TWI448353B - Method and apparatus of machine tools for intelligently compensating thermal error - Google Patents
Method and apparatus of machine tools for intelligently compensating thermal error Download PDFInfo
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本發明係與工具機熱誤差調適有關，特別是關於一種具暖機判斷與自適應學習的工具機熱誤差智慧調適裝置及其方法。The invention relates to the thermal error adjustment of the machine tool, in particular to a tool machine thermal error intelligent adapting device and method thereof for warming machine judgment and adaptive learning.
工具機10在進行加工過程，無論馬達、液壓系統和機械摩擦都是在進行能量轉換，不論轉換途徑為何，大多變成了熱，這些熱量造成機體內部與周遭的溫度變化，最後導致加工時工件尺寸或形狀的變位誤差，簡稱熱誤差(圖1至圖2所示)。The machine tool 10 is in the process of processing, no matter the motor, hydraulic system and mechanical friction are energy conversion, no matter the conversion route, most of them become heat, which causes temperature changes inside and around the body, and finally leads to the workpiece size during processing. Or the shape of the displacement error, referred to as thermal error (shown in Figure 1 to Figure 2).
熱誤差的問題是精密機械研發過程中永遠必須面對的課題。根據研究文獻[1]記載，工具機加工總誤差量約有40-70%是由熱誤差所貢獻，可見其對於工具機加工精度之影響，扮演著絕對關鍵的角色。The problem of thermal error is a problem that must always be faced in the development of precision machinery. According to the research literature [1], about 40-70% of the total error of tool machining is contributed by thermal error, which shows that it plays an absolutely crucial role in the impact of tool machining accuracy.
傳統上，為減少熱誤差干擾，需使機器溫升達到穩定狀態(或稱暖機狀態)之後，再開始進行加工作業。此過程多仰賴作員依經驗判斷暖機與否，如圖3、圖4所示。Traditionally, in order to reduce thermal error interference, it is necessary to start the machining operation after the temperature rise of the machine reaches a steady state (or warm state). This process relies on the judge to judge whether the warm-up is based on experience, as shown in Figure 3 and Figure 4.
近來，針對工具機熱誤差的因應策略主要可分為兩種方式，一種是採用被動補償方式，藉由建構工具機熱誤差預測模型，以軟體方式來進行誤差量的補償；另一種策略則是採用主動抑制方式，於設計階段即設法讓誤差產生量降低，其目的在於控制或避免熱誤差的生成。關於主動抑制與被動補償之研究與技術概況，分別歸納如表一與表二所列。Recently, the response strategy for tool machine thermal error can be divided into two ways. One is to use passive compensation method. By constructing the tool machine thermal error prediction model, the error amount is compensated in software mode; the other strategy is The active suppression method is used to reduce the amount of error generated during the design phase, and the purpose is to control or avoid the generation of thermal errors. The research and technical overview of active suppression and passive compensation are summarized in Tables 1 and 2 respectively.
相較於主動熱抑制的設計方式，採取熱誤差軟體補償之手段更具有便利性且符合經濟效益，它並非直接移除或減少工具機產生之熱誤差，而是利用實驗量測結果進行運算分析，藉由軟體方式來彌補誤差之影響，此種方法也廣受國外工具機廠使用，例如日本Mazak與Okuma、瑞士Mikron等。因此，如何改善現有補償技術，研發更精確、更可靠的熱誤差補償方法，乃是工具機業者長期以來持續投入的目標。Compared with the design method of active thermal suppression, it is more convenient and economical to adopt the method of thermal error software compensation. It does not directly remove or reduce the thermal error generated by the machine tool, but uses the experimental measurement results for operation analysis. This method is also widely used by foreign tool machine manufacturers, such as Mazak and Okuma in Japan and Mikron in Switzerland. Therefore, how to improve the existing compensation technology and develop a more accurate and reliable thermal error compensation method is a long-term goal of the tool machine industry.
然而，從過去之研究成果發現，對於工具機穩態的熱誤差問題，採用數學統計之靜態補償模型雖可獲得不錯之效果，但是對於暫(動)態之熱誤差問題，卻是相當棘手、不易處理，至今國內業者對於此類問題仍是無法解決。However, from the past research results, it is found that the static compensation model using mathematical statistics can obtain good results for the thermal error of the steady state of the machine tool, but it is quite tricky for the thermal error of the transient state. It is not easy to handle, and domestic companies still cannot solve such problems.
基於上述問題，發明人提出了一種工具機熱誤差智慧調適裝置及其方法，以克服現有技術的缺陷。Based on the above problems, the inventors have proposed a tool machine thermal error smart adaptation device and method thereof to overcome the deficiencies of the prior art.
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[15] Kang,Y.,Chang,C.W.,Huang,Y.,Hsu,C.L.,Nieh,I.F.,2007,“Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools,”Int. J. Mach. Tools Manufact.,Vol. 47,pp.376-387.[15] Kang, Y., Chang, CW, Huang, Y., Hsu, CL, Nieh, IF, 2007, "Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools," Int. J Mach. Tools Manufact., Vol. 47, pp. 376-387.
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[20] MIKRON website www.mikron.com [20] MIKRON website www.mikron.com
[21] FANUC website www.fanuc.co.jp [21] FANUC website www.fanuc.co.jp
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[24] Yi-Hung Liu,Yu-Kai Huang,and Ming-Jui Lee,“Automatic inline-defect detection for TFT-LCD array process using locally linear embedding and support vector data description,”Measurement Science and Technology,vol. 19,August 2008[24] Yi-Hung Liu, Yu-Kai Huang, and Ming-Jui Lee, "Automatic inline-defect detection for TFT-LCD array process using locally linear embedding and support vector data description," Measurement Science and Technology, vol. 19 , August 2008
[25] Yi-Hung Liu,Szu-Hsein Lin,Yi-Ling Hsueh,and Ming-Jiu Lee,“Automatic target defect identification for TFT-LCD array process inspection using kernel fuzzy c-means based fuzzy SVDD ensemble”,Expert Systems with Applications,vol. 40,2008[25] Yi-Hung Liu, Szu-Hsein Lin, Yi-Ling Hsueh, and Ming-Jiu Lee, "Automatic target defect identification for TFT-LCD array process inspection using kernel fuzzy c-means based fuzzy SVDD ensemble", Expert Systems With Applications, vol. 40, 2008
本發明目的在於提供一種以支持向量迴歸(Support Vector Regression，SVR)[22]當做熱誤差模型，利用支持向量資料描述(Support Vector Data Description，SVDD)[23]建構穩態模式範圍，以即時進行線上調整熱誤差的工具機熱誤差智慧調適裝置及其方法。The present invention aims to provide a thermal error model using Support Vector Regression (SVR) [22], and construct a steady state mode range using Support Vector Data Description (SVDD) [23] for immediate execution. Tool machine thermal error intelligent adjustment device and method for adjusting thermal error on line.
本發明的另一目的，在於提供一種可針對不同外部環境之溫度變化，以進行增量學習的工具機熱誤差智慧調適裝置及其方法。Another object of the present invention is to provide a machine tool thermal error smart adaptation device and method thereof that can perform incremental learning for temperature changes of different external environments.
為達上述目的，本發明係提供一種工具機熱誤差智慧調適裝置，包含：一暖機特徵描述建構單元，建立該工具機之一機體溫度分佈向量的一穩態模式範圍；一熱誤差模型建構單元，訓練出一非線性熱誤差模型；以及一暖機判斷與熱誤差運算單元，藉由讀取該工具機上若干溫度感測訊號以及該工具機之一運轉條件資訊，判斷該工具機之暖機狀態，計算出至少一節點之熱誤差補償量。In order to achieve the above object, the present invention provides a tooling machine thermal error intelligent adapting device, comprising: a warming machine feature description building unit, establishing a steady state mode range of a body temperature distribution vector of the machine tool; and constructing a thermal error model Unit, training a nonlinear thermal error model; and a warm-up judgment and thermal error calculation unit, by reading a plurality of temperature sensing signals on the machine tool and operating condition information of the machine tool, determining the machine tool In the warm state, the thermal error compensation amount of at least one node is calculated.
其中，該工具機熱誤差智慧調適裝置更包括一增量學習單元，可適應地作參數調整，以確保熱誤差模型之準確性。The tool error thermal intelligence adapting device further comprises an incremental learning unit, which can be adaptively adjusted to ensure the accuracy of the thermal error model.
為達上述目的，本發明更提供一種工具機熱誤差智慧調適方法，其步驟包含：萃取熱行為資料；由專家選定暖機穩定狀態；以穩定狀態溫度資訊建構暖機特徵描述；以暖機特徵描述篩選符合穩定狀態之熱行為資訊；以穩定狀態之熱行為資訊建構熱誤差模型；線上暖機判斷並且進行熱誤差運算。In order to achieve the above object, the present invention further provides a method for intelligently adjusting the thermal error of a machine tool, the steps comprising: extracting thermal behavior data; selecting a warm state of the warming machine by an expert; constructing a warming machine characteristic description by using steady state temperature information; Describe the thermal behavior information that matches the steady state; construct the thermal error model with the thermal behavior information of the steady state; judge the line and perform the thermal error calculation.
其中，該工具機熱誤差智慧調適方法更包括增量學習，藉以適應環境改變與機台參數飄移，類似概念請參考引用文獻[24][25]。Among them, the tool error thermal error adjustment method includes incremental learning to adapt to environmental changes and machine parameter drift. For similar concepts, please refer to the cited literature [24] [25].
雖然本發明使用了幾個較佳實施例進行解釋，但是下列圖式及具體實施方式僅僅是本發明的較佳實施例；應說明的是，下面所揭示的具體實施方式僅僅是本發明的例子，並不表示本發明限於下列圖式及具體實施方式。While the invention has been described in terms of several preferred embodiments, the preferred embodiments of the present invention It is not intended that the invention be limited to the following drawings and embodiments.
請參考圖5，表示本發明工具機熱誤差智慧調適裝置的方塊圖。Referring to FIG. 5, a block diagram of the thermal error intelligent adjustment device of the machine tool of the present invention is shown.
本實施例的工具機熱誤差智慧調適裝置1作用在一工具機10上主要包含一暖機特徵描述建構單元2、一熱誤差模型建構單元3、一暖機判斷與熱誤差運算單元4及一增量學習單元5。The machine tool thermal error intelligent adapting device 1 of the present embodiment functions on a power tool 10 mainly comprising a warm-up feature description construction unit 2, a thermal error model construction unit 3, a warm-up judgment and thermal error operation unit 4 and a Incremental learning unit 5.
請參考圖6，暖機特徵描述建構單元2以支持向量資料描述(SVDD)建立工具機之一機體溫度分佈向量的一穩態模式範圍，即以支持向量資料描述(SVDD)確認在工具機的各節點a~d的溫度狀態達到穩定狀態，直到支持向量資料描述(SVDD)之高維度空間特徵向量分布與預設之穩態模式範圍的相同。Referring to FIG. 6, the warm-up feature description construction unit 2 establishes a steady-state mode range of the body temperature distribution vector of the machine tool with the support vector data description (SVDD), that is, the support vector data description (SVDD) is confirmed in the machine tool. The temperature state of each node a~d reaches a steady state until the high dimensional spatial feature vector distribution of the support vector data description (SVDD) is the same as the preset steady state mode range.
以下對支持向量資料描述(SVDD)作詳細說明。The support vector data description (SVDD) is described in detail below.
■ SVDD之目的：由訓練資料估算出判斷函數D (x )，其中，■ Purpose of SVDD: Estimate the judgment function D ( x ) from the training data, where
■ SVDD模型使用：給輸入變數x ，判斷x 是否落入過往資料可解釋之範圍之中；D (x )0表示可適用，D (x )>0則否。■ SVDD model use: Give the input variable x to determine whether x falls within the range that can be explained by the previous data; D ( x ) 0 means applicable, D ( x )>0 then no.
■ 建模過程：■ Modeling process:
1).訂定參數σ、C 欲驗證範圍中的所有組合，挑選其中一組(σ，C )；1). Set all the combinations in the parameters σ, C to verify the range, select one of the groups (σ, C );
2).進行k-fold，將穩態的實驗資料隨機均分為k 組，取其中k -1組作為模型訓練(training)資料(記為，另一組加上暫態資料作為驗證(testing)之用(記為 2). Perform k-fold and randomly divide the steady-state experimental data into k groups, and take k -1 group as model training data (denoted as Another group plus transient data for testing (denoted as
3).對於第h 組的訓練與驗證組合，解二次規劃問題3). For the training and verification combination of the h group, solve the quadratic programming problem
等價於解其對偶問題，Equivalent to solving the dual problem,
決定出{}(支持向量，SVs，定義為『其對應的α i ≠0』)、N SV (支持向量個數)與α i Decide out { } (support vector, SVs, defined as "the corresponding α i ≠ 0"), N SV (the number of support vectors) and α i
4).選擇任一個ξ i =0的支持向量，計算R 4). Select any support vector with ξ i =0 , calculate R
5).將測試資料帶入模型測試，得到5). Bring the test data into the model test and get
計算F measure Calculate the F measure
6).對於k 組不同的訓練與驗證組合重複步驟3~5，平均所有的誤差，定義為此參數組合的分類表現；6). Repeat steps 3~5 for the different training and verification combinations of k groups, average all errors, define The classification performance of the combination of parameters;
7).對所有參數組合重複步驟1~6，挑選分類表現最佳的參數組合()；7) Repeat steps 1~6 for all parameter combinations to select the combination of parameters that best perform the classification ( );
8).完成SVDD建模。8). Complete SVDD modeling.
請參考圖7，在機器達到穩定狀態後再進行預測，熱誤差模型建構單元3以支持向量迴歸(SVR)訓練出一非線性熱誤差模型，支持向量迴歸(SVR)的運算參數少，在預測精準度方面，可以得到較低的MAPE(mean absolute percentage error)，其核心精神為核方法(kernel method)。Referring to FIG. 7, after the machine reaches a steady state, the prediction is performed, and the thermal error model construction unit 3 trains a nonlinear thermal error model by using support vector regression (SVR). The support vector regression (SVR) has fewer operational parameters and is predicted. In terms of accuracy, a lower MAPE (mean absolute percentage error) can be obtained, and its core spirit is the kernel method.
以下對支持向量迴歸(SVR)作詳細說明。。The following is a detailed description of Support Vector Regression (SVR). .
■ SVR目的：由訓練資料估算出函數y =f (x )，其中：■ SVR purpose: Estimate the function y = f ( x ) from the training data, where:
在上式中，每個training instance都有其對應的ξ i 及，用來決定該training instance是否可以落在ε的範圍之外。而C的作用則是用來調整訓練模型(training model)是否過份或不足調適資料(overfitting或underfitting)。當核心定義清楚後，有下列三個參數可以調整：In the above formula, each training instance has its corresponding ξ i and Used to determine whether the training instance can fall outside the scope of ε. The role of C is to adjust whether the training model is over or under adapted (overfitting or underfitting). When the core definition is clear, the following three parameters can be adjusted:
Gamma：調整高斯kernel函數之std，即上式裡之σ。Gamma: Adjust the std of the Gaussian kernel function, which is σ in the above equation.
C：用來調整訓練過程中誤差項之權衡量，可決定overfitting或underfitting。C: The weighting measure used to adjust the error term during training can determine overfitting or underfitting.
Epsilon：誤差寬容帶之大小，即上式裡之ε。Epsilon: The size of the error tolerance band, which is ε in the above equation.
■ SVR模型使用：給輸入變數x ，預測出對應的輸出變數的值f (x )。■ SVR model use: For the input variable x , the value f ( x ) of the corresponding output variable is predicted.
■ 建模過程：■ Modeling process:
1).訂定參數σ、C 、ε在欲驗證範圍中的所有組合，挑選其中一組(σ,C ,ε)；1). Set all the combinations of parameters σ, C , ε in the range to be verified, and select one of them (σ, C , ε);
2).進行k-fold，將實驗資料隨機均分為k 組，取其中k -1組作為模型訓練(training)資料(記為，另一組作為驗證(testing)之用(記為 2). Perform k-fold, randomly divide the experimental data into k groups, and take k -1 group as model training data (denoted as Another group for testing purposes (denoted as
3).對於第h 組的訓練與驗證組合，解二次規劃問題3). For the training and verification combination of the h group, solve the quadratic programming problem
等價於解其對偶問題，Equivalent to solving the dual problem,
決定出{}(支持向量，定義為『其對應的-αi ≠0』)、N SV (支持向量個數)、與α i ；Decide out { } (support vector, defined as "the corresponding -α i ≠0』), N SV (number of support vectors), With α i ;
4).選擇任一個ξ i =0或=0的支持向量，計算b ，4). Choose any ξ i =0 or =0 support vector , calculate b ,
5).計算誤差 5). Calculation error
6).對於k 組不同的訓練與驗證組合重複步驟3~5，平均所有的誤差，定義為此組參數組合的預測誤差；6). Repeat steps 3~5 for the different training and verification combinations of k groups, average all errors, define The prediction error of the combination of the parameters of this group;
7).對所有參數組合重複步驟1~6，挑選預測誤差最小時的參數組合()；7). Repeat steps 1~6 for all parameter combinations to select the parameter combination when the prediction error is the smallest ( );
8).完成SVR建模。8). Complete SVR modeling.
請參考圖8，暖機判斷與熱誤差運算單元4藉由讀取工具機上佈設在各節點的若干溫度感測訊號以及工具機之運轉條件資訊判斷工具機之暖機狀態，計算出其中至少一節點之熱誤差補償量後再進行加工。Referring to FIG. 8 , the warm-up determination and thermal error calculation unit 4 calculates at least one of the temperature sensing signals of each node and the operating condition information of the machine tool on the tool machine to determine the warm-up state of the machine tool. The thermal error compensation amount of one node is processed.
請參考圖9，在外部環境溫度變化，如工具機移動至其他工作廠區操作時，增量學習單元5可針對暖機特徵描述建構單元2之支持向量資料描述(SVDD)之原始資料，以及熱誤差模型建構單元3之支持向量迴歸(SVR)接收環境溫度變化之新資料，藉由暖機判斷與熱誤差運算單元4與已知的預定熱誤差資料比較確認是否進行增量學習，以適應地作參數調整確保熱誤差模型之準確性。Referring to FIG. 9, when the external environment temperature changes, such as the machine tool moves to other work site operations, the incremental learning unit 5 can describe the original data of the support vector data description (SVDD) of the construction unit 2 for the warm-up feature, and the heat. The support vector regression (SVR) of the error model construction unit 3 receives the new data of the environmental temperature change, and confirms whether the incremental learning is performed by comparing the warm error judgment and the thermal error calculation unit 4 with the known predetermined thermal error data. Parameter adjustments ensure the accuracy of the thermal error model.
其詳細操作流程將於後詳述。The detailed operation flow will be detailed later.
請參考圖10，本發明的工具機熱誤差智慧調適方法，其步驟包含：步驟S1：萃取熱行為資料；步驟S2：由專家選定暖機穩定狀態；步驟S3：以穩定狀態溫度資訊建構暖機特徵描述；步驟S4：以暖機特徵描述篩選符合穩定狀態之熱行為資訊；步驟S5：以穩定狀態之熱行為資訊建構熱誤差模型；步驟S6：線上暖機判斷並且進行熱誤差運算；步驟S7：增量學習，藉以適應環境改變與參數飄移。Referring to FIG. 10, a method for intelligently adjusting the thermal error of the machine tool according to the present invention includes the steps of: step S1: extracting thermal behavior data; step S2: selecting a warm state by an expert; and step S3: constructing a warming machine with steady state temperature information. Feature description; Step S4: Filtering the thermal behavior information according to the steady state with the warm-up feature description; Step S5: constructing the thermal error model with the thermal behavior information of the steady state; Step S6: determining the online warm-up and performing the thermal error calculation; Step S7 : Incremental learning, to adapt to environmental changes and parameter drift.
其中，步驟S1中之熱行為包括溫度及定位誤差等，步驟S2中的專家選定係可為在歷史資料中進行分類之選取，步驟S3中的暖機特徵描述係以支持向量資料描述(SVDD)方式建構，步驟S5的熱誤差模型係以支持向量回歸(SVR)所建構，步驟S6的線上暖機判斷係以讀取工具機上若干溫度感測訊號以及工具機運轉條件等資訊之方式進行。The thermal behavior in step S1 includes temperature and positioning error, etc., the expert selection system in step S2 can be used for classification in the historical data, and the warm-up feature description in step S3 is described by support vector data (SVDD). In the manner of construction, the thermal error model of step S5 is constructed by support vector regression (SVR), and the online warm-up judgment of step S6 is performed by reading information such as temperature sensing signals on the machine tool and operating conditions of the machine tool.
請參考圖11，以單軸進給工具機11進行說明；在工具機各部件設置溫度感測器T1~T24，分部位置如圖12~圖18所示；熱行為資訊萃取規劃如下：Please refer to FIG. 11 for description of the single-axis feeding machine 11; temperature sensors T1~T24 are arranged in each part of the machine tool, and the position of the parts is shown in FIG. 12 to FIG. 18; the thermal behavior information extraction plan is as follows:
行程規劃：全行程1100mm運轉。Itinerary planning: 1100mm full stroke.
進給速度規畫：共分9m/min、18m/min、27m/min及36m/min等四種進給速度。Feed speed planning: There are four feed speeds of 9m/min, 18m/min, 27m/min and 36m/min.
量測時間規畫：為利於分析系統暫態與穩態之熱誤差行為模式，開機後每隔15分鐘量測一次，擷取暫態資訊，待長時間(以150分鐘為例)運轉後，改以每30分鐘量測一次，擷取系統穩態資訊。Measurement time planning: In order to analyze the thermal error behavior mode of the transient and steady state of the system, it is measured every 15 minutes after starting up, and the transient information is taken. After a long time (taking 150 minutes as an example), Change it to measure every 30 minutes and retrieve the steady state information of the system.
請參考圖19，係表示熱行為資料萃取方法之流程圖。本發明之熱行為資料萃取方法步驟包括：步驟SA1：選定進給速度；步驟SA2：設定工具機參數及在工具機的若干節點佈設溫度感測器；步驟SA3：工具機開機並進入運轉模式；步驟SA4：運轉模式是否達到150分鐘(預定運轉時間)，若否，則進入每15分鐘(第一時間間隔)之量測模式(步驟SA41)；若是，則進入每30分鐘(第二時間間隔)之量測模式(步驟SA42)；步驟SA5：對工具機的各節點進行溫度量測；以及步驟SA6：儲存資料。Please refer to FIG. 19, which is a flow chart showing a method for extracting thermal behavior data. The thermal behavior data extraction method step of the present invention comprises: step SA1: selecting a feed speed; step SA2: setting a machine tool parameter and setting a temperature sensor at a plurality of nodes of the machine tool; step SA3: the machine tool is turned on and enters an operation mode; Step SA4: Whether the operation mode reaches 150 minutes (predetermined operation time), if not, enters the measurement mode every 15 minutes (first time interval) (step SA41); if yes, enters every 30 minutes (second time interval) Measuring mode (step SA42); step SA5: performing temperature measurement on each node of the machine tool; and step SA6: storing data.
其中，步驟SA6可以手動填寫表格或者是以溫度擷取程式自動記錄。Wherein, step SA6 can manually fill in the form or automatically record by the temperature capture program.
請參考圖12~18，工具機10包括鞍座11、螺桿12、滑軌13、伺服馬達14、底座15、地腳螺絲16，溫度變數的資訊擷取包括外部熱源(環境溫度)與內部熱源，故對於溫度變數的記錄，共有二十五項，一個為外部環境溫度、以及二十四個結構本體溫度，其中，二十四個結構本體溫度包含：五個鞍座11之滑塊端溫度、一個螺帽座(螺桿12處)溫度、十一個底座15兩側軌道(滑軌13)溫度、四個底座15地腳端結構(地腳螺絲16)溫度、一個馬達座軸承端溫度、一個尾座軸承端溫度、以及一個馬達介面座溫度，亦即二十四個溫度感測器T1~T24的各節點處；另外，根據熱力學動態觀點，應將溫度變化率(導數)列入參考。Referring to FIGS. 12-18, the machine tool 10 includes a saddle 11, a screw 12, a slide rail 13, a servo motor 14, a base 15, and a ground screw 16. The information of the temperature variable includes an external heat source (ambient temperature) and an internal heat source. Therefore, for the recording of temperature variables, there are twenty-five items, one is the external ambient temperature, and twenty-four structural body temperatures, wherein twenty-four structural body temperatures include: the slider end temperature of five saddles 11 , a nut seat (at the screw 12) temperature, eleven base 15 rails (slide 13) temperature, four base 15 foot end structure (foot screw 16) temperature, a motor seat bearing end temperature, The temperature of a tailstock bearing end, and the temperature of a motor interface seat, that is, the nodes of the twenty-four temperature sensors T1~T24; in addition, according to the thermodynamic dynamics, the temperature change rate (derivative) should be included in the reference. .
關於熱變位相關變數則是根據載具之行程進行規劃，為配合虛擬感測所建構之分割模型中節點的位置，實驗設定每間隔125 mm進行定位精度的量測，量測位置(mm)為：131,256,381,506,631,756,881,以及1006，如圖11所示。The thermal displacement related variables are planned according to the travel of the vehicle. In order to match the position of the nodes in the segmentation model constructed by the virtual sensing, the experiment sets the measurement of the positioning accuracy at intervals of 125 mm, and the measurement position (mm) They are: 131, 256, 381, 506, 631, 756, 881, and 1006, as shown in FIG.
本發明採用支持向量迴歸(SVR)當作熱誤差預測模型，利用支持向量資料描述(support vector data description,SVDD)來建構工具機之機體溫度分佈向量穩態模式範圍，用以界定熱誤差預測模型之適用性。，SVDD是一個新穎的機器學習演算法，其目的在於建構一個最小超球體(minimum-volume hypersphere)來包圍訓練集合，由於超球體是在特徵空間中建構，因此在輸入空間中此球體的表面變的非常有彈性。對於一個新進的工具機機體溫度分佈向量變數，我們只需要計算它到球心的距離，便可計算出此筆輸入相對應的暖機狀態之信心指數：距離越大，代表此筆輸入與訓練集合的相異程度越大，則輸出之信心指數就越小。利用SVDD的優勢有兩點：1)它的解不會有local minimum的問題，因為它的dual problem也是一個QP問題，因此不需要人為設定門檻值，2)無論輸入數據訓練集合的分布為何，利用SVDD都可以找到一個可以緊緊包圍它的邊界。其詳細技術細節已揭露如前。The invention adopts support vector regression (SVR) as a thermal error prediction model, and uses support vector data description (SVDD) to construct a steady state mode range of the body temperature distribution vector of the machine tool to define the thermal error prediction model. Applicability. SVDD is a novel machine learning algorithm whose purpose is to construct a minimum-volume hypersphere to surround the training set. Since the hypersphere is constructed in the feature space, the surface of the sphere changes in the input space. Very flexible. For a new machine tool body temperature distribution vector variable, we only need to calculate its distance to the center of the ball, we can calculate the confidence index of the warm-up state corresponding to this input: the greater the distance, the input and training The greater the difference in the set, the smaller the confidence index of the output. There are two advantages to using SVDD: 1) its solution does not have a local minimum problem, because its dual problem is also a QP problem, so there is no need to manually set the threshold, and 2) regardless of the distribution of the input data training set, With SVDD you can find a boundary that can tightly surround it. The detailed technical details have been revealed as before.
藉由上述結構及方法，透過在工具機各部件設置之溫度感測器擷取熱變位行為資料，建立穩態模式支持向量迴歸(SVR)熱誤差模型與支持向量資料描述(SVDD)超球體，能在環境溫度變異下判斷暖機狀態，直接在線上預測補償資訊以進行熱誤差的補償。According to the above structure and method, the thermal displacement model and the support vector data description (SVDD) hypersphere are established by using the temperature sensor set in the various components of the machine tool to obtain the thermal displacement behavior data. It can judge the warm-up state under the variation of the ambient temperature, and predict the compensation information directly on the line to compensate the thermal error.
雖然本發明以相關的較佳實施例進行解釋，但是這並不構成對本發明的限制。應說明的是，本領域的技術人員根據本發明的思想能夠構造出很多其他類似實施例，均在本發明的保護範圍之中。Although the present invention has been explained in connection with the preferred embodiments, it is not intended to limit the invention. It should be noted that many other similar embodiments can be constructed in accordance with the teachings of the present invention, and are within the scope of the present invention.
1．．．工具機熱誤差智慧調適裝置1. . . Tool machine thermal error wisdom adapting device
10．．．工具機10. . . Machine tool
11．．．鞍座11. . . Saddle
12．．．螺桿12. . . Screw
13．．．滑軌13. . . Slide rail
14．．．伺服馬達14. . . Servo motor
15．．．底座15. . . Base
16．．．地腳螺絲16. . . Foot screw
2．．．暖機特徵描述建構單元2. . . Warm-up feature description building unit
3．．．熱誤差模型建構單元3. . . Thermal error model construction unit
4．．．暖機判斷與熱誤差運算單元4. . . Warm-up judgment and thermal error calculation unit
5．．．增量學習單元5. . . Incremental learning unit
a~d．．．節點a~d. . . node
T1~T24．．．溫度感測器T1~T24. . . Temperature sensor
步驟S1~S7 依據本發明的工具機熱誤差智慧調適方法Steps S1 to S7 According to the present invention, the thermal error intelligent adjustment method of the machine tool
步驟SA1~SA6 依據本發明之萃取熱行為步驟Steps SA1 to SA6 According to the extraction heat behavior step of the present invention
圖1 表示習知工具機之一熱誤差示意圖。Figure 1 shows a schematic diagram of thermal error of a conventional machine tool.
圖2 表示習知工具機之另一熱誤差示意圖。Figure 2 shows another thermal error diagram of a conventional machine tool.
圖3 表示習知工具機熱誤差之曲線圖。Figure 3 shows a graph of the thermal error of a conventional machine tool.
圖4 表示習知工具機以操作員判斷熱誤差之曲線圖。Figure 4 shows a graph of a conventional machine tool that determines the thermal error by the operator.
圖5 表示本發明工具機熱誤差智慧調適裝置的方塊圖。Figure 5 is a block diagram showing the thermal error intelligent adjustment device of the machine tool of the present invention.
圖6 表示本發明工具機熱誤差智慧調適裝置以支持向量資料描述進行穩態描述的示意圖。Figure 6 is a schematic diagram showing the steady state description of the thermal error intelligent adaptation device of the machine tool of the present invention in support of vector data description.
圖7 表示本發明工具機熱誤差智慧調適裝置以支持向量迴歸描述進行穩態預測誤差量的曲線圖。Fig. 7 is a graph showing the thermal error intelligent adjustment device of the machine tool of the present invention for supporting the vector regression to describe the steady state prediction error amount.
圖8 表示本發明工具機熱誤差智慧調適裝置中暖機判斷與熱誤差運算單元的判斷運算示意圖。FIG. 8 is a schematic diagram showing the judgment operation of the warm-up judgment and thermal error calculation unit in the thermal error intelligent adjustment device of the machine tool of the present invention.
圖9 表示本發明工具機熱誤差智慧調適裝置中增量學習單元的判斷示意圖。FIG. 9 is a schematic diagram showing the judgment of the incremental learning unit in the thermal error intelligent adjustment device of the machine tool of the present invention.
圖10 表示本發明工具機熱誤差智慧調適方法的流程圖。Figure 10 is a flow chart showing the method of intelligently adjusting the thermal error of the machine tool of the present invention.
圖11 表示本發明以單軸進給工具機為例的行程規劃示意圖。Fig. 11 is a schematic view showing the stroke planning of the uniaxial feed tooling machine of the present invention.
圖12 表示本發明以單軸進給工具機為例的結構圖。Fig. 12 is a structural view showing the uniaxial feeding machine of the present invention as an example.
圖13 表示本發明在單軸進給工具機佈設溫度感測器的分布圖之一。Figure 13 shows one of the distribution diagrams of the present invention for deploying a temperature sensor in a single axis feed tool.
圖14 表示本發明在單軸進給工具機佈設溫度感測器的分布圖之二。Figure 14 shows the second distribution of the temperature sensor of the present invention in a single-axis feed tool machine.
圖15 表示本發明在單軸進給工具機佈設溫度感測器的分布圖之三。Figure 15 shows the third distribution of the temperature sensor of the present invention in a single-axis feed tool machine.
圖16 表示本發明在單軸進給工具機佈設溫度感測器的分布圖之四。Figure 16 shows the fourth distribution of the temperature sensor of the present invention in a single-axis feed tool machine.
圖17 表示本發明在單軸進給工具機佈設溫度感測器的分布圖之五。Figure 17 is a view showing the distribution of the temperature sensor of the present invention in a single-axis feed tool machine.
圖18 表示本發明在單軸進給工具機佈設溫度感測器的分布圖之六。Figure 18 is a diagram showing the distribution of the temperature sensor of the present invention in a single-axis feed tool machine.
圖19 表示本發明工具機熱誤差智慧調適方法中萃取熱行為資料步驟的流程圖。Figure 19 is a flow chart showing the steps of extracting thermal behavior data in the thermal error intelligent adaptation method of the machine tool of the present invention.
1．．．工具機熱誤差智慧調適裝置1. . . Tool machine thermal error wisdom adapting device
2．．．暖機特徵描述建構單元2. . . Warm-up feature description building unit
3．．．熱誤差模型建構單元3. . . Thermal error model construction unit
4．．．暖機判斷與熱誤差運算單元4. . . Warm-up judgment and thermal error calculation unit
5．．．增量學習單元5. . . Incremental learning unit
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| JPS57178641A (en) * | 1981-04-27 | 1982-11-02 | Shin Nippon Koki Kk | Correcting method of machining error from thermal displacement or the like |
| TW200944482A (en) * | 2008-04-21 | 2009-11-01 | Top Eng Co Ltd | Fragile substrate scribing apparatus and method |
| CN101573209A (en) * | 2006-11-16 | 2009-11-04 | 六边形度量衡股份公司 | Method and device for the compensation of geometrical errors in machining machinery |
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| TW201021959A (en) * | 2008-12-11 | 2010-06-16 | Ind Tech Res Inst | A thermal error compensation method for machine tools |
| CN101446994A (en) * | 2008-12-18 | 2009-06-03 | 浙江大学 | Modeling method of thermal error least squares support vector machine of numerically-controlled machine tool |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS57178641A (en) * | 1981-04-27 | 1982-11-02 | Shin Nippon Koki Kk | Correcting method of machining error from thermal displacement or the like |
| CN101573209A (en) * | 2006-11-16 | 2009-11-04 | 六边形度量衡股份公司 | Method and device for the compensation of geometrical errors in machining machinery |
| TW200944482A (en) * | 2008-04-21 | 2009-11-01 | Top Eng Co Ltd | Fragile substrate scribing apparatus and method |
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| US10481575B2 (en) | 2017-12-05 | 2019-11-19 | Industrial Technology Research Institute | Thermal compensation method and thermal compensation control system for machine tools |
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| CN102540884A (en) | 2012-07-04 |
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