Multi-station special-shaped part cold forging process

Abstract

The invention relates to a multi-station special-shaped part cold forging process, which belongs to the technical field of cold forging processes, and comprises the following steps of: 1, setting a plurality of stations with pressure monitoring modules; 2, setting a pressure threshold range for a plurality of stations; 3, pressure monitoring is conducted on the pressure of a plurality of stations in the machining process, when the monitoring value of a certain station exceeds a threshold value, the station is marked as an abnormal station, the step 4 is executed, and otherwise, the step 5 is executed; 4, reducing the pressure threshold range of the previous station of the abnormal station, improving the pressure judgment standard of the previous station of the abnormal station, and returning to the step 3; and 5, returning to the step 3.

Description

Technical Field

[0001] The invention belongs to the technical field of cold forging technology, and in particular relates to a multi-station special-shaped part cold forging technology. Background Art

[0002] Cold forging, also known as cold bulk forming, is a manufacturing process and processing method that mainly applies external force to cold-formed parts such as metal to form or deform the parts. A cold forging machine is a machine that realizes cold forging.

[0003] For cold forging, a multi-station form is generally adopted. The multi-station adopts a method similar to an assembly line. Each station adopts a process. The parts to be processed pass through several stations in sequence. After being processed by several stations, the process is completed. A general solution, such as a fully automatic multi-station cold forging machine disclosed in Chinese patent CN107584061B, includes cold forging heads for each process. The cold forging heads are arranged at equal intervals on the periphery of the whole machine base in sequence. A turntable is provided at the center of the whole machine base. Each of the cold forging heads is provided with a corresponding workpiece fixture, and the workpiece fixtures are distributed at equal intervals on the turntable; a loading mechanism is provided on the whole machine base, and the loading mechanism and the cold forging heads are arranged at equal intervals on the periphery of the whole machine base, and are also provided with corresponding workpiece fixtures; a unloading mechanism is provided below the loading mechanism. The above-mentioned multi-station cold forging machine is used for the production of eyeglass temples. The finished product can be obtained by simply putting the raw materials into the hopper as required, which can greatly improve efficiency and save labor costs.

[0004] However, in the process of multi-station special-shaped parts processing, it is sometimes necessary to process special-shaped parts. Since special-shaped parts have a higher diversity in shape and volume than traditional parts, a variety of processing methods are required. Therefore, for multi-station processing of special-shaped parts, the processing methods between each station are quite different, which poses a challenge to the monitoring of the processing process. For example, the previous station needs to perform high-pressure hammer forging on the workpiece, while the next station is to perform milling on the surface of the workpiece. The pressure or vibration amplitude and size generated by the two are different. If the pressure monitoring system of each station adopts the same set of judgment standards, there is a probability that the pressure monitoring system will not be able to monitor accurately. For this reason, a multi-station special-shaped parts cold forging process with accurate pressure monitoring is needed for multi-station cold forging of special-shaped parts. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a multi-station cold forging process for special-shaped parts, which has the characteristics of multi-station cold forging for special-shaped parts and accurate pressure monitoring.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A multi-station special-shaped part cold forging process includes the following steps: Step 1: Set up several workstations with pressure monitoring modules; Step 2: Set pressure threshold ranges for several workstations; Step 3: During the processing, the pressure of several workstations is monitored. When the monitoring value of a workstation exceeds the threshold, this workstation is marked as an abnormal workstation and step 4 is executed. Otherwise, step 5 is executed. Step 4: Narrow the pressure threshold range of the previous workstation of the abnormal workstation, increase the pressure judgment standard of the previous workstation of the abnormal workstation, and return to step 3; Step 5: Return to step 3.

[0007] As a preferred technical solution of the present invention, the step two also includes: setting a pressure magnitude threshold range, a pressure change rate threshold range, a pressure curve shape threshold range and a pressure variance threshold range for several workstations; the step three also includes: monitoring the pressure magnitude, change rate, pressure curve shape and pressure variance of several workstations. When one of the monitoring values ​​of a certain workstation exceeds the threshold, the workstation is recorded as an abnormal workstation, and the monitoring value exceeding the threshold is recorded as an abnormal parameter, and step four is executed, otherwise step five is executed; the step four also includes: narrowing the threshold range of the parameter corresponding to the abnormal parameter of the previous workstation of the abnormal workstation, and returning to step three.

[0008] As a preferred technical solution of the present invention, the step three also includes: after monitoring the pressure Fn of each station, storing the pressure values ​​in chronological order, drawing a curve Fn(t) showing the pressure value changing with time, and calculating the shape of the pressure curve. , calculate the pressure change rate dFn(t) / dt, and calculate the variance C using the latest uploaded pressure values ​​in reverse chronological order.

[0009] As a preferred technical solution of the present invention, step four also includes: adjusting the pressure threshold range of the previous workstation of the abnormal workstation to the original Fn0 / Fn times, adjusting the change rate threshold range to the original B0 / (dFn(t) / dt) times, adjusting the pressure curve shape threshold range to the original X0 / X times, and adjusting the variance threshold range to the original C0 / C times, wherein Fn0 is the current workstation pressure reference value, B0 is the current workstation change rate reference value, X0 is the current workstation pressure standard curve area, and C0 is the current workstation variance reference value.

[0010] As a preferred technical solution of the present invention, step three also includes: after monitoring the pressure size, change rate, pressure curve shape and pressure variance of several workstations, using weighted average to calculate the comprehensive monitoring parameter Z, and judging whether the comprehensive monitoring parameter exceeds the threshold, if so, issuing an alarm.

[0011] As a preferred technical solution of the present invention, the step three further includes: calculating the comprehensive monitoring parameter Z, where .

[0012] As a preferred technical solution of the present invention, step three also includes: the control module determines whether the long-term change trend values ​​of several parameters in the past exceed the threshold value. If the judgment result is yes, the values ​​of k1 and k3 are increased, otherwise the values ​​of k2 and k4 are increased.

[0013] The beneficial effects of the present invention are: (1) When a pressure anomaly is detected at a certain workstation, and there is a certain probability that the pressure threshold of the previous process is too loose, resulting in failure to complete the processing, the workstation is recorded as an abnormal workstation, and the pressure threshold range of the previous workstation of the abnormal workstation is narrowed, thereby improving the judgment standard of the previous workstation for pressure anomalies, completing the dynamic adjustment of pressure monitoring during the production process, avoiding the situation where the initially set threshold range cannot fully cover the actual needs when processing special-shaped parts, and improving the accuracy of pressure monitoring; (2) By separately monitoring the pressure magnitude, rate of change, pressure curve shape, and pressure variance, we can monitor pressure parameters in multiple dimensions to address the diverse sources of abnormalities in the processing of special-shaped parts. This allows us to more accurately capture the source of pressure anomalies and, in turn, the type of processing anomalies, further improving the accuracy of pressure monitoring. (3) By using weighted average to calculate the comprehensive monitoring parameter Z, we can avoid the situation where several parameters are close to the threshold range boundary, which is dangerous but cannot trigger an alarm; By increasing the values ​​of k1 and k3 when the long-term change trend values ​​of several parameters exceed the threshold, and conversely increasing the values ​​of k2 and k4, we can achieve the goal of increasing the weight of parameters that are more affected by long-term data changes, such as machine wear, in the calculation of comprehensive monitoring parameters when such situations occur. When there is no long-term data change and the impact of random errors is greater, we can increase the weight of parameters that are more affected by random errors in the calculation of comprehensive monitoring parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0015] Figure 1 This is a control loop block diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0017] See also Figure 1, a multi-station special-shaped part cold forging process, comprising the following steps: Step 1: Set up several workstations with pressure monitoring modules; Step 2: Set pressure threshold ranges for several workstations; Step 3: During the processing, the pressure of several workstations is monitored. When the monitoring value of a workstation exceeds the threshold, this workstation is marked as an abnormal workstation and step 4 is executed. Otherwise, step 5 is executed. Step 4: Narrow the pressure threshold range of the previous workstation of the abnormal workstation, increase the pressure judgment standard of the previous workstation of the abnormal workstation, and return to step 3; Step 5: Return to step 3; Specifically, several workstations are set up according to the needs of special-shaped parts processing. The pressure monitoring module set at each workstation is used to monitor the reaction force it receives when processing special-shaped parts, and then monitor the pressure on the parts during construction; When processing at multiple stations, there is a certain probability that the processing at the previous station is not in place, resulting in an abnormal shape of the workpiece. When the processing equipment at this station acts on the workpiece with an abnormal shape, the shape change makes it impossible for the processing equipment to act on the workpiece according to the predetermined plan, resulting in abnormal pressure. Therefore, when a certain station detects a pressure abnormality, there is a certain probability that the previous process has not been processed in place. If pressure monitoring modules are installed at all stations, the failure to process in place is likely due to the pressure threshold being too loose, resulting in the pressure monitoring module not being able to function normally. Therefore, by detecting a pressure abnormality at a certain workstation, and there is a certain probability that the pressure threshold of the previous process is too loose, resulting in failure to complete the processing, this workstation will be recorded as an abnormal workstation, and the pressure threshold range of the previous workstation of the abnormal workstation will be narrowed, and the judgment standard of the previous workstation for pressure abnormalities will be improved. Dynamic adjustment of pressure monitoring in the production process will be completed to avoid the situation where the initially set threshold range cannot fully cover the actual needs when processing special-shaped parts, thereby improving the accuracy of pressure monitoring.

[0018] During the specific test process, data in different dimensions present different problems. For example, when the pressure change rate is too high, there is a probability that the hardness of this batch of materials is too large. When the pressure variance is too large, that is, the pressure fluctuation is too large, there is a probability that the fixture of this workstation is loose. Therefore, it is necessary to analyze and judge the pressure data from multiple angles. To this end, step two also includes: setting a pressure magnitude threshold range, a pressure change rate threshold range, a pressure curve shape threshold range, and a pressure variance threshold range for several workstations; step three also includes: monitoring the pressure magnitude, change rate, pressure curve shape, and pressure variance of several workstations. When one of the monitoring values ​​of a workstation exceeds the threshold, this workstation is recorded as an abnormal workstation, and the monitoring value exceeding the threshold is recorded as an abnormal parameter, and step four is executed, otherwise step five is executed; step four also includes: narrowing the threshold range of the parameter corresponding to the abnormal parameter of the previous workstation of the abnormal workstation, and returning to step three.

[0019] By separately monitoring the pressure magnitude, rate of change, pressure curve shape, and pressure variance, we can address the diverse sources of abnormalities in the processing of special-shaped parts by monitoring pressure parameters in multiple dimensions. This allows us to more accurately capture the source of pressure anomalies and, in turn, the type of processing anomalies, further improving the accuracy of pressure monitoring. Specifically, step three also includes: after monitoring the pressure Fn of each station, storing the pressure values ​​in chronological order, drawing a curve Fn(t) showing the pressure value changing with time, and calculating the integral of the pressure over time. , calculate the pressure change rate dFn(t) / dt, and calculate the variance C using the most recently uploaded pressure values ​​in reverse chronological order. t0 is a pre-entered constant whose size is determined by the operator. t0 represents the cumulative length of the integral over time t when calculating the integral X. In this embodiment, the variance C is calculated using the J most recently uploaded pressure values ​​in reverse chronological order, where the value of J is predetermined by the operator; When determining whether the pressure curve shape exceeds the pressure curve shape threshold range, since X can represent the size of the area enclosed by the pressure curve and the horizontal axis, the determination is made based on the area size; By using integration to calculate the accumulation of pressure over a period of time, the impact of random fluctuations in pressure data on the output value can be reduced; Specifically, for the process of marking this workstation as an abnormal workstation in step 4 and narrowing the pressure threshold range of the previous workstation of the abnormal workstation, the pressure threshold range of the previous workstation of the abnormal workstation is adjusted to Fn0 / Fn times the original, the change rate threshold range is adjusted to B0 / (dFn(t) / dt) times the original, the pressure curve shape threshold range is adjusted to X0 / X times the original, and the variance threshold range is adjusted to C0 / C times the original, where Fn0 is the current workstation pressure reference value, B0 is the current workstation change rate reference value, X0 is the current workstation pressure standard curve area, and C0 is the current workstation variance reference value; For example, if the original rate-of-change threshold range is [A, B], then the adjusted rate-of-change threshold range is [A×(1+0.5×B0 / (dFn(t) / dt)), B×(1-0.5×B0 / (dFn(t) / dt))]. As the values ​​of A and B approach each other, the rate-of-change threshold range decreases, and the reduction ratio is B0 / (dFn(t) / dt). The complete rate-of-change threshold range is reduced to B0 / (dFn(t) / dt) times the original value. In some cases, there are several parameters that are close to the threshold range boundary. At this time, there is a high probability that an abnormal situation will occur. However, since it does not exceed the threshold range boundary, the alarm cannot be triggered, and thus no response to the abnormal situation can be made. To avoid such situations, step three also includes: after monitoring the pressure size, change rate, pressure curve shape and pressure variance of several workstations, the weighted average is used to calculate the comprehensive monitoring parameter Z to determine whether the comprehensive monitoring parameter exceeds the threshold. If so, an alarm is issued.

[0020] Specifically, for the process of calculating the comprehensive monitoring parameter Z using weighted average, ; Among them, k1, k2, k3 and k4 are weights, which are pre-entered into the control module by the operator based on the calculation results; By using weighted average to calculate the comprehensive monitoring parameter Z, we can avoid the situation where several parameters are close to the threshold range boundary, causing abnormalities but unable to trigger alarms and take measures.

[0021] There are many factors that lead to inability to process in place, such as long-term wear of the machine or random mechanical errors in a short period of time. When the machine suffers from long-term wear, it is often reflected in the pressure value and the shape of the pressure curve. Therefore, when the long-term change trend value of several parameters exceeds the threshold, it is necessary to increase the weight of the pressure value and the shape of the pressure curve that reflect the long-term change in the calculation. On the contrary, when the machine does not suffer from long-term wear, random mechanical errors in a short period of time are the main factor causing inability to process in place. The impact of random mechanical errors on pressure is often reflected in the rate of change and variance. At this time, it is necessary to increase the weight of the rate of change and variance that reflect the random error in the calculation. To this end, step three also includes: the control module determines whether the long-term change trend values ​​of the past parameters exceed the threshold value. If the judgment result is yes, the control module increases the values ​​of k1 and k3, otherwise increases the values ​​of k2 and k4; Specifically, when calculating the long-term change trend value, the control module calculates the difference between the current parameters and the parameters uploaded K times ago in reverse chronological order, and then calculates the ratio of the difference to the parameter to obtain the ratio value. The ratio value of the parameters is recorded as the long-term change trend value; This solution monitors pressure magnitude, rate of change, pressure curve shape, and pressure variance, a total of four parameters. Taking pressure magnitude as an example, the current pressure is Fn. The pressure value uploaded K times ago in reverse chronological order is 0.95Fn, and the difference is 0.05Fn. The difference accounts for 0.05 of this parameter, or 5%. Assuming that the difference in rate of change, pressure curve shape, and pressure variance accounts for 3%, 2%, and 6% of this parameter, respectively, the long-term trend value is (5%+3%+2%+6%) / 4=4%. By increasing the values ​​of k1 and k3 when the long-term change trend values ​​of several parameters exceed the threshold, and conversely increasing the values ​​of k2 and k4, we can achieve the goal of increasing the weight of parameters that are more affected by long-term data changes, such as machine wear, in the calculation of comprehensive monitoring parameters when such situations occur. When there is no long-term data change and the impact of random errors is greater, we can increase the weight of parameters that are more affected by random errors in the calculation of comprehensive monitoring parameters.

[0022] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A multi-station special-shaped part cold forging process, characterized by: The following steps are involved: Step 1: Set up several workstations with pressure monitoring modules; Step 2: Set pressure threshold ranges for several workstations; Step 3: During the processing, the pressure of several workstations is monitored. When the monitoring value of a workstation exceeds the threshold, this workstation is marked as an abnormal workstation and step 4 is executed. Otherwise, step 5 is executed. Step 4: Narrow the pressure threshold range of the previous workstation of the abnormal workstation, increase the pressure judgment standard of the previous workstation of the abnormal workstation, and return to step 3; Step 5: Return to step 3.

2. The multi-station special-shaped part cold forging process according to claim 1, characterized in that: The step two also includes: setting a pressure magnitude threshold range, a pressure change rate threshold range, a pressure curve shape threshold range and a pressure variance threshold range for several workstations; the step three also includes: monitoring the pressure magnitude, change rate, pressure curve shape and pressure variance of several workstations, and when one of the monitoring values ​​of a certain workstation exceeds the threshold, the workstation is recorded as an abnormal workstation, and the monitoring value exceeding the threshold is recorded as an abnormal parameter, and step four is executed, otherwise step five is executed; the step four also includes: narrowing the threshold range of the parameter corresponding to the abnormal parameter of the previous workstation of the abnormal workstation, and returning to step three.

3. The multi-station special-shaped part cold forging process according to claim 2, characterized in that: The step 3 also includes: after monitoring the pressure Fn of each station, storing the pressure values ​​in chronological order, drawing a curve Fn(t) showing the pressure value changing with time, and calculating the shape of the pressure curve , calculate the pressure change rate dFn(t) / dt, calculate the mean value based on the pressure value uploaded for each processing, and then calculate the variance C based on the latest several mean values.

4. The multi-station cold forging process for special-shaped parts according to claim 3, characterized in that: The step four also includes: adjusting the pressure threshold range of the previous workstation of the abnormal workstation to Fn0 / Fn times the original, adjusting the change rate threshold range to B0 / (dFn(t) / dt) times the original, adjusting the pressure curve shape threshold range to X0 / X times the original, and adjusting the variance threshold range to C0 / C times the original, wherein Fn0 is the pressure reference value of the current workstation, B0 is the change rate reference value of the current workstation, X0 is the area of ​​the standard curve of the pressure of the current workstation, and C0 is the variance reference value of the current workstation.

5. The multi-station special-shaped part cold forging process according to claim 4, characterized in that: The step three also includes: after monitoring the pressure size, change rate, pressure curve shape and pressure variance of several workstations, using weighted average to calculate the comprehensive monitoring parameter Z, and judging whether the comprehensive monitoring parameter exceeds the threshold, if so, issuing an alarm.

6. The multi-station cold forging process for special-shaped parts according to claim 5, characterized in that: The step three also includes: calculating the comprehensive monitoring parameter Z, where .

7. The multi-station special-shaped part cold forging process according to claim 6, characterized in that: The step three also includes: the control module determines whether the long-term change trend values ​​of several parameters in the past exceed the threshold value. If the judgment result is yes, the values ​​of k1 and k3 are increased, otherwise the values ​​of k2 and k4 are increased.

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