles or any other part of CNC machine with minor changes in the model.
In one embodiment of the present invention, the coolant of the chiller unit can be selected from group comprising water, oil-based Machine Coolants, polymer coolants, emulsions and/or mixture thereof.
Another aspect of the invention is low total cost associated with the implementation of the novel control method as the changes are mostly implemented in the software rather than hardware.
Yet another aspect of this invention is therefore to control the amount of cooling in order to match it with the real-time heat generation rate / heat generation. Although the heat dissipation / dissipation rate cannot be instantaneously made equal to the heat generation /generation rate at all times due to hardware limitation, they will be made to follow one another within a pre-specified threshold such that significant thermal distortion of spindle does not occur by taking advantage of relatively higher thermal time constant of the spindle.
The method also comprises of development of an empirical model-based adaptive cooling method for a chiller unit. A pre-requisite to this model-based control system is to be able to accurately estimate the heat generation rate and the corresponding heat dissipation rate in real time. Scheme 2 illustrates the schematic of the model-based control logic of the developed chiller control system.

Scheme 2

The invention can be deployed on various sub-systems of a CNC machine.A method for thermal stabilization of motorised spindle of a CNC machine is provided hereinto exemplify the method. Physics-based and empirical models are used to compute the above-mentioned quantities and are described in the following sections:
In a motorized spindle, the motor region [11] and the bearings [12] are the main heat generating sources apart from the frictional heat generated due to the eddies formed in the coolant channel and the surface friction. The heat generated in the motor region [11] is mainly because of the magnetic and the electrical losses and can be computed by taking into consideration the load on the spindle motor, the mechanical conversion efficiency and the rated power capacity of the motor as follows:
(Q_m ) ?=(1-?)*P*P_r ..........................................(Eqn. 1)
where, (Q_m ) ?is the heat generated from the motor in Watts, ? is the efficiency of the motor in the conversion from electrical power to mechanical work, P_ris the rated power in Watts and P is the proportional load onto the motor.
The heat generation at the bearings [12] is mainly due to the friction at the bearing contact points. According to Palmgren, the sum of torque due to the applied load (because of machining) and the viscous friction torque due to the grease lubrication in the bearing will lead to the heat generation. The torque due to the applied load in Nmm is given by M_1=f_1 F_ß d_m, where f_1=z(?F_s/C_s )?^y is a factor which depends on the design of the bearing and the relative bearing load, d_m is the pitch diameter of bearing in mm, F_s is the static equivalent load, C_s is the basic static load rating, z=0.001 and y=0.33 for angular contact bearing and F_ß is the axial load in Newton. While the viscous friction torque depends on the kinematic viscosity of the grease and the speed of spindle rotation:
M_v={¦(?10?^(-7) f_0 ?(?N)?^(2/3) d_m^3 ; ?N>2000@160*?10?^(-7) f_0 d_m^3 ; ?N=2000)¦……………………(Eqn. 2)
where, M_v is the viscous friction torque in Nmm, ? is the kinematic viscosity in centistokes, N is the spindle speed in rpm and f_0=4 for a pair of angular contact grease lubricated bearings. The total friction torque can then be computed as M=M_1+M_v and the total heat generation rate in the bearings is
(Q_b ) ?=1.047*?10?^(-4) NM…………………………….(Eqn. 3)
The total heat generation rate can then be computed as,
(Q_g ) ?=(Q_m ) ?+(Q_b………………………………………) ?(Eqn. 4)
The heat extraction rate can be computed by measuring the temperature of the coolant fluid that is inlet to the spindle and outlet from the spindle. For a specific case of this spindle and coolant circuit design, a part of the coolant fluid that enters the spindle motor region will return after cooling[6b], while the remaining part will cool the front bearing region and then will return back to the chiller unit [1]. Thus, the heat extraction rate from the motor region can be estimated as, (Q_dm ) ?=m ?C_p ?T_m, where m ? is the mass flow rate of the coolant fluid in kg/s, C_p is the specific heat capacity of the fluid in J/kg K and ?T_m is the temperature difference of the coolant liquid between the inlet and the outlet from the cooling channel near motor of the spindle. Similarly, the heat extraction rate from the bearing region [12] can be computed as,(Q_db ) ?= (m_b ) ?C_p ?T_b, where (m_b ) ? is the mass flow rate of the fluid entering the bearing region[12] and ?T_b is the temperature difference of fluid between the entry and exit points of the bearing region[12]. The total heat extraction rate is then computed as the sum of extraction rates from the bearing and the motor region ((Q_dm ) ?+(Q_db ) ?). It is to be noted that the material properties such as specific heat capacity and mass flow rate are functions of the coolant liquid temperature and therefore a look-up table is pre-computed in order to accommodate the variation.
When the CNC machine controller is switched ON, time counter, t will be initialized and the temperatures of the coolant liquid that is inlet and exit from the spindle unit are recorded through the PLC [9] and read into the in-house python code using serialcommunication. Fluid properties such as specific heat and mass flow rate are then computed for an average coolant temperature within the spindle unit using the look-up table. The real-time heat generation rate and the corresponding heat extraction quantities are then computed. The real-time heat generation rate or the cooling load in terms of percentage, L = (Q_g ) ?/(Q_c ) ? X 100, where (Q_c ) ? is the compressor cooling capacity in Watts, is communicated using an in-house IoT device to the controller of the cooler unit so that the VFD compressor operates at the load, L.The process is iterative and isrepeatedevery ?t seconds using the real-time temperature and CNC data.
The method can be implemented across any CNC controller platform through the in-house developed Internet of Things (IoT) device, IRIS. It is found that cooling method CTM performs better than conventional ATS method over a wide range of operating conditions.

Experimental:
Process and arrangements for physical measurements
The set-up includes a stand-alone motorized spindle clamped rigidly on a metallic V-block (Figure 2). The bearings [12] in the spindle are arranged in a double o-configuration and provision is made to cool the bearings and stator portion of the motor region [11] through a recirculation-type chiller unit. Resistance Temperature Detector (RTD) sensors [Tm, T1 to T17] are affixed at several critical points in the spindle and are monitored using a PLC [9]. In addition, a precision ground disk [13] is rigidly clamped onto spindle shaft to act as its extension and a steel fixture is designed to place precise capacitive sensors [14]so as to measure spindle distortion at three designated points (Figure 3; sensors Sa, Sb and Sc) on the rotating disk.
Experiments are performed under no-load condition and the resulting thermal displacements are measured using precision capacitive displacement sensors [14]. The displacements recorded from sensors (Da, Db and Dc) are then transformed to obtain thermal distortion, ?z, pitch and yaw angles (?x and ?y) using the following matrix equation:

where, R_0 is the radius from the spindle axis at which the capacitive sensors are affixed (Figure 3). Custom-built IoT system named IRIS is used to synchronize the data from FANUC CNC controller (such as spindle speed, load and motor temperature) along with temperature data and simultaneously, the spindle distortion is calculated through capacitive sensors. The CTM logic is executed through a Python code and the resulting thermal distortion is compared with the one obtained from a conventional ATS method for the same experiment.
Temperature values and CNC parameters are recorded every ?t seconds while the displacements are measured initially at a higher frequency and later at a constant interval of 10 minutes (at 60, 60, 60, 120, 300 and every 600 seconds) so that the transient variations during the initial phase of the experiment are captured well. Also, It is ensured that just before the displacement measurement as per the specified intermittent cycle, the spindle is stopped for a brief while and is oriented to the same angular position every time so that dynamic running errors or run-out of the precision disk are eliminated and thus the measured distortion is significantly due to thermal issues only.

Comparative study of performance of CTM and ATS methods in controlling the heat generation in spindle.
The speed dictates heat generation within the spindle, considering this aspect, the CTM and ATS methods are compared by performing air cutting experiments by varying the spindle speed with time.
Variation in spindle speed
The stand-alone spindle speed is varied from 2500 rpm to 15000 rpm in a step-wise fashion and then decreased to 2500 rpm in the same manner (Figure 4). The temperature evolution profile for ATS and CTM strategies shows interesting trends (Figure 5).The temperature near the motor region for ATS method is around 10 degrees higher than the one for CTM although the ambient temperature during both experiments remained almost constant at about 28°C. This is because of the fact that the coolant entry and exit temperatures from the spindle for CTM method are around 10 degrees lower than that for ATS method. It is seen that the inlet and exit temperatures for ATS method almost follows ambient temperature, whereas, in the CTM model it follows the heat generation rate. In the CTM method, the coolant entry temperature depends on the spindle speed and the higher the spindle speed, the lower is the temperature and vice-versa. Another interesting observation is the larger difference between the coolant inlet and the exit temperatures for CTM method, which indicates that heat extraction is higher when compared with the traditional ATS method. It is therefore observed that the CTM method dynamically accounts for the changes in heat generation and adapts so that it is reflected in heat dissipation and hence enables synchronization between heat generation and its extraction.
The resulting thermal distortion for both the ATS and CTM method is illustrated in Figure 6. The CTM method results in much lower thermal expansion ?z, pitch and yaw angles (?x and ?y) when compared with the ATS method. Specifically, the maximum thermal expansion of spindle reduces from 38 microns to 26 microns while the pitch angle reduces from 91 µradian to 52 µ radian and finally the yaw angle reduces from 62 µradian to 42 µradian when the spindle is running at 15000 rpm. However, the proportional thermal error reduction is much higher for CTM method than ATS method during other speed regimes.
Random speed variation
The efficacy of CTM method is further illustrated by another experiment with random speed variation, as shown in Figure 7. Along with the temperature evolution, the real-time variation of modified heat generation, Q?g along with the corresponding heat extraction rate Q?d, is also plotted. The plot aids in one-to-one comparison of both strategies in terms of heat extracted for same heat generated. It may be noted from the sub-plot (b) of Figure 8 that Q?gbecomes null at the end of every 10th minute because of the fact that the spindle is made to stop for a brief while before displacement readings are measured as mentioned earlier. Observations similar to the previous experiments are noted, which are illustrated in Figure. 9. Although adaptive nature of CTM method is reinforced, Q?dis however unable to closely follow Q?gat higher spindle speeds as Q?gincreases rapidly for higher N. Further, the bearing temperatures stabilize at a lesser temperature in CTM method when compared with ATS. Among the two methods, a significant reduction in the stabilization temperature of average front bearings by about 3 °C is observed in CTM method. Concurrently, a significant reduction in thermal distortion is observed in CTM when compared with ATS. Quantitatively, for the random speed profile, the maximum thermal expansion of spindle reduces from 43.7 microns to 25.1 microns while the pitch angle reduces from 53 µradian to 39 µradian and finally the yaw angle reduces from 49µradian to 36µradian.
The present invention provides a method for cost-effective high-performance cooling methodfor a conventional re-circulating type chiller unit of motorized spindle used in a machine tool. This cooling methodology does not require any sophisticated changes in the hardware of the cooler unit and can be downward compatible as well. The total cost associated with this technology is significantly lower when compared with other technologies.