Patent ID: 12214462

DETAILED DESCRIPTION

Each embodiment of the disclosure will be described in detail below and illustrated with drawings. In addition to these detailed descriptions, the disclosure may be broadly implemented in other embodiments, and any easy substitution, modification, or equivalent variation of the described embodiments is included in the scope of the disclosure and is covered by the scope of the claims thereafter. In the description of the specification, many specific details and examples of embodiments are provided to provide the reader with a more complete understanding of the disclosure; however, these specific details and examples of embodiments should not be considered as limitations of the disclosure. In addition, well known steps or components are not described in detail to avoid unnecessary limitations of the disclosure.

Referring toFIG.1, which is a simplified schematic diagram of a monitoring system100according to one embodiment of the disclosure. The monitoring system100includes a machine tool110, a signal sensing unit120, a signal processing unit130, a monitoring unit140, a parameter adjustment unit150, and a control device160. In the embodiment, the machine tool110includes a spindle112and a workpiece table114, and a tool (not shown) may be installed at the nose end of the spindle112. The control device160is coupled to the machine tool110to control the machine tool110to machine the workpiece WP on the workpiece table114. In the embodiment, the milling machine is exemplified for illustration, but the machine tool110of the disclosure is not limited thereto.

The signal sensing unit120is coupled to the spindle112of the machine tool110and is configured for detecting the vibration signal of the spindle112. The signal sensing unit120is, for example but not limited to, a sensor such as an accelerometer, a displacement meter, a microphone, etc. that may measure the vibration state. The signal processing unit130is coupled to the signal sensing unit120and is configured for receiving the vibration signal detected by the signal sensing unit120, and for processing the vibration signal. The monitoring unit140is coupled to the signal processing unit130, the parameter adjustment unit150and the control device160, and is configured for monitoring the signal processed by the signal processing unit130to keep track of the machining status of the machine tool110. If the current machining status is determined to be as expected, the control unit160is commanded to continue machining, and the control unit160may control the machine tool110based on the numerical control code. In case of poor machining status that may affect the surface quality of the workpiece WP, the parameter adjustment unit150is immediately commanded to adjust the machining parameter, and the numerical control code of the control device160may read the adjusted machining parameter stored in the temporary storage area to control the machine tool110, so as to ensure that the workpiece WP meets the desired surface quality.

FIG.2is a flowchart of a monitoring method S100according to one embodiment of the disclosure. Referring toFIG.1andFIG.2, in step S110, a threshold condition is first determined by a training model MD based on the predetermined surface quality of the workpiece WP. The threshold condition is used as a criterion for the monitoring unit140to monitor the machining status of the machine tool110. If the machining status meets the threshold condition, it means that the surface quality of the machined workpiece WP meets the desired predefined value. In some embodiments, the predetermined surface quality is related to the surface roughness of the workpiece WP, which may be, but is not limited to, the center-line average roughness (Ra), or the ten-point average roughness (Rz).

Herein, the training model MD may generate the threshold condition based on the predetermined surface quality of the workpiece WP. The training model MD may be constructed by a machine learning algorithm (e.g., neural network) for training. During the training process of the training model MD, the training model MD is trained using a machining quality database. Once the training is completed, the predetermined surface quality of the workpiece WP may be input into the training model MD by the user, and the training model MD may output the corresponding threshold condition for the monitoring unit140to monitor the machining status of the machine tool110.

FIG.3is a flowchart of a method S200for establishing a machining quality database according to one embodiment of the disclosure. Referring toFIG.1andFIG.3, in step S210, the machine tool110machines a specimen WPs with a machining condition. The machining condition may include the spindle speed and/or a feed speed of the machine tool110.

Next, in step S220, the signal sensing unit120detects a sample vibration signal of the spindle112of the machine tool110. Then, in step S230, the signal processing unit130obtains a sample vibration feature value VTof the sample vibration signal. Generally speaking, the sample vibration signal (original vibration signal) detected by the signal sensing unit120contains a lot of noise unrelated to cutting, so it is necessary to extract the sample vibration feature value VTby the signal processing unit130to obtain the sample cutting vibration signal related to cutting. The sample vibration feature value VTrepresents the vibration related to the machining area during machining of the machine tool110, but not the external vibration from the non-machining area.

Further, also refer toFIG.1,FIG.4,FIG.5AandFIG.5B.FIG.4is the step S230of obtaining the sample vibration feature value VTof the sample vibration signal according to one implementation.FIG.5Ashows the waveform of the sample vibration signal.FIG.5Bshows the waveform of the sample frequency-domain signal. As shown inFIG.5A, the sample vibration signal contains a lot of noise unrelated to cutting, so it is necessary to process this signal.

In step S231, the signal processing unit130acquires a sample time-domain signal of the sample vibration signal with a sample sampling frequency. For example, the signal processing unit130may sample the vibration value with a sample sampling frequency of 10,000 times per second to obtain the sample time-domain signal. Then, as in step S232, the signal processing unit130converts the sample time-domain signal into a sample frequency-domain signal using Fourier transform. For example, as shown inFIG.5B, the sample frequency-domain signal corresponding to one of the sampling intervals of the sample time-domain signal is shown.

Next, in step S233, the signal processing unit130obtains at least one sample vibration value corresponding to at least one frequency equal to N multiple of a fundamental frequency of the sample frequency-domain signal, wherein N is an integer of 1 or more. For example, as shown inFIG.5B, the waveform at 1 multiple of the fundamental frequency (cutting frequency multiplication A) usually has the maximum vibration amplitude (corresponding to the sample vibration value Va), and then the vibration amplitude decreases as the frequency multiplication increases. In the present example, at least 1 multiple of the fundamental frequency to 4 multiples of the fundamental frequency may be obtained, corresponding to the sample vibration value Va at the cutting frequency multiplication A, the sample vibration value Vb at the cutting frequency multiplication B, the sample vibration value Vc at the cutting frequency multiplication C, and the sample vibration value Vd at the cutting frequency multiplication D.

Next, in step S234, the signal processing unit130calculates a sample sum Vchof the at least one sample vibration value. For example, as shown inFIG.5B, the sample sum Vch=Va+Vb+Vc+Vd.

Next, in step S235, the signal processing unit130determines whether an additional sample vibration value Vcc exists beyond the N multiple of the fundamental frequency of the sample frequency-domain signal, and the additional sample vibration value Vcc is greater than the sample vibration value Va corresponding to 1 multiple of the fundamental frequency of the sample frequency-domain signal. If yes, step S236is performed; if not, step S237is performed.

For example, as shown inFIG.5B, there is an additional sample vibration value Vcc, which is larger than the sample vibration value Va, at a frequency E other than the frequency multiplication, and the vibration at frequency E may result from the chatter phenomenon caused by the machine tool110being in unstable cutting. Therefore, the additional sample vibration value Vccshould be taken into account when calculating the sample vibration feature value VT. As in step S236, the sample vibration feature value VTequals the sum of the sample sum Vchand the additional sample vibration value Vcc. On the contrary, if the additional sample vibration value Vccis not found, then the sample vibration feature value VTequals the sample sum Vch, as in step S237.

Returning toFIG.1andFIG.3, after the sample vibration feature value VTis obtained, step S240is performed to measure the sample surface quality of the specimen WPs under the machining condition. Afterwards, in step S250, the aforementioned machining condition, as well as the sample vibration feature value VTand the sample surface quality under the corresponding machining condition are recorded in the machining quality database.

Referring toFIG.1andFIG.2, after the threshold condition is determined, step S120is performed, and the machine tool110machines the workpiece WP. Next, in step S130, the signal sensing unit120detects the vibration signal of the spindle112of the machine tool110. Then, in step S140, the signal processing unit130obtains the vibration feature value of the vibration signal. In step S140, the vibration feature value is obtained as the manner described in the implementation of the step S230, and the vibration feature value is acquired so as to obtain the cutting vibration signal related to cutting. That is, the time-domain signal of the vibration signal is acquired with a sampling frequency; the time-domain signal is converted to a frequency-domain signal; at least one vibration value corresponding to at least one frequency equal to N multiple of a fundamental frequency of the frequency-domain signal is obtained, wherein N is an integer of 1 or more; the sum of the at least one vibration value is calculated; and whether an additional vibration value exists beyond the N multiple of the fundamental frequency is determined, and the additional vibration value is greater than the vibration value corresponding to 1 multiple of the fundamental frequency. If there is an additional vibration value, the vibration feature value equals the sum plus the additional vibration value; if there is no additional vibration value, the vibration feature value equals the sum. Examples will not be repeated here again.

Next, in step S150, the monitoring unit140determines whether the vibration feature value exceeds the threshold condition determined in step S110. If the vibration feature value does not exceed the threshold condition, it means that the surface quality of the workpiece WP machined with the current machining parameter may meet the desired predefined value. It returns to step S120, and the monitoring unit140commands the control unit160to continue machining the workpiece WP with the current machining parameter. If the vibration feature value exceeds the threshold condition, it means that the surface quality of the workpiece WP machined with the current machining parameter may not meet the desired predefined value. Then step S160is performed, and the monitoring unit140commands the parameter adjustment unit150to adjust the machining parameter of the machine tool110. In one embodiment, during the adjustment of the machining parameter, the monitoring unit140may command the control unit160to pause the machining of the workpiece WP by the machine tool110, and then command the control unit160to rotate the workpiece table114for a trial machining of a test piece WPt to find out the suitable machining parameter.

In one embodiment, step S160of adjusting the machining parameter of the machine tool110includes obtaining an optimized spindle speed of the machine tool110, and the optimized spindle speed is obtained as the manner shown inFIG.6, which is the step S160of adjusting the machining parameter of the machine tool110according to one implementation. Referring toFIG.1andFIG.6, in step S161, first, the parameter adjustment unit150sets a speed interval including any speed value between an initial spindle speed and a final spindle speed.

Next, the parameter adjustment unit150commands the control device160to iteratively execute step S162to step S166with different spindle speeds within the speed interval until all speed tests are completed.

In detail, in step S162, the parameter adjustment unit150first selects a predetermined spindle speed, and commands the control device160to perform trial machining on the test piece WPt with the predetermined spindle speed.

In step S163, the signal sensing unit120detects a test vibration signal of the spindle112of the machine tool110. Next, in step S164, the signal processing unit130obtains the test vibration feature value of the test vibration signal. In step S164, the test vibration feature value is obtained as the manner described in the implementation of the step S230, and the test vibration feature value is acquired so as to obtain the cutting vibration signal related to cutting. Examples will not be repeated here again. Then, in step S165, whether all speed tests are completed is determined. If not yet, the parameter adjustment unit150changes the spindle speed, as shown in step S166, and continues performing step S162according to the spindle speed. Conversely, if all speed tests in the speed interval are completed, step S167is performed and the parameter adjustment unit150selects the appropriate spindle speed based on the trial machining results.

Here, the parameter adjustment unit150may rank the spindle speed and the test vibration feature value based on the trial machining results, so as to obtain the optimized spindle speed. As shown inFIG.7, which shows the relationship between the spindle speed and the test vibration feature value. According to the trial machining results, the test vibration feature value under the spindle speed condition at point F is the smallest. Therefore, the parameter adjustment unit150considers the spindle speed at point F as the optimized spindle speed, updates the machining parameter accordingly, and commands the control unit160to return the workpiece WP to the previous pause position according to the updated machining parameter, so as to continue machining.

Of course, step S160of adjusting the machining parameter of the machine tool110is not limited to the above embodiment. For example, it is not necessary for the parameter adjustment unit150to find the optimized spindle speed. As long as the workpiece WP machined under the machining condition with the spindle speed meets its predetermined surface quality, the spindle speed may be considered as a suitable spindle speed.

The following is an example of cutting a stainless steel cell phone case with reference toFIG.8A,FIG.8BandFIG.8B.

FIG.8Ashows the waveform of the cutting vibration signal for machining a workpiece WP using the machine tool110, illustrating the situation when the monitoring system100according to one embodiment of the disclosure is not turned on. Here, the waveform of the cutting vibration signal may be obtained according to the vibration feature value obtained in step S140stated above, so as to show the cutting vibration signal related to cutting. In the embodiment, the predetermined surface quality (surface roughness Ra) of the workpiece WP is 0.8 μm, and the corresponding threshold condition determined by the training model MD is 0.91 g, wherein g is the acceleration value of gravity. As shown inFIG.8A, when the vibration feature value does not exceed 0.91 g, the measured surface roughness Ra of the workpiece WP meets the expected value of 0.8 μm. Once the vibration feature value exceeds 0.91 g, the measured surface roughness Ra of the workpiece WP does not meet the expected value.

FIG.8Bshows the waveform of the cutting vibration signal for machining the workpiece WP using the machine tool100, illustrating the situation when the monitoring system100according to one embodiment of the disclosure is turned on.FIG.8Cshows the relationship between the spindle speed and the test vibration feature value obtained by adjusting the machining parameter in the parameter adjustment interval. Referring toFIG.8B, in the initial machining zone Z1, the monitoring unit140determines that the vibration feature value at point P has exceeded the threshold condition, and then commands the parameter adjustment unit150to adjust the machining parameter. In the parameter adjustment zone Z2, the parameter adjustment unit150performs five sets of trial cutting tests to obtain the corresponding relationship for five sets of the spindle speeds and the test vibration feature values, as shown inFIG.8C. InFIG.8C, the test vibration feature value under the spindle speed condition at point H is the smallest. Therefore, the parameter adjustment unit150considers the spindle speed at point H as the optimal spindle speed and adjusts the machining parameters according to the optimized spindle speed. Referring toFIG.8B, the parameter adjustment unit150then commands the control device160to continue machining. In the successive machining zone Z3, the measured surface roughness Ra of the workpiece WP meet the expected value.

In summary, the monitoring method and monitoring system for a machine tool provided according to the disclosure may establish a threshold condition based on the predetermined surface quality of a workpiece. In this way, even if the machine tool is in a steady-state cutting situation, it is possible to adjust the machining parameter that is not as expected. Thus, the undesired surface quality of the workpiece may be prevented even when the machine tool is in a steady-state cutting situation.

It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed embodiments. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.