TW202021726A - Method of detecting cutter wear for machine tools - Google Patents

Method of detecting cutter wear for machine tools Download PDF

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TW202021726A
TW202021726A TW107144340A TW107144340A TW202021726A TW 202021726 A TW202021726 A TW 202021726A TW 107144340 A TW107144340 A TW 107144340A TW 107144340 A TW107144340 A TW 107144340A TW 202021726 A TW202021726 A TW 202021726A
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tool
wear
accelerometer
vibration signal
state
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TW107144340A
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TWI665051B (en
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謝錦聰
姚賀騰
汪正祺
林宜鴻
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國立勤益科技大學
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Abstract

A method of detecting cutter wear for machine tools includes steps of preparation, measurement, calculation, drawing, modeling and judgment. An accelerometer is attached to a clamp of a machine tool and a signal detected by the clamp is processed via chaotic signals synchronization through master and slave systems in order to produce dynamic error signals. Further, the dynamic error is used for drawing center point distribution graph of each states of the dynamic error and a model is made based on features of the wear condition of each cutter in order to compare the center point distribution graph in the following operation with the model, thereby judging the state of the cutter. Therefore, this method takes an advantage of only one accelerometer to reduce the calculation, obtain results quickly, achieve preferable accuracy and effectively decrease the detection cost.

Description

用於工具機之刀具磨損之檢測方法Tool wear detection method for machine tools

本發明是有關於一種檢測方法，特別是一種用於工具機之刀具磨損之檢測方法。The present invention relates to a detection method, particularly a detection method for tool wear of machine tools.

工具機是一種切削工件的自動化工具，在長時間的運轉狀態下，用於切削之刀具的損壞磨損是無可避免的，而當刀具磨損後，會導致刀具切削面與工具之接觸面增加，除了使阻力增加外，同時也會造成工件成品將不符合原始設計之規格，導致報廢品的產生；而為了降低此種狀況，一般主要是透過作業人員以人工方式檢視加工成品，若發現成品不符合規格時，將可判斷出目前刀具已經磨損，進而更換新的刀具。The machine tool is an automated tool for cutting workpieces. Under long-term operation, the damage and wear of the cutting tool is unavoidable. When the tool wears out, the contact surface between the cutting surface of the tool and the tool will increase. In addition to increasing the resistance, it will also cause the finished product to fail to meet the specifications of the original design, resulting in the production of scraps. In order to reduce this situation, it is usually mainly through the operator to manually inspect the processed product. When it meets the specifications, it can be judged that the current tool is worn, and then a new tool can be replaced.

然而，透過人力隨時檢視成品的作法，不僅提高人力成本，且若當人員未即時檢出不良品時，將造成許多不必要的浪費，以及成本提高，實有需改善，因此，如果能即時自動偵測、監控刀具磨損情形，將可有效降低不良品產生，同時也可以降低監控所需之人力成本，進而提高工具機之效率與生產良率，故如何讓工具機之刀具磨損能透過即時偵測而得知刀具磨損情形，且能快速、正確診斷刀具磨損程度，為眾所努力之目標。However, the practice of inspecting finished products through manpower at any time will not only increase labor costs, but also cause a lot of unnecessary waste when the personnel do not detect defective products immediately, and the cost will increase. There is a need for improvement. Therefore, if it can be automated immediately Detecting and monitoring tool wear can effectively reduce the production of defective products, and also reduce the labor cost required for monitoring, thereby improving the efficiency and production yield of the machine tool. Therefore, how to make the tool wear of the machine tool through real-time detection It is the goal of everyone's efforts to know the wear of the tool and to quickly and correctly diagnose the wear of the tool.

因此，本發明之目的，是在提供一種用於工具機之刀具磨損之檢測方法，其僅需使用單一感測器進行監測，即可快速分析出結果，達到良好之準確率，並降低監測所需之成本。Therefore, the purpose of the present invention is to provide a tool wear detection method for machine tools, which only needs to use a single sensor for monitoring, which can quickly analyze the results, achieve good accuracy, and reduce monitoring The cost of need.

於是，本發明用於工具機之刀具磨損之檢測方法，其包含有備具步驟、量測步驟、計算步驟、繪製步驟、建立模型步驟及判斷步驟；其中，該備具步驟，係於該夾具上貼附有一加速規，且該加速規內可分設有一主系統與一樸系統；另，該量測步驟則利用該加速規針對該夾具上之刀具振動進行量測，使該主系統可接收該加速規偵測該主軸初始轉動產生之初始振動訊號，以及該刀具切削第一刀所產生之車削振動訊號，至於該樸系統則可接收該加速規偵測該主軸持續轉動之轉動振動訊號，以及該刀具持續切削產生之切削振動訊號；另，該計算步驟具有一與該加速規訊號連接之處理裝置，該處理裝置可將輸入之該初始振動訊號、該車削振動訊號、該轉動振動訊號及該切削振動訊號等加以計算，採分數階混沌自我同步方式而陸續得到一動態誤差訊號，且針對該每一動態誤差訊號狀態之重心點予以標示出後，再將該每一動態誤差訊號狀態之重心點陸續顯示，而形成該每一動態誤差狀態之重心點分佈圖形（即繪製步驟），而後將前述該重心點分佈圖形建立一物元模型後，再備具不同刀具型態架設，且重覆前述該量測步驟、該計算步驟及該繪製步驟，以建立不同之物元模型，而前述該等物元模型可儲存於該處理裝置中（建立模型步驟）；最後，該判斷步驟備具另一刀具於該工具機上進行切削處理，前述該刀具經該量測步驟、該計算步驟及該繪製步驟等，而得到一重心點分佈圖形，且該重心點分佈圖形再與該等物元模型於該處理裝置進行比對，以判別該刀具使用狀態；是以，如此只需透過單一加速規感測器，即可減少計算量及快速分析出結果，達到良好之準確率，以及有效降低檢測所需之成本功效。Therefore, the method for detecting tool wear of a machine tool of the present invention includes a preparation step, a measurement step, a calculation step, a drawing step, a model building step, and a judgment step; wherein the preparation step is attached to the fixture There is an accelerometer attached to the accelerometer, and the accelerometer can be provided with a main system and a simple system; in addition, the measurement step uses the accelerometer to measure the tool vibration on the fixture, so that the main system can be Receive the accelerometer to detect the initial vibration signal generated by the initial rotation of the spindle, and the turning vibration signal generated by the tool cutting the first tool, and the plain system can receive the accelerometer to detect the rotation vibration signal of the continuous rotation of the spindle , And the cutting vibration signal generated by the continuous cutting of the tool; in addition, the calculation step has a processing device connected to the accelerometer signal, and the processing device can input the initial vibration signal, the turning vibration signal, and the rotation vibration signal And the cutting vibration signal, etc. are calculated, a fractional chaotic self-synchronization method is adopted to successively obtain a dynamic error signal, and the center of gravity of each dynamic error signal state is marked, and then each dynamic error signal state The center of gravity points are displayed one after another to form the center of gravity point distribution graph for each dynamic error state (that is, the drawing step), and then a matter-element model is established for the aforementioned center of gravity point distribution graph, and then different tool types are prepared, and Repeat the measurement step, the calculation step, and the drawing step to create different matter-element models, and the aforementioned matter-element models can be stored in the processing device (model building step); finally, the judgment step is prepared With another tool for cutting processing on the machine tool, the aforementioned tool undergoes the measurement step, the calculation step, the drawing step, etc., to obtain a center of gravity point distribution graph, and the center of gravity point distribution graph is then combined with the objects The meta-model is compared with the processing device to determine the state of use of the tool; therefore, only a single accelerometer sensor is needed to reduce the amount of calculation and quickly analyze the results, achieving good accuracy and effectiveness Reduce the cost-effectiveness required for testing.

有關本發明之前述及其他技術內容、特點與功效，在以下配合參考圖式之較佳實施例的詳細說明中，將可清楚的明白。The foregoing and other technical contents, features, and effects of the present invention will be clearly understood in the following detailed description of the preferred embodiment with reference to the drawings.

參閱圖1，本發明的一較佳實施例，用於工具機之刀具磨損之檢測方法，其用來偵測一工具機上之刀具磨損狀態，而該工具機具有一可轉動之主軸，一受該主轉轉動之夾具，以及一被該夾具夾持之刀具，該檢測方法依序包含有有備具步驟、量測步驟、計算步驟、繪製步驟、建立模型步驟及判斷步驟；其中，該備具步驟於該夾具上貼附有一加速規(Accelerometer)，例如為三軸加速規，且該加速規內可分設有一主系統與一樸系統，而本發明中所謂之主系統與樸系統是運用混沌理論進行運算。Referring to Figure 1, a preferred embodiment of the present invention is a method for detecting tool wear of a machine tool, which is used to detect the wear status of a tool on a machine tool, and the machine tool has a rotatable spindle, a For a fixture that is rotated by the main rotation and a tool held by the fixture, the inspection method sequentially includes a preparation step, a measurement step, a calculation step, a drawing step, a model building step, and a judgment step; wherein, the In the preparation step, an accelerometer, such as a three-axis accelerometer, is attached to the fixture, and the accelerometer can be separately provided with a main system and a basic system, and the so-called main system and the basic system in the present invention It uses chaos theory to perform operations.

仍續前述，該量測步驟利用該加速規針對該夾具上之刀具振動進行量測，而該主系統可接收該加速規偵測該主軸初始轉動產生之初始振動訊號，以及該刀具切削第一刀所產生之車削振動訊號（即為），至於該樸系統則可接收該加速規偵測該主軸持續轉動之轉動振動訊號，以及該刀具持續切削產生之切削振動訊號。Continuing the foregoing, the measurement step uses the accelerometer to measure the tool vibration on the fixture, and the host system can receive the accelerometer to detect the initial vibration signal generated by the initial rotation of the spindle, and the tool cuts first The turning vibration signal generated by the tool (that is), as for the plain system, the acceleration gauge can detect the rotation vibration signal of the continuous rotation of the spindle, and the cutting vibration signal generated by the continuous cutting of the tool.

仍續前述，該計算步驟其具有一與該加速規訊號連接之處理裝置，該處理裝置可將輸入之該初始振動訊號、該車削振動訊號、該轉動振動訊號及該切削振動訊號等加以計算，採分數階混沌自我同步方式而陸續得到一動態誤差訊號，且針對該每一動態誤差訊號狀態之重心點予以標示出；另，該繪製步驟為該每一動態誤差訊號狀態之重心點陸續顯示，而形成該每一動態誤差狀態之重心點分佈圖形（也稱為動態軌跡圖）。Continuing the foregoing, the calculation step has a processing device connected to the accelerometer signal, and the processing device can calculate the input initial vibration signal, the turning vibration signal, the rotation vibration signal, and the cutting vibration signal. The fractional chaotic self-synchronization method is adopted to obtain a dynamic error signal successively, and the center of gravity of each dynamic error signal state is marked; in addition, the drawing step is to successively display the center of gravity of each dynamic error signal state. And the center of gravity point distribution graph (also called dynamic trajectory graph) of each dynamic error state is formed.

仍續前述，建立模型步驟系將前述該重心點分佈圖形建立一物元模型（為特定狀態之動態軌跡圖）後，再備具不同刀具型態架設，且重覆前述該量測步驟、該計算步驟及該繪製步驟，以建立不同之物元模型，而前述該等物元模型可儲存於該處理裝置中；另，該判斷步驟則備具另一刀具於該工具機上進行切削處理，前述該刀具經該量測步驟、該計算步驟及該繪製步驟等，而得到一重心點分佈圖形，且該重心點分佈圖形再與該等物元模型於該處理裝置內進行比對，以判別該刀具使用狀態。Continuing the foregoing, the model building step is to create a matter-element model (a dynamic trajectory diagram of a specific state) from the aforementioned gravity center point distribution graph, and then prepare different tool types to set up, and repeat the aforementioned measurement steps, The calculation step and the drawing step are used to establish different matter-element models, and the aforementioned matter-element models can be stored in the processing device; in addition, the judgment step is to prepare another tool for cutting on the machine tool, The aforementioned tool undergoes the measurement step, the calculation step, and the drawing step to obtain a barycentric point distribution graph, and the barycentric point distribution graph is compared with the matter element models in the processing device to determine The tool usage status.

因此，而本發明實際運作時，使用加速規擷取振動訊號代入主、僕系統，其中，主系統(MS)為一刀具健康度理想之振動訊號，僕系統(SS)為即時擷取之振動訊號，經由主、僕系統相減可得出混沌動態誤差及其混沌吸引子，主僕混沌系統如式1所示：Therefore, in the actual operation of the present invention, an accelerometer is used to capture the vibration signal into the master and slave systems. The master system (MS) is a vibration signal with ideal tool health, and the slave system (SS) is a vibration captured in real time. The signal can be subtracted from the master and slave systems to obtain the chaotic dynamic error and its chaotic attractor. The master-slave chaotic system is shown in Equation 1:

MS=＞

Figure 02_image001
SS=＞
Figure 02_image003
(1)MS=＞
Figure 02_image001
SS=＞
Figure 02_image003
(1)

其中

Figure 02_image005
為理想刀具狀態之振動訊號，經由混沌系統運算後之狀態向量、
Figure 02_image007
為即時擷取之振動訊號，經由混沌系統運算後之狀態向量、為一
Figure 02_image009
矩陣、F(X)及F(Y)為非線性矩陣、u為非線性控制項，本專利主要取主、僕系統動態誤差作為刀具磨耗的辨識特徵，故u令為0，並將式2之混沌系統動態方程式轉換為主、僕沌系統型式可表示為式3：among them
Figure 02_image005
The vibration signal of the ideal tool state, the state vector after the chaotic system calculation,
Figure 02_image007
For the vibration signal captured in real time, the state vector after the chaotic system calculation is a
Figure 02_image009
Matrix, F(X) and F(Y) are non-linear matrices, u is the non-linear control item, this patent mainly takes the dynamic error of the master and slave system as the identification feature of tool wear, so u is set to 0, and formula 2 The dynamic equation of the chaotic system is converted into the main and the subordinate chaotic system type can be expressed as Equation 3:

Figure 02_image011
(2)
Figure 02_image011
(2)

Figure 02_image013
(3)
Figure 02_image015
Figure 02_image013
(3)
Figure 02_image015

式3中，

Figure 02_image017
，
Figure 02_image019
及
Figure 02_image021
為Chen-Lee系統之系統參數，經由Lyapunov exponent驗證，滿足
Figure 02_image023
之條件，該系統便具有混沌吸引子之特性，而本實施例中將健康狀況理想之訊號代入主系統，並且僕系統代入即時擷取出之振動訊號，個別經由混沌計算後，再經由主僕混沌系統所產生之結果相減，及可得出主僕混沌動態誤差及混沌吸引子，藉由該兩項結果即可作為分類之特徵依據；而本發明實施例舉例上述參數
Figure 02_image017
，
Figure 02_image019
，
Figure 02_image025
為(5,-10,-3.8)時，且
Figure 02_image027
，
Figure 02_image029
，
Figure 02_image031
初始條件為0.001時,系統產生混沌現象，如圖2、圖3，而其混沌系統主架構，
Figure 02_image033
，
Figure 02_image035
，其中
Figure 02_image037
，
Figure 02_image039
，
Figure 02_image041
，其主系統
Figure 02_image043
與樸系統
Figure 02_image045
為一個
Figure 02_image047
的系統矩陣，其主系統Ｘ如式(4)、僕系統Ｙ如式(5)：In formula 3,
Figure 02_image017
,
Figure 02_image019
and
Figure 02_image021
The system parameters of the Chen-Lee system, verified by Lyapunov exponent, satisfy
Figure 02_image023
Under the conditions, the system has the characteristics of a chaotic attractor. In this embodiment, signals with ideal health conditions are substituted into the master system, and the slave system is substituted into the vibration signal obtained in real time. After being individually calculated by the chaos, the master and slave chaos The results generated by the system are subtracted, and the master-slave chaotic dynamic error and the chaotic attractor can be obtained. The two results can be used as the characteristic basis for classification; and the embodiment of the present invention exemplifies the above-mentioned parameters
Figure 02_image017
,
Figure 02_image019
,
Figure 02_image025
When (5,-10,-3.8), and
Figure 02_image027
,
Figure 02_image029
,
Figure 02_image031
When the initial condition is 0.001, the system produces chaotic phenomena, as shown in Figure 2 and Figure 3. The main chaotic system architecture,
Figure 02_image033
,
Figure 02_image035
,among them
Figure 02_image037
,
Figure 02_image039
,
Figure 02_image041
, Its main system
Figure 02_image043
Yopu system
Figure 02_image045
For one
Figure 02_image047
The system matrix of, the main system X is as formula (4), and the slave system Y is as formula (5):

Figure 02_image053
(4)
Figure 02_image053
(4)

Figure 02_image055
(5)
Figure 02_image055
(5)

同時本發明中為了實現僕系統自我追蹤主系統同步動態誤差，因此設計控制項

Figure 02_image057
，且定義誤差狀態
Figure 02_image059
，其中
Figure 02_image061
，
Figure 02_image063
，
Figure 02_image065
，誤差系統經過整理後可以表示為式（5） ：At the same time, in order to realize the slave system self-tracking the synchronization dynamic error of the master system, the control item is designed
Figure 02_image057
, And define the error state
Figure 02_image059
,among them
Figure 02_image061
,
Figure 02_image063
,
Figure 02_image065
, The error system can be expressed as formula (5) after finishing:

Figure 02_image067
(5)
Figure 02_image067
(5)

其參數需滿足

Figure 02_image069
之條件，則系統誤差動態方程有奇異吸子之特性。Its parameters need to meet
Figure 02_image069
The condition of the system error dynamic equation has the characteristics of strange attractors.

其參數需滿足

Figure 02_image069
之條件，則系統誤差動態方程有奇異吸子之特性。Its parameters need to meet
Figure 02_image069
The condition of the system error dynamic equation has the characteristics of strange attractors.

另外，本發明中該分數階混沌自我同步動態誤差產生動態誤差，為了能夠更精細的表示刀具振動訊號各狀態特徵，其用分數階寫成式（6），其中

Figure 02_image071
為任意實數，
Figure 02_image073
為動態誤差，
Figure 02_image075
可以選擇所需階數。In addition, in the present invention, the fractional-order chaotic self-synchronization dynamic error generates dynamic error. In order to be able to more precisely represent the state characteristics of the tool vibration signal, it is written as a fractional-order equation (6), where
Figure 02_image071
Is any real number,
Figure 02_image073
Is the dynamic error,
Figure 02_image075
You can select the required order.

Figure 02_image077
(6)
Figure 02_image077
(6)

Figure 02_image079
為分數階階數，其中
Figure 02_image081
且滿足
Figure 02_image083
，
Figure 02_image085
為Gamma函數，為了使式(5)產生混沌吸引子，而將式改寫為式(7)，
Figure 02_image087
(7)
Figure 02_image079
Is the fractional order, where
Figure 02_image081
And satisfied
Figure 02_image083
,
Figure 02_image085
Is the Gamma function, in order to make equation (5) produce chaotic attractors, the equation is rewritten as equation (7),
Figure 02_image087
(7)

系統參數

Figure 02_image089
為非零常數，相位軌跡展示了分數階為
Figure 02_image091
時的各種動態行為，為了使式(7)實現自我同步追蹤，其動態誤差設定為:
Figure 02_image093
，
Figure 02_image095
，
Figure 02_image097
，
Figure 02_image099
，則
Figure 02_image101
可定義為式(8)System parameters
Figure 02_image089
Is a non-zero constant, the phase trajectory shows the fractional order as
Figure 02_image091
In order to achieve self-synchronization tracking of the various dynamic behaviors at time, the dynamic error is set as:
Figure 02_image093
,
Figure 02_image095
,
Figure 02_image097
,
Figure 02_image099
,then
Figure 02_image101
Can be defined as formula (8)

Figure 02_image103
(8)
Figure 02_image103
(8)

最後，該建立物元模型，則是採用可拓理論作為刀具損壞程度之判別法，藉由判斷經典域關聯函數大小，對照設定的物元模型，可拓理論之物元模型建立可表示為式(9)Finally, to establish the matter-element model, the extension theory is used as the method of judging the degree of tool damage. By judging the magnitude of the correlation function of the classical domain and comparing the set matter-element model, the matter-element model of the extension theory can be expressed as (9)

Figure 02_image105
(9)
Figure 02_image105
(9)

其中R代表為事物、N為自行定義的事物名稱、C為事物的特徵、V為事物的量值，本發明使用分數階混沌系統動態誤差所產生之混沌吸引子相平面座標系，作為建立可拓理論物元模型的量值，且而本實施例中，設定的物元模型有正常狀態、輕微磨損、中度磨損、重度磨損等四種進行判斷，其物元模型如下表：

Figure 107144340-A0304-0001
Where R represents a thing, N is a self-defined name of a thing, C is a characteristic of a thing, and V is a value of a thing. The present invention uses the phase plane coordinate system of the chaotic attractor generated by the dynamic error of the fractional chaotic system to establish the Extend the magnitude of the theoretical matter-element model, and in this embodiment, the set matter-element model has four types of judgments, such as normal state, light wear, moderate wear, and heavy wear. The matter element model is as follows:
Figure 107144340-A0304-0001

而當物元模型建立完成後，即定義出各個狀態之分數階混沌吸引子相平面座標分部狀況，借由物元模型之相平面座標系可定義出可拓集合中的經典域及節域如圖4，並經由式10及式11運算即可得出介於-1至1之可拓關聯度。After the matter-element model is established, the phase plane coordinates of the fractional chaotic attractor of each state are defined. The phase plane coordinate system of the matter-element model can be used to define the classical domain and the node domain in the extension set As shown in Figure 4, the extension correlation degree between -1 and 1 can be obtained through the calculation of Equation 10 and Equation 11.

Figure 02_image115
（10）
Figure 02_image115
(10)

Figure 02_image117
(11)
Figure 02_image117
(11)

上圖中，藍色框線為節域、綠色框線為重度磨耗經典域、紅色框線為中度磨耗經典域、橙色框線為為輕度磨耗經典域及黃色框線為狀態良好經典域，節域及經典域定義完成後，後續可代入其它振動訊號，經由混沌分析後取混沌吸引子，利用式10及式11計算與各狀態經典域之關聯度值，若關聯度大於0即可定義為該狀態，反之則不為，如此，將可判別狀態符合該程度最佳之特徵，診斷其關聯函數所對照到的刀具狀態。In the above figure, the blue border is the nodal domain, the green border is the classic domain with heavy wear, the red border is the classic domain with moderate wear, the orange border is the classic domain with mild wear, and the yellow border is the classic domain in good condition. , After the definition of the nodal domain and the classical domain is completed, other vibration signals can be substituted into it later. After chaos analysis, the chaotic attractor is obtained, and the correlation degree value with the classical domain of each state is calculated using equations 10 and 11, if the correlation degree is greater than 0 It is defined as this state, and vice versa. In this way, it will be judged that the state conforms to the characteristics of the best degree, and the tool state compared to the correlation function will be diagnosed.

因此，使用時，即經由備具步驟、量測步驟、計算步驟及建立模型步驟，以分別換上不同狀態之刀具後，本發明是以正常狀態(normal)、輕度磨損(slight wear)、中度磨損(moderate wear)以及重度磨損(severe wear)四種狀態建立物元模型做為比對之標的，因此換上刀具後利用加速規所測得知震動狀況，即該主系統訊號來源則為利用主軸開電靜止與刀具車削第一刀之震動訊號，僕系統訊號來源為當前量測之主軸與刀具訊號運轉時之震動訊號後，而輸入後至上述算式後，所產生

Figure 02_image119
的動差誤差軌跡圖，根據所定的四種狀態:正常狀態(normal)、輕度磨損(slight wear)、中度磨損(moderate wear)以及重度磨損(severe wear)，量測該狀態下切削狀態時之訊號，透過動態誤差
Figure 02_image121
繪製其動態軌跡圖，觀察其差異性，以此建立可拓理論之物元模型，作為判斷該訊號之狀態依據，最後根據不同的刀具狀態連續做切割動作，並且擷取其連續信號，最後可以得到如圖5與圖6之誤差值e1.e2變化量。Therefore, when in use, that is, through the preparation step, the measurement step, the calculation step, and the model building step, after the tools in different states are replaced, the present invention is based on the normal state (normal), light wear, and Four states of moderate wear and severe wear are established to establish a matter-element model as the target of comparison. Therefore, after the tool is replaced, the vibration condition is measured by the accelerometer, that is, the source of the main system signal In order to use the vibration signal of the spindle power-on and the tool turning the first tool, the signal source of the slave system is the vibration signal of the current measured spindle and tool signal when it is running, and then input to the above formula.
Figure 02_image119
The dynamic error trajectory diagram of the, according to the four states: normal, slight wear, moderate wear and severe wear, measure the cutting state in this state The signal of time, through dynamic error
Figure 02_image121
Draw its dynamic trajectory diagram and observe its differences to establish a matter-element model of the extension theory as a basis for judging the signal state. Finally, according to the state of different tools, the continuous cutting action is performed and the continuous signal is captured. Finally, Obtain the variation of the error value e1.e2 as shown in Figure 5 and Figure 6.

配合參閱圖7至圖17，使用分數階與整數階混沌系統處理刀具震動訊號之動態誤差軌跡圖，在0.7階至整數階可以發現較為線性變化，而0.1階至0.6階過於發散，因此首先排除0.1階至0.6階，再計算0.7階至整數階之動態誤差特徵量，並建立每一階數之物元模型，最後比較每一階數準確度，在與採用小波包分析、與離散傅立葉轉換方式進行比較，辦別故障診斷結果如下表：

Figure 107144340-A0304-0002
Refer to Figure 7 to Figure 17, using fractional and integer order chaotic systems to process the dynamic error trajectory diagram of the tool vibration signal. A relatively linear change can be found from 0.7 order to integer order, while 0.1 order to 0.6 order is too divergent, so first eliminate From 0.1 order to 0.6 order, then calculate the dynamic error characteristic quantity from 0.7 order to the integer order, and establish the matter element model of each order, and finally compare the accuracy of each order, and use wavelet packet analysis and discrete Fourier transform Comparison of different methods, the fault diagnosis results are as follows:
Figure 107144340-A0304-0002

仍續前述，故實驗時，可先安裝各狀態之刀具，並以相同速率與進給量運轉，擷取振動訊號建立各狀態之資料庫，並根據資料庫繪製混沌動態誤差軌跡圖，建立各狀態之可拓物元模型及各狀態可拓物元模型表：

Figure 107144340-A0304-0003
Continue the above, so in the experiment, you can install the tools in each state first, and run them at the same speed and feed rate, capture the vibration signal to establish a database of each state, and draw a chaotic dynamic error trajectory diagram based on the database to establish each The extension matter-element model of the state and the extension matter-element model table of each state:
Figure 107144340-A0304-0003

建構好資料庫後，本發明使用建立智慧型刀具狀態監測系統之人機介面，並透過人機介面可以令使用者得知目前刀具震動訊號及其狀態，於圖17至圖20可以看出系統能準確判斷其刀具狀態，並依所建立燈泡顯示，每個狀態都是運用各狀態之物元模型表的物元模型計算完可拓關聯函數後所得知結果，並且存取每次擷取刀具震動訊號給使用者判斷該工具機之狀態；是以，如此只需透過單一加速規感測器，即可減少計算量及快速分析出結果，達到良好之準確率，以及有效降低檢測所需之成本功效。After constructing the database, the present invention uses the man-machine interface that establishes the intelligent tool condition monitoring system, and through the man-machine interface, the user can know the current tool vibration signal and its status, as shown in Figure 17 to Figure 20. It can accurately judge the state of the tool and display it according to the established bulb. Each state uses the matter-element model of the matter-element model table of each state to calculate the result of the extension correlation function, and accesses the extracted tool every time The vibration signal allows the user to judge the state of the machine tool; therefore, only a single accelerometer sensor is needed to reduce the amount of calculation and quickly analyze the results, achieve good accuracy, and effectively reduce the amount of testing required Cost effectiveness.

歸納前述，本發明用於工具機之刀具磨損之檢測方法，其包含有備具步驟、量測步驟、計算步驟、繪製步驟、建立模型步驟及判斷步驟；其係透過工具機之夾具上貼附有一加速規，並且將該加速規所測得之訊號分別透過一樸系統、主系統進行混沌同步訊號處理，以產生動態誤差訊號後，運用上述動態誤差繪製動態誤差之各狀態重心點分布圖，並且依據各刀具磨損情形之特徵製作成物元模型，以供後續運轉時所產生之重心點分布圖能與該物元模型進行比對，即可判斷出該刀具之狀態；是以，如此只需透過單一加速規感測器，即可減少計算量及快速分析出結果，達到良好之準確率，以及有效降低檢測所需之成本功效。In summary, the method for detecting tool wear of the machine tool of the present invention includes preparation steps, measurement steps, calculation steps, drawing steps, model building steps, and judgment steps; it is attached to the fixture of the machine tool There is an accelerometer, and the signals measured by the accelerometer are processed by chaotic synchronization signals through a primitive system and a main system respectively to generate dynamic error signals, and then use the above dynamic errors to draw the center-of-gravity distribution map of each state of dynamic error. And make a matter-element model according to the characteristics of each tool wear situation, so that the center of gravity point distribution generated during subsequent operation can be compared with the matter-element model, and the state of the tool can be judged; A single accelerometer sensor is needed to reduce the amount of calculation and quickly analyze the results, achieve good accuracy, and effectively reduce the cost and effectiveness of detection.

惟以上所述者，僅為說明本發明之較佳實施例而已，當不能以此限定本發明實施之範圍，即大凡依本發明申請專利範圍及發明說明書內容所作之簡單的等效變化與修飾，皆應仍屬本發明專利涵蓋之範圍內。However, the above is only to illustrate the preferred embodiments of the present invention, and should not be used to limit the scope of implementation of the present invention, that is, simple equivalent changes and modifications made in accordance with the scope of the patent application of the present invention and the content of the description of the invention , Should still fall within the scope of the invention patent.

無no

圖1是本發明ㄧ較佳實施例之流程方塊圖。 圖2本發明一較佳實施例之Chen-Lee混沌系統二維圖。 圖3 本發明一較佳實施例之Chen-Lee混沌系統三維圖。 圖4本發明一較佳實施例之各狀態之經典域及節域圖。 圖5 本發明一較佳實施例之誤差值e1變化量。 圖6 本發明一較佳實施例之差值e2變化量。 圖7 本發明一較佳實施例之分數階(0.1階)各狀態動態軌跡圖。 圖8本發明一較佳實施例之分數階(0.2階)各狀態動態軌跡圖。 圖9本發明一較佳實施例之分數階(0.3階)各狀態動態軌跡圖。 圖10本發明一較佳實施例之分數階(0.4階)各狀態動態軌跡圖。 圖11本發明一較佳實施例之分數階(0.5階)各狀態動態軌跡圖。 　　圖12本發明一較佳實施例之分數階(0.6階)各狀態動態軌跡圖。 　　圖13本發明一較佳實施例之分數階(0.7階)各狀態動態軌跡圖。 　　圖14本發明一較佳實施例之分數階(0.8階)各狀態動態軌跡圖。 　　圖15本發明一較佳實施例之分數階(0.9階)各狀態動態軌跡圖。 　　圖16本發明一較佳實施例之整數階各狀態動態軌跡圖。 　　圖17狀態監測系統辦別訊號為正常狀態顯示圖。 　　圖18狀態監測系統辦別訊號為輕微磨損顯示圖。 圖19狀態監測系統辦別訊號為中度磨損狀態顯示圖。  圖20 狀態監測系統辦別訊號為重度磨損狀態顯示圖。Fig. 1 is a flow block diagram of a preferred embodiment of the present invention. Fig. 2 is a two-dimensional diagram of the Chen-Lee chaotic system in a preferred embodiment of the present invention. Fig. 3 A three-dimensional diagram of the Chen-Lee chaotic system in a preferred embodiment of the present invention. Fig. 4 is a diagram of the classical domain and the section domain of each state of a preferred embodiment of the present invention. Fig. 5 The variation of the error value e1 of a preferred embodiment of the present invention. Fig. 6 The variation of the difference e2 of a preferred embodiment of the present invention. Fig. 7 is a dynamic trajectory diagram of each state in a fractional order (0.1 order) of a preferred embodiment of the present invention. FIG. 8 is a diagram of the dynamic trajectory of each state in a fractional order (0.2 order) of a preferred embodiment of the present invention. Fig. 9 is a diagram showing the dynamic trajectory of each state in a fractional order (0.3 order) of a preferred embodiment of the present invention. FIG. 10 is a diagram of the dynamic trajectory of each state in the fractional order (0.4 order) of a preferred embodiment of the present invention. Fig. 11 is a diagram of the dynamic trajectory of each state in a fractional order (0.5 order) of a preferred embodiment of the present invention.　　 Fig. 12 is a fractional-level (0.6-level) dynamic trajectory diagram of each state of a preferred embodiment of the present invention.　　 Fig. 13 is a fractional-level (0.7-level) dynamic trajectory diagram of each state of a preferred embodiment of the present invention.　　 Fig. 14 is a fractional-level (0.8-level) dynamic trajectory diagram of each state of a preferred embodiment of the present invention.　　 Figure 15 is a fractional (0.9-order) dynamic trajectory diagram of each state of a preferred embodiment of the present invention.　　 Fig. 16 is an integer-level dynamic trajectory diagram of each state of a preferred embodiment of the present invention.　　 Figure 17 shows that the status monitoring system does not signal the normal status.　　 Figure 18 indicates that the status monitoring system signal is slightly worn. Figure 19 shows that the status monitoring system does not signal the moderate wear status. Figure 20 The status monitoring system does not indicate the signal is the display diagram of the severe wear status.

Claims (3)

一種用於工具機之刀具磨損之檢測方法，其用來偵測一工具機上之刀具磨損狀態，而該工具機具有一可轉動之主軸，一受該主轉轉動之夾具，以及一被該夾具夾持之刀具，該檢測方法依序有： 　　一備具步驟，其於該夾具上貼附有一加速規，且該加速規內可分設有一主系統與一樸系統； 一量測步驟，利用該加速規針對該夾具上之刀具振動進行量測，而該主系統可接收該加速規偵測該主軸初始轉動產生之初始振動訊號，以及該刀具切削第一刀所產生之車削振動訊號，至於該樸系統則可接收該加速規偵測該主軸持續轉動之轉動振動訊號，以及該刀具持續切削產生之切削振動訊號；一計算步驟，其具有一與該加速規訊號連接之處理裝置，該處理裝置可將輸入之該初始振動訊號、該車削振動訊號、該轉動振動訊號及該切削振動訊號等加以計算，採分數階混沌自我同步方式而陸續得到一動態誤差訊號，且針對該每一動態誤差訊號狀態之重心點予以標示出；一繪製步驟，前述該每一動態誤差訊號狀態之重心點陸續顯示，而形成該每一動態誤差狀態之重心點分佈圖形；及一建立模型步驟，其將前述該重心點分佈圖形建立一物元模型後，再備具不同刀具型態架設，且重覆前述該量測步驟、該計算步驟及該繪製步驟，以建立不同之物元模型，而前述該等物元模型可儲存於該處理裝置中；　一判斷步驟，備具另一刀具於該工具機上進行切削處理，前述該刀具經該量測步驟、該計算步驟及該繪製步驟等，而得到一重心點分佈圖形，且該重心點分佈圖形再與該等物元模型於該處理裝置進行比對，以判別該刀具使用狀態。A detection method for tool wear of a machine tool, which is used to detect the wear state of a tool on a machine tool, and the machine tool has a rotatable spindle, a clamp that is rotated by the main rotation, and a For the tools held by the fixture, the inspection method is as follows: 　　 a preparation step, which attaches an accelerometer to the jig, and the accelerometer can be divided into a main system and a simple system; a measurement step, Use the accelerometer to measure the tool vibration on the fixture, and the host system can receive the initial vibration signal generated by the accelerometer detecting the initial rotation of the spindle, and the turning vibration signal generated by the tool cutting the first tool, As for the simple system, the accelerometer can detect the rotation vibration signal of the continuous rotation of the spindle and the cutting vibration signal generated by the continuous cutting of the tool; a calculation step includes a processing device connected with the accelerometer signal, the The processing device can calculate the input initial vibration signal, the turning vibration signal, the rotation vibration signal, and the cutting vibration signal, etc., using fractional chaotic self-synchronization to obtain a dynamic error signal one after another, and for each dynamic The center of gravity of the error signal state is marked; in a drawing step, the center of gravity of each of the aforementioned dynamic error signal states is successively displayed to form the center of gravity point distribution graph of each dynamic error state; and a model building step, which will After establishing a matter-element model for the aforementioned center-of-gravity point distribution graph, different tool types are prepared for erection, and the measurement step, the calculation step, and the drawing step are repeated to establish different matter-element models. The object model can be stored in the processing device; 　a judgment step, another tool is prepared for cutting processing on the machine tool, and the tool is obtained through the measurement step, the calculation step, the drawing step, etc. A barycentric point distribution graph, and the barycentric point distribution graph is compared with the object model in the processing device to determine the use state of the tool. 根據申請專利範圍第1項所述用於工具機之刀具磨損之檢測方法，該分數階混沌自我同步方式計算時，所採用之最佳分數階為0.7階至整數階之動態誤差特徵量。According to the method for detecting tool wear of machine tools described in the scope of patent application, the optimal fractional order used in the calculation of the fractional chaotic self-synchronization method is from 0.7 order to integer order dynamic error characteristic quantities. 根據申請專利範圍第1項所述用於工具機之刀具磨損之檢測方法，該物元模型可分為正常狀態、輕微磨損、中度磨損、重度磨損。According to the detection method for tool wear of machine tools described in item 1 of the scope of patent application, the matter-element model can be divided into normal state, light wear, moderate wear, and heavy wear.
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