KR102036980B1 - A Method for Predicting Tool Wear Overlap Geometry - Google Patents

Abstract

The present invention relates to a method for predicting a tool wear shape, and more particularly, considering a influence of continuously changing surface inclination angles, and applying a tool wear shape even when machining complex shapes by applying probability theory, linear theory, and inverse deformation theory. A technique for predicting a tool wear shape for minimizing cutting errors caused by tool wear can be predicted.
Tool wear shape prediction method according to the present invention, by transforming each of the single wear predicted in the machining time distribution by cutting edge position of the cutting tool to a linear function of time to transform it into a linear wear and overlap with a simple sum and inverse deformation Solution By predicting the final wear shape where the single wears overlap, the tool wear shape can be predicted when machining complex shapes.

Description

A Method for Predicting Tool Wear Overlap Geometry}

The present invention relates to a method for predicting a tool wear shape, and more particularly, considering a influence of continuously changing surface inclination angles, and applying a tool wear shape even when machining complex shapes by applying probability theory, linear theory, and inverse deformation theory. A technique for predicting a tool wear shape for minimizing cutting errors caused by tool wear can be predicted.

Cutting finishing in the mold industry is a commonly used manufacturing process for producing high quality complex shapes with high productivity.

Cutting finishes with coated carbon ball end mills commonly used in manufacturing processes in the mold industry introduce complexity in predicting tool wear under overlap areas when having a continuously varying surface tilt angle. In practice, the tool contact points on the cutting edge change to the variety of surface inclination angles that affect tool wear, but are not considered in most current tool wear models.

Looking at the prior art for predicting the wear shape of the tool, as disclosed in Korean Patent Laid-Open Publication No. 10-2005-0030925, the acoustic sensor is formed by forming a thread to insert a PZT material into a tool, a tool stand or a workbench. By manufacturing and collecting in-processing signals and displaying them on the screen for visual observation, the process is predicted and the acoustic signals are analyzed to predict the replacement time and life of the tool due to wear or damage of the tool. An acoustic signal analysis system has been disclosed for surface defects and tool conditions that can improve production and improve productivity.

However, even in the case of the conventional technique, a technique for predicting tool wear under the overlap area is not disclosed in consideration of the continuously changing surface inclination angle, and the conventional technique is to monitor tool wear as the process proceeds, and cutting It is not a condition that predicts tool wear.

Republic of Korea Patent Publication No. 10-2005-0030925 (2005.03.31.)

Although many studies have been conducted on the method of predicting the shape of tool wear, studies for predicting the wear shape before the cutting process and for predicting the complex wear shape such as the case where the surface inclination angle is continuously changed are insufficient. The present invention predicts the final wear shape where the single wears predicted in the cutting time distribution by cutting edge position of the cutting tool are transformed into linear wears by transforming them into linear wears, overlapping with simple sums, and then inversely straining. In order to provide a more improved tool wear shape prediction method.

The problem to be solved by the present invention is not limited to the above-mentioned problem, another problem to be solved by the present invention not mentioned herein is to those skilled in the art from the following description. It will be clearly understood.

Tool wear shape prediction method according to the present invention is a method for predicting a tool wear shape using a computer, the first step of collecting a plurality of cutting conditions set in the cutting process, the tool wear shape model from the plurality of cutting conditions And a third step of predicting a shape of single wears from the established tool wear shape model, and a fourth step of predicting a final wear shape by overlapping the predicted single wear shapes. The cutting process is characterized in that the surface inclination angle between the cutting tool and the workpiece is continuously changed.

In addition, the fourth step of the present invention is characterized in that it comprises a step 4-1 that transforms the shape of the predicted single wear to a linear function over time to transform into a linear wear and overlap with a simple sum.

In addition, the fourth step of the present invention is characterized in that it comprises a step 4-2 after the step 4-1, inverse deformation of the linear wear superimposed by the simple sum to predict the final wear shape.

In addition, in the tool wear shape prediction method according to the invention, the shape of the predicted single wear is characterized in that the shape is predicted from the machining time distribution for each cutting edge position of the cutting tool.

In addition, in the tool wear shape prediction method according to the present invention, the tool wear shape model is characterized in that it comprises a machining time distribution for each cutting edge position of the cutting tool calculated through the machining path simulation.

In addition, the computer-readable recording medium having recorded thereon a program for executing the tool wear shape prediction method according to the present invention can be provided.

By solving the above problems, the tool wear shape prediction method of the present invention can predict the complicated and overlapping final wear shape of the cutting tool more precisely and accurately, thereby reducing the error occurring in a given cutting condition have.

의 측정값 및 계산값의 비교 그래프이다.
도 8은 본 발명의 입증 실험을 위한 절삭기계와 피절삭물 설치 상태를 나타낸 도면이다.
도 9는 본 발명의 피절삭물이 수평 하향 방향으로 절삭될 때 일정한 경사면과 볼록한 윤곽면의 경사면에 대한 절삭날의 표면 경사각 변화를 나타낸 도면이다.
도 10은 본 발명의 실험 중 그룹 Ⅰ의 테스트1에 따른 예측된 공구마모 겹침형상과 실험적으로 측정된 공구마모 겹침형상의 값 사이의 비교 그래프이다.
도 11은 본 발명의 실험 중 그룹 Ⅰ의 테스트2에 따른 예측된 공구마모 겹침형상과 실험적으로 측정된 공구마모 겹침형상의 값 사이의 비교 그래프이다.
도 12는 본 발명의 실험 중 그룹 Ⅱ의 테스트1에 따른 예측된 공구마모 겹침형상과 실험적으로 측정된 공구마모 겹침형상의 값 사이의 비교 그래프이다.
도 13은 본 발명의 실험 중 그룹 Ⅱ의 테스트2에 따른 예측된 공구마모 겹침형상과 실험적으로 측정된 공구마모 겹침형상의 값 사이의 비교 그래프이다.1 is a flowchart of a tool wear shape prediction method according to the present invention.
2 is a conceptual explanatory diagram of a tool wear shape prediction method according to the present invention.
Figure 3 is an enlarged view of the end 100 times to show the wear aspect of the cutting tool according to the present invention.
4 is a view showing the geometric elements of the cutting wear in the cutting operation of the cutting tool according to the present invention.
Figure 5 is a graph measuring the distribution of the tool wear shape of the cutting tool according to the present invention.
Figure 6 is a graph showing the trend of flank wear against cutting time and surface tilt angle in accordance with the present invention.
7 is a linearized tool wear according to the present invention.

Is a graph of comparison of measured and calculated values.
8 is a view showing a cutting machine and the workpiece installation state for the demonstration experiment of the present invention.
9 is a view showing a change in the inclination angle of the surface of the cutting edge with respect to the inclined surface of the constant inclined surface and convex contour surface when the workpiece of the present invention is cut in the horizontal downward direction.
10 is a comparison graph between the predicted tool wear overlap shape and the experimentally measured tool wear overlap shape according to Test 1 of Group I during the experiment of the present invention.
11 is a comparison graph between the predicted tool wear overlap shape and the experimentally measured tool wear overlap shape according to Test 2 of Group I during the experiment of the present invention.
12 is a comparison graph between the predicted tool wear overlap shape and the experimentally measured tool wear overlap shape according to Test 1 of Group II during the experiment of the present invention.
FIG. 13 is a comparison graph between predicted tool wear overlap shape and experimentally measured tool wear overlap shape according to Test 2 of Group II during the experiment of the present invention. FIG.

Specific matters including the problem to be solved, the solution to the problem, and the effects of the present invention as described above are included in the embodiments and drawings to be described below. Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings.

Tool wear shape prediction method according to an embodiment of the present invention, when a cutting tool, such as a ball-end mill in the cutting process, the surface inclination angle is continuously changed in the case of a workpiece of a complex shape, the tool wear under the overlapping area By predicting the shape of, the tool wear analysis takes into account the effects of continuously changing tool contacts and surface tilt angles along the cutting edge and overlap region.

Each step in the tool wear shape prediction method using a computer according to an embodiment of the present invention is as follows.

First step: collecting a plurality of cutting conditions set in the cutting process

Second step: establishing a tool wear shape model from the plurality of cutting conditions

Step 3: Predicting Shapes of Single Wears from the Established Tool Wear Shape Model

Step 4: predicting the final wear shape by overlapping the predicted single wear shapes

Hereinafter, a tool wear shape prediction method according to an exemplary embodiment of the present invention will be described in detail with reference to FIGS. 1 and 2.

First step: collecting a plurality of cutting conditions set in the cutting process

The most frequently used measure to assess tool condition is flank wear, shown in square shape in FIG. 3. Generally, tools are considered to have reached end of life when flank wear reaches a reference value. Flank wear can be measured by the distance between the top of the cutting edge and the bottom of the area where flank wear occurs. Due to the complexity of the actual cutting conditions, the cutting progress generates a significant part of the tool wear, and different cutting conditions have different effects on the tool wear condition. Tool wear is a function of material properties, tool material, tool geometry, operating conditions and other factors such as unexpected impacts due to poor control of the machine.

Step 2: establishing a tool wear shape model from a plurality of cutting conditions

Various theoretical, empirical, numerical or combined models have been developed to predict tool wear. Current tool wear models have been proposed based on average or maximum values of test flank wear results, but often it occurs that tool wear values are overestimated or underestimated. To solve this problem, a tool wear geometrical profile was developed under conditions where the inclination angle of the workpiece was constant, and it was found that the tool wear shape could be approximated by the distribution function.

Step 3: predicting the shape of the single wears from the established tool wear shape model

Since the tool wear shape can be approximated by a distribution function, the tool wear shape is calculated by Equation 1 below.

는 확률밀도함수(PDF)이다.here,

Is the probability density function (PDF).

The tool wear shape prediction method according to the present invention was originally adopted to directly measure the tool wear shape and then to determine the probability density function of the wear based on statistical theory. In practice, it is desirable to describe the distribution function by selecting one of established probability functions, such as a Gaussian distribution function and a wavelet distribution function. Various methods can be used to test whether the population described by the data set follows a given probability distribution.

Step 4: predicting the final wear shape by overlapping the predicted single wear shapes

A fourth step in the tool wear shape prediction method according to an embodiment of the present invention includes the following steps.

Step 4-1: transforming the shapes of the predicted single wears into a linear function over time, transforming them into linear wear and overlapping with a simple sum

Step 4-2: Preforming Final Wear Shape by Inverse Deformation of Linear Wear Overlaid by Simple Sum

Hereinafter, a fourth step of the tool wear shape prediction method according to an embodiment of the present invention will be described in detail with reference to FIGS. 1 and 2.

Step 4-1: transforming the shapes of the predicted single wears into a linear function over time, transforming them into linear wear and overlapping with a simple sum

In the design or operation of cutting edges, there is a high degree of interest in whether a cutting tool will fail under a given operating condition. Since the tool wear shape model set in the above-described third step is a nonlinear model, it is difficult to calculate the tool wear overlap shape for the overlap area. Therefore, in order to solve this problem, the tool wear shape model set in the above-described third step is linearized to generate a new variable capable of showing a linear graph.

모델에 대한 선형 함수

을 수학식 2와 같이 나타낼 수 있다.By applying linearization theory to Equation 1

Linear function for model

May be expressed as in Equation 2.

는 다른 물리적 파라미터의 조합과

변수에 해당하는

로 구성되는 공구마모 변수를 나타낸다. 즉, 수학식 1을 선형화하여 공구마모형상모델로부터 파라미터의 변화에 대한 선형 함수인

을 산출하는 것이다.here,

Is a combination of different physical parameters

Corresponding to a variable

Represents a tool wear parameter. That is, by linearizing Equation 1, it is a linear function of the change of parameters from the tool wear shape model.

To calculate.

On the other hand, the tool contact points change depending on the variety of surface inclination angles to create different tool wear shapes associated with areas that overlap or do not overlap during cutting. Overlapping areas mean areas that each have one or more common tool contacts with adjacent areas. If tool wear shape prediction is repeatedly performed for each zone, overlapping tool contact points may be commonly included in two or more zones. However, since the present invention converts the nonlinear tool wear model into a linear model, even if the tool contacts overlap, the overlapped wear shape of the linear wear tool can be calculated as a simple sum.

의 단순 합으로 도출되어, 하기의 수학식 3으로 나타낸다.Therefore, the linearized tool wear overlap feature is represented by the linearization model shown in Equation 2.

Derived by a simple sum of, it is represented by the following equation (3).

Equation 3 can be provided to calculate the linearized tool wear overlap shape for a given operating parameter at the tool contact along the cutting edge.

Step 4-2: Preforming Final Wear Shape by Inverse Deformation of Linear Wear Overlaid by Simple Sum

에서 산출된 겹침 모델로서, 실제 공구마모 겹침 형상과 다르다. 이 문제를 해결하기 위해 실제 공구마모 겹침 형상은 해당 선형공구마모모델의 역수를 취하여 계산할 수 있다. 따라서 절삭날의 최종 공구마모 형상은 하기의 수학식 4로 나타낸다.As described in step 4-1, the tool wear overlap shape may be calculated based on Equation 3. However, the calculated result is a linear tool wear model.

This is an overlap model calculated from, and is different from the actual tool wear overlap shape. To solve this problem, the actual tool wear overlap feature can be calculated by taking the inverse of the corresponding linear tool wear model. Therefore, the final tool wear shape of the cutting edge is represented by Equation 4 below.

As such, the surface inclination angle and cutting time in the operation of cutting tools such as ball end mills are variable values, which can result in different tool wear overlapping structures on the flank surface of the tool. Therefore, it is possible to calculate the tool wear overlap for a cutting cycle consisting of the same inclination angle and cutting time, and to extend the same process for several cutting cycles.

Example

Hereinafter, the tool wear shape prediction method according to an embodiment of the present invention will be described in detail by actually applying to a cutting tool.

First of all, the geometric elements of the cutting wear must be represented by the formula to apply to the actual cutting tool.

To develop a tool wear overlap model, the effects of the warp of the workpiece on the geometry of the basic ball end mill, the momentary change of the cutting surface and the workpiece, and the interaction between the cutting tool and the workpiece should be analyzed. . Figure 4 shows the geometric elements of the cutting wear in the cutting operation of the ball end mill. The instantaneous cutting angle can be calculated as shown in Equation 5 based on the basic equation.

과

은 각각 절삭공구의 반경과 절삭 깊이를 나타낸다.here

and

Denote the cutting tool radius and cutting depth, respectively.

와

는 하기 수학식 6 및 수학식 7로 나타난다.As shown in Figure 4, the position angle of the cutting surface

Wow

Are represented by Equations 6 and 7 below.

는 절삭의 스텝오버 깊이를 나타낸다.here

Represents the stepover depth of cutting.

In addition, the inlet and outlet contact positions of the tool along the cutting surface can be calculated by the following Equations (8) and (9).

As described above, the tool wear shape prediction method according to an embodiment of the present invention is applied to the cutting tool by using the geometric elements of the cutting wear calculated by the formula, and the single wear shape is predicted. Let's predict.

Single Wear Shape Prediction Using Tool Wear Shape Model

The characteristics of the tool wear shape referred to in the present invention means wear occurring on the side of the tool. The average value of two flutes is considered as an indicator of tool wear. The cutting parameters were set the same as in the finishing cutting process, with fixed operating parameters related to spindle speed, feed rate, step over depth and axis depth, and instantaneous changing surface inclination angles as variable parameters.

가 발생하는 것을 파악할 수 있다.Figure 5 is a measurement of the distribution of the tool wear shape, a single wear occurs around the tool contact point, it can be seen that the size of the single wear increases with time. The inlet and outlet contact positions of the tool for various inclination angles can be calculated according to Equations 5 to 9, in which tool wear starts at the entry point of the tool contact point and the maximum flank at the exit point of the tool contact under conditions where the tool inclination angle is constant. Wear

You can see what happens.

는 하기 수학식 10과 같이 산출된다.As shown in FIG. 5, it can be hypothesized that the measured tool wear shape data represents a normal distribution pattern. To test the hypothesis, the least-squares method was performed to fit the measured data using the Matlab program. According to the probability theory, the tool wear shape distribution is approximated by a Gaussian distribution, so the probability density function of the tool wear shape is

Is calculated as in Equation 10 below.

= 0.36은 고정 매개 변수의 영향을 고려한 실험 상수 값이고, l은 절삭공구의 중심 끝에서 절삭날을 따라 측정된 호길이이며,

는 공구의 최대 측면 마모를 나타낸다.here,

= 0.36 is the experimental constant value taking into account the influence of the fixed parameters, l is the arc length measured along the cutting edge at the center end of the cutting tool,

Represents the maximum lateral wear of the tool.

In this case, the distribution of the tool wear shape according to Equation 1 may be expressed as Equation 11.

를 계산하는 데 효과적인 모델은 아직 존재하지 않으나, 이는 측정된 데이터를 통해 간접적으로 계산될 수 있다.Tool wear features have generally been shown to follow a normal distribution. However, so far flank wear

There is no effective model yet for calculating P, but it can be calculated indirectly from the measured data.

의 증가를 나타내는데, 관찰 결과, 절삭 시간의 초기 단계에서 절삭 시간에 따라 플랭크 마모의 증가가 급격히 증가했다. 마모 경향은 다른 연구자들에 의해 보고된 패턴과 일치하였는데, 절단 초기에 마모는 가장자리의 불규칙성을 제거하는 경향이 있고 높은 압력이 발생하며 가장자리에 열이 집중된다. 이러한 최초의 가속 마모 후에 절삭 시간이 증가함에 따라 마모 속도가 느려지므로 코팅이 마모를 더디게 하는 것을 알 수 있다. 또한, 도 6에 따르면, 표면 경사각이 플랭크 마모에 영향을 미치는데, 절삭공구 마모는 표면 경사각이 증가함에 따라 감소하였다.6 shows flank wear for cutting time and surface tilt angle

As a result of observation, the increase in flank wear increased rapidly with the cutting time in the initial stage of cutting time. The tendency to wear was consistent with the pattern reported by other researchers. At the beginning of the cut, wear tends to eliminate edge irregularities, high pressures occur and heat concentrates on the edge. It can be seen that after this initial accelerated wear, the wear rate slows down as the cutting time increases, so that the coating slows down wear. In addition, according to FIG. 6, the surface inclination angle affects flank wear, and the cutting tool wear decreased with increasing surface inclination angle.

모델은 절삭 시간과 경사각을 고려하여 수학식 12와 같이 제안된다.Therefore, according to Taylor's expansion equation, flank wear

The model is proposed as in Equation 12 in consideration of the cutting time and the inclination angle.

는 고정 매개 변수의 영향을 고려한 또 다른 실험 상수 값이고, a는 시간 계수이며, b는 경사각 계수이다. 실험 상수 값은 전술한 바와 같이 근사 곡선(curving fitting)을 통해 산출되었으며,

=0.0054, a=0.497 및 b=-0.869 인 것으로 나타났다.here,

Is another experimental constant value considering the influence of fixed parameters, a is the time coefficient, and b is the tilt angle coefficient. The experimental constant values were calculated through approximating curves as described above.

= 0.0054, a = 0.497 and b = -0.869.

), 공구 접촉점(l) 및 절삭 시간(t)의 관점에서 생성된 공구마모 형상에 대한 예측 모델은 하기 수학식 13과 같습니다.Equation 12 is inserted into Equation 11, and the inclination angle (

), The tool contact point (l) and the cutting time (t) in terms of the predictive model for the resulting tool wear shape is expressed by the following equation (13).

라고 가정하면 공구마모형상모델은 하기 수학식 14와 같이 표현된다.here,

Assuming that the tool wear shape model is represented by Equation 14 below.

), 공구 접촉점(l) 및 절삭 시간(t)에 의해 직접 영향을 받는다는 것을 나타낸다.Equation 14 shows that the tool wear shape has an inclination angle (

), It is directly affected by the tool contact point l and the cutting time t.

Predicting the final wear shape by overlapping single wear features

In order to easily calculate the overlap of tool wear, the proposed nonlinear tool wear model is linearized to generate new variables that can show a straight line graph. Here, the linearized tool wear shape model is applied with a mapping function that fits the nonlinear wear curve to the linear curve, and equation (14) is converted into the following equation (15).

From Equation 2 described above, the linearized equation can be expressed as Equation 16 below.

을 산출할 수 있다. 도 7은 선형화된 공구마모

의 측정값 및 계산값의 비교 그래프를 나타낸다.Therefore, by linearizing equation (14), the equation is a linear function of the change of model parameters from the tool wear shape model.

Can be calculated. 7 is a linearized tool wear

The comparison graph of the measured value and the calculated value is shown.

Since the non-linear tool wear is converted to linear wear, the overlapped shape of the tool wear can be calculated under the given operating conditions by applying the above-described steps 4-1 and 4-2. First, the linearized tool wear overlap shape is calculated as in Equation 17 according to Equation 3 and Equation 15-16.

)의 분산의 역수를 취하여 계산할 수 있다. 수학식 4를 참조하여, 최종 공구마모 겹침형상은 수학식 17을 역변형하여 하기 수학식 18과 같이 주어진다.Next, the actual final tool wear overlap is calculated. As in the above-described step 4-2, the final tool wear overlap shape is a linearized tool wear overlap shape (

Can be calculated by taking the inverse of the variance of Referring to Equation 4, the final tool wear overlap shape is given by Equation 18 by inversely transforming Equation 17.

Where a is equal to a in Equation 12.

Experimental Example

Additional experiments were performed to verify the tool wear shape prediction method of the present invention.

First, the experimental equipment, the workpiece, and the measuring method for comparing the final wear shape and the actual wear shape according to the tool wear shape prediction method of the present invention will be described.

Cutting experiments were carried out in a three-axis vertical milling center (HWACHEON VESTA 1000) with a maximum spindle speed of 20,000 rpm and a feed rate of 24 m / min. In order to accelerate tool wear, all experiments were run dry and compressed air supplied through the nozzles was injected into the cutting zone. In addition, in order to minimize the dynamic influence of the tool deformation, the protruding length of the tool was kept as short as possible, and the dial indicator was used to obtain accurate workpiece tilt.

8 shows the cutting machine and the workpiece installation state for the experiment. In this experiment, as shown in FIG. 9, the difference between the inclined surface and the convex contour surface when the workpiece was cut in the horizontal downward direction was examined. The cutting parameters were set identical to the finishing process and fixed operating parameters related to spindle speed, feed rate, stepover depth and axis depth in horizontal downcutting were set. The variable parameter was an immediate tilt angle. The details of the cutting conditions used in this experiment are shown in Table 1.

Cutting condition  Cutting machine  VESTA 1000 (3-Axis Vertical Milling Center)  Ball end mill  Multilayer TiAlN carbide  Diameter (mm)  10  Number of cutting edges  2  Workpiece Material  Hardened HP1 steel HRC 53-57  Spindle speed (rpm)  6000  Feed rate (mm / min)  1200

(mm)Cutting axis depth,

(mm)  0.05 Cutting path spacing,

(mm)  0.2  Tilt angle (°)  30-40  Runout (μm)  <10  Tool length (mm)  30  Cutting work type  Downward  slush  Compressed Air Blow Dry

The workpiece material used in this experiment is hardened HP1 cast iron. Table 2 shows the chemical composition of HP1 with an average hardness of 43 HRC used in this experiment. This material was processed into a 100 × 100 × 100 mm rectangular block shape. Before starting the machining experiments, the workpieces were face milled on the surface, bottom and sides to remove surface defects and ensure flatness to prevent deflection of the results. As a cutting tool, a two-blade carbide ball end mill (OSG-GS series) was used for the experiment. The ball end mill was coated with a multilayered TiAlN film having a thickness of about 2.5 μm. The cutting edge has a diameter of 10 mm, an inclination angle of 30 ° and a clearance angle of -10 °.

Chemical composition of HP1, the material to be cut C Si Mn Cr Mo V 0.35 0.20 0.30 5.20 1.40 0.55

Tool wear geometry in terms of flank wear of the tool was measured using a digital microscope (INSIZE) at 100 × magnification and using image processing software throughout the machining test. After a certain time interval the cutting tool was removed from the tool holder to observe the shape of the tool wear. An illuminator was provided as a light source for the digital microscope, and images of the cutting edge were recorded and processed. Tool wear geometry could be measured in terms of flank wear along the cutting edge in contact with the machined surface. The maximum flank wear criterion for ball end milling was specified at about 0.1 mm. In addition, the wear of the two blades was measured separately and the average value was considered as an indicator of tool wear.

In order to confirm the accuracy and validity of the tool wear shape model according to the present invention, the experiment was conducted in two groups, group I and group II, as shown in FIG. Was performed.

Group I is constant slope milling and group II is convex contour milling. Fixed operating parameters related to spindle speed, feed rate, step over depth and axial depth are presented in Table 1 above. Experiments were performed at various tilt angles as described in Tables 3 and 4.

Variable operating parameters for group I Tilt angle (°) Cutting time (min) Test No.1 Test No.2 30 160 160 35 70 70 40 70 160

Variable operating parameters for group II Test
No. Changing angle of inclination (°) Cutting angle distance (°) Total cutting time (min) One 35.1 ～ 39.6 0.1 150 2 33.7-40.4 0.1 219

Therefore, all relevant cutting parameters can be inserted into Equations 5 to 18 to quantitatively predict the tool wear overlap.

Group I: Overlap of tool wear under constant inclined milling

A comparison between the predicted tool wear overlap and the experimentally measured tool wear overlap is shown in FIGS. 10 and 11. The single tool wear shape prediction of each zone iteratively calculates the equations (5) to (14), and then, in the overlapping area, the tool wear overlap shape is calculated through the equations (15) to (18). As shown in Figs. 10 and 11, the tool wear value increases as the cutting time increases from 70 minutes to 160 minutes at the surface tilt angle δ = 40 °, which determines the tool wear shape in the overlap area. The comparison of and experimental data shows that the cutting time plays an important role in determining tool wear overlap in constant milling. The predicted results are in good agreement with the general trend of the experimental results, and the maximum percentage error between the expected and experimental values is about 17%.

Group II: Overlap of tool wear under slope milling of convex contour

Tool wear overlap is a function of the instantaneous changing angle of inclination. A comparison between the predicted tool wear overlap shape and the experimentally measured tool wear overlap shape under sloped milling of the convex contour is shown in FIGS. 12 and 13. The total machining time is distributed at each instantaneously varying tilt angle. Tool wear overlap at fixed cutting parameters and various instantaneously varying surface tilt angles is predicted using the model described above. The predicted tool wear overlap tends to be in good agreement with the experimental results, and the overall trend of the experimental and predictive data is determined by the instantaneously changing surface inclination angle and cutting time in the inclined face milling of the convex contour. Shows that The maximum percentage error between the expected and experimental values is about 16%.

가 발생하고, 중첩 영역 내에서 공구마모 겹침형상은 즉각적인 표면경사각과 해당 절삭 시간에 의해 결정되는 것을 알 수 있었다.The present invention proposes a new tool wear shape prediction method considering the influence of the instantaneous surface inclination angle and overlapping area. Based on probability theory, linearization theorem, and inverse strain, the present invention has been demonstrated and validated through experiments of cutting hardened HP1 steel with coated carbide ball end mills. Based on the experiments performed, tool wear starts at the entry point of the tool contact point at constant tool slopes and maximum lateral wear at the exit point of the tool contact.

It was found that the tool wear overlap shape in the overlap region was determined by the instantaneous surface tilt angle and the corresponding cutting time.

The new tool wear shape prediction method applied to the present invention is desirable for design engineers to predict tool wear shapes associated with overlapping or non-overlapping areas in order to improve the surface quality of the workpiece and to compensate for machining errors due to tool wear.

As such, the technical configuration of the present invention described above will be understood by those skilled in the art that the present invention can be implemented in other specific forms without changing the technical spirit or essential features of the present invention.

Therefore, the above-described embodiments are to be understood as illustrative and not restrictive in all respects, and the scope of the present invention is indicated by the following claims rather than the detailed description, and the meaning and scope of the claims and their All changes or modifications derived from an equivalent concept should be construed as being included in the scope of the present invention.

S1: First step of collecting a plurality of cutting conditions set in the cutting process
S2: second step of establishing a tool wear shape model from a plurality of cutting conditions
S3: third step of predicting the shape of the single wears from the established tool wear shape model
S4-1: Step 4-1 in which the shapes of the predicted single wears are transformed into linear functions over time, transformed into linear wear, and overlap with simple sums
S4-2: step 4-2 of predicting the final wear shape by inverse deformation of the linear wear superimposed by a simple sum
S4: fourth step of predicting the final wear shape by overlapping the shape of the predicted single wear

Claims (6)

As a method of predicting a tool wear shape using a computer,
A first step of collecting a plurality of cutting conditions set in the cutting process;
A second step of establishing a tool wear shape model from the plurality of cutting conditions;
A third step of predicting shapes of single wears from the established tool wear shape model; And
And a fourth step of predicting a final wear shape by overlapping the predicted single wear shapes.
In the third step, the shape of the single wear is calculated as a probability density function as shown in Equation 1,
[Equation 1]

(W _i is the shape of the single wear, f _i (x) represents the probability density function.)
The cutting process is a process of continuously changing the inclination angle of the surface between the cutting tool and the workpiece,
The fourth step,
A step 4-1 of transforming the predicted shapes of the single wears into a linear function with respect to time, transforming the shapes into linear wear, and overlapping with a simple sum; And
After the step 4-1, and comprising the step 4-2 for predicting the final wear shape by inverse deformation of the linear wear superimposed by the simple sum,
In step 4-1, the linearization theory is applied to Equation 1 to represent a linear function as in Equation 2 below.
[Equation 2]

(WLi is a linear function and Wi is a tool wear variable.)
The linearized tool wear overlap shape is derived as the sum of the linearization models shown in Equation 2 as shown in Equation 3 below.
[Equation 3]

In step 4-2, the tool wear shape prediction method of claim 4, wherein the final wear shape is derived as shown in Equation 4 by taking the inverse of the overlapping shape of the tool wear as shown in Equation (3).
[Equation 4]

The method of claim 1,
The predicted shape of the single wear is a tool wear shape prediction method, characterized in that the shape predicted from the machining time distribution of each cutting edge position of the cutting tool. The method of claim 1,
The tool wear shape model includes a tool wear shape prediction method comprising a machining time distribution for each cutting edge position of the cutting tool calculated through a machining path simulation. A computer-readable recording medium having recorded thereon a program for executing the method according to any one of claims 1 to 3. delete delete

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