Manufacturing method, device and equipment of precision stretching part, storage medium and program product

Abstract

The invention discloses a manufacturing method, a manufacturing device and manufacturing equipment of a precision stretching part, a storage medium and a computer program product, and relates to the technical field of precision stretching parts. Whether the stretching defect exists or not is predicted according to the real-time process parameter data of the manufacturing equipment of the precision stretching part and the real-time state data of the precision stretching part being manufactured, and when the stretching defect exists, an adjusting scheme corresponding to the stretching defect is determined and implemented so as to remanufacture the precision stretching part. Compared with a method for determining stretching defects and adjusting suggestions depending on expert experience in the prior art, the method has the advantages that the stretching defects are predicted and the adjusting scheme corresponding to the stretching defects is determined through real-time manufacturing data, the method does not depend on the expert experience and is driven by data; therefore, the method for determining the stretching defects and adjusting the suggestions is accurate and timely enough, and the risk of misjudgment and delayed judgment is greatly reduced.

Description

Technical Field

[0001] The present application relates to the technical field of precision drawn parts, and particularly to a manufacturing method of precision drawn parts, a manufacturing device of precision drawn parts, a manufacturing equipment of precision drawn parts, a storage medium, and a computer program product. Background Art

[0002] Precision drawn parts are a common type of mechanical parts, widely used in fields such as automobiles, electrical appliances, machinery, and instruments. In different fields, the applications of precision drawn parts also vary. In the automotive industry, precision drawn parts are mainly used in parts such as engines, transmissions, steering gears, and suspension systems, providing accurate fit and stable performance for automobiles. In the electrical appliance industry, precision drawn parts are applied to aspects such as switch wiring and electronic component connection, having good electrical conductivity and corrosion resistance. In the machinery and instrument industry, precision drawn parts are mainly used for clamping, connection, assembly, etc., and can well improve the stability and reliability of equipment.

[0003] Precision drawn parts are produced by placing metal materials under specific stress states and utilizing the plastic deformation characteristics of the materials to produce thin-walled and complex-shaped parts. During the production and manufacturing process of precision drawn parts, for a scrapped precision drawn part obtained, its drawing defects are often determined and corresponding adjustment suggestions are given based on the manufacturing parameters of the manufacturing equipment and the component morphology of the scrapped drawn part, etc., by expert experience. This method of determining drawing defects and adjustment suggestions relying on expert experience is not accurate and timely enough, and there are risks of misjudgment and delayed judgment.

[0004] The above content is only used to assist in understanding the technical solution of the present application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present application is to provide a manufacturing method of precision drawn parts, a manufacturing device of precision drawn parts, a manufacturing equipment of precision drawn parts, a storage medium, and a computer program product, aiming to solve the technical problem that the current method of determining drawing defects and adjustment suggestions is not accurate and timely enough.

[0006] To achieve the above purpose, the present application proposes a manufacturing method of precision drawn parts, and the manufacturing method of precision drawn parts includes:

[0007] Obtain real-time manufacturing data during the manufacturing process of precision drawn parts, where the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment of precision drawn parts and real-time state data of the precision drawn parts being manufactured;

[0008] Based on the real-time manufacturing data, predict whether there are drawing defects in the precision drawn parts being manufactured, where the drawing defects include wrinkling, tearing, and cracking;

[0009] When there are stretching defects in the precision stretching parts being manufactured, after determining and implementing the adjustment plan corresponding to the stretching defects, remanufacture the precision stretching parts.

[0010] In one embodiment, before the step of predicting whether there are stretching defects in the precision stretching parts being manufactured based on the real-time manufacturing data, it includes:

[0011] Obtain the historical manufacturing data and historical stretching defects of the historical scrapped parts during the historical manufacturing process of the historical precision stretching parts;

[0012] Based on the historical manufacturing data and the historical stretching defects, train to obtain a prediction model;

[0013] The step of predicting whether there are stretching defects in the precision stretching parts being manufactured based on the real-time manufacturing data includes:

[0014] Based on the real-time manufacturing data, predict whether there are stretching defects in the precision stretching parts being manufactured through the prediction model.

[0015] In one embodiment, the step of obtaining the historical manufacturing data and historical stretching defects of the historical scrapped parts during the historical manufacturing process of the historical precision stretching parts; and training to obtain a prediction model based on the historical manufacturing data and the historical stretching defects includes:

[0016] Obtain the first historical manufacturing data and historical wrinkling defects of the historical wrinkled scrapped parts, the second historical manufacturing data and historical tearing defects of the historical torn scrapped parts, and the third historical manufacturing data and historical cracking defects of the historical cracked scrapped parts during the historical manufacturing process of the historical precision stretching parts;

[0017] Based on the first historical manufacturing data and the historical wrinkling defects, train to obtain a first prediction model;

[0018] Based on the second historical manufacturing data and the historical tearing defects, train to obtain a second prediction model;

[0019] Based on the third historical manufacturing data and the historical cracking defects, train to obtain a third prediction model.

[0020] In one embodiment, the step of predicting whether there are stretching defects in the precision stretching parts being manufactured through the prediction model based on the real-time manufacturing data includes:

[0021] Based on the real-time manufacturing data, predict whether there are wrinkling defects in the precision stretching parts being manufactured through the first prediction model;

[0022] Based on the real-time manufacturing data, predict whether there is a tearing defect in the precision drawing part being manufactured through the second prediction model;

[0023] Based on the real-time manufacturing data, predict whether there is a cracking defect in the precision drawing part being manufactured through the third prediction model.

[0024] In one embodiment, before the step of determining the adjustment scheme corresponding to the drawing defect, it includes:

[0025] Obtain the historical drawing defects of historical scrapped parts and the corresponding historical adjustment schemes, and construct a knowledge base;

[0026] The step of determining the adjustment scheme corresponding to the drawing defect includes:

[0027] According to the drawing defects existing in the precision drawing part being manufactured and the real-time manufacturing data, perform intelligent diagnosis on the precision drawing part with drawing defects during manufacturing in the knowledge base to obtain the adjustment scheme corresponding to the drawing defect.

[0028] In one embodiment, the manufacturing method of the precision drawing part further includes:

[0029] Obtain the real-time monitoring data of the die during the manufacturing process of the precision drawing part, wherein the real-time monitoring data includes vibration data, temperature data, surface data and dimension data;

[0030] When it is determined that the die needs to be replaced according to the real-time monitoring data, determine the remaining life of the die through the real-time monitoring data, and formulate a replacement plan for the die based on the remaining life.

[0031] In addition, to achieve the above object, the present application also proposes a manufacturing device for precision drawing parts, and the manufacturing device for precision drawing parts includes:

[0032] An acquisition module, configured to acquire real-time manufacturing data during the manufacturing process of precision drawing parts, wherein the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment of the precision drawing parts and real-time state data of the precision drawing parts being manufactured;

[0033] A prediction module, configured to predict whether there are drawing defects in the precision drawing parts being manufactured based on the real-time manufacturing data, wherein the drawing defects include wrinkling, tearing and cracking;

[0034] An adjustment module, configured to re-manufacture the precision drawing parts after determining and implementing the adjustment scheme corresponding to the drawing defect when there are drawing defects in the precision drawing parts being manufactured.

[0035] In addition, to achieve the above object, the present application further provides a manufacturing device for precision stretch parts, the device including: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the manufacturing method for precision stretch parts as described above.

[0036] In addition, to achieve the above object, the present application further provides a storage medium, the storage medium being a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the manufacturing method for precision stretch parts as described above.

[0037] In addition, to achieve the above object, the present application further provides a computer program product, the computer program product including a computer program, and when the computer program is executed by a processor, it implements the steps of the manufacturing method for precision stretch parts as described above.

[0038] One or more technical solutions proposed by the present application have at least the following technical effects:

[0039] In the present application, a manufacturing method for precision stretch parts for determining stretch defects and adjustment suggestions is given. In this method, based on the real-time process parameter data of the manufacturing device for precision stretch parts and the real-time state data of the precision drawing parts being manufactured, it is predicted whether there are stretch defects, and when there are stretch defects, the adjustment scheme corresponding to the stretch defects is determined and implemented to remanufacture the precision stretch parts. Compared with the method of determining stretch defects and adjustment suggestions that relies on expert experience in the prior art, predicting stretch defects through real-time manufacturing data and determining the adjustment scheme corresponding to the stretch defects, which does not rely on expert experience but is data-driven, makes the method of determining stretch defects and adjustment suggestions in the present application accurate and timely enough, and greatly reduces the risk of misjudgment and delayed judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0041] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a schematic flowchart provided for the first embodiment of the manufacturing method for precision stretch parts of the present application;

[0043] Figure 2Schematic flow chart provided for the second embodiment of the manufacturing method of precision drawing parts in this application;

[0044] Figure 3 Schematic flow chart provided for the third embodiment of the manufacturing method of precision drawing parts in this application;

[0045] Figure 4 Schematic flow chart provided for the fourth embodiment of the manufacturing method of precision drawing parts in this application;

[0046] Figure 5 Schematic module structure diagram of the manufacturing device for precision drawing parts in the embodiments of this application;

[0047] Figure 6 Schematic device structure diagram of the hardware operating environment involved in the manufacturing method of precision drawing parts in the embodiments of this application.

[0048] The implementation, functional features and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0050] In order to better understand the technical solutions of this application, the following will be described in detail with reference to the accompanying drawings of the specification and the specific implementation manners.

[0051] In the production and manufacturing process of precision drawn parts, for a certain scrapped precision drawing part obtained, its drawing defects are often determined and corresponding adjustment suggestions are given based on the manufacturing parameters of the manufacturing equipment and the component form of the scrapped drawn part, etc., by expert experience. This method of determining drawing defects and adjustment suggestions relying on expert experience is not accurate and timely enough, and there are risks of misjudgment and delayed judgment.

[0052] Specifically, in the production and manufacturing process of precision drawn parts, for a certain scrapped precision drawing part obtained, there may be more than one drawing defect at the same time. When there are multiple drawing defects at the same time, it is very difficult to determine each drawing defect based on the manufacturing parameters and the scrapped drawn part afterwards.

[0053] Specifically, in the production and manufacturing process of precision drawn parts, for a certain scrapped precision drawing part obtained, after it is determined that a scrapped drawn part has been manufactured, relevant data is often traced back in the data log and records, and it is difficult to make timely adjustments, and a large number of scrapped drawn parts are easily manufactured on the production line.

[0054] Based on this, the embodiments of this application provide a manufacturing method for precision drawing parts, referring to Figure 1 , Figure 1Schematic flowchart of the first embodiment of the manufacturing method of the precision drawing part of the present application.

[0055] In this embodiment, the manufacturing method of the precision drawing part includes steps S10 to S30:

[0056] Step S10: Obtain real-time manufacturing data during the manufacturing process of the precision drawing part, where the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment of the precision drawing part and real-time state data of the precision drawing part being manufactured;

[0057] The drawing type of the precision drawing part can be flanged hemispherical drawing, panel drawing, cylindrical drawing, elliptical drawing, mountain-shaped drawing, hill-shaped drawing, rectangular drawing, flange drawing, edge drawing, deep drawing, reverse drawing, conical drawing, rectangular redrawing, surface forming, etc. In this embodiment, the drawing type of the precision drawing part is not limited. For example, it can also include drawing types such as stepped drawing and ironing.

[0058] The real-time process parameter data of the manufacturing equipment includes data related to temperature such as die temperature, material heating temperature, and cooling medium temperature, data related to pressure and load such as hydraulic system pressure, blank holding force, and drawing force, data related to speed and synchronism such as drawing speed, die movement speed, and feeding speed, and data related to lubrication state such as lubricant spraying amount, coverage rate, and viscosity. In this embodiment, the real-time process parameter data of the manufacturing equipment is not limited. For example, it can also include data such as drawing time and die clearance.

[0059] The real-time state data of the drawn part includes data related to monitoring of material deformation amount such as thickness reduction rate, radial / circumferential strain, and springback amount, defect identification data related to scratches, wrinkles, orange peel texture, etc. such as scratch length and surface roughness, and data related to geometric accuracy feedback such as diameter, roundness, and flange edge height. In this embodiment, the real-time state data of the drawn part is not limited. For example, the stress distribution monitored by X-ray diffraction or magnetic measurement can also be included.

[0060] Step S20: Based on the real-time manufacturing data, predict whether there are drawing defects in the precision drawing part being manufactured, where the drawing defects include wrinkling, tearing, and cracking;

[0061] In the processing of precision drawn parts, due to the different stress and deformation conditions of each part during drawing, some unique phenomena occur in the drawing process. That is, the phenomena often occurring in precision drawn parts during the drawing process are: wrinkling, tearing, and cracking. Among them, 1. Wrinkling of precision drawn parts means that the tangential compressive stress in the flange part during drawing is large enough to exceed the buckling resistance of the material, and the material in the flange part will become unstable and bulge. 2. Tearing caused by uneven deformation during drawing: During drawing, the thickness of the material changes, and the change is uneven. The thickness change of the material at the outer edge of the flange is the largest. After the drawn part is formed, the material at the blank edge of the workpiece is the thickest, gradually thinning inward, while the material at the bottom becomes thinner less due to the friction preventing the elongation deformation of the material, and the material at the bottom corner is always subjected to the top force and bending action of the punch corner during drawing and is always under tensile stress during the whole drawing process, resulting in the largest thinning here. The cylindrical side wall plays the role of transmitting the punch tensile force to the flange. When the radial tensile stress in the force transmission area exceeds the material limit, tearing occurs. 3. Cracking caused by uneven hardening of metal materials: After drawing, the material undergoes plastic deformation, causing cold work hardening of the material. Due to the different deformation degrees of each part, the degree of cold work hardening is also different. Among them, the mouth part is the largest, and the hardening degree decreases downward. When approaching the bottom, due to the smaller tangential compressive deformation, the cold work hardening is the smallest, and the yield limit and strength of the material are relatively low, and cracking is most likely to occur here.

[0062] In this embodiment, the drawing defects existing in the precision drawn parts are not limited. For example, in addition to the above-mentioned deformation defects such as wrinkling and cracking defects such as tearing, it may also include drawing defects such as slag inclusions and dross, scratches and oxidation.

[0063] In this embodiment, a drawing defect prediction model is established by using machine learning algorithms such as neural networks and support vector machines. Through learning a large amount of historical data, this prediction model can predict whether there are defects in the drawn parts. Further, the real-time manufacturing data is input into this prediction model, and the model outputs the prediction result to give an early warning of possible drawing defects.

[0064] Step S30: When there are drawing defects in the precision drawn parts being manufactured, after determining and implementing the adjustment plan corresponding to the drawing defects, remanufacture the precision drawn parts.

[0065] When there are no drawing defects in the precision drawn parts being manufactured, continue with the subsequent manufacturing steps of the precision drawn parts. When there are drawing defects in the precision drawn parts being manufactured, after determining and implementing the adjustment plan corresponding to the drawing defects, remanufacture the precision drawn parts.

[0066] Furthermore, adjustments can be made based on the defect type, that is, according to the detected defect type, such as cracks, deformations, dimensional deviations, etc., to determine the corresponding adjustment plan. For example, for crack defects, measures such as repair welding and repair may be required; for dimensional deviations, it may be necessary to adjust the stretching process parameters or perform secondary processing, etc.

[0067] In this embodiment, when adjusting the real-time adjustment plan, the process parameters can be adjusted: according to the defect situation, adjust the stretching process parameters, such as stretching force, stretching speed, stretching temperature, etc. For example, when the stretched part is over-stretched and the size is too large, the stretching force or speed can be appropriately reduced; when there is insufficient stretching resulting in a too small size or unqualified performance, the stretching force or speed can be appropriately increased, etc. Equipment adjustment and maintenance can be carried out: check whether there are any faults or accuracy problems in the stretching equipment, such as whether the pressure sensors and displacement sensors of the stretching machine are accurate, and whether the molds are worn, etc. Adjust, repair or replace parts of the equipment to ensure the normal operation of the equipment and meet the requirements of the stretching process. Material treatment and replacement can be carried out: if the defect is caused by problems with the material itself, such as unqualified material quality or unstable material performance, consider treating the material, such as heat treatment, surface treatment, etc., to improve the material performance; or replace the qualified material and re-perform stretching manufacturing. Manual intervention and repair can be carried out: for some surface defects or minor internal defects, manual intervention can be used for repair, such as grinding, polishing, repair welding, etc. For example, for scratches or small cracks on the surface of the stretched part, the defects can be removed by methods such as grinding and polishing; for small internal cracks, repair welding and other methods can be used for repair.

[0068] In this embodiment, compared with the method of determining stretching defects and adjustment suggestions relying on expert experience in the prior art, predicting stretching defects through real-time manufacturing data and determining the adjustment plan corresponding to the stretching defects does not rely on expert experience, but is data-driven, making the method of determining stretching defects and adjustment suggestions in this application accurate and timely enough, and greatly reducing the risk of misjudgment and delayed judgment.

[0069] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the above first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 2 , before the step S20 includes:

[0070] Step S201: Obtain the historical manufacturing data and historical stretching defects of the historical scrap parts during the historical manufacturing process of the historical precision stretched parts;

[0071] Step S202: Based on the historical manufacturing data and historical stretching defects, train to obtain a prediction model;

[0072] The step S20 includes:

[0073] Step S203: Based on the real-time manufacturing data, predict whether there are drawing defects in the precision drawn parts being manufactured through a prediction model.

[0074] In this embodiment, a large amount of data of historical precision drawn parts during the drawing process, i.e., the historical manufacturing process, in a historical period is collected, including the historical manufacturing data of historical scrapped parts and historical drawing defects, such as material properties, die parameters, process parameters (such as drawing force, drawing speed, blank holding force, etc.), and the quality inspection results of drawn parts. Then, the collected data is analyzed to extract characteristic parameters related to drawing defects such as wrinkling, tearing, and cracking. Next, a prediction model is established using machine learning algorithms (such as neural networks, support vector machines, decision trees, etc.). Taking the extracted characteristic parameters as the input and whether the precision drawn parts have drawing defects such as wrinkling, tearing, and cracking as the output, the model is trained and optimized to enable it to accurately predict whether various drawing defects will occur during the drawing process. Thus, during the actual drawing production process of precision drawn parts, the real-time manufacturing data during the manufacturing process of precision drawn parts, that is, the process parameter data of manufacturing equipment and the state data of precision drawn parts, is collected in real time through pre-installed sensors and input into the trained prediction model, and the prediction model can then predict in real time whether drawing defects such as wrinkling, tearing, and cracking will occur.

[0075] In a feasible implementation manner, step S201 includes:

[0076] Obtain the first historical manufacturing data and historical wrinkling defects of historical wrinkled scrapped parts, the second historical manufacturing data and historical tearing defects of historical torn scrapped parts, and the third historical manufacturing data and historical cracking defects of historical cracked scrapped parts during the historical manufacturing process of historical precision drawn parts;

[0077] Step S202 includes:

[0078] Based on the first historical manufacturing data and historical wrinkling defects, train the first prediction model;

[0079] Based on the second historical manufacturing data and historical tearing defects, train the second prediction model;

[0080] Based on the third historical manufacturing data and historical cracking defects, train the third prediction model.

[0081] Further, prediction models corresponding to different types of stretching defects can be constructed respectively to improve the prediction accuracy of the models and reduce the training difficulty of the models. In one embodiment, a first prediction model is trained according to the first historical manufacturing data and historical wrinkling defects of historical wrinkled scrapped parts during the historical manufacturing process of historical precision stretch parts; a second prediction model is trained according to the second historical manufacturing data and historical tearing defects of historical torn scrapped parts during the historical manufacturing process of historical precision stretch parts; a third prediction model is trained according to the third historical manufacturing data and historical cracking defects of historical cracked scrapped parts during the historical manufacturing process of historical precision stretch parts.

[0082] In a feasible implementation manner, step S203 includes:

[0083] Based on the real-time manufacturing data, use the first prediction model to predict whether there is a wrinkling defect in the precision stretch part being manufactured;

[0084] Based on the real-time manufacturing data, use the second prediction model to predict whether there is a tearing defect in the precision stretch part being manufactured;

[0085] Based on the real-time manufacturing data, use the third prediction model to predict whether there is a cracking defect in the precision stretch part being manufactured.

[0086] Corresponding to the types of stretching defects, respectively based on the real-time manufacturing data, use the first prediction model to predict whether there is a wrinkling defect in the precision stretch part being manufactured, use the second prediction model to predict whether there is a tearing defect in the precision stretch part being manufactured, and use the third prediction model to predict whether there is a cracking defect in the precision stretch part being manufactured.

[0087] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar content as the above-mentioned embodiment one can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , before the step S30 includes:

[0088] Step S301: Obtain the historical stretching defects of historical scrapped parts and the corresponding historical adjustment schemes, and construct a knowledge base;

[0089] The step S30 includes:

[0090] Step S302: According to the stretching defects existing in the precision stretch part being manufactured and the real-time manufacturing data, perform intelligent diagnosis on the precision stretch part with stretching defects during manufacturing in the knowledge base to obtain the adjustment scheme corresponding to the stretching defect.

[0091] In this embodiment, after the stretching defects such as wrinkling, cracking, and tearing in the deep drawing process of precision stamping parts are determined in advance based on artificial intelligence, adjustment suggestions are given in a timely manner. First, a knowledge base is established. Various adjustment methods and empirical knowledge for stretching defects such as wrinkling, cracking, and tearing are collected and sorted to form a knowledge base. This knowledge includes aspects such as adjusting process parameters (such as increasing or decreasing the blank holder force, adjusting the deep drawing speed, etc.), replacing the die or material, and improving the deep drawing process. Then, intelligent diagnosis and suggestions are carried out. When the prediction model determines that a certain stretching defect will occur, intelligent diagnosis is performed based on the knowledge in the knowledge base combined with the current production actual situation (such as equipment capabilities, production efficiency requirements, etc.), and specific adjustment suggestions are given. For example, if it is predicted that a wrinkling stretching defect will occur, it can be suggested to increase the blank holder force, adjust the die clearance, or replace a more suitable material, etc. Further, after implementing the adjustment plan corresponding to the adjustment suggestion, the quality of the precision deep drawing parts is detected again, and the feedback of the detection results is collected. The effect of the adjustment suggestion is evaluated according to the feedback information. If the problem of the precision stretching defect is solved, the adjustment experience of this time is recorded; if the problem of the precision stretching defect still exists, diagnosis and adjustment are carried out again until the problem of the precision stretching defect is solved.

[0092] Based on the first embodiment of the present application, in the fourth embodiment of the present application, for the same or similar content as in the above-mentioned first embodiment, reference can be made to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 4 , the method further includes:

[0093] Step T10: Obtain real-time monitoring data of the die during the manufacturing process of the precision stamping parts, where the real-time monitoring data includes vibration data, temperature data, surface data, and dimension data;

[0094] Step T20: When it is determined according to the real-time monitoring data that the die needs to be replaced, determine the remaining life of the die through the real-time monitoring data, and formulate a replacement plan for the die based on the remaining life.

[0095] In this embodiment, by monitoring the die wear, the die is replaced in advance to avoid a batch of defective stamping parts produced by the die to be replaced.

[0096] First, monitor the wear-related indicators of the die. By installing vibration sensors, monitor the changes in vibration frequency and amplitude when the die is working. Abnormal vibration may indicate die wear. Use temperature sensors to monitor the temperature changes of the die. Excessive temperature may reflect increased wear. Adopt a vision inspection system or laser scanning technology to regularly check whether there are scratches, depressions, etc. on the die surface. Use a coordinate measuring machine (CMM) to regularly measure the key dimensions of the die to determine whether they exceed the tolerance range. In addition, the noise signal during the operation of the die and the mechanical data borne by the die during the deep drawing process can also be monitored.

[0097] Then, analyze the vibration frequency and temperature change trend to identify whether there are abnormalities in vibration and temperature. Analyze the die surface image through image processing technology to detect signs of wear. Compare the die size measurement results to evaluate the degree of wear. At the same time, warning thresholds for each real-time monitoring data have been preset in advance. When the real-time monitoring data exceeds the corresponding normal range, an alarm is triggered to prompt that the die may need to be replaced.

[0098] Next, when it is determined that the die needs to be replaced based on the real-time monitoring data, the remaining life of the die can be predicted by combining the real-time monitoring data with a pre-established die wear prediction model. Specifically, extract die wear characteristics, such as time-domain characteristics. The time-domain characteristics of the vibration signal include the mean acceleration, standard deviation, and band energy, and the time-domain characteristics of the temperature signal include the temperature rise rate and temperature fluctuation amplitude; such as frequency-domain characteristics, the frequency-domain characteristics of the vibration signal include the band energy distribution and frequency peak; such as statistical characteristics, the statistical characteristics of the vibration signal include kurtosis and energy concentration, and the statistical characteristics of the temperature signal include the temperature rise trend and fluctuation period. Further, select an appropriate model architecture according to the complexity and time series of the feature data. A prediction model based on machine learning, such as support vector machine (SVM), random forest (Random Forest), long short-term memory network (LSTM), etc., can be used, and through steps such as model training and model verification, a die wear prediction model is constructed.

[0099] Thereby, input the real-time monitoring data into the trained die wear prediction model, calculate the current wear state of the die, and output the degree of die wear (such as wear amount and wear rate). Based on the current wear state and historical wear data, calculate the remaining life of the die. Among them, linear or non-linear fitting methods can be used to predict the remaining service time of the die. When the predicted remaining life is lower than the safety threshold, a warning signal is triggered, and maintenance suggestions such as die replacement, grinding, or lubrication optimization are provided, thereby optimizing the maintenance cycle and reducing unexpected shutdowns.

[0100] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the manufacturing method of the precision stamping parts of the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0101] The present application also provides a manufacturing device for precision stamping parts. Please refer to Figure 5 , the manufacturing device for precision stamping parts includes:

[0102] An acquisition module 10, configured to acquire real-time manufacturing data during the manufacturing process of precision stamping parts, wherein the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment of precision stamping parts and real-time state data of the precision drawing parts being manufactured;

[0103] A prediction module 20, configured to predict whether there are stretching defects in the precision stretching parts being manufactured based on real-time manufacturing data, where the stretching defects include wrinkling, tearing, and cracking.

[0104] An adjustment module 30, configured to, when there are stretching defects in the precision stretching parts being manufactured, determine and implement an adjustment plan corresponding to the stretching defects and then remanufacture the precision stretching parts.

[0105] In one embodiment, the prediction module 20 is further configured to:

[0106] Before the step of predicting whether there are stretching defects in the precision stretching parts being manufactured based on real-time manufacturing data: Obtain the historical manufacturing data and historical stretching defects of the historical scrap parts in the historical manufacturing process of the historical precision stretching parts; Based on the historical manufacturing data and historical stretching defects, train a prediction model.

[0107] Predict whether there are stretching defects in the precision stretching parts being manufactured through the prediction model based on real-time manufacturing data.

[0108] In one embodiment, the prediction module 20 is further configured to:

[0109] Obtain the first historical manufacturing data and historical wrinkling defects of the historical wrinkled scrap parts, the second historical manufacturing data and historical tearing defects of the historical torn scrap parts, and the third historical manufacturing data and historical cracking defects of the historical cracked scrap parts in the historical manufacturing process of the historical precision stretching parts;

[0110] Train a first prediction model based on the first historical manufacturing data and historical wrinkling defects;

[0111] Train a second prediction model based on the second historical manufacturing data and historical tearing defects;

[0112] Train a third prediction model based on the third historical manufacturing data and historical cracking defects.

[0113] In one embodiment, the prediction module 20 is further configured to:

[0114] Predict whether there are wrinkling defects in the precision stretching parts being manufactured through the first prediction model based on real-time manufacturing data;

[0115] Predict whether there are tearing defects in the precision stretching parts being manufactured through the second prediction model based on real-time manufacturing data;

[0116] Predict whether there are cracking defects in the precision stretching parts being manufactured through the third prediction model based on real-time manufacturing data.

[0117] In one embodiment, the adjustment module 30 is further configured to:

[0118] Before the steps of determining the adjustment scheme corresponding to the stretching defect: Obtain the historical stretching defects of historical scrapped parts and the corresponding historical adjustment schemes, and construct a knowledge base;

[0119] According to the stretching defects existing in the precision stretching parts being manufactured and the real-time manufacturing data, perform intelligent diagnosis on the precision stretching parts with stretching defects during manufacturing in the knowledge base to obtain the adjustment scheme corresponding to the stretching defects.

[0120] In one embodiment, the manufacturing device of the precision stretching part further includes a replacement module for:

[0121] Obtain the real-time monitoring data of the mold during the manufacturing process of the precision stretching part, where the real-time monitoring data includes vibration data, temperature data, surface data, and dimension data;

[0122] When it is determined according to the real-time monitoring data that the mold needs to be replaced, determine the remaining life of the mold through the real-time monitoring data, and formulate a replacement plan for the mold based on the remaining life.

[0123] The manufacturing device of the precision stretching part provided by the present application adopts the manufacturing method of the precision stretching part in the above embodiment, and can solve the technical problem that the current methods for determining stretching defects and adjustment suggestions are not accurate and timely enough. Compared with the prior art, the beneficial effects of the manufacturing device of the precision stretching part provided by the present application are the same as those of the manufacturing method of the precision stretching part provided by the above embodiment, and other technical features in the manufacturing device of the precision stretching part are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0124] The present application provides a manufacturing device for precision stretching parts. The manufacturing device for precision stretching parts includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the manufacturing method of the precision stretching part in the first embodiment above.

[0125] Next, refer to Figure 6, which shows a schematic structural diagram of a manufacturing apparatus suitable for implementing the precision stretch part of the embodiments of the present application. The manufacturing apparatus for the precision stretch part in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The shown manufacturing apparatus for the precision stretch part is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0126] As Figure 6 shown, the manufacturing apparatus for the precision stretch part may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in the read-only memory 1002 or a program loaded from the storage device 1003 into the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the manufacturing apparatus for the precision stretch part are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. An input / output interface 1006 is also connected to the bus. Generally, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the manufacturing apparatus for the precision stretch part to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a manufacturing apparatus for the precision stretch part having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0127] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0128] The manufacturing equipment for precision stretch parts provided by the present application adopts the manufacturing method of precision stretch parts in the above embodiments, and can solve the technical problems that the current methods for determining stretch defects and adjustment suggestions are not accurate and timely enough. Compared with the prior art, the beneficial effects of the manufacturing equipment for precision stretch parts provided by the present application are the same as those of the manufacturing method of precision stretch parts provided by the above embodiments, and other technical features in the manufacturing equipment for precision stretch parts are the same as the features disclosed in the method of the previous embodiment, which will not be elaborated here.

[0129] It should be understood that the various parts disclosed in the present application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0130] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0131] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the manufacturing method of precision stretch parts in the above embodiments.

[0132] The computer-readable storage medium provided by this application can, for example, be a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0133] The above computer-readable storage medium can be included in the manufacturing equipment of precision stretch parts; it can also exist separately and not be assembled into the manufacturing equipment of precision stretch parts.

[0134] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the manufacturing equipment of precision stretch parts, the manufacturing equipment of precision stretch parts is enabled to: obtain real-time manufacturing data during the manufacturing process of precision stretch parts, where the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment of precision stretch parts and real-time status data of the precision drawn parts being manufactured; based on the real-time manufacturing data, predict whether there are stretching defects in the precision stretch parts being manufactured, where the stretching defects include wrinkling, tearing, and splitting; when there are stretching defects in the precision stretch parts being manufactured, determine and implement the adjustment plan corresponding to the stretching defects and then remanufacture the precision stretch parts.

[0135] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in a different order than that marked in the accompanying drawings. For example, two consecutively represented boxes may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0137] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0138] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned manufacturing method of precision tensile parts, and can solve the technical problems that the current methods for determining tensile defects and adjustment suggestions are not accurate and timely enough. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the manufacturing method of precision tensile parts provided by the above embodiments, and will not be elaborated here.

[0139] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the manufacturing method of the precision tensile part as described above.

[0140] The computer program product provided by the present application can solve the technical problem that the current methods for determining tensile defects and adjustment suggestions are not accurate and timely enough. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the manufacturing method of the precision tensile part provided in the above embodiments, and will not be elaborated herein.

[0141] The foregoing are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A manufacturing method of a precision stamping part, characterized in that, The manufacturing method of the precision stamping part includes: Obtaining real-time manufacturing data during the manufacturing process of the precision stamping part, where the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment for the precision stamping part and real-time status data of the precision deep-drawing part being manufactured; Based on the real-time manufacturing data, predicting whether there are stretching defects in the precision stamping part being manufactured, where the stretching defects include wrinkling, drawing breakage, and tearing; When there are stretching defects in the precision stamping part being manufactured, determining and implementing the adjustment plan corresponding to the stretching defects and then remanufacturing the precision stamping part.

2. The manufacturing method of the precision drawing part according to claim 1, characterized in that, Before the step of predicting whether there are stretching defects in the precision stamping part being manufactured based on the real-time manufacturing data, it includes: Obtaining the historical manufacturing data of historical scrap parts and historical stretching defects during the historical manufacturing process of historical precision stamping parts; Training a prediction model based on the historical manufacturing data and the historical stretching defects; The step of predicting whether there are stretching defects in the precision stamping part being manufactured based on the real-time manufacturing data includes: Based on the real-time manufacturing data, predicting whether there are stretching defects in the precision stamping part being manufactured through the prediction model.

3. The manufacturing method of the precision drawing part according to claim 2, wherein, The obtaining of the historical manufacturing data of historical scrap parts and historical stretching defects during the historical manufacturing process of historical precision stamping parts; The step of training a prediction model based on the historical manufacturing data and the historical stretching defects includes: Obtaining the first historical manufacturing data of historical wrinkling scrap parts and historical wrinkling defects, the second historical manufacturing data of historical drawing breakage scrap parts and historical drawing breakage defects, and the third historical manufacturing data of historical tearing scrap parts and historical tearing defects during the historical manufacturing process of historical precision stamping parts; Training a first prediction model based on the first historical manufacturing data and the historical wrinkling defects; Training a second prediction model based on the second historical manufacturing data and the historical drawing breakage defects; Training a third prediction model based on the third historical manufacturing data and the historical tearing defects.

4. The manufacturing method of the precision drawing part according to claim 3, characterized in that, The step of predicting whether there are stretching defects in the precision stamping part being manufactured through the prediction model based on the real-time manufacturing data includes: Based on the real-time manufacturing data, predicting whether there are wrinkling defects in the precision stamping part being manufactured through the first prediction model; Based on the real-time manufacturing data, predicting whether there are drawing breakage defects in the precision stamping part being manufactured through the second prediction model; Based on the real-time manufacturing data, predicting whether there are tearing defects in the precision stamping part being manufactured through the third prediction model.

5. The manufacturing method of the precision drawing part according to claim 1, characterized in that, Before the step of determining the adjustment plan corresponding to the stretching defects, it includes: Obtaining the historical stretching defects of historical scrap parts and the corresponding historical adjustment plans, and constructing a knowledge base; The step of determining the adjustment plan corresponding to the stretching defects includes: According to the stretching defects existing in the precision stamping part being manufactured and the real-time manufacturing data, performing intelligent diagnosis on the precision stamping part with stretching defects during manufacturing in the knowledge base to obtain the adjustment plan corresponding to the stretching defects.

6. The manufacturing method of the precision tensile part according to claim 1, characterized in that, The manufacturing method of the precision stamping part further includes: Obtain real-time monitoring data of the mold during the manufacturing process of precision stamping parts, wherein the real-time monitoring data includes vibration data, temperature data, surface data, and dimension data; When it is determined that the mold needs to be replaced based on the real-time monitoring data, determine the remaining life of the mold through the real-time monitoring data, and formulate a replacement plan for the mold based on the remaining life.

7. A manufacturing device for precision stretch parts, characterized in that, The manufacturing device for the precision stamping parts includes: An acquisition module, configured to acquire real-time manufacturing data during the manufacturing process of precision stamping parts, wherein the real-time manufacturing data includes real-time process parameter data of the manufacturing equipment for precision stamping parts and real-time status data of the precision deep-drawing parts being manufactured; A prediction module, configured to predict whether there are drawing defects in the precision stamping parts being manufactured based on the real-time manufacturing data, wherein the drawing defects include wrinkling, tearing, and cracking; An adjustment module, configured to, when there are drawing defects in the precision stamping parts being manufactured, determine and implement the adjustment plan corresponding to the drawing defects and then remanufacture the precision stamping parts.

8. A manufacturing device for precision stretch parts, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the manufacturing method for precision stamping parts according to any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by the processor, it implements the steps of the manufacturing method for precision stamping parts according to any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, it implements the steps of the manufacturing method for precision stamping parts according to any one of claims 1 to 6.

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