Patent Description:
In a field of aerospace products, due to an increase of product performance requirement, introduction of intermetallic compounds having high specific strength at high temperatures is progressing. Intermetallic compounds have a low toughness, in other words intermetallic compounds have fragility, thus cutting process thereto is very difficult. Such a material is called a low toughness material or a brittle material. As other examples of low toughness materials, there are glass, ceramics and the like. In this document, as an example, materials with a fracture toughness value equal to or less than 30MPa·m<NUM>/<NUM> will be collectively referred to as low toughness materials or brittle materials.

Among low toughness materials, intermetallic compounds in particular are difficult-to-cut materials with poor workability because of specific strength thereof that is relatively high at high temperature. As described above, because intermetallic compounds are low toughness materials and are difficult-to-cut materials also, a defect may relatively easily occur during a cutting process.

Milling process is known as a method of cutting a metallic material. In milling process, a workpiece is cut by rotating a tool such as an end mill at an appropriate rotation speed and moving the workpiece relative to the tool with an appropriate feed amount. During a cut process of an intermetallic compound by milling process, when the feed amount increases, on one hand cutting force increases, on the other hand likelihood of defect increases. However, when the feed amount decreases, a process efficiency of the cutting process (hereinafter also referred to as "cutting efficiency") decreases.

In relation with the above, <CIT> discloses a method of measuring a strength of a composite material containing fragile particles. This method of measuring a strength is characterized in generating a virtual test piece with a same quality as a real test piece from a content rate and particle size of the fragile particles and fracture toughness value of a matrix.

<CIT> describes an online prediction method for the damage depth of a hard and brittle material during rotary ultrasonic machining.

<CIT> discloses a method for determining a ceramic cutting temperature.

<CIT> discloses an apparatus and a method for aiding a programmer in preparing a machining program which represents the closest prior art.

A brittle workpiece cutting apparatus, a brittle workpiece manufacturing method and a recording medium storing a brittle workpiece manufacturing program should be provided to predict an occurrence of defect and/or a non-occurrence of defect before cutting process of brittle material. Other objectives and new features will be clear from disclosures of the present description and attached diagrams.

This object is solved by a brittle workpiece cutting apparatus with the features of claim <NUM>, a brittle workpiece manufacturing method with the features of claim <NUM> and a recording medium storing a brittle workpiece manufacturing program with the features of claim <NUM>. Preferred embodiments follow from the other claims.

According to the present disclosure, a brittle workpiece manufacturing program includes:
preparing tool data that represent physical characteristics and a shape of a tool, cutting data that represent a group of parameters of a cutting process to be performed to a workpiece by use of the tool and material data that represent physical characteristics and a shape of the workpiece; performing, based on the tool data, the cutting data and the material data, an analysis of deformation of the workpiece due to a cutting force and an analysis of fracture due to the deformation, performing a prediction of an occurrence of defect and/or a non-occurrence of defect of the workpiece due to the cutting process; and outputting a result of the prediction. Each step of the brittle workpiece manufacturing program is executed by a computer.

An occurrence of defect and/or a non-occurrence of defect can be predicted before a cutting process of a brittle material. In the following description, "low toughness" and "brittle" are used in a synonymous way.

Embodiments to carry out a low toughness workpiece cutting apparatus, a low toughness workpiece manufacturing method and a low toughness workpiece manufacturing program according to the present invention will be described below with reference to attached drawings.

When performing cutting process to a low toughness material under a condition including a combination of a plurality of parameters, it will be predicted whether an undesired defect occurs or not by a computer simulation before performing the cutting process.

More specifically, from a perspective of fracture mechanics, it is considered as follows based on an assumption in that a defect such as an initial crack exists inside a workpiece. At first, a surface energy of the workpiece needs to increase for the crack to grow. Next, by a growth of such a crack, an elastic strain energy (hereinafter also referred to as "strain energy") of the workpiece is released and decreased. Herein, the inventors have focused on that, when an incremental of the surface energy and a decremental of the strain energy are compared, it can be considered that a defect occurs when the latter becomes greater than the former, in other words a prediction is established in that no defect occurs as long as the latter is smaller than the former.

In addition, from another perspective of fracture mechanics, a fracture toughness value K1C is defined as a physic characteristics value of a workpiece. The fracture toughness value K1C is a value that represents physical characteristics of toughness against fracture of the material. Furthermore, when cutting a workpiece by use of a tool, a stress intensity factor K based on parameters related to the cutting is defined. The stress intensity factor K is a physical quantity used in a field of fracture mechanics and the like to evaluate a strength of a material in which a crack or a defect exists and represents a strength of stress distribution near a tip of the crack or the defect. Herein, the inventors have focused on that, when the stress intensity factor K and the fracture toughness value K1C are compared, it can be considered that a defect occurs when the former becomes greater than the latter, in other words a prediction is established in that no defect occurs as long as the stress intensity factor K is smaller than the fracture toughness value K1C.

Predicting an occurrence of defect by comparing an incremental of a surface energy and a decremental of a strain energy and predicting an occurrence of defect by comparing a fracture toughness value and a stress intensity factor are essentially the same. However, those two predictions differ in methods that are actually taken. Therefore, the former method and the latter method will be described as a first embodiment and a second embodiment, respectively.

A configuration of the low toughness workpiece cutting apparatus <NUM> in <FIG> will be described. The low toughness workpiece cutting apparatus <NUM> is provided with a processing apparatus <NUM> and a control device <NUM>. The processing apparatus <NUM> is configured to be able to perform a process including a cutting to a low toughness workpiece under control of the control device <NUM>. The processing apparatus <NUM> is a Computer Numerical Control (CNC) Milling machine, for example. The control device <NUM> is configured to be able to control the processing apparatus <NUM> by executing a desired program stored in a non-illustrated storage device by a non-illustrated processor to generate a desired control signal. The control device <NUM> is provided with a defect prediction device <NUM> and an interface <NUM>. The defect prediction device <NUM> predicts whether any defect occurs or not to a workpiece by a process performed by the processing apparatus <NUM> under a predetermined condition, before starting the process. It is preferable that the control device <NUM> generates, based on a result of this prediction, a control signal so that no defect occurs. This control signal may be a cutting program for operating the processing apparatus <NUM>, or may be a group of parameters included in this cutting program. The interface <NUM> outputs this control signal. It is preferable that the processing apparatus <NUM> and the control device <NUM> are connected so as to be able to transmit or receive the control signal.

A configuration example of the processing apparatus <NUM> in <FIG> will be described. <FIG> is a block diagram that shows a configuration example of a processing apparatus <NUM> according to an embodiment. The processing apparatus <NUM> according to the present embodiment is, for example, a knee-type CNC milling machine. The processing apparatus <NUM> is provided with a body <NUM>, a table <NUM>, a table movement mechanism <NUM>, a spindle <NUM>, a spindle movement mechanism <NUM>, a control circuit <NUM> and an interface circuit <NUM>. In an embodiment, the body <NUM> is installed on the ground on one hand and supports the spindle movement mechanism <NUM> and the table movement mechanism <NUM> on the other hand. The table <NUM> is configured on one hand to fix a workpiece by use of a workpiece fixing mechanism <NUM> and is connected on the other hand to the body <NUM> through the table movement mechanism <NUM> so as to be movable with respect to the body <NUM> within a predetermined range. The table movement mechanism <NUM> controls a position of the table <NUM> under a control of the control circuit <NUM>. The spindle <NUM> supports on one hand a tool <NUM> by a non-illustrated vise or the like, interchangeably, and is connected on the other hand to the spindle movement mechanism <NUM> that is fixed to the body <NUM>, so as to be rotatable with respect to the spindle movement mechanism <NUM>. Furthermore, the spindle <NUM> may be connected so as to be movable with respect to the spindle movement mechanism <NUM> within a predetermined range. The spindle movement mechanism <NUM> transmits a rotational power to the tool <NUM> connected to the spindle <NUM> by rotating the spindle <NUM> under a control of the control circuit <NUM>. Furthermore, the spindle movement mechanism <NUM> may move the spindle <NUM> within a predetermined range under a control of the control circuit <NUM>. The control circuit <NUM> is configured to receive a control signal from the control device <NUM> via the interface circuit <NUM> and control the table movement mechanism <NUM> and the spindle movement mechanism <NUM> in accordance with the control signal. It should be noted that the table <NUM> can be operated by a manual control of a user separately from the control by the control circuit <NUM>. In addition, the spindle movement mechanism <NUM> can be operated by a manual control of a user separately from the control by the control circuit <NUM>.

An operation example of the processing apparatus <NUM> in <FIG> will be described. <FIG> is a perspective view that shows an example of a positional relationship of the table <NUM>, the tool <NUM> and the workpiece <NUM> according to an embodiment. In an embodiment, the tool <NUM> is an end mill. An end mill is a tool used for a process such as cutting, has an approximately cylindrical shape and is provided with one or more blades on parts corresponding to a side surface and a bottom surface thereof. The end mill cuts, when rotating around a rotation axis <NUM> in a predetermined rotation direction <NUM>, a part of the workpiece <NUM> in contact with a blade of the end mill. The end mill can continuously cut the workpiece <NUM> by moving one or both of the end mill and the workpiece <NUM>. At that time, a direction in which the rotation axis <NUM> of the end mill moves with respect to the workpiece <NUM> is referred to as feed direction <NUM>. In the example of <FIG>, Z axis of the cartesian coordinate XYZ and the rotation axis <NUM> are parallel, a plane XY and the surface of the table <NUM> are parallel, and Y direction and the feed direction <NUM> are parallel.

<FIG> is a partial cross section to describe a cutting force <NUM> that is generated when the tool <NUM> according to an embodiment cuts the workpiece <NUM>. The partial cross section of <FIG> shows an example of a state of a tip of the blade of the end mill in contact with an inside corner surface of the workpiece <NUM>. At that time, a cutting force <NUM> is generated by a concentration of a rotation energy of the end mill and a kinetic energy of a relative movement of the end mill and the workpiece <NUM>, at a part where the end mill and the workpiece <NUM> are in contact. In the example of <FIG>, the cutting force <NUM> acts in a direction from the end mill to the workpiece <NUM>. However, to be more precise, the cutting force <NUM> does not necessarily have to be distributed in a same plane.

(Definition of a first energy amount) When the cutting force <NUM> is applied to the workpiece <NUM>, a part of the workpiece <NUM> is separated from the workpiece <NUM> to become a chip <NUM>, and a desired shape is formed on the surface of the workpiece <NUM>. The cutting force <NUM>, that is applied to the workpiece <NUM>, can be calculated by a computer simulation to which parameters related to the workpiece <NUM>, the tool <NUM> and the cutting condition are inputted. In addition, a decremental of the strain energy, that is reduced when a crack grows without changing a relative position of the tool <NUM> with respect to the workpiece <NUM> in a region where the cutting force <NUM> causes a deformation, can be calculated by a computer simulation. It should be noted that a distribution of strain in this region and strain energy stored in this region can be analyzed by a finite element method for example. The decremental of the strain energy calculated based on the cutting force <NUM> as described above will be hereinafter referred to as a first energy amount.

An example of another method of calculating the first energy amount will be described with reference to <FIG> and <FIG>. <FIG> is a partial cross section of the workpiece <NUM> to describe a method of calculating a cutting force <NUM> acting to the workpiece <NUM>. <FIG> is a partial cross section of the workpiece <NUM> to describe a movement of a tool <NUM> and a strain energy of the workpiece <NUM>.

<FIG> shows the workpiece <NUM> extracted from <FIG>. In other words, the illustration of the tool <NUM> is omitted in <FIG>. When the tool <NUM> and the workpiece <NUM> relatively move and a part of the workpiece <NUM> is deformed to become the chip <NUM>, a force referred to as a specific cutting resistance Kc (vector) and a force referred to as edge force Fe (vector) act from the workpiece <NUM> to the tool <NUM>. Herein, symbols that are supposed to be expressed as vectors are described with "(vector)" immediately after. The specific cutting resistance Kc (vector) is a cutting resistance per unit area. The cutting resistance is a force that tries to push the tool <NUM> back when the workpiece <NUM> is being cut and is proportional to a cutting thickness h and a cutting width b. The edge force Fe (vector) is a force caused by a roundness of the tip of the blade of the tool <NUM> and is proportional to the cutting width b of the chip <NUM>. It should be note that in the example in <FIG> an illustration in a direction of the cutting width b is omitted because this direction is parallel to an axis perpendicular to the paper surface. In the example of <FIG>, the direction of the cutting width b is noted as J axis, a direction of the movement of the tool <NUM> that is omitted from illustration is noted as I axis, and a direction orthogonal to both I axis and J axis is noted as K axis.

As described above, a force that acts from the workpiece <NUM> to the tool <NUM> when trying to cut the workpiece <NUM> by use of the tool <NUM> can be defined by the specific cutting resistance Kc (vector), the edge force Fe (vector), the cutting width b and the cutting thickness h. This force is in balance with a combined cutting force R (vector) that acts from the tool <NUM> to the workpiece <NUM>. Therefore, the following equation holds.

<FIG> also shows, similarly to the case of <FIG>, the workpiece <NUM> extracted from <FIG>. Herein, three following states are considered. At first, in a first state (first moment), the tip of the blade of the tool <NUM> is cutting the workpiece <NUM>, and a crack inherent in the workpiece <NUM> from the beginning exists at the edge of the tip of the blade (when no single crack growth is tolerated, a largest crack is assumed in a most dangerous direction). It is assumed that the combined cutting force from the tool <NUM> to the workpiece <NUM> is R (vector) and the elastic strain energy inside the workpiece <NUM> is U<NUM>. Herein, it is considered that the cutting motion is suddenly stopped from the first state and the combined cutting force R (vector) applied between the tool <NUM> and the workpiece <NUM> is reduced until it becomes zero (unloaded). At that time, the tip of the blade of the tool <NUM> is in contact with the surface of the workpiece <NUM> while no force is acting. This is referred to as a zeroth state and a relative position of the tool <NUM> with respect to the workpiece <NUM> at that time is referred to as an origin, for convenience. When a force is applied, again from this zeroth state, between the tool <NUM> and the workpiece <NUM> in a direction of the combined cutting force R (vector), and when this force is increased until the magnitude thereof becomes R, the relative position moves from the origin to a position λ1 (vector) and the state returns to the state <NUM>. During this time, the force increases in proportion to the traveled distance. Therefore, the elastic strain energy U<NUM> in the first state can be calculated by the following equation.

Next, it is assumed that, in the first state (first moment), a small crack growth has occurred by keeping the same combined cutting force R (vector). This state will be referred to as a second state. As this crack grows, a relative position vector of the tool <NUM> with respect to the workpiece <NUM> moves from the position λ<NUM> (vector) to a position λ<NUM> (vector) by a small displacement Δλ (vector). Therefore, a strain energy U<NUM> in the second state can be calculated, similarly to the case of the first state, by the following equation.

At that time, as the elastic strain energy changes from U<NUM> to U<NUM> while the combined cutting force R (vector) performs a work of R (vector) ·Δλ, (vector), a released mount ΔU of the elastic strain energy can be calculated by the following equation.

(Definition of a second energy amount) A surface energy of a new surface that is generated by the assumed small crack growth is referred to as a crack growth energy or simply a second energy amount. When the above-mentioned first energy amount becomes larger than this second energy amount, the crack may grow by consuming the energy of the elastic deformation. In other words, it is predicted that an undesired defect occurs when the released amount of the elastic strain energy becomes larger than this crack growth energy (Griffith's condition).

This second energy amount can be directly quantified by an assumption of a crack growth amount. That is, this second energy amount is a surface energy of a new surface that is generated in the workpiece <NUM> by an assumed crack growth. Therefore, the second energy amount can be calculated by multiplying a surface energy per unit area that the workpiece <NUM> has by a surface area of an assumed crack growth. In other words, the crack growth energy or the second energy amount can be also calculated by a calculation procedure, a computer simulation or the like to which parameters related to the workpiece <NUM> are inputted.

It should be noted that when a brittle fracture occurs in a common brittle material, a plastic deformation also may occur therewith. In this case, the surface energy per unit area in the definition of the second energy amount may be replaced with an effective surface energy per unit area. Herein, the effective surface energy per unit area is a sum of a surface energy per unit area and a plastic strain energy per unit area. In this case, when a decremental of a strain energy due to an assumed crack growth exceeds an incremental of an effective surface energy due to the crack growth, this crack growth occurs (Griffith-Orowan-Irwin's condition).

In the present embodiment, by respectively calculating the first energy amount and the second energy amount based on parameters related to the tool <NUM> and the workpiece <NUM> and comparing them, an occurrence of defect can be predicted before starting an actual cutting process and consider changing parameters so that no defect occurs.

A configuration example of a defect prediction device <NUM> in <FIG> will be described. <FIG> is a block circuit diagram that shows a configuration example of a defect prediction device <NUM> according to an embodiment. The defect prediction device <NUM> according to the present embodiment is provided with a bus <NUM>, an interface <NUM>, a processor <NUM> and a storage device <NUM>. The bus <NUM> is electrically connects to each of the interface <NUM>, the processor <NUM> and the storage device <NUM> and is configured so that the interface <NUM>, the processor <NUM> and the storage device <NUM> can communicate to each other. The interface <NUM> is electrically connected to the interface <NUM> of the control device <NUM> and the interface circuit <NUM> of the processing apparatus <NUM> and performs communication between the defect prediction device <NUM>, the control device <NUM> and the processing apparatus <NUM>. The interface <NUM> may be communicably connected to further other input/output devices. The further other input/output devices may include, for example, an output device such as a display device or a printer and an input device such as a keyboard or a mouse, and the like. The processor <NUM> is configured to read out a program <NUM> stored in the storage device <NUM> to execute and perform operations specified in this program. The storage device <NUM> is configured to readablely store a program <NUM> that the processor <NUM> executes. The storage device <NUM> may be configured to further store an operation result by the processor <NUM>. The program <NUM> may be read out from a recording medium <NUM> to be stored in the storage device <NUM>.

An operation of the low toughness workpiece cutting apparatus <NUM> according the present embodiment, that is, the low toughness workpiece manufacturing method and the low toughness workpiece manufacturing program according to the present embodiment will be described. <FIG> is a flowchart that shows a configuration example of a low toughness workpiece manufacturing method and a low toughness workpiece manufacturing program according to an embodiment.

The flowchart in <FIG> will be described. The flowchart in <FIG> includes a total of seven steps consisting of a first step S01 to a seventh step S07. When the flowchart in <FIG> starts, the first step S01 is executed.

In the step S01, tool data are set to the defect prediction device <NUM>. Information related to the tool data is stored in the storage device <NUM>, and by reading this information from the storage device <NUM>, the processor <NUM> becomes able to apply the tool data when the processor <NUM> performs a prediction of the first energy amount described later.

The tool data will be described. The tool data includes tool physical characteristics data that represent physical characteristics of a material constituting the tool <NUM>, tool shape data that define a shape of the tool <NUM>, and the like. As an example, when the tool <NUM> is an end mill with an approximately cylindrical shape, the shape of the tool <NUM> is defined by a diameter of a blade part of the end mill, a length in the rotation axis direction of the blade part, a blade number, a blade helix angle, a cutting-edge angle, a rake angle, a clearance angle, and the like. However, the tool data are not limited to those examples. Next to the first step S01, a second step S02 will be executed.

In the step S02, material data are set to the defect prediction device <NUM>. Similarly to the case of the tool data in the first step S01, information of the material data is also stored in the storage device <NUM>, and by reading from this information from the storage device <NUM>, the processor <NUM> sets the material data to the defect prediction device <NUM>.

The material data will be described. The material data includes material physical characteristics data that represent physical characteristics of a material constituting the workpiece <NUM>, material shape data that defines a shape of the workpiece <NUM>, and the like.

The material physical characteristics data will be described. As an example, the workpiece <NUM> is entirely or partially constituted of a low toughness material. A low toughness material has a low toughness and fragility, and an undesired part thereof may be defected during a cutting process, depending on a cutting method. In this sense, a process of cutting a workpiece <NUM> constituted of a low toughness material is very difficult. Therefore, in the present embodiment, from a perspective of fracture mechanics, it is assumed that initial defects exist at random inside the workpiece <NUM>. That is, it is assumed that a plurality of initial defects defined with various sizes and various directions exist inside the workpiece <NUM> with a predetermined probability distribution. It will be considered about a case in which an initial defect among the plurality of initial defects defined as above, of which a condition that is the worst from a perspective of suppressing an occurrence of defect, exists in a region of the workpiece <NUM> where the cutting force <NUM> by the tool <NUM> acts. Herein, the initial defect with a worst condition may be for example an initial defect with a largest scale among all assumed initial defects, an initial defect pointing in a direction closest to a direction in which a crack growth is likely occur due to the cutting force <NUM> or an initial defect of which a result of performing a predetermined weighting operation on a size of the scale and a proximity of direction becomes maximal. If no crack growth due to the cutting force <NUM> occurs in the region where the initial defect with a worst condition exists, that is, if no defect occurs, it can be predicted that no defect due to the same cutting force <NUM> occurs in regions where other initial defects exist.

It is preferable that parameters representing physical characteristics of a low toughness material include, for example, at least a part of a specific cutting resistance, an edge force, a shear strength, a coefficient of friction between the tool <NUM> and the chip <NUM>, a density, a Young's modulus, a Poisson's ratio and the like that are related to the calculation of the cutting force <NUM>. In addition, it is preferable that the parameters representing physical characteristics of a low toughness material specifically includes, in addition to the above parameters, a bulk modulus, a shear modulus of elasticity, fracture toughness value K1C and the like of the low toughness material constituting the workpiece <NUM> that are related to crack growth.

Material shape data will be described. The cutting force <NUM> applied from the tool <NUM> to the workpiece <NUM> may change depending on the shape of the workpiece <NUM>. It is preferable that the shape of the workpiece <NUM> at each timing can be grasped as needed because the shape of the workpiece <NUM> carries on changing as the cutting process progresses. As an example, information that is set as material shape includes information that represents a shape of the workpiece <NUM> before starting the cutting and a position of the workpiece <NUM> with respect to the table <NUM>. The information that is set as the material shape may further include, when a process including cutting is executed in accordance with a control signal based on cutting data, information that represent a shape during the cutting of the workpiece <NUM> of which the shape carries on changing from immediately before the start of this process to immediately after the end of this process.

In a third step S03, the cutting data is set to the defect prediction device <NUM>. Information related to a setting of the cutting data is also stored in the storage device <NUM>. By reading this information from the storage device <NUM>, the processor <NUM> sets the cutting data to the defect prediction device <NUM>.

The cutting data will be described. The cutting data includes a group of parameters used by the control device <NUM> to control the processing apparatus <NUM>. As a more detailed specific example, the cutting data includes information defining a plurality of processes specified by a plurality of parameters including a timing, a direction, a speed, a distance and the like by which the table <NUM> and the tool <NUM> of the processing apparatus <NUM> is moved under a control of the control device <NUM>, and information that specifies a timing, an order and the like of executing the plurality of processes. The cutting data includes information to define a cutting path. A cutting path is a path where the tool <NUM> relatively moves with respect to the workpiece <NUM> by cutting the workpiece <NUM>. However, the content of the cutting data is not limited to these examples.

It should be noted that each of the first step S01 to the third step S03 may be executed independently, therefore the order of execution may be changed, and some or all of them may be executed in parallel. When all of the first step S01 to the third step S03 are completed, a fourth step S04 is executed next.

In the fourth step S04, the processor <NUM> executes a first energy amount prediction program to calculate the first energy amount based on the cutting force <NUM> applied from the tool <NUM> to the workpiece <NUM>. More specifically, by predicting a state of the cutting process to the workpiece <NUM> by the tool <NUM> by a computer simulation based on the tool data, the material data and the cutting data that are set in the first step S01 to the third step S03, the cutting force <NUM> which is predicted that the tool <NUM> applies to the workpiece <NUM> is calculated. The cutting force <NUM> can be calculated similarly to the case of the above-described combined cutting force R, for example. Then, based on the predicted cutting force <NUM>, a decremental of the strain energy of the workpiece <NUM> is calculated, that is, the first energy amount is calculated. The first energy amount can be calculated similarly to the case of the above-described released amount ΔU of the elastic strain energy, for example.

In a fifth step S05, a second energy amount prediction program is executed to calculate the second energy amount. More specifically, by predicting a surface energy of a new surface of the workpiece <NUM>, that is generated by an assumed crack growth, by a computer simulation based on the material physical characteristics value that is set in the second step S02, the second energy amount, that is predicted to be necessary, when a crack exists in the workpiece <NUM>, for this crack to grow, is calculated.

It should be noted that each of the fourth step S04 and the fifth step S05 may be independently executed, therefore the order of execution may be changed, and some or all of them may be executed in parallel. When the fourth step S04 and the fifth step S05 are completed, a sixth step S06 is executed.

In the sixth step S06, a prediction of an occurrence of defect and/or a non-occurrence of defect is performed. More specifically, the first energy amount calculated in the fourth step S04 and the second energy amount calculated in the fifth step S05 are compared. As a result, if the first energy amount is equal to or greater than the second energy amount, the defect prediction device <NUM> predicts an occurrence of defect because a prediction holds in that a defect due to the cutting process occurs in the workpiece <NUM>. In other words, if the first energy amount is less than the second energy amount, the defect prediction device <NUM> predicts a non-occurrence of defect because a prediction holds in that no defect due to the cutting process occurs in the workpiece <NUM>.

When it is predicted that a defect occurs (YES), the first step S01 to the sixth step S06 will be executed again after the sixth step S06. At that time, a modification processing section <NUM> described later reviews and modifies one or more parameters among the tool data, the material data and the cutting data. A review of parameters may be automatically performed by the modification processing section <NUM> by executing a predetermined program, may be automatically performed by the modification processing section <NUM> by using an Artificial Intelligence (AI), or may be manually performed by a user, for example. In any case, it is preferable that the modification processing section <NUM> stores a result of reviewing parameters in the storage device <NUM>. As an example, an upper limit value and a lower limit value that are available for each parameter to be modified may be preliminary stored in the storage device <NUM>, and each parameter may be automatically incremented or decremented within a corresponding range or each parameter may be automatically selected at random from the corresponding range, by a predetermined program. In addition, as another example, an order of priority to review the plurality of parameters may be preliminary stored in the storage device <NUM> and the parameters may be reviewed in the order of priority while other parameters are fixed, or a plurality of parameters may be reviewed at a same time. As a further other example, a result of machine learning performed about a relationship between a combination of the plurality of parameters and/or a combination of modifications of the plurality of parameters and an occurrence or non-occurrence of defect may be preliminarily stored in the storage device <NUM> and an inference engine realized by the modification processing section <NUM> may search for a combination of parameters with which a non-occurrence of defect is expected. On the contrary, when it is predicted that no defect occurs (NO), a seventh step S07 will be executed after the sixth step S06.

In the seventh step S07, the process including the cutting is performed by use of the combination of parameters with which it is predicted that no defect occurs. More specifically, a non-illustrated processor of the control device <NUM> executes a predetermined program to generate a desired control signal, and the processing apparatus <NUM> cuts the workpiece <NUM> without occurrence of defect by processing the workpiece <NUM> according to this control signal. When the seventh step S07 ends, the flowchart in <FIG> also ends.

A configuration example of the defect prediction device <NUM> in <FIG> will be described from a perspective of the steps included in the flowchart in <FIG>. <FIG> is a block circuit diagram that shows a configuration example of the defect prediction device <NUM> in <FIG> from a perspective of functional blocks. As shown in <FIG>, the defect prediction device <NUM> in <FIG> is provided with the bus <NUM>, the interface <NUM>, the processor <NUM> and the storage device <NUM>. The defect prediction device <NUM> in <FIG> is provided with a data setting section <NUM>, a first energy amount prediction processing section <NUM>, a second energy amount prediction processing section <NUM>, a defect prediction processing section <NUM> and a modification processing section <NUM>. The data setting section <NUM> is a virtual functional block that executes the first step S01 to the third step S03 of the flowchart in <FIG>. The first energy amount prediction processing section <NUM> is a virtual functional block that executes the fourth step S04 of the flowchart in <FIG>. The second energy amount prediction processing section <NUM> is a virtual functional block that executes the fifth step S05 of the flowchart in <FIG>. The defect prediction processing section <NUM> is a virtual functional block that executes the sixth step S06 of the flowchart in <FIG>. The modification processing section <NUM> is a virtual functional block that executes a part of the sixth step S06 of the flowchart in <FIG>. As other components shown in <FIG> are similar to the components shown in <FIG>, further detailed description thereof is omitted.

It should be noted that while a case in which the processing apparatus <NUM> is a knee-type CNC milling machine is herein described, the present embodiment is not limited to this example and may be applied to any process including cutting.

According to the present embodiment, it can be predicted whether a defect occurs in a cutting process of a low toughness material by a computer simulation based on cutting data, tool data and material data before performing the cutting process. In addition, when it is predicted that a defect occurs due to the cutting process, each of parameters related to the cutting process can be reviewed until it is predicted that no defect occurs. That is, parameters with which it is predicted that no defect occurs can be outputted as a prediction result before performing the cutting process, and by performing the cutting process by use of this condition, the cutting process can be performed by preliminarily suppressing an occurrence of defect. In addition, a plurality of groups of parameters with which it is predicted that no defect occurs can be displayed on the defect prediction device <NUM> and select a condition for setting a higher processing efficiency of the cutting process. Alternatively, the defect prediction device <NUM> can be made to display a condition for setting a higher cutting efficiency. As a result, an improvement of yield in cutting process of low toughness material is expected while satisfying a high cutting efficiency.

(Second embodiment) In the present embodiment also it is predicted whether an undesired defect occurs when performing a cutting process to a low toughness material under a predetermined cutting condition in which parameters related to the tool data, the cutting data and the material data are combined, by a computer simulation before performing this cutting process. However, in the present embodiment, this prediction is performed based on a criterion in that an undesired defect may occur in the low toughness material when a stress intensity factor K becomes larger than a fracture toughness value K1C. This criterion means in other words that no defect occurs as long as the stress intensity factor K is smaller than the fracture toughness value K1C. Herein, a determination in the present embodiment about an occurrence of defect based on a result of comparison between the stress intensity factor K and the fracture toughness value K1C is essentially the same as the determination in the first embodiment about an occurrence of defect based on a result of comparison between the first energy amount and the second energy amount, while methods of calculations performed therefor are different. It should be noted that in the present embodiment a process efficiency of the cutting under this cutting condition can be predicted furthermore.

As the low toughness workpiece cutting apparatus <NUM> used in the present embodiment is of a same configuration as the one used in the first embodiment, further detailed description thereof will be omitted. Similarly, as the processing apparatus <NUM> and the control device <NUM> used in the present embodiment are also of same configurations as the ones used in the first embodiment, further detailed descriptions thereof will be omitted.

The stress intensity factor K will be described. The stress intensity factor K is a physical quantity used in a field of fracture mechanics and the like to evaluate a strength of a material in which a crack or a defect exists, and represents a strength of a stress distribution in a proximity of a tip of a crack or a defect. The stress intensity factor K is calculated based on a plurality of parameters included in a cutting condition of a process performed by the processing apparatus <NUM> to the workpiece <NUM> under a control of the control device <NUM>. The plurality of parameters includes a type of material constituting the tool <NUM>, a clearance angle of a blade of the tool <NUM>, a relative feed amount of the tool <NUM> with respect to the workpiece <NUM>, a depth of cut, a path angle, a lead angle and the like.

The path angle of the tool <NUM> will be described. <FIG> is a top view that shows an example of a group of parameters included in cutting data in a low toughness workpiece manufacturing method according to an embodiment. In the example of <FIG>, the workpiece <NUM> has a rectangular parallelepiped shape and is disposed so that each side thereof is parallel to any one of X axis, Y axis or Z axis of a cartesian coordinate. Herein, the Z axis is parallel to a vertical direction for example and the rotation axis <NUM> of the tool <NUM> is approximately parallel to the Z axis. In addition, the Y axis is orthogonal to the Z axis and is parallel to a feed direction 51A of the tool <NUM> as a reference, as described later. The X axis is orthogonal to both the Y axis and the Z axis. The lead angle between the rotation axis <NUM> of the tool <NUM> and the Z axis will be described later.

In the example of <FIG>, three feed directions 51A to 51C are shown as relative directions in which the tool <NUM> moves with respect to the workpiece <NUM>. The first feed direction 51A is parallel to the Y axis. In other words, a surface 50A, where the tool <NUM> comes in contact with the workpiece <NUM> at first when the tool <NUM> relatively moves with respect to the workpiece <NUM> along the feed direction 51A and the cutting starts, is perpendicular to the feed direction 51A. In addition, a surface 50B, where the tool <NUM> finally separates from the workpiece <NUM> when the tool <NUM> relatively moves with respect to the workpiece <NUM> along the feed direction 51A and the cutting ends, is also perpendicular to the feed direction 51A. Herein, the path angle is defined with respect to this feed direction 51A as a reference. In other words, a path angle of the feed direction 51A is zero degree.

In the example of <FIG>, the second feed direction 51B is separated from the first feed direction 51A by an angle 52B in an anti-clockwise direction when viewed from the tool <NUM>. This angle 52B is referred to as a path angle 52B corresponding to the feed direction 51B. On the contrary, the third feed direction 51C is separated from the first feed direction 51A by an angle 52C in a clockwise direction. This angle 52C is referred to as a path angle 52C corresponding to the feed direction 51C. Hereinafter, when the feed directions 51A to 51C are not distinguished, they may be simply referred to as a feed direction <NUM>. Similarly, when the path angles 52B and 52C are not distinguished, they may be simply referred to as a path angle <NUM>.

The stress intensity factor K depends to a shape around a part of the workpiece <NUM> where the blade of the tool <NUM> comes in contact, a direction of a force applied from the tool <NUM> to the workpiece <NUM> at this part, and the like. It will be considered about a case in which the tool <NUM> moves, in the example of <FIG>, when the tool <NUM> which rotates in a rotation direction <NUM> in a clockwise rotation direction when viewed from the +Z direction cuts the workpiece <NUM>, along any feed direction <NUM> when viewed from the workpiece <NUM>, so as to enter by the surface 50A and leave from the surface 50B. When the tool <NUM>, that was until then separated from the workpiece <NUM>, moves by rotating along a feed direction <NUM> and a blade of the tool <NUM> enters in the surface 50A of the workpiece <NUM>, a defect is more likely to occur if an angle between the surface 50A and the feed direction <NUM> is an acute angle (for example, acute angle section 53A), and conversely, a defect is less likely to occur if this angle is obtuse (for example, obtuse angle section 54C). Similarly, when the tool <NUM>, that was until then cutting the workpiece <NUM>, moves by rotating along a feed direction <NUM> and leaves from the surface 50B of the workpiece <NUM> and the blade of the tool <NUM> finally contacts the surface 50B from outside the workpiece <NUM>, a defect is more likely to occur if an angle between the surface 50B and the feed direction <NUM> is an acute angle (for example, acute angle section 54B), and conversely, a defect is less likely to occur at a part where this angle is an obtuse angle (for example, obtuse angle section 53D). Similarly, when the tool <NUM>, that was until then cutting the workpiece <NUM>, moves by rotating and contacts the surface 50A or the surface 50B from inside the workpiece <NUM>, a defect is more likely to occur if an angle between the surface 50A or the surface 50B and the feed direction <NUM> is an acute angle (for example, acute angle sections 53C and 54D) and conversely, a defect is less likely to occur if this angle is an obtuse angle (for example, obtuse angle sections 54A and 53B). Those angles vary depending on a path angle <NUM>
corresponding to the feed direction <NUM> or the like. In addition, if other parameters are the same, a cutting force <NUM> in an acute angle section 53C in a direction in which a blade of the tool <NUM> pulls the acute angle section 53C is more likely to make a defect occur than a cutting force <NUM> in an acute angle section 53A in a direction in which a blade of the tool <NUM> pushes the acute angle section 53A.

The lead angle of the tool <NUM> will be described. <FIG> is a side view that shows an example of a parameters included in cutting data in a low toughness workpiece manufacturing method according to an embodiment. In the example of <FIG>, the rotation axis <NUM> of the tool <NUM> is tilted by a predetermined angle with respect to a surface perpendicular to the feed direction <NUM>. This angle is referred to as the lead angle <NUM>. In addition, although it is not illustrated, an angle by which the rotation axis <NUM> is tilted with respect to a direction perpendicular to a pick feed direction in a plan perpendicular to the feed direction <NUM> is referred to as a tilt angle. By changing this lead angle and this tilt angle, a cutting thickness at a blade tip section and an angle between an end surface and the processed surface (a surface including the blade tip and the cutting direction) of the workpiece <NUM> slightly change when the cutting ends. For this reason, the lead angle <NUM> and the tilt angle are also parameters of the cutting condition that modify the stress intensity factor K.

It should be noted that a helix angle of a blade of an end mill, that is, an angle between a ridge of the blade and the rotation axis <NUM>, may be referred to as "lead angle" and therefore distinction should be noted. It should be noted that a curve drawn by an end of a blade of an end mill during a cutting process is for example a trochoid curve when the lead angle (the lead angle shown in <FIG>) <NUM> is zero degree and may be an intermediate curve between a trochoidal curve and a spiral when the lead angle <NUM> is an angle other than zero degree.

A comparison between the stress intensity factor K calculated from each parameter of the cutting process and the fracture toughness value K1C of the low toughness material constituting the workpiece <NUM> will be described. Herein, to make the description easier, it will be described about calculating the stress intensity factor K in each of cases in which only the path angle and the feed amount, among the parameters related to the stress intensity factor K, are modified. However, in reality, to calculate the stress intensity factor K of each different cutting condition, two other parameters included in the cutting condition may be modified, or more than two parameters may be modified.

<FIG> is a perspective bar graph that shows an example of stress intensity factor K corresponding to a combination of a path angle and a feed amount. In <FIG>, a total of twenty bar graphs arranged in five rows and four columns are drawn. Those bar graphs represent by heights thereof twenty kinds of values of stress intensity factor K that are expected to be respectively generated by combining five kind of path angles and four kinds of feed amounts, respectively.

Values of the path angle <NUM> to the path angle <NUM> are indicated on the coordinate axis of the path angles. In the path angle <NUM> to the path angle <NUM>, the larger the attached number is, the larger the corresponding path angle is, not necessarily proportional to the attached number. It is similarly in the feed amount <NUM> to the feed amount <NUM> indicated on the coordinate axis of the feed amount.

Some of the bar graphs of twenty bar graphs shown in <FIG> are painted in two colors. The bar graphs painted in two colors are a total of eleven bar graphs including ten bar graphs of which the feed amount is the feed amount <NUM> or the feed amount <NUM> and a bar graph of which the feed amount is equal to the feed amount <NUM> and the path angle is equal to the path angle <NUM>. In all of eleven bar graphs that are painted separately, the boundary of two colors corresponds to a same value of the stress intensity factor K. This value is equal to a threshold value of a condition for the low toughness material to defect. In other words, those eleven bar graphs show that the stress intensity factor K represented by the length thereof is larger than the fracture toughness value K1C. On the contrary, the remaining nine bar graphs show that the stress intensity factor K represented by the length thereof is smaller than the fracture toughness value K1C.

In the present embodiment, a combination of a plurality of parameters is selected as a candidate of the cutting condition within a range in which the corresponding stress intensity factor K is less than the fracture toughness value K1C. Furthermore, in the present embodiment, it is preferable to select a combination with a highest processing efficiency among the candidates as the cutting condition. As an example of criterion to select a combination with a highest cutting efficiency, it can be considered at first to select a cutting condition that requires a time as short as possible for processing. In the example of <FIG>, among the feed amounts <NUM> to <NUM>, feed amounts <NUM> and <NUM> can be selected so that the stress intensity factor K is lower than the fracture toughness value K1C. Herein, as a time required for processing is shorter in the case of the feed amount <NUM> than the case of the feed amount <NUM>, it is preferable to select the feed amount <NUM> from a perspective of making the cutting efficiency higher. Next, if the time required for processing is the same, it can be considered to select a cutting condition in which a factor of safety is higher from a perspective of suppressing a defect or the like. In the example of <FIG>, the path angles <NUM> to <NUM> can be selected among the path angles <NUM> to <NUM> so that the stress intensity factor K is lower than the fracture toughness value K1C after selecting the feed amount <NUM>. Herein, the path angle at which the stress intensity factor K becomes minimal is the path angle <NUM>. Therefore, in the example of <FIG>, it is preferable to select a combination of the feed amount <NUM> and the path angle <NUM>. However, in reality, a criterion for selecting a combination of parameters according to the present embodiment is not limited to the above example because various parameters other than the feed amount and the path angle shown in <FIG>, such as cutting thickness and a cutting width may be included in the cutting condition.

An operation of the low toughness workpiece cutting apparatus <NUM> according to the present embodiment, that is, a low toughness workpiece manufacturing method and a low toughness workpiece manufacturing program according to the present embodiment will be described. <FIG> is a flowchart that shows a configuration example of a low toughness workpiece manufacturing method and a low toughness workpiece manufacturing program according to an embodiment.

The flowchart in <FIG> will be described. The flowchart in <FIG> includes a total of five steps consisting of a first step S11 to a fifth step S15. When the flowchart in <FIG> start, the first step S11 is executed.

In the first step S11, the defect prediction device <NUM> calculates the stress intensity factor K based on the combination of a plurality of parameters included in the tool data, the cutting data and the material data. A second step S12 is executed after the first step S11.

In the second step S12, the defect prediction device <NUM> compares the fracture toughness value K1C and the stress intensity factor K based on a combination, that is the same as the first step S11, of the plurality of parameters included in the tool data, the cutting data and the material data. A third step S13 is executed after the second step S12.

In the third step S13, the defect prediction device <NUM> calculates a cutting efficiency of each of the plurality of combinations, that are same as the first step S11 and the second step S12, of the plurality of parameters included in the tool data, the cutting data and the material data. A fourth step S14 is executed after the third step S13.

It should be noted that each of the first step S11 to the third step S13 may be independently executed, therefore an order of execution may be changed, and in addition some or all of them may be executed in parallel. When all of the first step S11 to the third step S13 are completed, the fourth step S14 is executed next.

In the fourth step S14, the defect prediction device <NUM> determines a cutting condition for performing the cutting process, based on the result obtained in the first step S11 to the third step S13, and decides the cutting condition if a result of this determination satisfies a predetermined condition. At that time, the defect prediction device <NUM> selects to decide as the cutting condition a combination of which a cutting efficiency is the highest among a plurality of combinations of parameters, within a range in that the corresponding stress intensity factor K is lower than the fracture toughness value K1C. A fifth step S15 is executed after the fourth step S14.

In the fifth step S15, a process including the cutting is performed by use of the combination of parameters with which it is predicted that no defect occurs. More specifically, a non-illustrated processor of the control device <NUM> generates a desired control signal by executing a predetermine program and the workpiece <NUM> is cut without occurrence of defect by the processing device <NUM> that processes the workpiece <NUM> according to this control signal. When the fifth step S15 ends, the flowchart in <FIG> ends also.

A configuration example of the defect prediction device <NUM> in <FIG> will be described from a perspective of the steps included in the flowchart in <FIG>. <FIG> is a block circuit diagram that shows a configuration example of the defect prediction device <NUM> in <FIG> from a perspective of functional blocks. The defect prediction device <NUM> in <FIG> is provided with the bus <NUM>, the interface <NUM>, the processor <NUM> and the storage device <NUM>, as shown in <FIG>. The defect prediction device <NUM> in <FIG> is provided with a stress intensity factor calculating section <NUM>, a comparison section <NUM>, a cutting efficiency calculating section <NUM>, a cutting condition deciding section <NUM> and a cutting process performing section <NUM>. The storage device <NUM> is provided with a fracture toughness value database <NUM>. The fracture toughness value database <NUM> readablely stores fracture toughness values K1C of low toughness materials and the like. The stress intensity factor calculation section <NUM> is a virtual functional block that executes the first step S11 of the flowchart in <FIG>. The comparison section <NUM> is a virtual functional block that executes the second step S12 of the flowchart in <FIG>. Herein, the comparison section <NUM> may read out the fracture toughness value K1C corresponding to the material of the workpiece <NUM> from the fracture toughness value database <NUM>. The cutting efficiency calculation section <NUM> is a virtual functional block that executes the third step S13 of the flowchart in <FIG>. The cutting condition deciding section <NUM> is a virtual functional block that executes the fourth step S14 of the flowchart in <FIG>. The cutting process performing section <NUM> is a virtual functional block that executes the fifth step S15 of the flowchart in <FIG>. As other components shown in <FIG> are similar to the components shown in <FIG>, further detailed description will be omitted.

According to the present embodiment, it is expected to improve a yield and a cutting efficiency at the same time by selecting a combination of parameters so that the cutting efficiency becomes maximal or a value equivalent to the maximum within a range in which no undesired defect occurs due to the cutting process. For example, in consideration of safety to prevent an occurrence of defect, a combination with which a cutting efficiency becomes maximal within a predetermined range excluding a predetermined safety margin from a range in which the stress intensity factor K does not exceed the fracture toughness value K1C may be selected from combination of parameters.

Claim 1:
A brittle workpiece cutting apparatus (<NUM>) comprising a control device (<NUM>) that comprises a defect prediction device (<NUM>),
wherein the defect prediction device (<NUM>) comprises:
a storage device (<NUM>) configured to store tool data that represent physical characteristics and a shape of a tool (<NUM>), cutting data that represent a group of parameters of a cutting process to be performed to a workpiece by use of the tool (<NUM>) and material data that represent physical characteristics and a shape of the workpiece (<NUM>); and
an interface (<NUM>) configured to output a result of a prediction,
characterized in that
the defect prediction device further comprises
a processor (<NUM>) configured to perform, based on the tool data, the cutting data and the material data, an analysis of deformation of the workpiece (<NUM>) due to a cutting force and an analysis of fracture due to the deformation, and perform the prediction of an occurrence of defect and/or a non-occurrence of defect of the workpiece (<NUM>) due to the cutting process; wherein
the processor (<NUM>) is configured to generate, based on the prediction result of the defect prediction device (<NUM>), a control signal for controlling a processing apparatus (<NUM>) that performs a processing including the cutting process by use of the tool (<NUM>) to the workpiece (<NUM>),
the interface (<NUM>) is configured to output the control signal, and the processing apparatus (<NUM>) is configured to perform the processing of the control signal.