Intelligent wind field wind speed control method and device for additive manufacturing and medium

Abstract

The invention relates to the technical field of additive manufacturing, and discloses an intelligent wind field wind speed control method and device for additive manufacturing and a medium. According to the intelligent wind field wind speed control method and device for additive manufacturing, firstly, through a fuzzy relation self-learning method, corresponding relation data of the fan rotating speed and the wind field wind speed can be permanently stored in a target rotating speed relation database; and secondly, the target fan rotating speed corresponding to the preset control wind speed can be inquired through the target rotating speed relation database, and the axial flow fan is controlled to rotate until the rotating speed of the axial flow fan meets the preset condition. And finally, when the rotating speed of the axial flow fan meets the preset condition, the target fan rotating speed is used for controlling the wind field to be stable. Therefore, by implementing the wind field intelligent control method, dynamic response and precision requirements can be considered while intelligent control of the wind field is realized by combining a fuzzy relation self-learning method and a self-adaptive control method.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to an intelligent wind field wind speed control method, device and medium for additive manufacturing. Background Art

[0002] In additive manufacturing laser melting equipment, when lasers melt and sinter different metal powders, sintering powder splashes, and a large amount of large solid particles remain on the powder surface, which will affect the sintering quality of the workpiece. Using a parallel wind field with a constant wind speed to bring large solid particles splashing when the powder surface is sintered into the filtration system can greatly improve the sintering quality. This technology has been widely used in large and medium-sized equipment. With the market demand for the size of additive manufacturing laser melting workpieces gradually increasing, large and ultra-large additive manufacturing laser melting equipment has been launched on the market, and at the same time, higher requirements are placed on the control efficiency and stability of the wind field.

[0003] With the increasing requirements for wind farm control, simple PID control is difficult to take into account both dynamic response and accuracy requirements. Summary of the invention

[0004] In view of this, the present invention provides an intelligent wind field wind speed control method, device and medium for additive manufacturing to solve the problem that simple PID control is difficult to take into account both dynamic response and accuracy requirements.

[0005] In a first aspect, the present invention provides a method for controlling wind speed in an intelligent wind field for additive manufacturing, wherein the wind field is powered by an axial flow fan, so that a control fluid circulates in a closed channel and is formed in a sintering chamber, the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel; the method is used for a control system, and the control system is connected to the wind speed sensor; the method comprises:

[0006] A preset control wind speed and initial rotational speed relationship database is obtained, and the initial rotational speed relationship database is used to characterize the relationship between the fan speed and the wind speed in the wind field; based on the initial rotational speed relationship database, a target rotational speed relationship database is obtained after being processed by a fuzzy relationship self-learning method; based on the target rotational speed relationship database, a target fan speed corresponding to the preset control wind speed is determined; based on the preset control wind speed and target rotational speed relationship database, the axial flow fan is controlled to rotate until the speed of the axial flow fan meets the preset conditions, and based on the target fan speed and the preset control wind speed, the wind field is stabilized through the axial flow fan control.

[0007] The intelligent wind farm wind speed control method for additive manufacturing provided by the present invention can firstly store the corresponding relationship data between the fan speed and the wind farm wind speed in a target speed relationship database permanently through a fuzzy relationship self-learning method. Secondly, the target fan speed corresponding to the preset control wind speed can be queried through the target speed relationship database and the axial flow fan can be controlled to rotate until the speed of the axial flow fan is equal to the preset condition. Finally, when the speed of the axial flow fan is equal to the preset condition, the target fan speed is used to control the wind farm to be stable. Therefore, by implementing the present invention, by combining the fuzzy relationship self-learning method and the adaptive control method, it is possible to achieve intelligent control of the wind farm while taking into account the dynamic response and accuracy requirements.

[0008] In an optional implementation, based on the initial speed relationship database, a target speed relationship database is obtained through processing by a fuzzy relationship self-learning method, including:

[0009] The initial fan speed is obtained in the initial speed relationship database according to a preset order; the initial fan speed is used to control the rotation of the axial flow fan until the axial flow fan rotates stably, the stable rotation speed of the axial flow fan is obtained and the measured wind speed in the wind field sent by the wind speed sensor is received; the stable rotation speed and the measured wind speed in the wind field are stored in the initial speed relationship database according to the corresponding relationship, and the step of obtaining the initial fan speed is returned to iterate repeatedly until all fan speeds in the initial speed relationship database have been traversed or the measured wind speed in the wind field is greater than the preset maximum wind speed, and the target speed relationship database is obtained.

[0010] The intelligent wind farm wind speed control method for additive manufacturing provided by the present invention obtains the initial fan speed in the initial speed relationship database in a preset order and controls the rotation of the axial flow fan, and stores the corresponding stable rotation speed of the axial flow fan during stable rotation and the measured wind speed of the wind farm in the initial speed relationship database in a corresponding relationship, until all fan speeds in the initial speed relationship database have been traversed or the measured wind speed of the wind farm is greater than the preset maximum wind speed, and obtains the target speed relationship database. Therefore, by implementing the present invention, the corresponding relationship data between the fan speed and the wind farm speed can be permanently stored in the target speed relationship database through the fuzzy relationship self-learning method.

[0011] In an optional implementation, the initial fan speed is used to control the rotation of the axial flow fan until the axial flow fan rotates stably, the stable rotation speed of the axial flow fan is obtained, and the measured wind speed of the wind field sent by the wind speed sensor is received, including:

[0012] The initial fan speed is used to control the rotation of the axial flow fan and obtain the measured fan speed of the axial flow fan; based on the measured fan speed and the preset fan speed, whether the rotation of the axial flow fan is stable is determined; when the rotation of the axial flow fan is stable, the stable rotation speed of the axial flow fan is obtained and the measured wind field wind speed sent by the wind speed sensor is received.

[0013] In an optional implementation, based on a preset control wind speed and target speed relationship database, the axial flow fan is controlled to rotate until the speed of the axial flow fan meets a preset condition, and based on the target fan speed and the preset control wind speed, the wind field is stabilized by the axial flow fan control, including:

[0014] Based on the preset control wind speed and target speed relationship database, the control speed is calculated; the rotation of the axial flow fan is controlled by the control speed, and it is determined whether the speed of the axial flow fan meets the preset conditions; when the speed of the axial flow fan meets the preset conditions, the wind field is stabilized by controlling the axial flow fan based on the target fan speed and the preset control wind speed.

[0015] The intelligent wind farm wind speed control method for additive manufacturing provided by the present invention first calculates the control speed by using a preset control wind speed and target speed relationship database. Secondly, the axial flow fan is controlled to rotate according to the calculated control speed until the speed of the axial flow fan meets the preset conditions, and then the axial flow fan is controlled by using the target fan speed. At the same time, the wind farm can be stably controlled in combination with the preset control wind speed, while taking into account the dynamic response and accuracy requirements.

[0016] In an optional implementation, when the rotation speed of the axial flow fan meets a preset condition, based on the target fan rotation speed and the preset control wind speed, the wind field is controlled to be stable by the axial flow fan, including:

[0017] When the speed of the axial flow fan meets the preset conditions, the target fan speed is used to control the stable rotation of the axial flow fan; the measured fan speed of the axial flow fan during the stable rotation process is obtained; based on the measured fan speed and the preset control wind speed, it is determined whether the wind field is stable; when the wind field is unstable, return to the step of controlling the rotation of the axial flow fan by controlling the speed, and iterate repeatedly until the wind field is stable.

[0018] The intelligent wind field wind speed control method for additive manufacturing provided by the present invention controls the axial flow fan using the target fan speed when the speed of the axial flow fan meets the preset conditions. Furthermore, if the wind field is unstable during the control process, it is necessary to repeatedly iterate the control until the wind field is stable, thereby achieving stable control of the wind field while taking into account the dynamic response and accuracy requirements.

[0019] In an optional implementation, judging whether the wind farm is stable based on the measured wind turbine speed and the preset control wind speed includes:

[0020] Determine whether the measured fan speed is within the fan speed instability range, and determine whether the preset control wind speed has changed; when the measured fan speed is not within the fan speed instability range, or when the preset control wind speed has changed, determine that the wind farm is unstable.

[0021] In a second aspect, the present invention provides an intelligent wind field wind speed control device for additive manufacturing, wherein the wind field is powered by an axial flow fan, so that a control fluid circulates in a closed channel and is formed in a sintering chamber, wherein the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel; the device is used to control the system, and the control system is connected to the wind speed sensor; the device comprises:

[0022] An acquisition module is used to acquire a preset control wind speed and an initial speed relationship database, wherein the initial speed relationship database is used to characterize the relationship between the fan speed and the wind speed of the wind field; a processing module is used to obtain a target speed relationship database based on the initial speed relationship database after processing by a fuzzy relationship self-learning method; a determination module is used to determine a target fan speed corresponding to the preset control wind speed based on the target speed relationship database; a control module is used to control the rotation of the axial flow fan based on the preset control wind speed and the target speed relationship database until the speed of the axial flow fan meets the preset conditions, and then control the wind field to be stable through the axial flow fan based on the target fan speed and the preset control wind speed.

[0023] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the intelligent wind farm wind speed control method for additive manufacturing of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the intelligent wind farm wind speed control method for additive manufacturing of the above-mentioned first aspect or any corresponding embodiment thereof.

[0025] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the intelligent wind farm wind speed control method for additive manufacturing according to the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a schematic flow chart of a smart wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention;

[0028] Figure 2 is a flow chart of another intelligent wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention;

[0029] Figure 3 is a flow chart of another intelligent wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of control hardware of a smart wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention;

[0031] Figure 5A is a self-learning control principle diagram according to an embodiment of the present invention;

[0032] Figure 5B is another self-learning control principle diagram according to an embodiment of the present invention;

[0033] Figure 6 is a structural block diagram of an intelligent wind farm wind speed control device for additive manufacturing according to an embodiment of the present invention;

[0034] Figure 7 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] With the increasing requirements for wind farm control, simple PID control is difficult to take into account both dynamic response and precision requirements. In view of the unique properties of the wind farm as a control object, the introduction of intelligent control methods to maintain an efficient and stable wind farm is essential to promote the intelligent processing technology of additive manufacturing laser melting equipment.

[0037] Therefore, an embodiment of the present invention provides an intelligent wind farm wind speed control method for additive manufacturing, which combines a fuzzy relationship self-learning method and an adaptive control method to achieve the effect of taking into account dynamic response and precision requirements while realizing intelligent control of the wind farm.

[0038] According to an embodiment of the present invention, an embodiment of an intelligent wind farm wind speed control method for additive manufacturing is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] In this embodiment, a method for controlling wind speed in an intelligent wind field for additive manufacturing is provided, wherein the wind field is powered by an axial flow fan, so that a control fluid circulates in a closed channel and is formed in a sintering chamber. Furthermore, the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel.

[0040] The method is used for a control system, which is connected to a wind speed sensor; Figure 1 is a flow chart of a smart wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0041] Step S101, obtaining a preset control wind speed and initial rotation speed relationship database.

[0042] Specifically, the preset control wind speed represents the preset control speed of the wind farm, and the rationality of the preset control wind speed can be ensured by a preset wind speed limiter, etc.; the initial speed relationship database is used to characterize the relationship between the wind turbine speed and the wind farm speed, as shown in the following relationship:

[0043] Relationship[1..N]=(FanSpeed[WindSpeed)

[0044] Wherein: Relationship represents the relationship between the fan speed and the wind speed in the wind farm; FanSpeed ​​represents the fan speed, which is divided into N parts according to the rated wind speed of the fan, and each part is ΔFanSpeed; WindSpeed ​​represents the wind speed in the wind farm measured by the wind speed sensor at different fan speeds.

[0045] In one example, the rated wind speed of the fan is 6220 rpm; N = 20; ΔFanSpeed ​​= 331 rpm;

[0046] Relationship[1..N]

[0047] ={(311|0.67), (622|1.32), (933|2.97), (1244|4.13), (4976|5.65), (1555|8.24), (1866|10.96), (2177|13.01), (2488|15.26), (2799|17.89), (3110|20.1 0),(3421|22.78),(3732|25.21),(4043|27.73),(4354|30.14),(4665|32.89),(4976|35.65),(5287|37.49),(5598|39.53),(5909|41.57),(6220|43.24)}.

[0048] Step S102, based on the initial speed relationship database, a target speed relationship database is obtained through processing using a fuzzy relationship self-learning method.

[0049] Specifically, during the equipment preparation stage, the fan is started, and the fan speed is controlled step by step through the relationship data between the fan speed and the wind speed in the wind field stored in the known initial speed relationship database. The wind speed corresponding to each level is measured, and the corresponding relationship data is established and permanently stored in the target speed relationship database of the control system.

[0050] Step S103, determining a target fan speed corresponding to a preset control wind speed based on a target speed relationship database.

[0051] Specifically, on the basis of obtaining the preset control wind speed, the target fan speed corresponding to the preset control wind speed can be queried in the target speed relationship database.

[0052] Step S104, based on the preset control wind speed and target speed relationship database, the axial flow fan is controlled to rotate until the speed of the axial flow fan meets the preset conditions, and the wind field is stabilized by controlling the axial flow fan based on the target fan speed and the preset control wind speed.

[0053] Specifically, based on a known preset control wind speed and target speed relationship database, the axial flow fan is first controlled to rotate until the axial flow fan can rotate stably, that is, when the speed of the axial flow fan is close to the target fan speed, it is determined that the speed of the axial flow fan meets the preset conditions.

[0054] Furthermore, the target wind turbine speed is used to control the wind farm to be stable. At the same time, combined with the preset control wind speed, the stable control of the wind farm can be achieved, while taking into account the dynamic response and accuracy requirements.

[0055] The intelligent wind farm wind speed control method for additive manufacturing provided in this embodiment can firstly store the corresponding relationship data between the fan speed and the wind farm wind speed in a target speed relationship database permanently through a fuzzy relationship self-learning method. Secondly, the target fan speed corresponding to the preset control wind speed can be queried through the target speed relationship database and the axial flow fan can be controlled to rotate until the speed of the axial flow fan meets the preset conditions. Finally, when the speed of the axial flow fan is equal to the preset conditions, the target fan speed is used to control the wind farm to be stable. Therefore, by implementing the present invention, by combining the fuzzy relationship self-learning method and the adaptive control method, it is possible to achieve intelligent control of the wind farm while taking into account the dynamic response and accuracy requirements.

[0056] In this embodiment, a method for controlling wind speed in an intelligent wind field for additive manufacturing is provided, wherein the wind field is powered by an axial flow fan, so that a control fluid circulates in a closed channel and is formed in a sintering chamber. Furthermore, the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel.

[0057] The method is used for a control system, which is connected to a wind speed sensor; Figure 2 is a flow chart of a smart wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0058] Step S201, obtain the preset control wind speed and initial speed relationship database. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0059] Step S202: Based on the initial speed relationship database, a target speed relationship database is obtained by processing it using a fuzzy relationship self-learning method.

[0060] Specifically, the above step S202 includes:

[0061] Step S2021, obtaining the initial fan speed in the initial speed relationship database according to a preset order.

[0062] Specifically, the corresponding initial fan speeds are extracted step by step from the initial speed relationship database in order from high to low.

[0063] Step S2022, using the initial fan speed to control the rotation of the axial flow fan until the axial flow fan rotates stably, obtaining the stable rotation speed of the axial flow fan and receiving the measured wind speed in the wind field sent by the wind speed sensor.

[0064] Specifically, the extracted initial fan speed is used to control the rotation of the axial flow fan, and after the axial flow fan rotates stably, the stable rotation speed of the axial flow fan is obtained. At the same time, the wind speed sensor measures the current wind speed of the wind farm in real time and sends it to the control system.

[0065] In some optional implementations, the above step S2022 includes:

[0066] Step a1: Use the initial fan speed to control the rotation of the axial flow fan and obtain the actual fan speed of the axial flow fan.

[0067] Step a2: judging whether the rotation of the axial flow fan is stable based on the measured fan speed and the preset fan speed.

[0068] Step a3: when the rotation of the axial flow fan is stable, the stable rotation speed of the axial flow fan is obtained and the measured wind speed in the wind field sent by the wind speed sensor is received.

[0069] Specifically, the extracted initial fan speed is used to control the rotation of the axial flow fan, and at the same time, the measured fan speed of the axial flow fan is obtained.

[0070] Furthermore, it can be determined whether the fan speed is stable by checking that the difference between the actual fan speed and the preset fan speed reaches an allowable range for a period of time.

[0071] Further, if the fan speed is stable, the stable rotation speed of the axial flow fan when it is rotating stably is obtained. At the same time, the wind speed sensor measures the wind speed of the wind field when the axial flow fan is rotating stably in real time and sends it to the control system.

[0072] Step S2023, store the stable rotation speed and the measured wind speed in the wind field in the initial speed relationship database according to the corresponding relationship, return to the step of obtaining the initial fan speed, and iterate repeatedly until all fan speeds in the initial speed relationship database have been traversed or the measured wind speed in the wind field is greater than the preset maximum wind speed, and obtain the target speed relationship database.

[0073] Specifically, the obtained stable rotation speed and the measured wind speed in the wind field are stored in an initial rotation speed relationship database according to a corresponding relationship.

[0074] Further, return to step S2021 to repeat the operation until all wind turbine speeds in the initial speed relationship database have been traversed, or the actual wind speed in the wind field measured by the wind speed sensor is greater than the preset maximum wind speed, the fuzzy relationship self-learning is completed, and a corresponding target speed relationship database is formed that stores all stable rotation speeds and actual wind speeds in the wind field stored according to the corresponding relationship.

[0075] Step S203: Determine the target fan speed corresponding to the preset control wind speed based on the target speed relationship database. Figure 1Step S103 of the illustrated embodiment will not be described in detail here.

[0076] Step S204: Based on the preset control wind speed and target speed relationship database, the axial flow fan is controlled to rotate until the speed of the axial flow fan meets the preset conditions, and the wind field is stabilized by the axial flow fan control based on the target fan speed and the preset control wind speed. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0077] The intelligent wind field wind speed control method for additive manufacturing provided in this embodiment obtains the initial fan speed in the initial speed relationship database in accordance with the preset order and controls the rotation of the axial flow fan, and stores the corresponding stable rotation speed of the axial flow fan during stable rotation and the measured wind field wind speed in the initial speed relationship database in accordance with the corresponding relationship, until all fan speeds in the initial speed relationship database have been traversed or the measured wind field wind speed is greater than the preset maximum wind speed, the fuzzy relationship self-learning is completed and the target speed relationship database is obtained. Secondly, the target fan speed corresponding to the preset control wind speed can be queried through the target speed relationship database and the axial flow fan is controlled to rotate until the speed of the axial flow fan meets the preset conditions. Finally, when the speed of the axial flow fan is equal to the preset conditions, the target fan speed is used to control the stability of the wind field. Therefore, by implementing the present invention, by combining the fuzzy relationship self-learning method and the adaptive control method, the dynamic response and accuracy requirements can be taken into account while realizing the intelligent control of the wind field.

[0078] In this embodiment, a method for controlling wind speed in an intelligent wind field for additive manufacturing is provided, wherein the wind field is powered by an axial flow fan, so that a control fluid circulates in a closed channel and is formed in a sintering chamber. Furthermore, the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel.

[0079] The method is used for a control system, which is connected to a wind speed sensor; Figure 3 is a flow chart of a smart wind farm wind speed control method for additive manufacturing according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0080] Step S301, obtain the preset control wind speed and initial speed relationship database. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0081] Step S302: Based on the initial speed relationship database, the target speed relationship database is obtained through fuzzy relationship self-learning method. Figure 3 Step S302 of the illustrated embodiment will not be described in detail here.

[0082] Step S303: Determine the target fan speed corresponding to the preset control wind speed based on the target speed relationship database. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0083] Step S304, based on the preset control wind speed and target speed relationship database, control the axial flow fan to rotate until the speed of the axial flow fan meets the preset conditions, and then control the wind field to be stable through the axial flow fan based on the target fan speed and the preset control wind speed.

[0084] Specifically, the above step S304 includes:

[0085] Step S3041, calculating the control speed based on the preset control wind speed and target speed relationship database.

[0086] Specifically, the corresponding relationship can be queried in the target speed relationship database according to the preset control wind speed SetWindSpeed, as shown in the following relationship:

[0087] SetWindSpeed∈[Relationship[n](FanSpeedWindSpeed),Relationship[n+1](FanSpeed|WindSpeed)]

[0088] Furthermore, the fan speed control related parameters are calculated and a straight line equation is constructed within the queried interval, as shown in the following relationship:

[0089]

[0090] Furthermore, the relationship (ControlFanSpeed|SetWindSpeed) can be obtained, where ControlFanSpeed ​​represents the control speed. At the same time, the steady-state adjustment fan speed range [ControlFanSpeed-ΔFanSpeed, ControlFanSpeed+2*ΔFanSpeed] can be obtained. Furthermore, the fan speed instability range [FanSpeed low ,FanSpeed high ].

[0091] Step S3042, controlling the rotation speed of the axial flow fan, and determining whether the rotation speed of the axial flow fan meets a preset condition.

[0092] Specifically, based on the obtained steady-state adjustment fan speed range and fan speed instability range, a fan speed limiter can be used to ensure the rationality of the speed output, and after meeting the requirements, the axial flow fan is controlled to rotate according to the calculated control speed.

[0093] Furthermore, in the process of controlling the axial flow fan to rotate according to the calculated control speed, it is continuously determined whether the real-time speed of the axial flow fan is close to the target fan speed, that is, whether the real-time speed of the axial flow fan meets the preset condition.

[0094] Step S3043: When the rotation speed of the axial flow fan meets the preset conditions, the wind field is stabilized by controlling the axial flow fan based on the target fan rotation speed and the preset control wind speed.

[0095] Specifically, if the rotation speed of the axial flow fan meets the preset condition, that is, the rotation speed of the axial flow fan is close to the target fan rotation speed, it means that the wind speed of the current wind farm is close to the preset control wind speed.

[0096] Furthermore, by combining the target fan speed and the preset control wind speed at this time, the rotation of the axial flow fan can be controlled, thereby achieving stable control of the wind farm.

[0097] In some optional implementations, the above step S3043 includes:

[0098] Step b1, when the rotation speed of the axial flow fan meets the preset conditions, the target fan rotation speed is used to control the axial flow fan to rotate stably.

[0099] Specifically, if the speed of the axial flow fan meets the preset conditions, that is, the speed of the axial flow fan is close to the target fan speed, it means that the wind speed of the current wind field is close to the preset control wind speed. At this time, the difference between the target fan speed and the preset control wind speed can be used to determine whether the speed of the axial flow fan is stable.

[0100] Furthermore, when the rotation speed of the axial flow fan is stabilized, the target fan rotation speed can be used to control the axial flow fan to rotate stably.

[0101] Step b2, obtaining the actual measured fan speed of the axial flow fan during stable rotation.

[0102] Step b3, judging whether the wind farm is stable based on the measured wind turbine speed and the preset control wind speed.

[0103] In some optional implementations, the above step b3 includes:

[0104] Step b31, determining whether the measured fan speed is within the fan speed instability interval, and determining whether the preset control wind speed changes.

[0105] Step b32: When the measured wind turbine speed is not within the wind turbine speed instability interval, or when the preset control wind speed changes, it is determined that the wind field is unstable.

[0106] Specifically, in the process of controlling the stable rotation of the axial flow fan by using the target fan speed, if the measured fan speed exceeds the fan speed instability interval [FanSpeedlow ,FanSpeed high ], or if the preset controlled wind speed changes, the wind speed control can be considered unstable.

[0107] Step b4, when the wind field is unstable, return to the step of controlling the rotation of the axial flow fan by controlling the speed, and iterate repeatedly until the wind field is stable.

[0108] Specifically, if it is determined that the wind speed control is unstable, return to step S3042 to perform repeated iterations until the wind field is stable.

[0109] The intelligent wind farm wind speed control method for additive manufacturing provided in this embodiment firstly uses a fuzzy relationship self-learning method to permanently store the corresponding relationship data between the fan speed and the wind farm wind speed in a target speed relationship database. Secondly, the target fan speed corresponding to the preset control wind speed can be queried through the target speed relationship database to control the rotation of the axial flow fan. Finally, when the speed of the axial flow fan meets the preset conditions, the target fan speed is used to control the axial flow fan. Furthermore, if the wind field is unstable during the control process, it is necessary to repeatedly iterate the control until the wind field is stable, thereby achieving stable control of the wind field while taking into account dynamic response and accuracy requirements.

[0110] In one example, a smart wind field wind speed control method for additive manufacturing is provided, which is used for a controller. The method is used to control the circulation of fluid in a fully closed-loop wind duct and form a stable wind field in a laser sintering chamber. The wind field is powered by an axial flow fan. The actual wind speed is detected by installing a wind speed sensor in the pipeline. The fan adopts a vector control mode, which makes the wind field output control more accurate. At the same time, a filtering system is equipped at the end of the pipeline to filter large solid particles.

[0111] Furthermore, the method comprises the following steps:

[0112] (1) Fuzzy relationship self-learning: During the equipment preparation stage, the fan is started, and the fan speed is controlled step by step. The corresponding wind speed at each level is measured to establish the relationship data and store it permanently in the controller.

[0113] (2) Adaptive control: Adaptive control is divided into two parts: pre-control and steady-state control. In the pre-control stage, according to the set wind speed, the relationship data is queried to find the corresponding fan speed, and the fan is controlled to reach the speed smoothly and quickly. After stabilization, the pre-control stage is completed. Then it enters steady-state control, and the wind speed is controlled to enter steady state through PID adjustment. During the steady-state process, due to external interference or changes in the wind speed setting value, the steady-state imbalance is caused, and the control will enter the pre-control stage, so that it can quickly reach or return to the steady-state range, and then enter steady-state control again.

[0114] Specifically, the hardware of this method is as follows Figure 4As shown, 101-sintering chamber and 102-full closed loop pipeline form a closed channel for fluid. 103-axial flow fan provides fluid circulation power, and the axial flow fan is controlled by a variable speed inverter. 104-filter element is used to filter large solid particles mixed in the fluid. Finally, 106-wind field is formed in 101-sintering chamber. At the same time, 105-wind speed sensor measures the wind speed at the air outlet as feedback for control.

[0115] Furthermore, relationship self-learning is required during the equipment commissioning phase or after the filter element is cleaned. Figure 5A As shown, the 201-relationship data is shown in the following relationship:

[0116] Relationship[1..N]=(FanSpeed|WindSpeed)

[0117] Wherein: Relationship represents the relationship between the fan speed and the wind speed in the wind farm; FanSpeed ​​represents the fan speed, which is divided into N parts according to the rated wind speed of the fan, and each part is ΔFanSpeed; WindSpeed ​​represents the wind speed in the wind farm measured by the wind speed sensor at different fan speeds.

[0118] Furthermore, the fan speed step-by-step setting control unit 202 extracts parameters of each level from the 201-relationship data, and controls the rotation of the fan in order from low to high. After each fan speed is given, the fan speed stability judgment unit 203 judges that the fan speed is stable after the difference between the actual speed of the fan and the given speed reaches the allowable range for a period of time. At this time, the wind speed is measured by the 105-wind speed sensor, and the 204-speed-wind speed relationship data is established. Then the 202-fan speed step-by-step setting control unit extracts the next level of parameters from the 201-relationship data, and repeats steps 203 and 204 until all levels in the 201-relationship data have been traversed or the measured wind speed is greater than the set maximum wind speed. After self-learning is completed, the 201-relationship data will be permanently maintained in the controller.

[0119] Furthermore, in the process of wind speed control, if Figure 5B As shown, it is divided into 309-pre-control and 310-steady-state control. In 309-pre-control, the wind speed is set through 301-setting wind speed limiter to ensure the rationality of the set wind speed. After meeting the requirements, the corresponding relationship is queried in 201-relation data according to the set wind speed according to 302-query relationship number method:

[0120] SetWindSpeed∈[Relationship[n](FanSpeed|WindSpeed),Relationship[n+1](FanSpeed|WindSpeed)]

[0121] Then 303- calculates the fan speed control related parameters and constructs a straight line equation within the queried interval:

[0122]

[0123] Get the relationship (ControlFanSpeed|SetWindSpeed). At the same time, the steady-state adjustment fan speed interval [ControlFanSpeed-ΔFanSpeed, ControlFanSpeed+2*ΔFanSpeed] is calculated to obtain the fan speed instability interval [FanSpeed low ,FanSpeed high ]. The fan speed limiter is controlled by 305 to ensure the rationality of the speed output. After the requirements are met, the fan is controlled to rotate according to the calculated control speed, and the fan is quickly started to make the wind speed close to the set wind speed value. When the difference between the fan speed and the fan control speed calculated by the pre-control reaches the allowable range and lasts for a period of time, the fan speed is judged to be stable. After stabilization, it enters 310-steady-state control, and 308-steady-state PID controller takes over the control to ensure the stability of the wind speed within a small range.

[0124] Furthermore, in the steady-state control process, 306-the fan speed stability judgment unit is the fan control speed currently outputted exceeds the instability range [FanSpeed low ,FanSpeed high ] when the wind speed control is unstable. At the same time, 307-the set wind speed change judgment unit judges that the set wind speed has changed and also determines that the wind speed control is unstable. When it is unstable, the control is transferred from steady-state control to pre-control. After the pre-control is stabilized, it enters 310-steady-state control and realizes wind speed control according to this mode.

[0125] In this embodiment, an intelligent wind farm wind speed control device for additive manufacturing is also provided, which is used to implement the above embodiments and preferred embodiments, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0126] This embodiment provides an intelligent wind field wind speed control device for additive manufacturing. The wind field is powered by an axial flow fan, so that the control fluid circulates in a closed channel and is formed in a sintering chamber. The closed channel is constructed by a sintering chamber and a fully closed-loop pipeline. A wind speed sensor is installed in the closed channel. The device is used to control the system, and the control system is connected to the wind speed sensor. Figure 6 As shown, the device comprises:

[0127] The acquisition module 601 is used to acquire a preset control wind speed and an initial rotation speed relationship database, where the initial rotation speed relationship database is used to characterize the relationship between the wind turbine rotation speed and the wind farm wind speed.

[0128] The processing module 602 is used to obtain a target speed relationship database based on the initial speed relationship database through a fuzzy relationship self-learning method.

[0129] The determination module 603 is used to determine the target wind turbine speed corresponding to the preset control wind speed based on the target speed relationship database.

[0130] The control module 604 is used to control the rotation of the axial flow fan based on the preset control wind speed and target speed relationship database until the speed of the axial flow fan meets the preset conditions, and then control the wind field stability through the axial flow fan based on the target fan speed and the preset control wind speed.

[0131] In some optional implementations, the processing module 602 includes:

[0132] The acquisition submodule is used to acquire the initial fan speed in the initial speed relationship database according to a preset order.

[0133] A first control submodule is used to control the rotation of the axial flow fan using the initial fan speed until the axial flow fan rotates stably, obtain the stable rotation speed of the axial flow fan and receive the measured wind speed of the wind field sent by the wind speed sensor;

[0134] The iterative submodule is used to store the stable rotation speed and the measured wind speed in the wind field in the initial speed relationship database according to the corresponding relationship, return to the step of obtaining the initial fan speed, and iterate repeatedly until all fan speeds in the initial speed relationship database have been traversed or the measured wind speed in the wind field is greater than the preset maximum wind speed, and obtain the target speed relationship database.

[0135] In some optional implementations, the first control submodule includes:

[0136] The first control unit is used to control the rotation of the axial flow fan using the initial fan speed and obtain the actual measured fan speed of the axial flow fan.

[0137] The first judgment unit is used to judge whether the rotation of the axial flow fan is stable based on the measured fan speed and the preset fan speed.

[0138] The first acquisition unit is used to acquire the stable rotation speed of the axial flow fan and receive the measured wind speed in the wind field sent by the wind speed sensor when the rotation of the axial flow fan is stable.

[0139] In some optional implementations, the control module 604 includes:

[0140] The calculation submodule is used to calculate the control speed based on a preset control wind speed and target speed relationship database.

[0141] The judging submodule is used to control the rotation of the axial flow fan by controlling the rotation speed, and to judge whether the rotation speed of the axial flow fan meets a preset condition.

[0142] The second control submodule is used to control the wind field stability through the axial flow fan based on the target fan speed and the preset control wind speed when the speed of the axial flow fan meets the preset conditions.

[0143] In some optional implementations, the second control submodule includes:

[0144] The second control unit is used to control the axial flow fan to rotate stably using the target fan speed when the speed of the axial flow fan meets the preset conditions.

[0145] The second acquisition unit is used to acquire the actual measured fan speed of the axial flow fan during the stable rotation process.

[0146] The second judgment unit is used to judge whether the wind farm is stable based on the measured wind turbine speed and the preset control wind speed.

[0147] The iteration unit is used to return to the step of controlling the rotation of the axial flow fan by controlling the speed when the wind field is unstable, and iterate repeatedly until the wind field is stable.

[0148] In some optional implementations, the second determination unit includes:

[0149] The judgment subunit is used to judge whether the measured fan speed is within the fan speed instability range, and to judge whether the preset control wind speed changes.

[0150] The determination subunit is used to determine that the wind farm is unstable when the measured wind turbine speed is not within the wind turbine speed instability interval or when the preset control wind speed changes.

[0151] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0152] The intelligent wind farm wind speed control device for additive manufacturing in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0153] The embodiment of the present invention also provides a computer device having the above Figure 6 The intelligent wind farm wind speed control device for additive manufacturing is shown.

[0154] See also Figure 7 , Figure 7 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 7 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0155] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0156] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0157] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0158] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0159] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0160] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0161] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0162] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for controlling wind speed in an intelligent wind field for additive manufacturing, wherein the wind field is powered by an axial flow fan, so that a control fluid circulates in a closed channel and is formed in a sintering chamber, wherein the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel; characterized in that: Used for a control system, the control system is connected to the wind speed sensor; the method comprises: Acquire a preset control wind speed and an initial speed relationship database, wherein the initial speed relationship database is used to characterize the relationship between the wind turbine speed and the wind field wind speed; Based on the initial speed relationship database, a target speed relationship database is obtained by processing with a fuzzy relationship self-learning method; Determining a target fan speed corresponding to the preset control wind speed based on the target speed relationship database; Based on the preset control wind speed and the target speed relationship database, the axial flow fan is controlled to rotate until the speed of the axial flow fan meets a preset condition, and based on the target fan speed and the preset control wind speed, the wind field is controlled to be stable through the axial flow fan.

2. The method according to claim 1, characterized in that Based on the initial speed relationship database, a target speed relationship database is obtained through fuzzy relationship self-learning method processing, including: Acquire the initial fan speed in the initial speed relationship database according to a preset order; Using the initial fan speed to control the rotation of the axial flow fan until the axial flow fan rotates stably, obtaining the stable rotation speed of the axial flow fan and receiving the measured wind speed of the wind field sent by the wind speed sensor; The stable rotation speed and the measured wind speed in the wind field are stored in the initial rotation speed relationship database according to the corresponding relationship, and the step of obtaining the initial fan speed is returned to and iterated repeatedly until all fan speeds in the initial rotation speed relationship database have been traversed or the measured wind speed in the wind field is greater than the preset maximum wind speed, thereby obtaining the target rotation speed relationship database.

3. The method according to claim 2, characterized in that The method comprises: controlling the rotation of the axial flow fan by using the initial fan speed until the axial flow fan rotates stably, obtaining the stable rotation speed of the axial flow fan and receiving the measured wind speed of the wind field sent by the wind speed sensor, including: Using the initial fan speed to control the rotation of the axial flow fan and obtain the measured fan speed of the axial flow fan; Determining whether the rotation of the axial flow fan is stable based on the measured fan speed and the preset fan speed; When the rotation of the axial flow fan is stable, the stable rotation speed of the axial flow fan is acquired and the measured wind speed of the wind field sent by the wind speed sensor is received.

4. The method according to claim 1, characterized in that: Based on the preset control wind speed and the target speed relationship database, the axial flow fan is controlled to rotate until the speed of the axial flow fan meets a preset condition, and based on the target fan speed and the preset control wind speed, the wind field is controlled to be stable by the axial flow fan, including: Calculating a control speed based on the preset control wind speed and the target speed relationship database; Controlling the rotation of the axial flow fan by using the controlled rotation speed, and determining whether the rotation speed of the axial flow fan meets a preset condition; When the rotation speed of the axial flow fan meets a preset condition, the wind field is controlled to be stable through the axial flow fan based on the target fan rotation speed and the preset control wind speed.

5. The method according to claim 4, characterized in that When the rotation speed of the axial flow fan meets a preset condition, based on the target fan rotation speed and the preset control wind speed, the wind field is controlled to be stable by the axial flow fan, including: When the rotation speed of the axial flow fan meets a preset condition, the target fan rotation speed is used to control the axial flow fan to rotate stably; Obtaining the actual measured fan speed of the axial flow fan during stable rotation; Based on the measured wind turbine speed and the preset control wind speed, determining whether the wind farm is stable; When the wind field is unstable, return to the step of controlling the rotation of the axial flow fan by controlling the rotation speed, and iterate repeatedly until the wind field is stable.

6. The method according to claim 5, characterized in that Based on the measured wind turbine speed and the preset control wind speed, judging whether the wind farm is stable includes: Determining whether the measured fan speed is within the fan speed instability interval, and determining whether the preset control wind speed changes; When the measured wind turbine speed is not within the wind turbine speed instability interval, or when the preset control wind speed changes, it is determined that the wind farm is unstable.

7. An intelligent wind field wind speed control device for additive manufacturing, wherein the wind field is powered by an axial flow fan, so that the control fluid circulates in a closed channel and is formed in a sintering chamber, the closed channel is constructed by a sintering chamber and a fully closed-loop pipeline, and a wind speed sensor is installed in the closed channel; characterized in that, Used for a control system, the control system is connected to the wind speed sensor; the device comprises: An acquisition module, used to acquire a preset control wind speed and an initial speed relationship database, wherein the initial speed relationship database is used to characterize the relationship between the wind turbine speed and the wind farm wind speed; A processing module, used for obtaining a target speed relationship database based on the initial speed relationship database through a fuzzy relationship self-learning method; A determination module, configured to determine a target fan speed corresponding to the preset control wind speed based on the target speed relationship database; A control module is used to control the rotation of the axial flow fan based on the preset control wind speed and the target speed relationship database until the speed of the axial flow fan meets the preset conditions, and based on the target fan speed and the preset control wind speed, control the wind field to be stable through the axial flow fan.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the intelligent wind farm wind speed control method for additive manufacturing according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the intelligent wind farm wind speed control method for additive manufacturing according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to cause a computer to execute the intelligent wind farm wind speed control method for additive manufacturing according to any one of claims 1 to 6.

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