Anomaly detection method and apparatus, and 3D printing device, electronic device and storage medium

Abstract

An anomaly detection method and apparatus (300), and a 3D printing device (400), an electronic device (500) and a storage medium. The method comprises: acquiring the current torque current of an electric motor of a 3D printing device (011); on the basis of a target mapping relationship corresponding to the current operating condition of the 3D printing device, and the current extrusion speed of printing consumables, determining a reference torque current (012), wherein the target mapping relationship is any one of a plurality of preset mapping relationships, and the preset mapping relationships comprise mapping relationships between extrusion speeds of the printing consumables and torque currents when the 3D printing device operates normally under different preset operating conditions; and on the basis of the current torque current and the reference torque current, performing anomaly detection on the 3D printing device, so as to determine an anomaly detection result (013).

Description

Anomaly detection methods, devices, 3D printing equipment, electronic devices and storage media

[0001] Priority information

[0002] This application claims priority and benefits to patent application No. 202510295936X, filed with the China National Intellectual Property Administration on March 11, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application belongs to the field of 3D printing equipment technology, and particularly relates to an anomaly detection method, detection device, 3D printing equipment, electronic equipment and computer-readable storage medium. Background Technology

[0004] Currently, during the printing process, 3D printing equipment may experience nozzle clogging (i.e., the nozzle is blocked) due to impurities in the printing filament or nozzle, or unreasonable nozzle temperature. It may also cause dry printing (i.e., the gripping wheel cannot hold the filament, preventing the nozzle from ejecting material) because the diameter of the printing filament is too small (either the material itself is small or it becomes smaller due to continuous friction from the gripping wheel after the nozzle is blocked).

[0005] If problems such as nozzle blockage or dry printing are not addressed promptly, they can affect the normal operation of 3D printing equipment and even damage it. Therefore, timely and accurate detection of abnormal problems in 3D printing equipment has become a crucial issue to address in maintaining its stable operation. Summary of the Invention

[0006] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an anomaly detection method, detection device, 3D printing equipment, electronic equipment, and computer-readable storage medium, which can perform anomaly detection in a timely and accurate manner by detecting the current torque current of the motor of the 3D printing equipment, thereby helping to ensure the stable operation of the 3D printing equipment.

[0007] In a first aspect, this application provides an anomaly detection method, comprising: acquiring the current torque current of the motor of a 3D printing device; determining a reference torque current based on a target mapping relationship corresponding to the current operating condition of the 3D printing device and the current extrusion speed of the printing filament, wherein the target mapping relationship is any one of a plurality of preset mapping relationships, the preset mapping relationships including the mapping relationship between the extrusion speed of the printing filament and the torque current when the 3D printing device is operating normally under different preset operating conditions; and performing anomaly detection on the 3D printing device based on the current torque current and the reference torque current to determine the anomaly detection result.

[0008] Secondly, this application provides a detection device, which includes:

[0009] The acquisition module is used to acquire the current torque current of the motor of the 3D printing equipment;

[0010] The determination module is used to determine a reference torque current based on the target mapping relationship corresponding to the current operating condition of the 3D printing equipment and the current extrusion speed of the printing consumable. The target mapping relationship is any one of a plurality of preset mapping relationships. The preset mapping relationship includes the mapping relationship between the extrusion speed of the printing consumable and the torque current when the 3D printing equipment is operating normally under different preset operating conditions.

[0011] An anomaly detection module is used to perform anomaly detection on the 3D printing equipment based on the current torque current and the reference torque current, so as to determine the anomaly detection result.

[0012] Thirdly, this application provides a 3D printing device, including a motor, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned anomaly detection method when executing the program.

[0013] Fourthly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described anomaly detection method.

[0014] Fifthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described anomaly detection method.

[0015] The anomaly detection method, detection device, 3D printing equipment, electronic equipment, and computer-readable storage medium provided in this application first establish a mapping relationship between torque current and the extrusion speed of printing consumables during normal operation. Then, during detection, the current torque current of the motor of the 3D printing equipment is obtained. Based on the difference between the reference torque current during normal operation and the current torque current corresponding to the current extrusion speed, anomaly detection of the 3D printing equipment is achieved. For example, nozzle clogging or dry printing will cause abnormal torque current. If the difference between the reference torque current and the current torque current is too large, it can be determined that there is an anomaly in the 3D printing equipment.

[0016] Due to the real-time nature of torque current, anomaly detection in 3D printing equipment based on torque current has high real-time performance, ensuring timely anomaly detection. Furthermore, torque current has high accuracy, resulting in high detection accuracy when using it for anomaly detection. Therefore, timely and accurate anomaly detection in 3D printing equipment ensures stable operation.

[0017] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0019] Figure 1 is a schematic diagram of the operating principle of the motor provided in an embodiment of this application;

[0020] Figure 2 is a schematic diagram of the application scenario of the anomaly detection method provided in the embodiments of this application;

[0021] Figure 3 is a first flowchart of the anomaly detection method provided in an embodiment of this application;

[0022] Figure 4 is a second flowchart of the anomaly detection method provided in an embodiment of this application;

[0023] Figure 5 is a schematic diagram of the third process of the anomaly detection method provided in the embodiments of this application;

[0024] Figure 6 is a schematic diagram of a scenario for the anomaly detection method provided in an embodiment of this application;

[0025] Figure 7 is a schematic diagram of the fourth process of the anomaly detection method provided in the embodiments of this application;

[0026] Figure 8 is a schematic diagram of the detection device provided in an embodiment of this application;

[0027] Figure 9 is a schematic diagram of the structure of the 3D printing equipment provided in the embodiment of this application;

[0028] Figure 10 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application; and

[0029] Figure 11 is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application. Embodiments of the present invention

[0030] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0031] To facilitate understanding, the technical background and application scenarios of this application will be introduced below:

[0032] 3D printing equipment is a machine that can deposit material layer by layer according to a digital model to create a three-dimensional solid object. 3D printing technology (additive manufacturing technology) has developed rapidly in the past few decades and has been widely used in many fields such as prototyping, manufacturing, medical, and education.

[0033] The following section details the components and working principle of 3D printing equipment.

[0034] (1) Components of 3D printing equipment

[0035] The print head (including the nozzle) is responsible for heating and extruding printing materials (such as plastic filaments, metal powder, etc.).

[0036] Motion System: Includes motion control mechanisms along the X, Y, and Z axes to ensure the printhead can move precisely to the designated position.

[0037] Control Unit: Typically controlled by an embedded system or computer, it is responsible for processing G-code (numerical control programming language) and controlling the coordinated operation of various components.

[0038] Heated Bed: Used to maintain the temperature of the printing platform, helping the first layer of material adhere better and reducing warping.

[0039] Frame: The structural foundation that supports all components and ensures the stability of the equipment.

[0040] Filament Feeder: Stores and supplies printing consumables.

[0041] (2) Working principle of 3D printing

[0042] Model design: Design a 3D model using CAD software.

[0043] Slicing: The 3D model is converted into a series of cross sections (slices), each slice representing a layer of material.

[0044] Print preparation: Convert the slice information into a machine-readable format (such as G-code) and load it into the 3D printer.

[0045] Material deposition: The print head deposits material layer by layer according to instructions. After each layer is completed, the platform descends a certain height, and this process is repeated until the model is completely printed.

[0046] Two common problems during the printing process of 3D printing equipment are nozzle blockage and dry printing.

[0047] (1) Head clogging: This refers to a situation where the nozzle of a 3D printer becomes clogged for various reasons, preventing the normal extrusion of filament material. This situation may affect the normal progress of 3D printing, leading to printing failure or a decrease in print quality. Head clogging is a common problem in the 3D printing process, and its causes are varied, mainly including:

[0048] a. Impurities in consumable materials or nozzles: Impurities that may be present in consumable materials or nozzles, such as aluminum shavings or other metal particles, as well as the mixed use of consumable materials from different brands, may cause nozzle clogging.

[0049] b. Inappropriate selection of consumable temperature during printing, impurities, etc. For example, when using PLA (Polylactic Acid) consumable temperature to print PETG (Polyethylene Terephthalate Glycol), insufficient melting of the consumable filament leads to increased extrusion resistance and difficulty in extrusion.

[0050] c. When printing ordinary PLA, if the cavity temperature of the 3D printer is too high, the filament will soften, causing extrusion difficulties or even nozzle blockage.

[0051] (2) Dry printing: During the printing process, the material may soften due to reasons such as the filament diameter being too thin or the filament being damp, making it impossible for the nozzle to extrude material, resulting in dry printing problems in the 3D printer. Alternatively, the meshing part between the motor drive shaft and the biting wheel that is to be printed with the filament may fail (such as wear of the biting wheel), causing the motor to be unable to drive the biting wheel to rotate or the biting wheel to rotate too slowly when driven, thus resulting in dry printing problems.

[0052] If problems such as nozzle blockage or dry printing are not addressed promptly, they can affect the normal operation of the 3D printing equipment or even damage it.

[0053] The inventors of this application discovered that the working principle of a servo motor reflects the relationship between load force and torque current. Specifically:

[0054] The closed-loop control of a servo motor typically includes a position loop, a speed loop, and a current loop, as shown in Figure 1. The explanation proceeds from the outer loop to the inner loop. The position command and position feedback are processed by the position loop PID (Proportional, Integral, Derivative) controller to calculate the speed command. The speed command and speed feedback are then processed by the speed loop PID controller to calculate the torque (iq) current command. The iq current command and current feedback are then processed by the current loop PID controller to calculate the required output voltage. Subsequently, through relevant modules, the control voltage is converted into the three-phase duty cycle of the inverter, thereby controlling the motor to operate at the commanded position.

[0055] During motor operation, if the load torque of the motor increases, the current iq will also increase in order to follow the position command and overcome the load torque. If the load force decreases, the current iq will also decrease. From this characteristic, it can be seen that by detecting the magnitude of the current iq, the magnitude of the external load force can be known.

[0056] In 3D printer applications, the extrusion motor output shaft drives a gear mechanism, which in turn drives the filament gripping wheel to engage the filament at the hot end of the extrusion nozzle. If the hot end becomes clogged, increasing the extrusion resistance, the load torque on the extrusion motor will also increase. To follow position commands, the extrusion motor will increase its drive current (iq) to overcome the load torque. Therefore, monitoring changes in the current (iq) during printing can detect the extrusion resistance. An abnormally high current indicates increased extrusion resistance, possibly due to a nozzle blockage. An abnormally low current, almost indicating the motor is running under no-load, suggests the printer may be printing dry, meaning the filament is not being gripped by the gripping wheel or the filament has been worn into grooves.

[0057] The motor of the 3D printing equipment in this application can be a servo motor. Based on this, the anomaly detection method of this application detects abnormal changes in the nozzle load force by detecting abnormal changes in the torque current, thereby realizing timely and accurate anomaly detection of the 3D printing equipment. If the load force abnormally increases, it is determined to be a nozzle blockage anomaly; if the load force abnormally decreases, it is determined to be a dry-printing anomaly.

[0058] Please refer to Figure 2, which is an application scenario diagram of an anomaly detection method provided in an embodiment of this application. The application scenario provided in this application includes a terminal device 101, a server 102, and a 3D printing device 103. The anomaly detection method provided in this application can be executed by at least one of the terminal device 101, the server 102, and the 3D printing device 103.

[0059] The terminal devices may include, but are not limited to: smartphones (such as Android phones, iOS phones, etc.), tablets, laptops, desktop computers, smart speakers, smartwatches, portable personal computers, mobile internet devices (MIDs), smart voice interaction devices, smart home appliances, vehicle terminals, aircraft, wearable devices, etc., but this application embodiment does not limit them.

[0060] The terminal device may integrate a client, which can be a client with the function of displaying data information such as text, images, audio and video, including but not limited to a device control client associated with the 3D printing equipment, so as to realize communication and control with the 3D printing equipment. The client can be a standalone client or an embedded sub-client integrated into a client (e.g., a social client), and there is no limitation here.

[0061] The terminal device can communicate with the 3D printing equipment to obtain the real-time torque current of the 3D printing equipment, thereby realizing anomaly detection.

[0062] The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. This application does not limit this.

[0063] The server can also communicate with the 3D printing equipment to obtain the real-time torque current of the 3D printing equipment, thereby realizing anomaly detection.

[0064] 3D printing equipment can integrate its own anomaly detection function, which can be achieved by acquiring the torque current of the motor.

[0065] It should be noted that the number of terminal devices and servers in Figure 2 is for illustrative purposes only, and the number of terminal devices and servers can be more or less, which is not limited here. The terminal devices and servers can be connected directly or indirectly through wired or wireless communication, which is not limited here.

[0066] The anomaly detection method involved in this application can be implemented using cloud technology.

[0067] Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or local area network to achieve data computing, storage, processing, and sharing.

[0068] Cloud technology is a collective term for network technologies, information technologies, integration technologies, management platform technologies, and application technologies applied to the cloud computing business model. It can form resource pools, providing flexible and convenient on-demand access. Cloud computing technology will become a crucial support. Backend services of technical network systems require substantial computing and storage resources, such as video websites, image websites, and many portal websites. With the rapid development and application of the internet industry, every item may have its own identification mark in the future, requiring transmission to backend systems for logical processing. Data at different levels will be processed separately, and various industry data will all require robust system support, which can only be achieved through cloud computing.

[0069] The anomaly detection method in this application can be implemented based on cloud computing. Cloud computing is a computing model that distributes computing tasks across a resource pool composed of a large number of computers, enabling various application systems to obtain computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, resources in the "cloud" are infinitely scalable, readily available, on-demand, expandable, and pay-as-you-go.

[0070] As a provider of fundamental cloud computing capabilities, a cloud resource pool (referred to as a cloud platform, generally called an IaaS (Infrastructure as a Service) platform) is established. Various types of virtual resources are deployed in the resource pool for external customers to choose from. The cloud resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, and network devices.

[0071] The anomaly detection method in this application embodiment can be executed by an electronic device, which can be at least one of a server and a terminal device. That is, the method can be executed by the server or the terminal device alone, or by both the server and the terminal device. Therefore, the executing entity of each step will not be described again below.

[0072] It should be noted that the example of the anomaly detection method in the following text is based on the example of the 3D printing equipment performing the anomaly detection method on its own. Based on the understanding of the following text, those skilled in the art can apply the anomaly detection method provided in the embodiments of this application to other types of scenarios (such as the terminal device or server performing the anomaly detection method, or the terminal device cooperating with the server to jointly perform the anomaly detection method, or the 3D printing equipment cooperating with the terminal device to jointly perform the anomaly detection method, or the 3D printing equipment cooperating with the terminal device and the server to jointly perform the anomaly detection method).

[0073] Based on the above description of the relevant scenarios, this application provides an anomaly detection method, which will be described in detail below:

[0074] Please refer to Figure 3. The anomaly detection method provided in this application embodiment is implemented by steps 011 to 013, which are described in detail below.

[0075] Step 011: Obtain the current torque current of the motor of the 3D printing equipment.

[0076] Torque current typically refers to the current component directly related to the torque generated by a motor during operation. Measuring torque current is crucial for understanding the motor's operating status, assessing its efficiency, and diagnosing faults.

[0077] The current torque current is the torque current collected in real time in the 3D printing equipment. For example, the drive board of the motor in the 3D printing equipment is equipped with a current sampling device (current sensor as shown in Figure 1), such as a sampling resistor. The sampling current is determined by detecting the voltage across the sampling resistor, and then the torque current is calculated from the sampling current. The sampling current can be calculated by measuring the phase current of the motor (ia and ib in Figure 1).

[0078] Alternatively, the motor can be a servo motor.

[0079] Optionally, the motor includes an extrusion motor, which is located in the print head. The extrusion motor has the following functions:

[0080] (1) Material conveying: The main function of the extrusion motor is to convey the filamentous printing material from the tray to the nozzle of the print head. It uses the power generated by the rotation of the motor to bite the material through the engagement of the engagement rollers and push it forward at a stable speed and force to ensure that the material can reach the nozzle smoothly for melting and extrusion.

[0081] (2) Precise control of extrusion amount: In the 3D printing process, it is necessary to precisely control the extrusion amount of each layer of material to ensure the accuracy and quality of the printed model. The extrusion motor works in conjunction with the control system of the 3D printer to precisely control the rotation angle and speed according to the printing instructions, thereby achieving precise control of the material extrusion amount. For example, when printing complex curved surfaces or small structures, the extrusion motor can precisely adjust the extrusion amount according to the shape and size of the model, so that the printed model has a smooth surface and accurate structure.

[0082] (3) Matching the printing speed: The extrusion motor needs to match the moving speed of the print head. When the print head moves quickly, the extrusion motor needs to increase the material feeding speed to ensure that the material can be extruded and filled into the corresponding position in time; when the print head moves slowly or pauses, the extrusion motor also needs to reduce its speed or stop feeding accordingly to avoid excessive material extrusion or accumulation. This matching can ensure the continuity and stability of the printing process and improve the printing quality.

[0083] Specifically, 3D printing equipment can collect the current torque current of the motor based on a preset sampling frequency. For example, the preset sampling frequency could be 60 Hz, 120 Hz, etc. It can be understood that the higher the preset sampling frequency, the higher the real-time performance of the detection.

[0084] Step 012: Based on the target mapping relationship corresponding to the current operating condition of the 3D printing equipment and the current extrusion speed of the printing filament, determine the reference torque current. The target mapping relationship is any one of multiple preset mapping relationships. The preset mapping relationships include the mapping relationship between the extrusion speed of the printing filament and the torque current when the 3D printing equipment is running normally under different preset operating conditions.

[0085] The preset operating conditions refer to the operating conditions under which a 3D printing equipment performs 3D printing. Different preset operating conditions are based on at least one of the following: the type of printing consumables (such as PLA, PETG, ABS (Acrylonitrile Butadiene Styrene copolymer), PC (Polycarbonate), the nozzle diameter of the 3D printing equipment (such as 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm, etc.), and the preset heating temperature of the nozzle (such as 180 degrees, 220 degrees, etc.). When at least one of the following three conditions is different, the 3D printing equipment is in a different preset operating condition.

[0086] Optionally, each preset working condition has a corresponding preset mapping relationship, which is the mapping relationship between the extrusion speed of the printing consumable and the torque current when the 3D printing equipment is operating normally under the preset working condition.

[0087] It is understandable that a preset mapping relationship under various preset working conditions can be established through experiments. For example, multiple sets of extrusion speeds and torque currents of the 3D printing equipment under various test conditions can be obtained, with the test conditions being any preset working condition; then, the multiple sets of extrusion speeds and torque currents are fitted to generate the corresponding mapping relationship for each test condition, thereby obtaining the preset mapping relationship for each preset working condition.

[0088] The extrusion speed of the printing consumables refers to the speed at which the printing consumables are ejected from the nozzle. In this embodiment, the detection of nozzle blockage or dry printing can be achieved even with an external material tray. The external material tray can be considered as a situation without a feeding device or odometer. The judgment is made by the extrusion speed / current of the motor that drives the printing consumables into the hot end, which enriches the application scenarios of this application and has strong applicability.

[0089] Optionally, the current extrusion speed is determined based on the current rotational speed of the motor.

[0090] It is understandable that the faster the motor rotates, the faster the engagement wheel rotates, the faster the engagement wheel engages with the printing filament, and the faster the printing filament is extruded.

[0091] Therefore, the current extrusion speed can be quickly determined based on the current rotational speed of the motor. For example, the current extrusion speed = k * current rotational speed, where k is a pre-calibrated constant. Based on different nozzle diameters, different types of consumables, etc., the corresponding k can be calibrated to ensure the accuracy of the current extrusion speed calculation.

[0092] Optionally, the motor is equipped with a position sensor that can record the motor's position at different times. Based on the motor's position at different times, the motor speed can be quickly calculated. For example, the motor speed at each moment can be obtained by differentiating the motor's position over time.

[0093] Optionally, the 3D printing equipment also includes a feeding device equipped with an odometer. The odometer records the length of printing filament that has been supplied by the feeding device, and the current extrusion speed is determined based on the odometer's recorded mileage at different times.

[0094] It's understandable that the extrusion speed of printing consumables can be directly calculated based on the mileage at different times. However, the mileage is abnormal when there is nozzle blockage or dry printing. Since the extrusion speed generally doesn't change much over a period of time, a more accurate extrusion speed can be calculated using historical mileage data when an abnormal increase or decrease in torque current is detected.

[0095] The current operating condition is determined based on at least one of the following: the type of printing filament currently being used, the diameter of the nozzle currently being used in the 3D printing equipment, and the preset heating temperature of the nozzle currently being used in the 3D printing equipment. In this way, the preset operating condition corresponding to the current operating condition can be determined.

[0096] Once the preset working condition corresponding to the current working condition is determined, the preset mapping relationship corresponding to the preset working condition corresponding to the current working condition can be used as the target mapping relationship corresponding to the current working condition.

[0097] Specifically, after determining the target mapping relationship for the current working condition from the preset mapping relationships corresponding to each preset working condition, the reference torque current can be quickly calculated based on the current extrusion speed and the target mapping relationship. The reference torque current refers to the torque current that the 3D printing equipment should have when operating normally at the current extrusion speed.

[0098] Step 013: Based on the current torque current and the reference torque current, perform anomaly detection on the 3D printing equipment to determine the anomaly detection results.

[0099] The anomaly detection results are used to characterize anomaly-related information of the 3D printing equipment. For example, whether an anomaly exists and the specific type of the anomaly.

[0100] Specifically, after calculating / collecting the current torque current and the reference torque current, the current torque current and the reference torque current can be compared to realize the anomaly detection of the 3D printing equipment and determine the anomaly detection result.

[0101] Optionally, referring to Figure 4, step 013 includes:

[0102] Step 0131: If the current torque current is less than the reference torque current and the difference is greater than the preset difference, it is determined that the 3D printing equipment has a dry-printing abnormality.

[0103] Step 0132: If the current torque current is greater than the reference torque current and the difference is greater than the preset difference, it is determined that there is a blockage abnormality in the 3D printing equipment.

[0104] Taking abnormal detection results including plug abnormality and dry running abnormality as an example, if the current torque current is less than the reference torque current and the difference (specifically the absolute value of the difference) is greater than the preset difference, it indicates that the torque current is abnormally reduced and the load force of the nozzle is abnormally reduced. At this time, it can be determined that the nozzle has a dry running abnormality.

[0105] If the current torque current is greater than the reference torque current, and the difference (specifically the absolute value of the difference) is greater than the preset difference, it indicates that the torque current has increased abnormally and the load force of the nozzle has increased abnormally. At this time, it can be determined that the nozzle has a clogging problem.

[0106] Furthermore, if the current torque current and the reference torque current match (e.g., the current torque current is within the range of [reference torque current - preset difference, reference torque current + preset difference]), it can be assumed that the current torque current has not increased or decreased abnormally, and the load force of the nozzle is within the normal range. At this point, it can be determined that the 3D printing equipment is operating normally.

[0107] Thus, by monitoring whether the current torque currents collected in real time increase or decrease abnormally, the 3D printing equipment can perform anomaly detection, which can improve the real-time performance of anomaly detection.

[0108] Optionally, referring to Figure 5, step 013 includes:

[0109] Step 0133: If the current torque current is less than the corresponding reference torque current within a preset time period, it is determined that the 3D printing equipment has a dry-printing abnormality.

[0110] Step 0134: If the current torque current is greater than the corresponding reference torque current within a preset time period, it is determined that there is a blockage abnormality in the 3D printing equipment.

[0111] Taking abnormal detection results, including plug abnormalities and dry-running abnormalities, as examples, current fluctuations that may occur during normal operation or due to non-abnormal issues (such as sampling current errors) can easily lead to misjudgments when performing abnormality detection based on a single torque current. However, the abnormal fluctuations in torque current caused by plug abnormalities or dry-running abnormalities are continuous. Therefore, abnormality detection can be performed by determining whether each current torque current within a preset time period is less than or greater than the corresponding reference torque current.

[0112] For example, if all current torque currents within a preset time period (such as 0.1 seconds (S), 0.5S, 1S, 2S, 5S, 10S, etc., which can be determined based on the sampling frequency of the current torque current) are less than the corresponding reference torque current, it can be determined that the nozzle load force is abnormally decreasing within the preset time period. In this case, it can be accurately determined that the 3D printing equipment has a dry-printing abnormality. If all current torque currents within a preset time period are greater than the corresponding reference torque current, it can be determined that the nozzle load force is abnormally increasing within the preset time period. In this case, it can be accurately determined that the 3D printing equipment has a nozzle clogging abnormality.

[0113] In one example, please refer to Figure 6. Curve 1 represents the torque current within a preset time period under the abnormal blockage condition, curve 2 represents the target mapping curve under the current operating condition, and curves 3 and 4 represent the torque current within a preset time period under the abnormal dry running condition.

[0114] It can be seen that the torque currents of curve 1 are all greater than the reference torque currents corresponding to curve 2 (i.e., the torque currents corresponding to the same extrusion speed), while the torque currents of curves 3 and 4 are all less than the reference torque currents corresponding to curve 2.

[0115] Therefore, compared to current fluctuations caused by non-abnormal issues, which typically only affect one or two torque currents, detecting anomalies by sampling multiple torque currents within a short preset time period can ensure accuracy without significantly impacting the timeliness of anomaly detection. For example, with a preset time period of 0.5 seconds and a preset sampling frequency of 120Hz, 60 current torque currents can be collected within this preset time period. By comparing the magnitudes of these 60 current torque currents with their corresponding reference torque currents, the anomaly detection results can be quickly obtained.

[0116] Optionally, if a preset percentage (e.g., 70%, 80%, 90%) of the current torque currents collected within a preset time period are all less than the corresponding reference torque current, it is determined that the 3D printing equipment has a dry-printing abnormality; if a preset percentage of the current torque currents collected within a preset time period are all greater than the corresponding reference torque current, it is determined that the 3D printing equipment has a head-clogging abnormality.

[0117] In this way, the influence of noise currents (such as noise caused by sampling errors) in each current torque current collected within a preset time period can be avoided, further improving the accuracy of anomaly detection and avoiding missed detections.

[0118] The anomaly detection method of this application first establishes a mapping relationship between torque current and the extrusion speed of printing consumables during normal operation. Then, during detection, the current torque current of the motor of the 3D printing equipment is obtained. Based on the difference between the reference torque current during normal operation corresponding to the current extrusion speed and the current torque current, the anomaly detection of the 3D printing equipment is realized. For example, nozzle clogging or dry running will cause abnormal torque current. If the difference between the reference torque current and the current torque current is too large, it can be determined that there is an anomaly in the 3D printing equipment.

[0119] Due to the real-time nature of torque current, anomaly detection in 3D printing equipment based on torque current has high real-time performance, ensuring timely anomaly detection. Furthermore, torque current has high accuracy, resulting in high detection accuracy when performing anomaly detection based on torque current. Thus, timely and accurate anomaly detection of the 3D printing equipment ensures its stable operation. Moreover, as shown in Figure 6, this application considers both motor current and motor speed, judging the printing equipment's operating status from two dimensions. This differs from judging the printing equipment's operating status solely based on the magnitude of the current threshold, making the detection results of this application more accurate.

[0120] In some embodiments, please refer to Figure 7. The anomaly detection method further includes step 014, which is described in detail below.

[0121] Step 014: Based on the anomaly detection results, control the 3D printing equipment to stop running and / or issue a prompt message.

[0122] Specifically, upon detecting abnormal results, the 3D printing equipment can be stopped in a timely manner to prevent the abnormality from escalating.

[0123] Furthermore, it can issue alerts. For example, it can issue an alarm through the sound device of the 3D printing equipment to prompt maintenance personnel to handle the issue promptly. Another example is sending alert messages to the maintenance personnel's electronic devices to prompt them to address the problem immediately.

[0124] Once the maintenance personnel resolve the issue, printing can resume, reducing the impact of the anomaly on printing efficiency and ensuring a high success rate.

[0125] According to the method described in the above embodiments, this application also provides a detection device 300 for performing the steps in the above-described anomaly detection method. Please refer to FIG8, which is a schematic diagram of the modules of the detection device 300 provided in this application embodiment. The detection device 300 includes:

[0126] The acquisition module 301 is used to acquire the current torque current of the motor of the 3D printing equipment;

[0127] The determination module 302 is used to determine the reference torque current based on the target mapping relationship corresponding to the current working condition of the 3D printing equipment and the current extrusion speed of the printing consumable. The target mapping relationship is any one of multiple preset mapping relationships. The preset mapping relationships include the mapping relationship between the extrusion speed of the printing consumable and the torque current when the 3D printing equipment is running normally under different preset working conditions.

[0128] The anomaly detection module 303 is used to perform anomaly detection on the 3D printing equipment based on the current torque current and the reference torque current, so as to determine the anomaly detection result.

[0129] It should be noted that the specific details of each module unit in the above-mentioned detection device have been described in detail in the embodiments of the above-mentioned anomaly detection method, and will not be repeated here.

[0130] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0131] In some embodiments, the detection device in this application can be implemented in hardware, such as an electronic device or a component in an electronic device, such as an integrated circuit or a chip; the detection device can also be implemented in software, such as as an application installed in an electronic device.

[0132] In some embodiments, referring to FIG9, the 3D printing device 400 of this application includes a motor 401, a processor 402, and a memory 403. The memory 403 stores a computer program 404 that can run on the processor 402. When the program 404 is executed by the processor 402, it implements the various processes of the embodiments of the above-described anomaly detection method and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0133] Optionally, the print head of the 3D printing equipment 400 includes a nozzle 405, a gripping wheel 406, and a feeding device 407. Driven by a motor 401, the gripping wheel 406 grips the printing material provided by the feeding device 407 and feeds it to the nozzle 405. The nozzle 405 is used to heat the printing material at a preset heating temperature and eject it.

[0134] In some embodiments, please refer to FIG10, which is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 500 includes a processor 501 and a memory 502. The memory 502 stores a computer program 503 that can run on the processor 501. When the program 503 is executed by the processor 501, it implements the various processes of the embodiments of the above-described anomaly detection method and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0135] Please refer to Figure 11, which is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. This electronic device can be a terminal or a server. Exemplarily, the electronic device 700 includes a Central Processing Unit (CPU) 701, a system memory 704 including Random Access Memory (RAM) 702 and Read-Only Memory (ROM) 703, and a system bus 705 connecting the system memory 704 and the CPU 701.

[0136] In some embodiments, the electronic device 700 may also include a basic input / output system 706 that helps transmit information between various devices within the computer, and a mass storage device 707 for storing the operating system 713, the client 714, and other program modules 715.

[0137] In some embodiments, the basic input / output system 706 includes a display 708 for displaying information and an input device 709 for user input, such as a touch panel and other input devices. A touch panel is also called a touchscreen. A touch panel may include both a touch device and a touch controller. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described further here.

[0138] Both the display 708 and the input device 709 are connected to the central processing unit 701 via an input / output controller 710 connected to the system bus 705. The basic input / output system 706 may also include the input / output controller 710 for receiving and processing input from touch panels, other input devices, etc. Similarly, the input / output system 706 also includes output devices such as displays, printers, or other types of output devices.

[0139] Mass storage device 707 is connected to central processing unit 701 via a mass storage controller (not shown) connected to system bus 705. Mass storage device 707 and its associated computer-readable media provide non-volatile storage for electronic device 700. That is, mass storage device 707 may include computer-readable media (not shown) such as hard disk or compact disc read-only memory (CD-ROM) drive.

[0140] According to various embodiments of this application, the electronic device 700 can also be connected to a remote computer on a network, such as the Internet. That is, the electronic device 700 can be connected to a network 717 via a network interface unit 716 connected to the system bus 705, or the network interface unit 716 can be used to connect to other types of networks or remote computer systems (not shown).

[0141] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described anomaly detection method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0142] The processor can be the processor in the electronic device described in the above embodiments. The computer-readable storage medium can be a computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0143] Computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid-state storage technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types.

[0144] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described anomaly detection method. The processor may be a processor in the electronic device described in the above embodiments. When executed by the processor, the computer program implements various processes of the embodiments of the above-described anomaly detection method and achieves the same technical effects; therefore, to avoid repetition, further details are omitted here.

[0145] It is understood that in the specific implementation of this application, data related to user identity or characteristics is involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. An anomaly detection method, wherein, The anomaly detection method is applicable to 3D printing equipment, which includes a motor and a hot end assembly, wherein the motor is used to drive printing consumables into the hot end; The anomaly detection method includes: Obtain the current torque current of the motor in the 3D printing equipment; Based on the target mapping relationship corresponding to the current operating condition of the 3D printing equipment and the current extrusion speed of the printing consumable, a reference torque current is determined. The target mapping relationship is any one of a plurality of preset mapping relationships. The preset mapping relationship includes the mapping relationship between the extrusion speed of the printing consumable and the torque current when the 3D printing equipment is operating normally under different preset operating conditions. Based on the current torque current and the reference torque current, anomaly detection is performed on the 3D printing equipment to determine the anomaly detection result.

2. The anomaly detection method according to claim 1, wherein, The current operating condition is determined based on at least one of the following: the type of printing consumables currently used by the 3D printing equipment, the nozzle diameter of the 3D printing equipment, and the preset heating temperature of the nozzle.

3. The anomaly detection method according to claim 1 or 2, wherein, The anomaly detection results include end-plug anomalies and dry-printing anomalies. The anomaly detection of the 3D printing equipment based on the current torque current and the reference torque current to determine the anomaly detection results includes: If the current torque current is less than the reference torque current and the difference is greater than a preset difference, it is determined that the 3D printing equipment has a dry-printing abnormality. If the current torque current is greater than the reference torque current and the difference is greater than a preset difference, it is determined that the 3D printing equipment has a clogging abnormality.

4. The anomaly detection method according to claim 1 or 2, wherein, The anomaly detection results include end-plug anomalies and dry-printing anomalies. The anomaly detection of the 3D printing equipment based on the current torque current and the reference torque current to determine the anomaly detection results includes: If the current torque current is less than the corresponding reference torque current within a preset time period, it is determined that the 3D printing equipment has a dry-printing abnormality. If the current torque current is greater than the corresponding reference torque current within a preset time period, it is determined that the 3D printing equipment has a blockage abnormality.

5. The anomaly detection method according to any one of claims 1-4, wherein, Also includes: Based on the anomaly detection results, the 3D printing equipment is controlled to stop operating and / or a prompt message is issued.

6. The anomaly detection method according to claim 1, wherein, Also includes: The extrusion speed and torque current of the 3D printing equipment are obtained during normal operation under test conditions, wherein the test conditions are any of the preset conditions. Multiple sets of extrusion speeds and torque currents are fitted to generate the mapping relationship corresponding to the test conditions.

7. The anomaly detection method according to claim 1, wherein, The motor includes a servo motor.

8. The anomaly detection method according to claim 1, wherein, The 3D printing equipment also includes a drive board for driving the motor, and the drive board is equipped with a current detection device for detecting the current torque current of the motor.

9. The anomaly detection method according to claim 1, wherein, The current extrusion speed is determined based on the current rotational speed of the motor; Alternatively, the 3D printing equipment may further include a feeding device equipped with an odometer, the odometer recording mileage representing the length of printing filament supplied by the feeding device, and the current extrusion speed being determined based on the mileage recorded by the odometer at different times.

10. The anomaly detection method according to claim 9, wherein, The motor also includes a position sensor, and the current rotational speed is determined based on the position information detected by the position sensor.

11. A detection device, wherein, include: The acquisition module is used to acquire the current torque current of the motor of the 3D printing equipment; The determination module is used to determine a reference torque current based on the target mapping relationship corresponding to the current operating condition of the 3D printing equipment and the current extrusion speed of the printing consumable. The target mapping relationship is any one of a plurality of preset mapping relationships. The preset mapping relationship includes the mapping relationship between the extrusion speed of the printing consumable and the torque current when the 3D printing equipment is operating normally under different preset operating conditions. An anomaly detection module is used to perform anomaly detection on the 3D printing equipment based on the current torque current and the reference torque current, so as to determine the anomaly detection result.

12. A 3D printing device, wherein, It includes an extrusion motor, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the anomaly detection method as described in any one of claims 1-10.

13. The 3D printing apparatus according to claim 12, wherein, The 3D printing equipment includes a print head, which includes an extrusion motor, a nozzle, a gripping roller, and a feeding device. Driven by the extrusion motor, the gripping roller grips the printing filament provided by the feeding device and feeds it to the nozzle. The nozzle is used to heat the printing filament at a preset heating temperature and eject it.

14. An electronic device, wherein, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the anomaly detection method as described in any one of claims 1-10.

15. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the anomaly detection method as described in any one of claims 1-10.

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