A broaching surface quality assessment method based on chip curvature measurement

Abstract

The present invention discloses a method for evaluating the quality of a broaching surface based on the measurement of the degree of chip curvature. The process of the evaluation method is as follows: 1. Collect chips during the broaching process, and perform edge extraction to obtain an edge characteristic curve of the chips. 2. Extract multiple feature points on the edge characteristic curve obtained in step 1. 3. Measure the distance L and the curve length S from each feature point to the starting point of the edge characteristic curve. 4. Perform quadratic function fitting on the distance L and the curve length S corresponding to each measured feature point to obtain a chip morphology characteristic curve. 5. Take the distance corresponding to the highest point of the chip morphology characteristic curve as the characteristic height h. Substitute the characteristic height h into the surface roughness-characteristic height relationship function obtained in advance to obtain the surface roughness on the broaching surface when the cutting is produced. The direct measurement object of the present invention is the chips generated during broaching, so real-time evaluation of the workpiece processing surface quality can be achieved when the workpiece is not completed.

Description

Technical Field

[0001] The invention belongs to the field of broaching surface quality assessment, and in particular relates to a broaching surface quality assessment method based on chip bending degree measurement. Background Art

[0002] Broaching technology has the advantages of high machining precision, wide machining range, high production efficiency and simple operation. It is widely used in high-precision machining and manufacturing fields such as marine equipment, automotive parts, aerospace equipment and nuclear industry. However, due to the increasingly fierce competition in the global industrial market, products are becoming more and more complex, the requirements for machining quality are getting higher and higher, and the high-precision standards of key components are gradually increasing. At present, the surface machining quality of broached workpieces is mainly evaluated by surface roughness. Common test methods for surface roughness include contact measurement method and non-contact measurement method. The contact method, also known as the stylus method, refers to the use of a stylus surface profiler to contact the measured surface in one direction, converting the displacement signal into an electrical signal to record the precise measurement of the microscopic scale; the non-contact method is divided into optical measurement method and electronic measurement method. The optical measurement instrument is relatively expensive, has low measurement efficiency, and has a small single measurement area. The electronic measurement method performs non-contact relative motion on the workpiece surface, and the feedback signal of the capacitive sensor represents the morphological information of the workpiece surface under the electrode diameter coverage, and the measurement efficiency is low. The above measurement methods have relatively low measurement efficiency, and the evaluation time required during the processing is relatively long. It is also difficult to measure the surface roughness of the machined surface while broaching. During the broaching process, the chip morphology is closely related to the cutting heat. During the broaching process, the chips will continuously absorb cutting heat. The more cutting heat they absorb, the higher the chip temperature, the stronger the plastic deformation ability, and the more curled the shape. Compared with the measurement of the workpiece processing surface quality, the measurement and observation of chip morphology is more convenient.

[0003] There is also a lot of research on the assessment of machining quality in cutting processes. Publication No. CN113770805A proposes a method for predicting turning surface roughness based on tool parameters and material parameters. By measuring tool parameters such as turning tool waviness, tool tip radius, and cutting edge blunt radius, and material parameters such as hardness and elasticity model, the surface roughness components corresponding to the tool cutting edge profile and plastic lateral flow are calculated. Finally, these surface roughness components are combined with non-deterministic surface roughness components to accurately predict the turning surface roughness. Publication No. CN106407669A proposes a method for predicting cutting surface roughness. By selecting a portion of variable parameters and the corresponding surface roughness, a variable probability distribution function is determined. The copula optimal function and the variable probability distribution function are synthesized to derive a surface roughness conditional probability distribution function based on the variable parameters. Local correlation analysis is performed, the prediction model is corrected, and the surface roughness is evaluated. As can be seen from the above publications, the assessment methods of machined surface quality have attracted the attention of many researchers, but none of the above studies have paid attention to the relationship between chip morphology and machined surface quality.

[0004] With the improvement of the degree of automation in mechanical processing, higher and higher requirements are placed on the online measurement of surface roughness. In many areas of industrial production, in order to save energy and materials, avoid or reduce the scrap rate of parts during processing, monitor the processing process, and improve product quality, it is required that the inspected surface must not be damaged, and the surface quality needs to be monitored in real time during the processing. Real-time detection of the surface roughness of parts during processing can help workers to grasp the quality status of the surface of parts during processing at any time, and adjust the corresponding processing parameters to control the surface quality of parts to ensure that their quality reaches the expected goal. Therefore, real-time detection of the surface roughness of parts during processing is even more important. In addition, real-time acquisition of the surface roughness of the processed surface can reflect the wear status of some tools and facilitate operators to change tools in time to ensure the continuity and quality of processing. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a broaching surface quality assessment method based on chip bending degree measurement.

[0006] A method for evaluating the quality of a broached surface based on chip curvature measurement comprises the following steps:

[0007] Step 1: Collect chips during the broaching process and perform edge extraction to obtain the edge characteristic curve of the chips.

[0008] Step 2: Extract multiple feature points on the edge feature curve obtained in step 1.

[0009] Step 3: Measure the distance L from each feature point to the starting point of the edge feature curve and the curve length S.

[0010] Step 4: Perform quadratic function fitting on the distance L and curve length S corresponding to each measured characteristic point to obtain the chip morphology characteristic curve.

[0011] Step 5: Take the distance corresponding to the highest point of the chip morphology characteristic curve as the characteristic height h. Substitute the characteristic height h into the pre-fitted surface roughness-characteristic height relationship function to obtain the surface roughness of the broached surface during the cutting process.

[0012] Preferably, the surface roughness-feature height relationship function described in step 5 is a linear equation, and the process for obtaining the linear equation is as follows: broaching the same workpiece under various machining environments, collecting chips from each workpiece, and measuring the surface roughness Ra of the machined surface. The chips are used to extract the feature height h; and the obtained surface roughness Ra is fitted to the feature height h to obtain the surface roughness-feature height relationship function.

[0013] Preferably, in step 2, the edge characteristic curve is obtained by dividing the edge characteristic curve into equal-angle segments with the starting point as the center.

[0014] Preferably, by collecting chips and calculating the surface roughness, the surface roughness of the broaching surface is monitored without stopping the broaching operation. When the surface roughness of the machined surface becomes abnormal, the tool is changed or the machining parameters are adjusted.

[0015] Preferably, in step 2, the specific process of extracting feature points is as follows: extending a starting line tangent to the edge feature curve through the starting point of the edge feature curve, rotating the starting line along the cutting direction with the starting point as the center of the circle, and taking the intersection of the starting line and the edge feature curve as a feature point for each rotation angle θ, until the starting line rotates to the end point of the edge feature curve or the rotation angle reaches 270°; wherein, 15°≤θ≤45°.

[0016] The beneficial effects of the present invention are:

[0017] 1. Based on the experimental conclusion that there is a moderate correlation between chip morphology and surface roughness, the present invention links the degree of chip curvature with the surface quality of the workpiece, elevating the surface roughness measurement from the microscopic level to the macroscopic level, making the measurement easier.

[0018] 2. The direct measurement object of the present invention is the chips generated during broaching, so real-time evaluation of the workpiece surface quality can be achieved before the workpiece is processed. This can help workers promptly discover problems that occur during broaching, so that they can change tools or adjust processing parameters in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of the present invention.

[0020] Figure 2 This is the result diagram of the chips after edge recognition and feature point marking in Example 1 (wherein, parts A and D are chips from an air-cooled environment, parts B and E are chips from a dry-cut environment, and parts C and F are chips from an oil-cooled environment).

[0021] Figure 3 The chips formed by broaching under different broaching environments are fitted with the curve length S as the horizontal axis and the distance L as the vertical axis. DETAILED DESCRIPTION

[0022] The present invention will be further described below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, a broaching surface quality assessment method based on chip bending degree measurement includes the following steps:

[0024] Step 1. Carry out broaching tests on the same workpiece with the same broach under three different broaching environments. The conditions of the broaching test are as follows: Use a horizontal machine tool with model LG612Ya-800 to perform broaching motion on the same workpiece with a lathe pulling force of 20KN and a broaching speed of 80mm / s under dry cutting, cold air, and oil-lubricated processing environments, and repeat the experiment 3 times in each working environment. The material of the workpiece is 45# steel. While broaching the workpiece, the chips generated by the processing are collected. The surface roughness of the workpiece processed under each working condition is measured using an SJ-210 surface roughness tester. The surface roughness of the same workpiece is measured three times and the average value is taken.

[0025] At the same time, the collected chips are edge identified to obtain the contour edge characteristic curve of each chip; the contour edge characteristic curve is as follows: Figure 2 The different broaching environments are dry cutting, cold air and oil-lubricated processing environments.

[0026] Step 2: Take the inner endpoint of the contour edge characteristic curve as the starting point and extend it downward to form its realization starting line; rotate the starting line around the starting point until the starting line passes through the outer endpoint of the contour edge characteristic curve or the rotation angle reaches 270°; leave a segmented line at each position where the starting line rotates 30°, forming multiple segmented lines with an angle of 30° in sequence. The intersection of each segmented line and the contour edge characteristic curve is used as a feature point; extract the distance L between each feature point and the starting point and the curve length S (that is, the length of the contour edge characteristic curve between the two points). Each segmented line is as follows Figure 2 shown.

[0027] Step 3: Use Origin2019b software to perform quadratic function fitting on the measured distance L and the curve length S to obtain the characteristic equation L = a·S 2 +bS+c; where a is the coefficient of the quadratic term of the fitting function, b is the coefficient of the linear term of the fitting function, and c is the constant term of the fitting function. The fitting curve is drawn with the chip curve length S as the horizontal axis and the distance L as the vertical axis. Figure 3 shown.

[0028] According to the characteristic parameters a, b, and c in the characteristic equation obtained by fitting, the characteristic height h is calculated as follows:

[0029]

[0030] The characteristic heights calculated under the three different machining environments are h1, h2, and h3, and the surface roughness of the workpiece surfaces are Ra1, Ra2, and Ra3, respectively. Multiple tests were repeated for each of the three different machining environments.

[0031] Step 4: Fit the chip characteristic height h produced by three different processing environments with the surface roughness Ra of the workpiece processing surface through a linear equation, and obtain the relationship function between the surface roughness Ra and the chip characteristic height h as follows:

[0032] Ra=k·h+t

[0033] Among them, k and t are two fitting coefficients in the relationship function.

[0034] Step 5: During the actual machining process, chips are collected and their corresponding characteristic chip height h is extracted. This characteristic chip height h is substituted into the relationship function obtained in Step 4 to calculate the workpiece surface roughness Ra. This allows for real-time monitoring of the surface roughness of the broached workpiece without stopping the machine. If abnormal surface roughness is detected, the operator can promptly change the tool or adjust the machining parameters. The parameters of the workpiece and broach used in the actual machining process are consistent with those used in the broaching test to ensure the validity of the relationship function.

[0035] The accuracy of the present invention is described below through specific test data:

[0036] First, the chips generated in the first experiment under air cooling, dry cutting, and oil cutting environments were collected, characteristic curves were extracted, characteristic points were extracted, and the distance L and curve length S corresponding to each characteristic point were measured (as shown in Table 1).

[0037] Table 1

[0038]

[0039] The characteristic curve fitting and characteristic curve height h are calculated for the existing experimental data, and the roughness of the machined workpiece in each experiment is measured (as shown in Table 2).

[0040] Table 2

[0041] Experiment No. Air cooling first test First dry cooling experiment Oil cooling first experiment constant term 0.0145 0.31069 0.0893 Linear coefficient 0.97811 0.35631 0.87162 Quadratic term coefficient -0.31944 -0.12816 -0.2446 Feature Height 0.763232 0.558342965 0.865793688 Actual roughness 1.581 1.126 1.267

[0042] By linearly fitting the characteristic height h obtained from the above three environments with the actual roughness Ra, the roughness fitting relationship function is simulated:

[0043] Ra=0.70988*h+0.80707

[0044] A second experiment with the same machining parameters was conducted under air cooling, dry cooling, and oil cooling environments. By collecting cutting data and extracting characteristic curves, the distance L and curve length S corresponding to each characteristic point were measured and summarized as follows (as shown in Table 3).

[0045] Table 3

[0046]

[0047] By fitting the values ​​of each characteristic point of the second set of repeated experiments, the predicted roughness can be calculated by substituting it into the fitting relationship function, and the deviation rate can be calculated by comparing it with the actual roughness (as shown in Table 4). The three curves obtained are as follows: Figure 3 shown.

[0048] Table 4

[0049] Experiment No. Second air cooling test Second dry cooling experiment Oil cooling second experiment Constant term coefficient 0.1086 0.2341 0.0587 Linear coefficient 0.7292 0.4794 0.8880 Quadratic term coefficient -0.2396 -0.1653 -0.2542 Characteristic curve height 0.6634 0.5817 0.8343 Actual roughness 1.6620 1.5570 1.1850 Calculating roughness 1.2780 1.2200 1.3993 Deviation rate 23.106％ 21.644％ 18.085％

[0050] The surface roughness of the machined surface was evaluated by using the characteristic height of the chips obtained in subsequent experiments and compared with the surface roughness actually measured after machining. The results are shown in Table 4. The actual data table shows that the error rate is between 18% and 23%, indicating that this method can play a role in roughly estimating the machining quality in the actual machining process.

Claims

1. A method for evaluating the quality of broached surfaces based on chip curvature measurement, characterized by: The following steps are involved: Step 1: Collect chips during the broaching process and perform edge extraction to obtain the edge characteristic curve of the chips; Step 2: extract multiple feature points on the edge feature curve obtained in step 1; Step 3: Measure the distance L from each feature point to the starting point of the edge feature curve and the curve length S; Step 4: Perform quadratic function fitting on the distance L and curve length S corresponding to each characteristic point measured to obtain a chip morphology characteristic curve; Step 5: Take the distance corresponding to the highest point of the chip morphology characteristic curve as the characteristic height h ; Set the feature height h Substituting the pre-fitted surface roughness-characteristic height relationship function, the surface roughness of the broaching surface when the chip is generated is obtained; The surface roughness-feature height relationship function is a linear equation, and its acquisition process is: broaching the same workpiece under a variety of different machining environments, collecting chips respectively, and measuring the surface roughness of the machined surface. Ra ; Extract feature height using chips h ; The surface roughness obtained Ra With feature height h The surface roughness-feature height relationship function is obtained by fitting.

2. The broaching surface quality assessment method based on chip curvature measurement according to claim 1, characterized in that: In step 2, the edge feature curve is obtained by dividing it into equal-angle segments with the starting point as the center.

3. The broaching surface quality assessment method based on chip curvature measurement according to claim 1, characterized in that: By collecting chips and calculating the surface roughness, the surface roughness of the broached surface is monitored without stopping the broaching operation. When the surface roughness of the machined surface becomes abnormal, the tool is changed or the machining parameters are adjusted.

4. The method for evaluating broaching surface quality based on chip curvature measurement according to claim 1, wherein: In step 2, the specific process of extracting feature points is as follows: through the starting point of the edge feature curve, extend a starting line tangent to the edge feature curve, and with the starting point as the center of the circle, rotate the starting line along the cutting direction. θ Angle, take the intersection of the starting line and the edge characteristic curve as a characteristic point, until the starting line rotates to the end point of the edge characteristic curve or the rotation angle reaches 270°; where 15°≤ θ ≤45°.

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