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    METHODS FOR DETERMINING AND MACHINING WORKED SURFACE OF PLATE-LIKE MATERIAL AND APPARATUS FOR THESE METHODS
    2.
    发明公开
    METHODS FOR DETERMINING AND MACHINING WORKED SURFACE OF PLATE-LIKE MATERIAL AND APPARATUS FOR THESE METHODS 有权
    程序处理和板状材料的处理过的表面和设备这一过程

    公开(公告)号：EP1785793A1

    公开(公告)日：2007-05-16

    申请号：EP05765163.0

    申请日：2005-06-27

    申请人： Nippon Mining & Metals Co., Ltd.

    发明人： NAKASHIMA, Koichi Isohara Fa.of Nikko Mat. Co.,Ltd ,  KOMACHI, Nobuyoshi Isohara Fa.of Nikko Mat.Co.,Ltd

    IPC分类号： G05B19/404

    CPC分类号： G05B19/401 ,  B23Q15/14 ,  G01B11/30 ,  G05B19/404 ,  G05B2219/37049 ,  G05B2219/37402 ,  G05B2219/37596 ,  G05B2219/50046 ,  G05B2219/50063 ,  Y02P80/40

    摘要： Provided is a surface treatment method for performing machining such as cutting work, grinding, and electrical discharging to a plate-like material with two- or three-dimensional deformation to realize a uniform thickness. This method includes the steps of mounting the plate-like material on a surface plate, setting a coordinate axis in a plane direction of the plate-like material to be X, Y and setting a coordinate axis in a height direction of the plate-like material to be Z, virtualizing a surface containing an origin of the measured Z direction, measuring a height Z 1-n from the origin in an arbitrary plane position, and inclining and cutting the plate-like material so that an absolute value of a difference between a maximum value Z max and a minimum value Z min of the obtained height data will be minimum. Although a ceramic sintered plate such as a sputtering target or a metal plate prepared by metal rolling or forging, in most instances, is subject to two- or three-dimensional deformation as a result of thermal stress or machining stress during the manufacturing process, this invention is able to obtain a flat plate-like material having a uniform thickness and minimal machining costs from a plate-like material with two-or three-dimensional deformation.

    摘要翻译： 提供了一种用于执行机械加工的表面处理方法：如切削加工，磨削用二维或三维变形以实现均匀的厚度，和电放电到板状材料。 该方法包括安装在表面板的板状材料的步骤，设定坐标轴在板状材料的平面方向是X，Y和的高度方向设置的坐标轴的板状 材料为Z，虚拟化表面包含测量Z方向的原点，测定从原点的高度Z 1-N中在任意平面位置，和倾斜和切割板状材料也是如此的差的绝对值 最大值Z max和高度所获得的数据的最小值Z min的将是最小的。 虽然陶瓷烧结板：如溅射靶或由金属轧制或锻造制备的金属板，在大多数情况下，是受两轮或在制造过程中三维变形热应力或机械加工应力的结果，这 发明能够获得具有由板状材料的厚度均匀和最小加工成本的平板状的材料具有两或三维变形。

    Artificial intelligence for adaptive machining control of surface finish
    6.
    发明公开
    Artificial intelligence for adaptive machining control of surface finish 失效
    KünstlicheIntelligenzfürdie adaptive BearbeitungssteuerungfürOberflächen-Endbearbeitung。

    公开(公告)号：EP0359378A2

    公开(公告)日：1990-03-21

    申请号：EP89307587.9

    申请日：1989-07-26

    申请人： FORD MOTOR COMPANY LIMITED ,  FORD FRANCE S. A. ,  FORD-WERKE AKTIENGESELLSCHAFT

    发明人： Wu, Charles LaiSau ,  Haboush, Roger Kent

    IPC分类号： G05B19/18

    CPC分类号： G05B19/4163 ,  G05B2219/33002 ,  G05B2219/37402 ,  Y10S706/904

    摘要： A method of using a mathematical model to adaptively control surface roughness when machining a series of workpieces or segments by: (a) linearizing a geometrical surface roughness model; (b) initializing said model essentially as a function of feed; and (c) subjecting the initialized model to computerized estimation based on roughness and feed values taken from the last machined workpiece, thereby to determine the largest allowable feed for attaining a desired surface roughness in subsequently machined workpieces or segments of the series. The mathematical model is an algorithm of the form R = [1262.79]f²/r. The model is linearized and initialized to give the form R = β₁s R f+β₂R max where R is the actual roughness and R max is desired roughness, f is actual feed, β₁ and β₂ are coefficients to be updated by estimation, and s R is a scale factor chosen to make the first term have the same order of magnitude as the second term. Computerized estimation is carried out by converting the above initialized linear model to vector matrix notation with provision for the estimated coefficients in the form R n = ϑ T x n , where R n is measured roughness, x n is a computed vector taken from measured feed, and ϑ is a vector to be estimated with "T" denoting the transpose of the vector. After inserting roughness and feed values into such vector model, taken from the last machined workpiece, the coefficients are recursively estimated by sequential regression analysis.

    摘要翻译： 一种使用数学模型来自动控制加工一系列工件或段的表面粗糙度的方法：（a）线性化几何表面粗糙度模型; （b）基本上将饲料初始化为模型; 和（c）基于从最后加工的工件获取的粗糙度和进给值对初始化的模型进行计算机化估计，从而确定用于在后续加工的工件或该系列的段中获得期望的表面粗糙度的最大允许进给量。 数学模型是R = [1262.79] f 2 / r形式的算法。 该模型被线性化并初始化以得到R =β1sRf +β2Rmax的形式，其中R是实际粗糙度，Rmax是所需粗糙度，f是实际进料，β1和β2是通过估计更新的系数，sR是 选择第一项的比例因子与第二项具有相同的数量级。 通过将上述初始化的线性模型转换为向量矩阵符号来进行计算机化估计，其中提供了以Rn =θXn形式的估计系数，其中Rn是测量粗糙度，xn是从测量进给获得的计算向量，以及 theta是用“T”表示向量的转置来估计的向量。 在将粗糙度和进给值插入到从最后加工的工件中取得的向量模型中后，通过顺序回归分析递归地估计系数。

    Nachgeführte Fräsvorrichtung an einer Durchlaufmaschine
    9.
    发明公开
    Nachgeführte Fräsvorrichtung an einer Durchlaufmaschine 有权
    用跟踪的铣削装置运行机

    公开(公告)号：EP1038644A2

    公开(公告)日：2000-09-27

    申请号：EP00104733.1

    申请日：2000-03-04

    申请人： IMA Maschinenfabriken Klessmann GmbH

    发明人： Heidemann, Hans-Dieter ,  Nope, Thomas ,  Hielscher, Ulrich

    IPC分类号： B27C5/02 ,  B23Q35/128

    CPC分类号： B27D5/006 ,  B23Q15/007 ,  B23Q17/2233 ,  B23Q17/2471 ,  B23Q35/128 ,  G05B2219/37402 ,  G05B2219/37571 ,  G05B2219/45145

    摘要： Eine solche Vorrichtung, mit der vornehmlich plattenförmige Werkstücke aus Holz oder Holzersatzstoffen bearbeitet werden, hat ein Fräsaggregat, dessen Fräser den Oberflächenunebenheiten der durchlaufenden Werkstücke nachgeführt wird. Dafür ist eine entsprechend die Oberflächenunebenheiten der Werkstücke abtastende, den Fräser steuernde Abtasteinrichtung vorhanden. Um die Werkstückoberseiten berührungslos abtasten und das Fräsaggregat und/oder den Fräser reaktionsschnell verfahren zu können, besteht die Abtasteinrichtung aus einem optischen Sensor und sind das Fräsaggregat und/oder der Fräser an einem Support mittels eines Stellantriebes relativ zu der Ebene der durchlaufenden Werkstücke verfahrbar angeordnet. Hierbei tastet der optische Sensor an einer Stelle entgegen der Durchlaufrichtung in Abstand vor dem Fräser die Werkstückoberseiten ab und steuert entsprechend zeitverzögert über eine Auswerteinheit den Stellantrieb. Ferner ist der optische Sensor in räumlichem Abstand vom Fräser angeordnet.

    摘要翻译： 这样的装置，都与由木材或木材替代品的主要板状工件进行处理，具有铣削单元，铣刀被跟踪到经过的工件的表面不规则性。 这需要相应的工件扫描的表面不规则性，路由器控制扫描本。 围绕工件顶样品接触，并且能够操作铣削单元和/或响应的路由器，有一个光学传感器和所述铣削单元和/或通过致动器的装置中的切割器支撑件的扫描装置相对于所述平面的通过工件移动布置。 这里，光学传感器扫描的位置处相反的通路的方向以一定距离铣刀前，工件的顶部和通过评估单元对应的时间延迟到该致动器的控制。 此外，光学传感器被布置在距铣刀的空间距离。

    Artificial intelligence for adaptive machining control of surface finish
    10.
    发明公开
    Artificial intelligence for adaptive machining control of surface finish 失效
    人工智能表面处理自适应加工控制

    公开(公告)号：EP0359378A3

    公开(公告)日：1990-11-28

    申请号：EP89307587.9

    申请日：1989-07-26

    申请人： FORD MOTOR COMPANY LIMITED ,  FORD FRANCE S. A. ,  FORD-WERKE AKTIENGESELLSCHAFT

    发明人： Wu, Charles LaiSau ,  Haboush, Roger Kent

    IPC分类号： G05B19/18

    CPC分类号： G05B19/4163 ,  G05B2219/33002 ,  G05B2219/37402 ,  Y10S706/904

    摘要： A method of using a mathematical model to adaptively control surface roughness when machining a series of workpieces or segments by: (a) linearizing a geometrical surface roughness model; (b) initializing said model essentially as a function of feed; and (c) subjecting the initialized model to computerized estimation based on roughness and feed values taken from the last machined workpiece, thereby to determine the largest allowable feed for attaining a desired surface roughness in subsequently machined workpieces or segments of the series. The mathematical model is an algorithm of the form R = [1262.79]f²/r. The model is linearized and initialized to give the form R = β₁s R f+β₂R max where R is the actual roughness and R max is desired roughness, f is actual feed, β₁ and β₂ are coefficients to be updated by estimation, and s R is a scale factor chosen to make the first term have the same order of magnitude as the second term. Computerized estimation is carried out by converting the above initialized linear model to vector matrix notation with provision for the estimated coefficients in the form R n = ϑ T x n , where R n is measured roughness, x n is a computed vector taken from measured feed, and ϑ is a vector to be estimated with "T" denoting the transpose of the vector. After inserting roughness and feed values into such vector model, taken from the last machined workpiece, the coefficients are recursively estimated by sequential regression analysis.