A multi-station cold forging process for irregularly shaped parts

Abstract

This invention relates to a multi-station cold forging process for irregularly shaped parts, belonging to the field of cold forging technology, and includes the following steps: Step 1: Set up several stations equipped with pressure monitoring modules; Step 2: Set pressure threshold ranges for several stations; Step 3: Monitor the pressure of several stations during processing. When the monitoring value of a station exceeds the threshold, this station is recorded as an abnormal station, and Step 4 is executed; otherwise, Step 5 is executed; Step 4: Narrow the pressure threshold range of the station preceding the abnormal station, increase the pressure judgment standard of the station preceding the abnormal station, and return to Step 3; Step 5: Return to Step 3.

Description

Technical Field

[0001] This invention belongs to the field of cold forging technology, specifically relating to a multi-station cold forging process for irregularly shaped parts. Background Technology

[0002] Cold forging, also known as cold volume forming, is a manufacturing process and processing method that mainly involves applying external force to cold-formed parts such as metals to shape or deform the parts. A cold forging machine is the machine that performs cold forging.

[0003] For cold forging, a multi-station configuration is generally adopted, similar to an assembly line. Each station performs a specific process, and the part to be processed sequentially passes through several stations, being processed by each station to complete the process. A typical solution, such as the fully automatic multi-station cold forging machine disclosed in Chinese Patent CN107584061B, includes cold forging heads for each process. These cold forging heads are arranged sequentially and at equal intervals around the machine base. A rotary table is located at the center of the machine base. Each cold forging head is equipped with a corresponding workpiece clamp, which is evenly distributed around the rotary table. A feeding mechanism is provided on the machine base, and this feeding mechanism, along with the cold forging heads, is also evenly spaced around the machine base and is also equipped with corresponding workpiece clamps. A discharging mechanism is located below the feeding mechanism. This multi-station cold forging machine is used for the production of eyeglass temple threads. Simply placing the raw materials into the hopper according to regulations yields the finished product, significantly improving efficiency and saving labor costs.

[0004] However, in the multi-station machining of irregularly shaped parts, it is sometimes necessary to process these parts. Compared with traditional parts, irregularly shaped parts have a higher diversity in shape and volume, requiring the use of multiple processing methods. Therefore, for multi-station machining of irregularly shaped parts, the processing methods between each station are quite different, which poses a challenge to the monitoring of the machining process. For example, the previous station needs to perform high-pressure hammer forging on the workpiece, while the next station performs milling on the surface of the workpiece. The pressure or vibration amplitude and magnitude generated by the two are different. If the pressure monitoring system of each station uses the same set of judgment criteria, there is a possibility that the pressure monitoring system will not be able to accurately monitor the pressure. Therefore, a multi-station cold forging process for irregularly shaped parts with accurate pressure monitoring is needed. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a multi-station cold forging process for irregularly shaped parts, which features accurate pressure monitoring for multi-station cold forging of irregularly shaped parts.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A multi-station cold forging process for irregularly shaped parts includes the following steps:

[0008] Step 1: Set up several workstations equipped with pressure monitoring modules;

[0009] Step 2: Set pressure threshold ranges for several workstations;

[0010] Step 3: During the processing, pressure is monitored at several workstations. When the monitored value at a certain workstation exceeds the threshold, this workstation is marked as an abnormal workstation and Step 4 is executed; otherwise, Step 5 is executed.

[0011] Step 4: Narrow the pressure threshold range of the previous workstation of the abnormal workstation, raise the pressure judgment standard of the previous workstation of the abnormal workstation, and return to Step 3.

[0012] Step 5: Return to Step 3.

[0013] As a preferred embodiment of the present invention, step two further includes: setting threshold ranges for pressure magnitude, pressure change rate, pressure curve shape, and pressure variance for several workstations; step three further includes: monitoring the pressure magnitude, change rate, pressure curve shape, and pressure variance for several workstations; when one of the monitored values ​​for a certain workstation exceeds the threshold, this workstation is recorded as an abnormal workstation, and the monitored value exceeding the threshold is recorded as an abnormal parameter, and step four is executed; otherwise, step five is executed; step four further 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.

[0014] As a preferred embodiment of the present invention, step three further includes: after monitoring the pressure Fn at each workstation, storing the pressure values ​​in chronological order, plotting the pressure value as a function of time curve Fn(t), 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.

[0015] As a preferred embodiment of the present invention, step four further includes: adjusting the pressure threshold range of the previous station of the abnormal station to the original Fn0 / Fn times, adjusting the rate of change 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 pressure reference value of the current station, B0 is the rate of change reference value of the current station, X0 is the area of ​​the pressure standard curve of the current station, and C0 is the variance reference value of the current station.

[0016] As a preferred technical solution of the present invention, step three further includes: after monitoring the pressure magnitude, rate of change, pressure curve shape and pressure variance of several workstations, calculating the comprehensive monitoring parameter Z using a weighted average, determining whether the comprehensive monitoring parameter exceeds the threshold, and issuing an alarm if so.

[0017] As a preferred embodiment of the present invention, step three further includes: calculating the comprehensive monitoring parameter Z, wherein... .

[0018] As a preferred technical solution of the present invention, step three further includes: the control module determines whether the long-term change trend value of several parameters exceeds the threshold. If the determination result is yes, the values ​​of k1 and k3 are increased; otherwise, the values ​​of k2 and k4 are increased.

[0019] The beneficial effects of this invention are as follows:

[0020] (1) When a pressure abnormality is detected at a certain workstation, and there is a certain probability that the pressure threshold judgment of the previous process is too lenient, resulting in the failure to process to the required level, this workstation is recorded as an abnormal workstation, and the pressure threshold range of the previous workstation is narrowed, thereby improving the judgment standard of the previous workstation for pressure abnormality. This completes the dynamic adjustment of pressure monitoring during the production process, avoids the situation where the initially set threshold range cannot fully cover the actual needs when processing irregular parts, and improves the accuracy of pressure monitoring.

[0021] (2) By monitoring the pressure magnitude, rate of change, shape of the pressure curve and pressure variance respectively, pressure parameters of multiple dimensions are monitored to address the problem of diverse sources of abnormality in the processing of irregular parts. This allows for more accurate capture of the sources of pressure abnormality and, consequently, more accurate capture of the types of processing abnormality, thereby further improving the accuracy of pressure monitoring.

[0022] (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 quite dangerous but cannot trigger the alarm.

[0023] By increasing the values ​​of k1 and k3 when the long-term trend values ​​of several parameters exceed the threshold, and conversely increasing the values ​​of k2 and k4 when the long-term trend values ​​exceed the threshold, the weight of parameters more affected by such situations in the calculation of comprehensive monitoring parameters is increased. This is achieved when situations that lead to long-term data changes occur, such as machine wear, and the weight of parameters more affected by random errors is increased in the calculation of comprehensive monitoring parameters when no long-term data changes occur and the impact of random errors is greater. Attached Figure Description

[0024] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0025] Figure 1This is a block diagram of the control loop of the present invention. Detailed Implementation

[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0027] Please see Figure 1 A multi-station cold forging process for irregularly shaped parts includes the following steps:

[0028] Step 1: Set up several workstations equipped with pressure monitoring modules;

[0029] Step 2: Set pressure threshold ranges for several workstations;

[0030] Step 3: During the processing, pressure is monitored at several workstations. When the monitored value at a certain workstation exceeds the threshold, this workstation is marked as an abnormal workstation and Step 4 is executed; otherwise, Step 5 is executed.

[0031] Step 4: Narrow the pressure threshold range of the previous workstation of the abnormal workstation, raise the pressure judgment standard of the previous workstation of the abnormal workstation, and return to Step 3.

[0032] Step 5: Return to Step 3;

[0033] Specifically, several workstations are set up according to the needs of processing irregular parts. Each workstation is equipped with a pressure monitoring module to monitor the reaction force it receives when processing irregular parts, and thus monitor the pressure on the parts during construction.

[0034] When processing at multiple workstations, there is a certain probability that the previous workstation may not have completed the processing, resulting in an abnormal shape of the workpiece. When the processing equipment at this workstation acts on the abnormally shaped workpiece, the shape change prevents the equipment from acting on the workpiece according to the predetermined plan, resulting in abnormal pressure. Therefore, when an abnormal pressure is detected at a certain workstation, there is a certain probability that the previous process has not completed the processing. When pressure monitoring modules are set up at all workstations, the failure to complete the processing may be due to the pressure threshold judgment being too lenient, causing the pressure monitoring module to malfunction.

[0035] Therefore, when an abnormal pressure is detected at a certain workstation, and there is a certain probability that the failure to complete the processing is due to an overly lenient pressure threshold judgment in the previous process, this workstation is marked as an abnormal workstation. The pressure threshold range of the workstation preceding the abnormal workstation is then narrowed, thereby improving the judgment standard for the previous workstation regarding abnormal pressure. This allows for dynamic adjustment of pressure monitoring during the production process, avoiding situations where the initially set threshold range cannot fully cover the actual needs when processing irregularly shaped parts, and improving the accuracy of pressure monitoring.

[0036] In the specific testing process, the data from different dimensions presents different problems. For example, when the pressure change rate is too high, it may indicate that the material in this batch is too hard. When the pressure variance is too large, that is, the pressure fluctuation is too large, it may indicate that the fixture at this station is loose. Therefore, it is necessary to analyze and judge the pressure data from multiple perspectives. To this end, step two also includes: setting threshold ranges for pressure magnitude, pressure change rate, pressure curve shape, and pressure variance for several stations. Step three also includes: monitoring the pressure magnitude, change rate, pressure curve shape, and pressure variance of several stations. When one of the monitored values ​​of a certain station exceeds the threshold, this station is recorded as an abnormal station, and the monitored value exceeding the threshold is recorded as an abnormal parameter. Step four is then executed; otherwise, step five is executed. Step four also includes: narrowing the threshold range of the abnormal parameter corresponding to the abnormal parameter of the previous station and returning to step three.

[0037] By monitoring pressure magnitude, rate of change, pressure curve shape, and pressure variance respectively, this study addresses the diverse sources of abnormalities in the processing of irregularly shaped parts by monitoring pressure parameters across multiple dimensions. This allows for more precise identification of the sources of pressure anomalies and, consequently, more accurate identification of the types of processing anomalies, thereby further improving the accuracy of pressure monitoring.

[0038] Specifically, step three also includes: after monitoring the pressure Fn at each workstation, storing the pressure values ​​in chronological order, plotting the pressure value as a function of time (Fn(t)), and calculating the integral of the pressure over time. Calculate the pressure change rate dFn(t) / dt, and calculate the variance C using the latest uploaded pressure values ​​in reverse time order. Here, t0 is a pre-input constant, the size of which is determined by the operator. t0 represents the cumulative length of the integral over time t when calculating the integral X.

[0039] In this embodiment, the variance C is calculated using the J most recently uploaded pressure values ​​in reverse chronological order, and the value of J is predetermined by the operator.

[0040] When determining whether the shape of the pressure curve exceeds the threshold range of the pressure curve shape, since X can represent the size of the area enclosed by the pressure curve and the horizontal axis, the judgment is made by the size of the area.

[0041] By using integral calculations to calculate the cumulative stress over a period of time, the impact of random fluctuations in stress data on the output value can be reduced.

[0042] Specifically, in step four, the process of marking this workstation as an abnormal workstation and narrowing the pressure threshold range of the previous workstation is as follows: the pressure threshold range of the previous workstation is adjusted to Fn0 / Fn times the original value, the rate of change threshold range is adjusted to B0 / (dFn(t) / dt) times the original value, the pressure curve shape threshold range is adjusted to X0 / X times the original value, and the variance threshold range is adjusted to C0 / C times the original value. Here, Fn0 is the current workstation pressure reference value, B0 is the current workstation rate of change reference value, X0 is the area of ​​the current workstation pressure standard curve, and C0 is the current workstation variance reference value.

[0043] For example, if the original change rate threshold range is [A, B], then the adjusted change rate threshold range is [A×(1+0.5×B0 / (dFn(t) / dt)), B×(1-0.5×B0 / (dFn(t) / dt))]. The values ​​of A and B are closer to each other, the change rate threshold range is reduced, and the reduction factor is B0 / (dFn(t) / dt), thus reducing the change rate threshold range to B0 / (dFn(t) / dt) times the original value.

[0044] In some cases, several parameters are close to the threshold range boundary. At this time, there is a high probability of an abnormal situation. However, since the threshold range boundary has not been exceeded, the alarm cannot be triggered, and thus the abnormal situation cannot be responded to. To avoid such a situation, step three also includes: after monitoring the pressure magnitude, rate of change, pressure curve shape and pressure variance of several workstations, the comprehensive monitoring parameter Z is calculated using a weighted average. It is then determined whether the comprehensive monitoring parameter exceeds the threshold. If it does, an alarm is issued.

[0045] Specifically, regarding the process of calculating the comprehensive monitoring parameter Z using a weighted average, ;

[0046] Among them, k1, k2, k3 and k4 are weights, which are pre-input into the control module by the operators based on the calculation results;

[0047] By using a weighted average to calculate the comprehensive monitoring parameter Z, we can avoid situations where several parameters are close to the threshold range boundary, causing anomalies but failing to trigger alarms and take measures.

[0048] There are many factors that can cause the machine to fail to complete the processing, such as long-term wear and tear of the machine or random mechanical errors in a short period of time. When the machine experiences long-term wear and tear, it is often reflected in the pressure value and the shape of the pressure curve. Therefore, when the long-term trend 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 long-term changes in the calculation. Conversely, when the machine does not experience long-term wear and tear, random mechanical errors in a short period of time are the main factor causing the machine to fail to complete the processing. The impact of random mechanical errors on pressure is often reflected in the rate of change and variance. In this case, it is necessary to increase the weight of the rate of change and variance that reflect random errors in the calculation.

[0049] Therefore, step three also includes: the control module determines whether the long-term trend values ​​of several parameters exceed the threshold. If the determination result is yes, the control module increases the values ​​of k1 and k3; otherwise, it increases the values ​​of k2 and k4.

[0050] Specifically, when calculating the long-term trend value, the control module calculates the difference between the current parameters and the parameters uploaded K times ago in reverse chronological order. Then, it calculates the proportion of the difference to the parameter and obtains the proportion value. The proportion value of the parameters is recorded as the long-term trend value.

[0051] This scheme monitors four parameters: pressure magnitude, rate of change, pressure curve shape, and pressure variance. 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, so the difference is 0.05Fn. The difference accounts for 0.05% of this parameter, or 5%. Assuming that the differences in the rate of change, pressure curve shape, and pressure variance account for 3%, 2%, and 6% of this parameter, respectively, the long-term trend value is (5%+3%+2%+6%) / 4=4%.

[0052] By increasing the values ​​of k1 and k3 when the long-term trend values ​​of several parameters exceed the threshold, and conversely increasing the values ​​of k2 and k4 when the long-term trend values ​​exceed the threshold, the weight of parameters more affected by such situations in the calculation of comprehensive monitoring parameters is increased. This is achieved when situations that lead to long-term data changes occur, such as machine wear, and the weight of parameters more affected by random errors is increased in the calculation of comprehensive monitoring parameters when no long-term data changes occur and the impact of random errors is greater.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-station cold forging process for irregularly shaped parts, characterized in that: Includes the following steps: Step 1: Set up several workstations equipped with pressure monitoring modules; Step 2: Set pressure threshold ranges for several workstations; Step 3: During the processing, pressure is monitored at several workstations. When the monitored value at a certain 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, raise the pressure judgment standard of the previous workstation of the abnormal workstation, and return to Step 3. Step 5: Return to Step 3; Step two further includes: setting threshold ranges for pressure magnitude, pressure change rate, pressure curve shape, and pressure variance for several workstations; Step three further includes: monitoring the pressure magnitude, change rate, pressure curve shape, and pressure variance for several workstations; when one of the monitored values ​​for a certain workstation exceeds the threshold, this workstation is recorded as an abnormal workstation, and the monitored value exceeding the threshold is recorded as an abnormal parameter, and step four is executed; otherwise, step five is executed; Step four further 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; Step three also includes: after monitoring the pressure Fn at each workstation, storing the pressure values ​​in chronological order, plotting the pressure value as a function of time curve Fn(t), 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. t0 represents the cumulative length of the integral over time t when calculating the integral X. Step four further includes: adjusting the pressure threshold range of the previous station of the abnormal station to the original Fn0 / Fn times, the change rate threshold range to the original B0 / (dFn(t) / dt) times, the pressure curve shape threshold range to the original X0 / X times, and the variance threshold range to the original C0 / C times, where Fn0 is the pressure reference value of the current station, B0 is the change rate reference value of the current station, X0 is the area of ​​the pressure standard curve of the current station, and C0 is the variance reference value of the current station; Step three also includes: after monitoring the pressure magnitude, rate of change, pressure curve shape and pressure variance of several workstations, calculating the comprehensive monitoring parameter Z using a weighted average, determining whether the comprehensive monitoring parameter exceeds the threshold, and issuing an alarm if so.

2. The multi-station cold forging process for irregularly shaped parts according to claim 1, characterized in that: Step three also includes: calculating the comprehensive monitoring parameter Z, where k1, k2, k3, and k4 are weights that are pre-input to the control module.

3. The multi-station cold forging process for irregularly shaped parts according to claim 2, characterized in that: Step three also includes: the control module determines whether the long-term trend values ​​of several parameters exceed the threshold. If the determination result is yes, the values ​​of k1 and k3 are increased; otherwise, the values ​​of k2 and k4 are increased.

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