Preparation method, device and equipment of appliance and storage medium

Abstract

The invention discloses a preparation method, device and equipment of an orthodontic appliance and a storage medium. The preparation method comprises the following steps: obtaining cutting line data of the orthodontic appliance and cutting quality evaluation data of an industrial robot corresponding to at least one position on a cutting line; the evaluation data is determined according to the deviation or the matching degree between the processing state of the industrial robot at the corresponding position and the expected state; when the evaluation data meets a first correction condition, determining a motion correction parameter of the industrial robot at the first position; and based on the motion correction parameters, controlling motion parameters of the industrial robot, so that the industrial robot cuts the appliance along the cutting line. According to the preparation method provided by the invention, the machining precision can be actually improved in a targeted manner and in accordance with the machining track.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices, and in particular to a preparation method, device, equipment and storage medium of an orthodontic appliance. BACKGROUND

[0002] In the preparation of medical devices, industrial robots are gradually widely used due to their low cost and high flexibility. In scenarios such as cutting of orthodontic appliances, industrial robots are required to run along a complex and different trajectory, maintain the accuracy of the trajectory posture (including position and orientation), and maintain a certain motion speed to meet the efficiency requirements. However, in actual processing, the industrial robot has different accuracy performance at different positions in the trajectory. In some trajectory segments, the end of the industrial robot used to perform the task will appear to shake beyond the error allowed range, resulting in uneven or even non-compliant accuracy of the industrial robot along the trajectory.

[0003] The prior art provides optimization of the motion trajectory of the industrial robot according to the angle, structure, model, etc. of the motion of the industrial robot, but the above optimization is only for the fixed results generated by the fixed configuration, which not only deviates from the actual processing of the industrial robot, but also can only globally optimize the processing trajectory. In particular for orthodontic appliances, the prior art can only adjust the motion of the industrial robot based on the dental model area (e.g., anterior teeth area, posterior teeth area, lingual side area or buccal side area), which not only cannot cope with the changing orthodontic product situation, but also lacks targeted optimization, resulting in poor overall efficiency and process optimization effect, and cannot further improve the processing accuracy of the orthodontic appliance. SUMMARY

[0004] One of the purposes of the present application is to provide a preparation method of an orthodontic appliance to solve the problem that in the prior art, the processing accuracy cannot be improved in a targeted and actual manner according to the processing trajectory when processing and preparing the orthodontic appliance.

[0005] One of the purposes of the present application is to provide a preparation device of an orthodontic appliance.

[0006] One of the purposes of the present application is to provide an electronic device.

[0007] One of the purposes of the present application is to provide a storage medium.

[0008] To achieve one of the above-mentioned purposes, one embodiment of the present application provides a method for manufacturing an appliance, comprising: obtaining cutting line data of the appliance and cutting quality evaluation data of an industrial robot at at least one position corresponding to the cutting line; the evaluation data is determined according to the deviation or matching degree of the processing state of the industrial robot at the corresponding position from the expected state; when the evaluation data meets a first correction condition, determining a motion correction parameter of the industrial robot at the first position; and controlling the motion parameter of the industrial robot based on the motion correction parameter, so that the industrial robot cuts the appliance along the cutting line.

[0009] To achieve one of the above-mentioned purposes, one embodiment of the present application provides a device for manufacturing an appliance, comprising: a first module for obtaining cutting line data of the appliance and cutting quality evaluation data of an industrial robot at at least one position corresponding to the cutting line; the evaluation data is determined according to the deviation or matching degree of the processing state of the industrial robot at the corresponding position from the expected state; a second module for determining a motion correction parameter of the industrial robot at the first position when the evaluation data meets a first correction condition; and a third module for controlling the motion parameter of the industrial robot based on the motion correction parameter, so that the industrial robot cuts the appliance along the cutting line.

[0010] To achieve one of the above-mentioned purposes, one embodiment of the present application provides an electronic device, comprising a processor, a memory and a communication bus, characterized in that the processor and the memory communicate with each other through the communication bus; the memory is used to store an application program; and the processor is used to implement the steps of the manufacturing method according to any one of the above-mentioned technical solutions when executing the application program stored in the memory.

[0011] To achieve one of the above-mentioned purposes, one embodiment of the present application provides a storage medium having an application program stored thereon, and the application program is executed to implement the steps of the manufacturing method according to any one of the above-mentioned technical solutions.

[0012] Compared with the prior art, the manufacturing method of the appliance provided by the present application can control the industrial robot to cut the appliance according to the cutting line, and control the motion parameter of the industrial robot according to the motion correction parameter, so that the industrial robot can be controlled to move according to the actual situation of cutting along the cutting line, and since the motion parameter corresponds to the evaluation data of the specific position on the cutting line, the motion control can be dynamically performed according to the cutting quality of the industrial robot, and compared with the action adjustment based on the global evaluation of the trajectory, the processing precision can be quickly and conveniently improved. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is a schematic diagram of the cutting line in the Cartesian coordinate system in one embodiment of the present application.

[0014] Figure 2 is a schematic diagram of a cutting line and an actual path of a cutting line cut by an industrial robot in an embodiment of the present application.

[0015] Figure 3 is a schematic diagram of a structure of an electronic device in an embodiment of the present application.

[0016] Figure 4 is a schematic diagram of a structure of an appliance for preparing an appliance in an embodiment of the present application.

[0017] Figure 5 is a schematic diagram of a step of a method for preparing an appliance in an embodiment of the present application.

[0018] Figure 6 is a schematic diagram of a step of a first embodiment of a method for preparing in an embodiment of the present application.

[0019] Figure 7 is a schematic diagram of a relationship between evaluation data and an arc length parameter of a trajectory in an embodiment of the present application.

[0020] Figure 8 is a schematic diagram of a relationship between evaluation data and an arc length parameter of a trajectory in an embodiment of the present application.

[0021] Figure 9 is a schematic diagram of a relationship between a coefficient of evaluation data and an angle of a workpiece in an embodiment of the present application.

[0022] Figure 10 is a schematic diagram of a relationship between a joint component and an arc length parameter of a trajectory in an embodiment of the present application.

[0023] Figure 11 is a schematic diagram of a step of a second embodiment of a method for preparing in an embodiment of the present application.

[0024] Figure 12 is a schematic diagram of a step of a specific embodiment of a method for preparing in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present application will be described in detail below with reference to specific embodiments shown in the drawings. However, these embodiments do not limit the present application, and modifications of structure, method, or function made by those skilled in the art based on these embodiments are included in the scope of the present application.

[0026] It should be noted that the term "comprising" or any other variant is intended to cover non-exclusive inclusion, so that processes, methods, articles, or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles, or devices.

[0027] The terms “first,” “second,” “third,” etc., are used for descriptive purposes only and should not be interpreted as indicating or implying relative importance.

[0028] The main idea of ​​this application is as follows: Considering that when industrial robots optimize the performance of cutting tasks, they usually only focus on the fixed results produced by fixed configurations and only consider the global optimization effect, often ignoring the actual processing of the industrial robot, failing to achieve targeted optimization, and unable to cope with the changing conditions of the products to be cut; based on this, the orthodontic device manufacturing method provided in this application determines the motion correction parameters used to control the movement of the industrial robot based on the evaluation data of the cutting quality of the corresponding industrial robot along the cutting line. This can correlate the processing state of the corresponding cutting line of the industrial robot with the actual movement, and realize autonomous correction of the cutting line to adapt to the changing orthodontic device cutting conditions; furthermore, because the evaluation data and the motion correction parameters and motion parameters determined therefrom correspond to at least one position on the cutting line, targeted optimization of the cutting line and its specific positions can be achieved, making the cutting process of the industrial robot more in line with the actual situation of orthodontic device manufacturing.

[0029] Figure 1 The cutting line is shown.

[0030] In the industrial manufacturing sector, especially when performing high-precision machining tasks, in addition to relying on CNC machine tools, industrial robots can also be used to perform machining according to the cutting line to complete some machining tasks.

[0031] In one scenario, industrial robots are used to perform the task of cutting and straightening devices. Specifically, industrial robots can move along... Figure 1 The cutting is performed along the indicated trajectory; the motion trajectories of industrial robots are complex and varied. To achieve efficient processing while maintaining a certain speed, industrial robots can be configured to process along the cutting line while maintaining accuracy in trajectory posture, such as position and orientation.

[0032] In this embodiment, corresponding Figure 1 The lines shown can be used to cut the orthodontic appliance.

[0033] Cutting lines can be digitally represented as cutting line data.

[0034] In one embodiment, the cutting line data includes the position and orientation information of the robot end effector.

[0035] In one embodiment, the cutting line data may be a series of discrete points or a continuous function used to describe the trajectory of the cutting line.

[0036] In one embodiment, the cleaving line data can be represented in the form of a special Euclidean group SE(3). In another embodiment, the cleaving line data can be represented in the form of a Lie algebra se(3) corresponding to the special Euclidean group SE(3).

[0037] In one embodiment, the cutting line data forms a cutting line relative to a predetermined reference frame. For example, the cutting line data forms a cutting line in a reference frame determined according to a robot base; for example, the cutting line data forms a cutting line in a reference frame determined according to a first workpiece, which is a predetermined workpiece in an industrial robot.

[0038] In one embodiment, the cutting line or cutting line data is determined based on computer-aided design (CAD).

[0039] In one embodiment, the cutting line or cutting line data is determined based on the points obtained from the teaching.

[0040] In one embodiment, the cut line or cut line data is extracted from an existing physical object or a completed task through reverse engineering.

[0041] In one embodiment, the cutting line or cutting line data is automatically generated according to an algorithm; for example, the algorithm may be a path planning algorithm such as A*, Dijkstra's algorithm, or Rapid Expanding Random Tree (RRT).

[0042] In one embodiment, the cutting line or cutting line data is determined by external tracking sensors of an industrial robot or other equipment; for example, the cutting line or cutting line data can be determined by measuring real-time data of the environment and the target using sensors such as lidar, cameras, and infrared sensors.

[0043] In one embodiment, the cutting line or cutting line data is determined by measurement data from within an industrial robot or other equipment; for example, the cutting line or cutting line data can be determined by measurement data from a joint encoder and a torque sensor.

[0044] Figure 1 Image (a) shows a cutting line. Figure 1 Another cutting line is shown in (b).

[0045] for Figure 1 In (a) and (b), the solid lines represent the cutting lines; the three dashed lines corresponding to different positions of the cutting lines constitute reference frames; the three dashed lines corresponding to different positions of the cutting lines represent the orientation changes of the industrial robot at that position.

[0046] In the application of cutting to manufacture orthotics, both types of cutting lines exhibit complex shapes. When objective conditions such as the control and execution capabilities of industrial robots remain unchanged or have reached a relatively optimal level, different trajectories will directly affect the motion accuracy of the robot's end effector.

[0047] Figure 2 A magnified schematic diagram of a portion of the cutting line is shown. This takes into account the robot's joint movements and changes in rigidity. Figure 2 This can be used to illustrate that different positions of the trajectory may exhibit drastically different accuracy characteristics during actual machining.

[0048] At certain points along the cutting line, the deviation between the end effector of the industrial robot and the corresponding position on the cutting line remains within a preset reasonable range; however, at other points along the cutting line, the end effector may exhibit deviations and wobbling that exceed the allowable error range.

[0049] For example, Figure 2 The solid line represents the actual trajectory LR generated by an industrial robot moving along the cutting line; the dashed line represents the cutting line LT in three-dimensional space; the shaded area around the cutting line represents the reasonable deviation range set based on the cutting line. It can be seen that at the first location P1 of the cutting line LT, the actual trajectory LR falls within the deviation range of the first location P1, indicating that this deviation is acceptable; at the second location P2 of the cutting line LT, the actual trajectory LR is outside the deviation range of the second location P2, indicating that this is a location prone to deviation or that the cutting quality is poor at this location.

[0050] One embodiment of this application provides a storage medium.

[0051] Storage media can be computer storage media. Storage media can be any available medium that a computer can access data on, or it can be a storage device such as a server or data center that integrates one or more available media. Available media can be magnetic media such as floppy disks, hard disks, and magnetic tapes; optical media such as DVDs (Digital Video Discs); or semiconductor media such as SSDs (Solid State Disks). Storage media can be installed in a computer or other computing device; storage media can store application programs.

[0052] In one embodiment, when the application is executed, it implements the steps of a method for fabricating an orthodontic appliance. In a specific embodiment, the method for fabricating the orthodontic appliance includes at least one of the following steps:

[0053] Obtain the cutting line data of the orthodontic device, and the cutting quality evaluation data of the industrial robot at at least one position on the corresponding cutting line; the evaluation data is determined based on the deviation or matching degree between the processing state of the industrial robot at the corresponding position and the expected state.

[0054] When the evaluation data meets the first correction condition, the motion correction parameters of the industrial robot at the first position are determined;

[0055] Based on motion correction parameters, the motion parameters of the industrial robot are controlled so that the industrial robot cuts the straightener along the cutting line.

[0056] The storage content of the storage medium can also be configured based on the preparation method of the orthodontic appliance in any of the technical solutions provided below.

[0057] One embodiment of this application provides an electronic device, such as... Figure 3 As shown.

[0058] Electronic device 100 can be a computer, mobile phone, tablet computer, etc., and this application does not limit the specific type of electronic device 100.

[0059] The electronic device 100 includes at least one processor 11, at least one memory 12, and a communication bus 13.

[0060] At least one processor 11 and at least one memory 12 communicate with each other via a communication bus 13.

[0061] Memory 12 is used to store application programs.

[0062] In one embodiment, the processor 11 is configured to implement the steps of a method for fabricating an orthodontic appliance when executing an application program stored in the memory 12. In this embodiment, the electronic device 100 may be defined as an apparatus for fabricating an orthodontic appliance or at least a portion thereof.

[0063] In one specific embodiment, the method for manufacturing the orthodontic appliance includes at least one of the following steps:

[0064] Obtain the cutting line data of the orthodontic device, and the cutting quality evaluation data of the industrial robot at at least one position on the corresponding cutting line; the evaluation data is determined based on the deviation or matching degree between the processing state of the industrial robot at the corresponding position and the expected state.

[0065] When the evaluation data meets the first correction condition, the motion correction parameters of the industrial robot at the first position are determined;

[0066] Based on motion correction parameters, the motion parameters of the industrial robot are controlled so that the industrial robot cuts the straightener along the cutting line.

[0067] The electronic device 100, particularly the processor 11 therein which executes the steps implemented by the application program, can also be configured according to the method of preparing the orthodontic appliance in any of the technical solutions provided below.

[0068] In one embodiment, the communication bus 13 may include any number of buses and bridge circuits. In some embodiments, in addition to connecting the processor and memory, the communication bus may also be used to connect peripheral devices or other peripheral circuits.

[0069] In one embodiment, the electronic device 100 may include a user interface 14 and at least one network interface 15. The user interface 13 may include a display, keyboard, mouse, trackball, click wheel, buttons, a touchpad, or a touch screen, etc.

[0070] like Figure 4 As shown, one embodiment of this application provides an apparatus for preparing an orthodontic device.

[0071] The apparatus for preparing orthodontic appliances includes at least one of the following components:

[0072] The first module 21 is used to obtain the cutting line data of the orthodontic appliance and the cutting quality evaluation data of the industrial robot at at least one position on the corresponding cutting line.

[0073] The second module 22 is used to determine the motion correction parameters of the industrial robot at the first position when the evaluation data meets the first correction condition.

[0074] The third module 23 is used to control the motion parameters of the industrial robot based on motion correction parameters, so that the industrial robot cuts the straightener along the cutting line.

[0075] The evaluation data is determined based on the deviation or matching degree between the processing state of the industrial robot at the corresponding position and the expected state.

[0076] In one embodiment, the third module 23 is further configured to control the movement speed of at least one of the industrial robot, the end effector of the industrial robot, the cutting tool of the industrial robot, or the joint of the industrial robot at a corresponding position based on motion correction parameters.

[0077] In one embodiment, the first module 21 is further configured to establish a quality evaluation function based on the motion parameters of the industrial robot and the cutting line data, and to determine at least one evaluation data of the industrial robot on the cutting line based on the quality evaluation function.

[0078] The quality evaluation function is used to characterize the deviation that may occur when the industrial robot moves along at least one position of the cutting line.

[0079] The quality evaluation function is related to the motion accuracy of at least one joint of the industrial robot.

[0080] In one embodiment, the apparatus for preparing the orthodontic appliance further includes a fourth module.

[0081] The fourth module is used to implement at least one of the following:

[0082] This is used to adjust the trajectory position where the evaluation data does not meet the set conditions, using the overall evaluation data of the corresponding cutting line as the optimization target, until the processing trajectory where the overall evaluation data meets the set conditions is determined.

[0083] Used to obtain several processing trajectories and several sets of overall evaluation data for several industrial robots corresponding to cutting lines, and to identify industrial robots that meet set conditions based on the overall evaluation data.

[0084] Used to obtain machining trajectory and overall evaluation data corresponding to several sets of workpiece coordinates, workpiece angles or redundant degrees of freedom of industrial robots, and to determine the workpiece coordinates, workpiece angles or redundant degrees of freedom of industrial robots that meet the set conditions of the overall evaluation data.

[0085] This method is used to obtain the contribution rate of the joint position of an industrial robot to the overall evaluation data of the machining trajectory. It determines the arc length range of the machining trajectory corresponding to the joint of the industrial robot with a contribution rate higher than the preset value. Using the evaluation data of this arc length range as the optimization target, it adjusts the joint position, joint speed and acceleration of the industrial robot with a contribution rate higher than the preset value until a machining trajectory that meets the set conditions is determined.

[0086] The apparatus for preparing the orthodontic appliance can also be configured based on the orthodontic appliance preparation method in any of the technical solutions provided below. Specifically, related steps can be implemented in the same or different modules based on the correlation between the steps.

[0087] In one embodiment, an electrical connection or a communication connection can be established between the first module 21, the second module 22, and the third module 23 to achieve data transmission.

[0088] In one embodiment, electrical or communication connections can be established between the first module 21, the second module 22, the third module 23, and the fourth module 24 to enable data transmission.

[0089] The device, or its modules or units, may be implemented by a computer chip or physical entity, or by a product with corresponding functions. While the device is described in terms of multiple modules, in some embodiments, the functions of the modules may be implemented in one or more software or hardware components.

[0090] like Figure 5 As shown, one embodiment of this application provides a method for preparing an orthodontic appliance.

[0091] The application or instructions corresponding to this method can be mounted in electronic devices, storage media and / or orthodontic device manufacturing apparatus, in carriers of other related methods, or in industrial robots to achieve the corresponding technical effects.

[0092] The preparation method of the orthodontic appliance may specifically include the following steps.

[0093] Step S1: Obtain the cutting line data of the orthodontic device and the cutting quality evaluation data of the industrial robot at at least one position on the corresponding cutting line.

[0094] The cutting task is performed by an industrial robot.

[0095] Industrial robots used to perform cutting tasks can have pre-set brand and model, equipment layout, and processing parameters (e.g., cutting speed, movement speed, etc.). The cutting method used by industrial robots can be either tool cutting or laser cutting.

[0096] The appliance can be any device with orthodontic function. In one embodiment, the appliance can be used for orthodontic treatment; in another embodiment, the appliance can be used for oral tissue functional training.

[0097] The orthodontic appliance may include an appliance body for forming a cavity to accommodate teeth; in this embodiment, the appliance may be specifically defined as a shell-shaped appliance. In some specific embodiments, the appliance may also include attachments that are fixed to the teeth or fixed to the appliance body.

[0098] The placement of the orthodontic appliance can be inside the actual human oral cavity, or it can be a simulated physical model or digital model of the oral cavity.

[0099] Orthodontic appliances can be physical instruments, or they can be digital or physical models.

[0100] When the orthodontic appliance is a digital model, the cutting process can also be a cutting process simulated based on the digital model. This cutting process can be rendered and output to a computer screen for user reference.

[0101] Cutting line data, or the corresponding cutting line, can serve as the target for an industrial robot to perform motion or cutting. By working along the cutting line data and the corresponding cutting line, an industrial robot can form a processing trajectory.

[0102] In one embodiment, the evaluation data may correspond to the cutting line itself. In this embodiment, the "cutting quality of the industrial robot at at least one position on the cutting line" may refer to the cutting quality affected by the shape or other characteristics of the cutting line itself, and the corresponding evaluation data is used to quantify the cutting quality.

[0103] In one embodiment, the evaluation data may correspond to a processing trajectory determined based on the cutting line. In this embodiment, the "cutting quality of the industrial robot corresponding to at least one position on the cutting line" may refer to the cutting quality affected by the trajectory shape, direction, speed, or other characteristics defined by the processing trajectory, and the corresponding evaluation data is used to quantify this cutting quality. Since the processing trajectory corresponds to the cutting line, and the processing trajectory has positions corresponding to at least one position on the cutting line, the evaluation data determined in this embodiment also corresponds to the cutting quality of the industrial robot at at least one position on the cutting line.

[0104] As can be seen, the evaluation data includes any data that can reflect the cutting quality at at least one position on the cutting line corresponding to the industrial robot.

[0105] The evaluation data can be used to assess whether the industrial robot is prone to deviation at at least one position on the cutting line. This deviation relates to the cutting quality and can be any deviation factor that can affect the cutting quality; for example, the deviation can be a deviation in the overall movement position, speed, acceleration, or direction of the industrial robot; or, for example, a deviation can be a deviation in the position, speed, acceleration, or direction of the end effector, cutting tool, or joints of the industrial robot.

[0106] The evaluation data is determined based on the deviation between the processing state of the industrial robot at the corresponding position and the expected state.

[0107] Evaluation data indicates that the smaller the deviation between the industrial robot's current processing state and the expected state, the better the cutting quality at that position can be considered. Conversely, the larger the deviation, the worse the cutting quality can be considered.

[0108] The evaluation data can be used to assess whether the industrial robot can operate stably by matching the cutting line at at least one position on the cutting line. This stable operation refers to the cutting process and can be any factor that can affect the stable operation of matching the cutting line.

[0109] The evaluation data is determined based on the degree of matching between the processing state of the industrial robot at the corresponding position and the expected state.

[0110] Evaluation data indicates that the higher the degree of match between the industrial robot's current processing state and the expected state, the better the cutting quality at that location can be considered. Conversely, the lower the degree of match, the worse the cutting quality can be considered.

[0111] The cutting quality can be related to the motion accuracy of the industrial robot; the better the setting of the cutting line, at least one position at the cutting line, the processing trajectory, and at least one position at the processing trajectory is, the better the corresponding cutting quality will be.

[0112] In one embodiment, the magnitude of the evaluation data is positively correlated with the quality of the cutting.

[0113] When the evaluation data is small, the cutting quality of the industrial robot at the position on the cutting line corresponding to that evaluation data (or the position on its corresponding machining trajectory) is poor. When the evaluation data is large, the cutting quality of the industrial robot at the position on the cutting line corresponding to that evaluation data (or the position on its corresponding machining trajectory) is good.

[0114] In one embodiment, the magnitude of the evaluation data is negatively correlated with the quality of the cutting.

[0115] When the evaluation data is large, the cutting quality of the industrial robot at the position on the cutting line corresponding to that evaluation data (or the position on its corresponding machining trajectory) is poor. When the evaluation data is small, the cutting quality of the industrial robot at the position on the cutting line corresponding to that evaluation data (or the position on its corresponding machining trajectory) is good.

[0116] The correspondence between evaluation data and cutting lines differs, resulting in different amounts of evaluation data generated by the corresponding cutting lines.

[0117] In one embodiment, for a set of cutting lines or cutting line data, there is evaluation data. This evaluation data can be used to evaluate the overall cutting quality of the industrial robot for that cutting line.

[0118] In one specific embodiment, the quality evaluation function can be used to determine multiple evaluation data corresponding to different positions of the cutting line. The mean, maximum or minimum value of the multiple evaluation data can be used to determine the evaluation data for evaluating the overall cutting quality.

[0119] In one embodiment, for a set of cutting lines or cutting line data, there are multiple evaluation data. This evaluation data can be used to evaluate the cutting quality at different positions along the cutting line; it can also be used to evaluate the ability of an industrial robot to maintain motion accuracy at different positions along the cutting line.

[0120] The evaluation data is used by the industrial robot to cut the corrector along the cutting line.

[0121] In one embodiment, the evaluation data can be used to evaluate the process of an industrial robot cutting a corrector along the cutting line.

[0122] In one specific embodiment, the evaluation data can be used to evaluate the entire cutting line or a portion thereof. In another specific embodiment, the evaluation data can be used to evaluate an industrial robot or the conditions it is in. In yet another specific embodiment, the evaluation data can be used to evaluate the ability of an industrial robot to maintain motion accuracy during the cutting process.

[0123] In one embodiment, the evaluation data can be used to guide the process of an industrial robot cutting a corrector along the cutting line.

[0124] In one specific embodiment, the evaluation data can be used to provide guidance for adjusting the overall cutting line or the processing trajectory of the industrial robot, or for adjusting a portion of its position. In another specific embodiment, the evaluation data can be used to provide guidance for configuring the industrial robot or configuring its operating conditions. In yet another specific embodiment, the evaluation data can be used to provide guidance for motion control during the cutting process of the industrial robot.

[0125] Step S2: When the evaluation data meets the first correction condition, determine the motion correction parameters of the industrial robot at the first position.

[0126] The first correction condition is used to determine whether to correct or control the process of the industrial robot cutting straightener.

[0127] The first correction condition concerns the evaluation data. When the evaluation data meets the first correction condition, it is determined whether to correct or control the process of the industrial robot cutting straightener.

[0128] In one embodiment, the first correction condition may be set as a preset value for the evaluation data.

[0129] In one specific embodiment, when the evaluation data exceeds the preset value, it is determined that the process of the industrial robot cutting corrector should be corrected or controlled. Poor cutting quality at the corresponding cutting line location / cutting quality failing to meet the target (e.g., failing to achieve the expected motion accuracy) can be characterized by evaluation data exceeding the preset value.

[0130] In one specific embodiment, when the evaluation data is lower than the preset value, it is determined that the process of the industrial robot cutting corrector should be corrected or controlled. Poor cutting quality at the corresponding cutting line location / cutting quality failing to meet the target (e.g., failing to achieve the expected motion accuracy) can be characterized by the evaluation data being lower than the preset value.

[0131] In some specific embodiments, the evaluation data can also be characterized by being equal to the preset value.

[0132] In one embodiment, the first correction condition can be set regarding the trend of change in the evaluation data.

[0133] In one specific embodiment, when the evaluation data corresponding to the current position is higher than the evaluation data corresponding to the previous position, it is determined that the process of the industrial robot cutting corrector should be corrected or controlled. Poor cutting quality / failure to meet the target (e.g., failure to achieve the expected motion accuracy) at the corresponding cutting line position can be characterized by an increasing trend in the evaluation data.

[0134] In one specific embodiment, when the evaluation data corresponding to the current position is lower than the evaluation data corresponding to the previous position, it is determined that the process of the industrial robot cutting corrector should be corrected or controlled. Poor cutting quality / failure to meet the target (e.g., failure to achieve the expected motion accuracy) at the corresponding cutting line position can be characterized by a decreasing trend in the evaluation data.

[0135] In some specific embodiments, the evaluation data can also be characterized by invariance.

[0136] The first position can be either on the cutting line or on the machining trajectory corresponding to the cutting line.

[0137] The first position can be related to the first correction condition.

[0138] In one embodiment, a first correction condition can be set for a first position. When the evaluation data of the first position on the corresponding cutting line or processing trajectory meets the first correction condition, the motion correction parameters of the industrial robot at the first position are determined. Other correction conditions are then set for other evaluation data corresponding to other positions on the cutting line or processing trajectory.

[0139] In one embodiment, a first correction condition can be set for other locations on the cutting line or machining trajectory, including the first location. In this embodiment, evaluation data at at least two locations can be used to determine their relationship with the first correction condition.

[0140] In one embodiment, other correction conditions, including a first correction condition, can be set for the first position. In this embodiment, the evaluation data at the first position is used in one case to determine its relationship with the first correction condition, and in another case to determine its relationship with other correction conditions. These cases can be different configurations of the industrial robot itself.

[0141] Motion correction parameters can be used to control the motion parameters of industrial robots.

[0142] In one embodiment, the motion correction parameter can be determined as a motion parameter, or the motion parameter can be determined by calculation based on the motion correction parameter.

[0143] In one embodiment, factors affecting motion parameters can be determined based on motion correction parameters.

[0144] In one specific embodiment, the initial or final value of the motion parameter can be determined based on the motion correction parameter.

[0145] In one specific embodiment, a control scheme for the motion parameters can be determined based on the motion correction parameters. For example, the control scheme is an adjustment function for the motion parameters.

[0146] When the motion parameter is the motion speed related to the industrial robot, the adjustment function can be a speed regulation function. In one example, the speed regulation function is characterized by motion correction parameters; in another example, the speed regulation function is characterized by evaluation data.

[0147] In one specific embodiment, the maximum value of the motion parameter change can be determined based on the motion correction parameters. In another specific embodiment, the minimum value of the motion parameter change can be determined based on the motion correction parameters.

[0148] In one specific embodiment, the difference between the motion parameters at the current position and the motion parameters at adjacent positions can be determined based on the motion correction parameters. For example, the difference between the motion parameters at the current position and the motion parameters at the previous position can be determined; another example is the difference between the motion parameters at the current position and the motion parameters at the next position.

[0149] Motion correction parameters can be used to reduce the aforementioned differences. For example, they can limit these differences to a set range; or, for example, they can smooth the changes in motion parameters corresponding to adjacent positions.

[0150] Step S3: Based on the motion correction parameters, control the motion parameters of the industrial robot so that the industrial robot cuts the straightener along the cutting line.

[0151] Motion correction parameters are used to control the motion parameters of industrial robots.

[0152] In one embodiment, motion correction parameters are used to determine the initial or final values ​​of motion parameters. The initial value of the motion parameter can be set to the motion parameter at a corresponding position, or the motion parameter at a corresponding position can be set based on the initial value. The final value of the motion parameter can be set to the motion parameter at a corresponding position, or the motion parameter at other positions can be set based on the final value.

[0153] In one specific embodiment, motion parameter control of an industrial robot includes: first determining initial values ​​of motion parameters based on motion correction parameters, and then controlling the motion parameters at corresponding positions according to the initial values ​​of the motion parameters. The method of controlling according to the initial values ​​of the motion parameters may include: smoothing the initial values ​​of motion parameters at different positions, and setting maximum and / or minimum values ​​for the changes in the initial values ​​of motion parameters at different positions.

[0154] In one embodiment, the motion correction parameters are used to determine a control scheme for the motion parameters. For example, the control scheme is an adjustment function for the motion parameters.

[0155] In one embodiment, the motion parameters of the industrial robot are controlled based on motion correction parameters to improve evaluation data. The evaluation data is determined based on the motion parameters of the industrial robot at corresponding locations, or based on parameters containing information about the industrial robot's motion parameters.

[0156] In this embodiment, motion correction parameters are used to improve the cutting quality of the industrial robot at corresponding locations. Evaluation data is determined based on or at least related to the motion parameters.

[0157] In one embodiment, motion parameters of the industrial robot are controlled based on motion correction parameters to improve the motion accuracy of the industrial robot at that position.

[0158] In this embodiment, motion correction parameters are used to improve the motion accuracy of the industrial robot at corresponding positions. Evaluation data is not required to be related to motion parameters, which are related to motion accuracy.

[0159] The industrial robot cuts the orthodontic device along the cutting line based on motion parameters.

[0160] In one embodiment, an industrial robot is used to cut materials to form a corrector.

[0161] In one embodiment, an industrial robot is used to cut the orthodontic appliance for further processing.

[0162] Industrial robots can perform actual cutting operations, or they can simulate the trajectory corresponding to the cutting line through digital simulation.

[0163] The motion parameters can be any motion-related parameters of the industrial robot during the cutting process along the cutting line.

[0164] Motion parameters can be determined by kinematic modeling of industrial robots and / or their auxiliary equipment. Alternatively, it can be considered that the relevant schemes utilizing motion parameters embody the idea of ​​kinematic modeling of industrial robots and / or their auxiliary equipment.

[0165] Motion parameters can be taken as the entire industrial robot. In this case, the motion parameters can be the position, direction of movement, speed, acceleration, and rate of change of acceleration of the industrial robot itself.

[0166] Motion parameters can be applied to a local part of the industrial robot. In one embodiment, the motion parameters can be the joints of the industrial robot, in which case the motion parameters can be the joint's position, direction of movement, velocity, acceleration, and rate of change of acceleration. In another embodiment, the motion parameters can be the end effector of the industrial robot, in which case the motion parameters can be the end effector's position, direction of movement, velocity, acceleration, and rate of change of acceleration.

[0167] In some embodiments, the end effector of an industrial robot can also be considered a type of joint.

[0168] Motion parameters can be taken from the components that work with the industrial robot. In one embodiment, a cutting tool that works with the industrial robot can be taken as the object, and the motion parameters can be the position, direction of movement, speed, acceleration, and rate of change of acceleration of the cutting tool.

[0169] By implementing steps S1, S2, and S3, motion correction parameters can be determined based on the evaluation data. The motion parameters of the industrial robot can then be controlled based on these motion correction parameters. Since the evaluation data reflects the cutting quality of the industrial robot, controlling the robot's motion in this way allows it to dynamically adjust to follow the cutting quality, thereby improving the overall cutting quality.

[0170] Since the evaluation data is determined based on the deviation or matching degree between the processing state and the expected state, controlling the movement of the industrial robot in this way can adjust the processing state of the industrial robot based on the expected state, so that it can achieve the corrector cutting according to the expected goal.

[0171] Because the evaluation data, and the motion correction parameters and motion parameter control process determined accordingly, correspond to the position on the cutting line, controlling the motion of the industrial robot in this way can achieve targeted adjustments to specific positions. Compared with motion control based on global evaluation, this is more in line with the actual processing of industrial robots and can achieve positional adjustments.

[0172] One embodiment of this application provides a method for preparing an orthodontic appliance, comprising at least one of the following steps.

[0173] Step S31: Based on the motion correction parameters, control the movement speed of the industrial robot at the corresponding position.

[0174] In this step, motion speed can refer to the overall motion speed of the industrial robot. Motion speed can represent the relationship between the position and time of the industrial robot as a whole relative to the cutting line or the processing trajectory determined by the cutting line.

[0175] In this step, the motion correction parameter represents the speed parameter corresponding to the overall motion speed of the industrial robot. It can be a specific value, a change, or a speed regulation function used to characterize the motion parameter control scheme.

[0176] In this step, the speed of movement can affect the cutting quality of the industrial robot along the cutting line or processing trajectory; the speed of movement can also affect the motion accuracy of the industrial robot along the cutting line or processing trajectory.

[0177] If, after setting the motion speed, the overall motion of the industrial robot better conforms to the cutting line or processing trajectory, it indicates that the motion speed enables the industrial robot to move in a more consistent manner with expectations, or that the motion speed enables the industrial robot to achieve better cutting quality.

[0178] In one embodiment, when evaluation data indicates that the processing state of the industrial robot differs from the expected state, motion correction parameters are determined to control the industrial robot to have a smaller overall speed.

[0179] In one specific embodiment, the evaluation data is determined based on the deviation between the processing state and the expected state of the industrial robot at a corresponding position. For example, the higher the evaluation data, the greater the deviation. A parameter that reduces the movement speed at that position or makes it lower than the movement speed at other positions with lower evaluation data is determined as a motion correction parameter, controlling the movement speed to have a smaller value. Conversely, the lower the evaluation data, the greater the deviation. A parameter that reduces the movement speed at that position or makes it lower than the movement speed at other positions with lower evaluation data is determined as a motion correction parameter, controlling the movement speed to have a smaller value.

[0180] In one specific embodiment, the evaluation data is determined based on the degree of matching between the processing state of the industrial robot at a corresponding position and the expected state. For example, the lower the evaluation data, the lower the degree of matching, and a parameter that can reduce the movement speed at that position or make it lower than the movement speed at other positions with lower evaluation data is determined as a motion correction parameter, controlling the movement speed to have a small value. Conversely, the higher the evaluation data, the lower the degree of matching, and a parameter that can reduce the movement speed at that position or make it lower than the movement speed at other positions with lower evaluation data is determined as a motion correction parameter, controlling the movement speed to have a small value.

[0181] Step S32: Based on the motion correction parameters, control the movement speed of the end effector of the industrial robot at the corresponding position.

[0182] In this step, motion speed can refer to the motion speed of the industrial robot's end effector. Motion speed can represent the relationship between the position of the industrial robot's end effector relative to the cutting line or the processing trajectory determined by the cutting line and time.

[0183] In this step, the motion correction parameter represents the velocity parameter of the corresponding industrial robot end effector, which can be a specific value, a change, or a speed regulation function used to characterize the motion parameter control scheme.

[0184] In this step, the movement speed can affect the cutting quality of the end effector of the industrial robot along the cutting line or processing trajectory; the movement speed can also affect the movement accuracy of the end effector of the industrial robot along the cutting line or processing trajectory.

[0185] In one embodiment, when evaluation data indicates that the processing state of the industrial robot differs from the expected state, motion correction parameters are determined to control the industrial robot to have a smaller end-effector speed.

[0186] In one specific embodiment, the evaluation data is determined based on the deviation between the processing state and the expected state of the end effector of the industrial robot at the corresponding position.

[0187] In one specific embodiment, the evaluation data is determined based on the degree of matching between the processing state of the industrial robot's end effector at the corresponding position and the expected state.

[0188] Step S33: Based on the motion correction parameters, control the movement speed of the cutting tool of the industrial robot at the corresponding position.

[0189] In this step, motion speed can refer to the movement speed of the industrial robot cutting tool. Motion speed can represent the relationship between the position of the industrial robot cutting tool relative to the cutting line or the processing trajectory determined by the cutting line and time.

[0190] In this step, the motion correction parameter represents the speed parameter of the corresponding industrial robot cutting tool's movement speed, which can be a specific value, a change, or a speed regulation function used to characterize the motion parameter control scheme.

[0191] In this step, the speed of movement can affect the cutting quality of the industrial robot's cutting tool along the cutting line or processing trajectory; the speed of movement can also affect the accuracy of the industrial robot's cutting tool along the cutting line or processing trajectory.

[0192] In one embodiment, when evaluation data indicates that the processing state of the industrial robot differs from the expected state, motion correction parameters are determined to control the industrial robot to have a lower cutting tool speed.

[0193] In one specific embodiment, the evaluation data is determined based on the deviation between the processing state and the expected state of the cutting tool of the industrial robot at the corresponding position.

[0194] In one specific embodiment, the evaluation data is determined based on the degree of matching between the processing state and the expected state of the industrial robot's cutting tool at the corresponding position.

[0195] Step S34: Based on the motion correction parameters, control the movement speed of the joints of the industrial robot at the corresponding positions.

[0196] In this step, motion speed can refer to the motion speed of the industrial robot joints. Motion speed can represent the relationship between the position of the industrial robot joints relative to the cutting line or the machining trajectory determined by the cutting line and time.

[0197] In this step, the motion correction parameter represents the velocity parameter of the corresponding industrial robot joint movement speed, which can be a specific value, a change, or a speed regulation function used to characterize the motion parameter control scheme.

[0198] In this step, the movement speed can affect the cutting quality of the industrial robot's joints along the cutting line or processing trajectory; the movement speed can also affect the movement accuracy of the industrial robot's joints along the cutting line or processing trajectory.

[0199] In one embodiment, when evaluation data indicates that the processing state of the industrial robot differs from the expected state, motion correction parameters are determined to control the industrial robot to have a smaller joint speed.

[0200] In one specific embodiment, the evaluation data is determined based on the deviation between the processing state and the expected state of the joints of the industrial robot at the corresponding positions.

[0201] In one specific embodiment, the evaluation data is determined based on the degree of matching between the processing state and the expected state of the joints of the industrial robot at the corresponding positions.

[0202] In one embodiment, at least one of steps S31 to S34 is included in the method for preparing the orthodontic appliance.

[0203] In one embodiment, steps S31 to S34 are executed simultaneously.

[0204] In one embodiment, steps S31 to S34 are executed in a preset order.

[0205] In one embodiment, steps S31 to S34 are included in step S3.

[0206] One embodiment of this application provides a method for preparing an orthodontic appliance, comprising at least one of the following steps.

[0207] Based on the relationship between the evaluation data and preset values, the movement speed of the industrial robot at the corresponding position is determined. The movement speed can be the movement speed of at least one of the following: the overall movement of the industrial robot, its end effector, its cutting tool, or its joints.

[0208] The cutting quality of the industrial robot at a corresponding position is characterized by the relationship between evaluation data and preset values. The motion accuracy of the industrial robot at a corresponding position is characterized by the relationship between evaluation data and preset values.

[0209] The first correction condition can be determined based on the preset value. Requirements regarding cutting quality or motion accuracy can be characterized by the first correction condition.

[0210] Step S41: When the evaluation data is higher than the preset value, increase the movement speed of the industrial robot at the corresponding position.

[0211] The evaluation data meeting the first correction condition can be characterized by the evaluation data being higher than the preset value.

[0212] Evaluation data that meets the first correction condition can be used to characterize whether the cutting quality or motion accuracy of the industrial robot at the corresponding position meets the set requirements. Furthermore, based on this, the movement speed of the industrial robot at that position can be increased to improve the real-time efficiency of the cutting process.

[0213] The fact that the evaluation data meets the first correction condition can not only indicate that the industrial robot has good cutting quality and motion accuracy at the corresponding position, but the first correction condition can also set higher requirements, such as that the cutting quality and motion accuracy at the corresponding position are stable, and that even if the influencing factor of higher speed is introduced, it will not lead to an excessive reduction in cutting quality or motion accuracy.

[0214] Step S41 can be set independently of steps S1 to S3.

[0215] Step S41 can be set in step S2. The motion correction parameters in step S2 can be configured according to the technical solution of step S41. When the evaluation data meets the first correction condition, which is characterized by the evaluation data being higher than the preset value, motion correction data can be configured to increase the motion speed, which is the motion parameter, at the corresponding position.

[0216] Step S41 can be set in step S3. The method of controlling the motion parameters in step S3 can be configured according to the technical solution of step S41. When the motion correction parameter is determined based on the evaluation data being higher than the preset value, it can control the motion speed, which is the motion parameter, to increase at the corresponding position.

[0217] Step S42: When the evaluation data is lower than the preset value, reduce the movement speed of the industrial robot at the corresponding position.

[0218] The evaluation data meeting the first correction condition can be characterized by the evaluation data being lower than the preset value.

[0219] Evaluation data that meets the first correction condition can be used to characterize whether the cutting quality or motion accuracy of the industrial robot at the corresponding position does not meet the set requirements. Furthermore, the movement speed of the industrial robot at that position can be reduced, which helps the industrial robot maintain stable operation and improve the cutting quality or motion accuracy at that position.

[0220] The fact that the evaluation data meets the first correction condition can not only indicate that the industrial robot has poor cutting quality and motion accuracy at the corresponding position, but the first correction condition can also set higher requirements. For example, if the cutting quality and motion accuracy at the corresponding position are unstable or fail to meet the set requirements, cutting at a normal speed or a higher speed will cause the cutting quality or motion accuracy to fail to meet the set requirements or to be further reduced.

[0221] Step S42 can be set independently of steps S1 to S3.

[0222] Step S42 can be set in step S2. The motion correction parameters in step S2 can be configured according to the technical solution of step S42. When the evaluation data meets the first correction condition, which is characterized by the evaluation data being lower than the preset value, motion correction data can be configured to reduce the motion speed, which is the motion parameter, at the corresponding position.

[0223] Step S42 can be incorporated into step S3. The method of controlling the motion parameters in step S3 can be configured according to the technical solution of step S42. When the motion correction parameter is determined based on the evaluation data being lower than a preset value, it can control the motion speed, which is the motion parameter, to decrease at the corresponding position.

[0224] Based on the changing trends of the evaluation data, the movement speed of the industrial robot at the corresponding position is determined. This movement speed can be the movement speed of at least one of the following: the overall movement of the industrial robot, its end effector, the cutting tool, or its joints.

[0225] The cutting quality of an industrial robot at a given location is characterized by the changing trends of evaluation data at different locations. The motion accuracy of an industrial robot at a given location is also characterized by the changing trends of evaluation data at different locations.

[0226] The first correction condition can be determined based on the changing trend. Requirements regarding cutting quality or motion accuracy can be characterized by the first correction condition.

[0227] Step S43: When the evaluation data at the current position is higher than the evaluation data at the previous position, increase the movement speed of the industrial robot at the corresponding position.

[0228] Evaluation data that meets the first correction condition can be used to improve the representation. Evaluation data that meets the first correction condition can be represented by the evaluation data at the current position being higher than the evaluation data at the previous position.

[0229] Evaluation data that meets the first correction condition can be used to characterize whether the cutting quality or motion accuracy of the industrial robot at the corresponding position meets the set requirements. Furthermore, based on this, the movement speed of the industrial robot at that position can be increased to improve the real-time efficiency of the cutting process.

[0230] The fact that the evaluation data meets the first correction condition can not only indicate that the industrial robot has improved cutting quality and motion accuracy at the corresponding position, but the first correction condition can also set higher requirements, such as the cutting quality and motion accuracy at the corresponding position being higher than the set requirements. Even if the influencing factor of higher speed is introduced, it will not lead to an excessive reduction in cutting quality or motion accuracy.

[0231] Specifically, the movement speed of the industrial robot at the current position can be increased. In one embodiment, both the movement speed of the industrial robot at the previous position and the movement speed at the current position can be increased.

[0232] Of course, the statement "when the evaluation data at the current position is higher than the evaluation data at the previous position" in step S43 can also be replaced with: "when the evaluation data at the next position is higher than the evaluation data at the previous position".

[0233] Specifically, the movement speed of the industrial robot at the next position can be increased. In one embodiment, both the movement speed of the industrial robot at the current position and the movement speed at the next position can be increased.

[0234] Step S43 can be set independently of steps S1 to S3.

[0235] Step S43 can be set in step S2. The motion correction parameters in step S2 can be configured according to the technical solution of step S43. When the evaluation data satisfies the first correction condition, which is characterized by the evaluation data at the current position being higher than the evaluation data at the previous position, motion correction data can be configured to increase the motion speed, which is the motion parameter, at the corresponding position.

[0236] Step S43 can be set within step S3. The method of controlling the motion parameters in step S3 can be configured according to the technical solution of step S43. When the motion correction parameter is determined based on the state that the evaluation data corresponding to the current position is higher than the evaluation data at the previous position, it can control the motion speed, which is a motion parameter, to increase at the corresponding position.

[0237] Step S44: When the evaluation data at the current position is lower than the evaluation data at the previous position, reduce the movement speed of the industrial robot at the corresponding position.

[0238] Evaluation data satisfying the first correction condition can be characterized by a decrease in evaluation data. Evaluation data satisfying the first correction condition can also be characterized by the evaluation data at the current position being lower than the evaluation data at the previous position.

[0239] Evaluation data that meets the first correction condition can be used to characterize whether the cutting quality or motion accuracy of the industrial robot at the corresponding position does not meet the set requirements. Furthermore, the movement speed of the industrial robot at that position can be reduced, which helps the industrial robot maintain stable operation and improve the cutting quality or motion accuracy at that position.

[0240] The fact that the evaluation data meets the first correction condition can not only indicate that the industrial robot has reduced cutting quality and motion accuracy at the corresponding position, but the first correction condition can also set higher requirements. For example, if the cutting quality and motion accuracy at the corresponding position are lower than the set requirements, cutting at a normal speed or a higher speed will cause the cutting quality or motion accuracy to fail to meet the set requirements or to be further reduced.

[0241] Specifically, the movement speed of the industrial robot at the current position can be reduced. In one embodiment, both the movement speed of the industrial robot at the previous position and the movement speed at the current position can be reduced.

[0242] Of course, the statement in step S43, "when the evaluation data at the current position is lower than the evaluation data at the previous position", can also be replaced with: "when the evaluation data at the next position is lower than the evaluation data at the previous position".

[0243] Specifically, the movement speed of the industrial robot at the next position can be reduced. In one embodiment, both the movement speed of the industrial robot at the current position and the movement speed at the next position can be reduced.

[0244] Step S44 can be set independently of steps S1 to S3.

[0245] Step S44 can be set in step S2. The motion correction parameters in step S2 can be configured according to the technical solution of step S44. When the evaluation data satisfies the first correction condition, which is characterized by the evaluation data at the current position being lower than the evaluation data at the previous position, motion correction data can be configured to reduce the motion speed, which is used as a motion parameter, at the corresponding position.

[0246] Step S42 can be set in step S3. The method of controlling the motion parameters in step S3 can be configured according to the technical solution of step S42. When the motion correction parameter is determined based on the state that the evaluation data corresponding to the current position is higher than the evaluation data at the previous position, it can control the motion speed, which is the motion parameter, to decrease at the corresponding position.

[0247] Controlling the motion parameters of an industrial robot can also mean controlling its motion speed to stabilize.

[0248] In one embodiment, the movement speed of the industrial robot at different positions is controlled to change gradually.

[0249] In one specific embodiment, controlling the stability of the industrial robot's movement speed can be implemented simultaneously with controlling the increase or decrease of the industrial robot's movement speed. Controlling the stability of the industrial robot's movement speed can be used to control the amount of change in the industrial robot's movement speed. For example, limiting the increase in the industrial robot's speed to a preset range; or, for example, limiting the decrease in the industrial robot's speed to a preset range.

[0250] Step S45: Adjust the speed difference between the current position and the previous position to a preset range.

[0251] Step S46: Adjust the speed difference between the current position and the next position to a preset range.

[0252] In one embodiment, controlling the speed difference within a preset range may also involve a corresponding first correction condition.

[0253] In this embodiment, the evaluation data can be the motion parameters of the industrial robot. In one specific embodiment, the motion parameter is the motion speed.

[0254] In this embodiment, the evaluation data satisfying the first correction condition can be characterized by unstable or drastic changes in motion speed. In another specific embodiment, the evaluation data satisfying the first correction condition can be characterized by the difference in motion speed between the current position and the previous position not being within a preset range. In yet another specific embodiment, the evaluation data satisfying the first correction condition can be characterized by the difference in motion speed between the current position and the next position not being within a preset range.

[0255] It can be seen that step S45 or step S46 can be a step that is performed under any conditions, or a step that is performed under specific conditions.

[0256] Step S45 can be set independently of steps S1 to S3.

[0257] Step S46 can be set independently of steps S1 to S3.

[0258] Step S45 can be set in step S3. The method of controlling the motion parameters in step S3 can be configured according to the technical solution of step S45.

[0259] Step S46 can be set in step S3. The method of controlling the motion parameters in step S3 can be configured according to the technical solution of step S46.

[0260] In one embodiment, at least one of steps S41 to S46 is included in the method for preparing the orthodontic appliance.

[0261] In one embodiment, at least two of steps S41 to S46 are executed simultaneously. For example, steps S41 and S45 are executed simultaneously.

[0262] In one embodiment, steps S41 to S46 are executed in a preset order. For example, step S43 is executed first, followed by step S46.

[0263] In one embodiment, steps S41 to S46 are included in step S3.

[0264] One embodiment of this application provides a method for preparing an orthodontic appliance, comprising:

[0265] Step S30: Establish a first function based on the motion parameters and evaluation data of at least one position on the cutting line of the industrial robot, and control the motion parameters of the industrial robot based on the first function.

[0266] In this embodiment, the aforementioned motion correction parameters can be characterized by a first function, or the aforementioned motion correction parameters can be included in the first function.

[0267] In this embodiment, the first function can characterize the control scheme of the motion parameters; the first function can be an adjustment function of the motion parameters.

[0268] In this embodiment, the first function can be used to characterize the relationship between motion parameters and evaluation data. In one specific embodiment, after determining the evaluation data corresponding to the first position, the target motion parameters, motion parameter adjustment amounts, or other parameters corresponding to the first position can be determined based on the first function and the evaluation data, so as to control the motion parameters of the industrial robot accordingly.

[0269] Step S30 may be included in step S3.

[0270] When the preparation method of the orthodontic device includes any one of steps S41 to S44, step S30 can be set to achieve motion parameter control through the scheme of step S30 when the evaluation data is higher than the preset value, lower than the preset value, increased or decreased, and it is determined that the motion parameters of the industrial robot need to be controlled, such as increasing speed or decreasing speed.

[0271] In one specific embodiment, the method for preparing the orthodontic appliance includes at least one of the following steps.

[0272] Step S301: Using the evaluation data as the independent variable and the motion speed as the dependent variable, adjust the motion speed of the industrial robot at the corresponding position based on the first function.

[0273] In this step, the first function determines the relationship between the evaluation data and the motion speed, and a control scheme for fixing the motion speed is established to control the motion parameters of the industrial robot.

[0274] In the first specific example, the first function is used to set the correlation between evaluation data and motion speed.

[0275] For example, when the evaluation data indicates that the cutting quality or motion accuracy at that location is high, the first function is set to a high motion speed corresponding to that location.

[0276] It can be specifically configured to smoothly transition between movement speeds at different positions.

[0277] In the second specific example, the first function is used to set a threshold for the motion speed.

[0278] For example, the first function is used to set the movement speed of the industrial robot along the cutting line to never exceed a first threshold. For example, the first function is used to set the movement speed of the industrial robot at a corresponding first position to never exceed a first threshold.

[0279] For example, the first function is used to set the movement speed of the industrial robot along the cutting line to always be no less than a second threshold. For example, the first function uses a preset setting to ensure that the movement speed of the industrial robot at the corresponding first position is no less than the second threshold.

[0280] In one specific example, the first function is used to implement both of the above functions simultaneously.

[0281] In one embodiment, the first function is a logistic function. Configuring the logistic function ensures that the industrial robot's movement speed satisfies a correlation, remains within a set threshold range, and further enables a smooth transition of movement speed at different positions.

[0282] In one specific embodiment, the evaluation data is represented by Q, and the motion speed is represented by v. The first function can be expressed as:

[0283]

[0284] The first function curve set in this way can be a smoothly transitioning S-shaped curve.

[0285] The first function curve set in this way can be a centrally symmetric S-shaped curve.

[0286] The first parameter l represents the distance (or height) between the upper and lower branches of the first function curve, which is shaped like an S. The first parameter l can be set according to the threshold requirement for the motion speed.

[0287] The second parameter k represents the slope of the middle segment of the first function curve, which is shaped like an S. This middle segment connects the upper and lower branches of the first function curve. The second parameter k can be set according to the required range of change in the motion speed.

[0288] The third parameter q0 represents the position of the center point of the first function curve, which is shaped like an S. The third parameter q0 can be set according to the initial value requirements of the motion velocity.

[0289] In a specific example, the evaluation data is a quantity that varies with the trajectory arc length parameter, and can be represented as Q(s); the motion velocity is a quantity that varies with the trajectory arc length parameter, and can be represented as v(s). The first function can be expressed as:

[0290]

[0291] In one embodiment, the calculation result determined by the first function based on evaluation data can be directly used as the target motion speed to control the motion parameters of the industrial robot. In this embodiment, the motion parameters include motion speed.

[0292] In one embodiment, the motion parameters of an industrial robot can be controlled based on the calculation results determined by a first function based on evaluation data.

[0293] In one specific embodiment, the first function, based on the calculation results determined by the evaluation data, is processed and determined as the target motion parameters of the industrial robot.

[0294] For example, the first function determines several motion velocities corresponding to several positions based on evaluation data, performs smoothing processing on these motion velocities, and thereby determines several target motion velocities corresponding to these positions. The smoothing processing can specifically be Gaussian smoothing or spline smoothing.

[0295] Gaussian smoothing uses a Gaussian function as a smoothing kernel to smooth motion velocity. In some embodiments, Gaussian smoothing includes at least one of the following steps: determining the Gaussian kernel; constructing the Gaussian kernel matrix; normalizing the Gaussian kernel; and convolving the Gaussian kernel with the motion velocity data to determine the Gaussian-smoothed motion data.

[0296] Spline smoothing uses spline functions to smooth motion velocities. In some embodiments, spline smoothing includes at least one of the following steps: determining the spline type (e.g., B-splines or natural splines, the former having better numerical stability and local control, and the latter having natural extension at the boundaries); determining the spline order (e.g., cubic); establishing spline basis functions; determining the design matrix based on the spline basis functions and the motion velocity; solving the linear system and determining the spline-smoothed motion data.

[0297] For example, the first function determines the corresponding movement speed based on the evaluation data, and determines the target movement speed for the corresponding position based on the relationship between the movement speed and the set interval. Specifically, when the movement speed exceeds the set maximum value, the target movement speed for that position is set to the set maximum value; when the movement speed is lower than the set minimum value, the target movement speed for that position is set to the set minimum value.

[0298] For example, after the first function determines the corresponding motion speed based on the evaluation data, it first performs smoothing processing, and then determines the target motion speed corresponding to the position based on the relationship between the smoothing processing result and the set interval.

[0299] The calculation process described in this application can be performed offline or online by executing a computer program based on modeling and related parameters, so as to adjust the motion speed for each different cutting line or processing trajectory.

[0300] like Figure 6 As shown, one embodiment of this application provides a method for preparing an orthodontic appliance, which includes at least one of the following steps.

[0301] By using evaluation data as the optimization target and adjusting the processing trajectory, the cutting quality of industrial robots can be maximized. In one embodiment, the evaluation data is used to set the optimization target for the processing trajectory.

[0302] Adjusting the processing trajectory can be achieved through direct modification, resampling, or smoothing, at least one of these methods.

[0303] The optimization process of the machining trajectory can be established under preset constraints.

[0304] The constraints can be determined based on the quantitative relationship between the machining trajectory and the evaluation data. These constraints can be used to determine the optimization algorithm corresponding to the machining trajectory.

[0305] Step S01: Using the overall evaluation data of the corresponding cutting line as the optimization target, adjust the trajectory positions where the evaluation data does not meet the set conditions until the processing trajectory where the overall evaluation data meets the set conditions is determined.

[0306] Different evaluation data can be determined for different positions along the cutting line. For the entire cutting line, the overall evaluation data can be determined based on the evaluation data for each position. The optimization process for the machining trajectory or cutting line can be implemented based on this overall evaluation data, allowing for comparison and determination of the optimal cutting line or machining trajectory.

[0307] For orthodontic appliances, the cutting lines are pre-set and fixed, so the process of determining the optimal cutting lines can be a process of screening cutting lines.

[0308] For orthodontic appliances or cutting lines, the machining trajectory can be adjusted in shape and position. Therefore, the process of determining the optimal machining trajectory can be a process of adjusting and optimizing the machining trajectory.

[0309] Therefore, although step S01 discloses the process of determining the machining trajectory, it can be done by first selecting the optimal cutting line and then determining the machining trajectory corresponding to the cutting line as the output, or it can be done by adjusting the machining trajectory of the corresponding cutting line as the output.

[0310] The process of adjusting the trajectory position can be iterative. The conditions for determining the processing trajectory that meets the criteria can be that the updated evaluation data converges to the set conditions, or that the iterative loop reaches the set number of times.

[0311] Figure 7 The figure shows the relationship between the evaluation data of the original trajectory (solid line) and the optimized trajectory (dashed line) and the trajectory arc length parameter. In the figure, when the trajectory arc length parameter is between 2100 and 2300, the evaluation data for the original trajectory has a region with values ​​less than -20, indicating poor cutting quality or low motion accuracy. The evaluation data for the optimized trajectory, however, is more evenly distributed between 0 and -15, eliminating the significantly low evaluation data regions and improving the overall cutting quality and motion accuracy.

[0312] In one embodiment, the distribution state of the evaluation data or the set of evaluation data can be defined as the overall evaluation data.

[0313] In one specific embodiment, the evaluation data is arranged according to the corresponding position or trajectory arc length parameter on the cutting line, or according to the corresponding position or trajectory arc length parameter on the processing trajectory.

[0314] Step S01 can be set independently of steps S1 to S3. For example, it can be set before step S1 to form a control architecture of "adjusting the cutting line or machining trajectory first, and then adjusting the motion parameters".

[0315] Step S01 can be set in step S1. After obtaining the cutting line data of the orthodontic appliance in step S1, the cutting line or processing trajectory is first optimized by implementing step S01, and then the evaluation data is determined based on the optimized cutting line or processing trajectory.

[0316] Step S01 can be set in step S3. After controlling the motion parameters of the industrial robot in step S3, the cutting line or machining trajectory is first optimized by implementing step S01, and then the cutting corrector is cut according to the optimized cutting line or machining trajectory.

[0317] In one embodiment, selecting or adjusting an industrial robot based on evaluation data helps to maximize the cutting quality of the industrial robot. In another embodiment, the evaluation data is used to determine the model of the industrial robot performing orthodontic cutting.

[0318] Step S02: Obtain several processing trajectories and several sets of overall evaluation data corresponding to the cutting lines of several industrial robots, and determine the industrial robots whose overall evaluation data meets the set conditions.

[0319] Different evaluation data can be determined at different positions along the cutting line. For the entire cutting line, the overall evaluation data can be determined based on the evaluation data at different positions.

[0320] Several different industrial robots can all achieve the cutting requirement, but they may generate different evaluation data when targeting a specific cutting line. Based on this, the industrial robot that best matches the current cutting line can be determined by analyzing the evaluation data of different industrial robots.

[0321] In one embodiment, the differences among several industrial robots may be reflected in aspects such as model, structure, performance, and functional configuration.

[0322] In one scenario, several industrial robots come from the same manufacturer, have similar performance, and can all meet the process requirements for orthodontic cutting. However, due to differences in structure, their kinematic models differ, resulting in different cutting quality or motion accuracy when dealing with cutting lines or processing trajectories. The cutting quality or motion accuracy is characterized by evaluation data.

[0323] In one specific embodiment, the method for preparing the orthodontic appliance may include at least one of the following steps: acquiring multiple sets of cutting lines; controlling or simulating multiple different industrial robots to cut along the multiple sets of cutting lines; determining several overall evaluation data corresponding to different industrial robots; and extracting key statistics and determining the industrial robot based on the several overall evaluation data.

[0324] Figure 8 The figure shows the relationship between the evaluation data of the first robot (solid line portion) and the second robot (dashed line portion) and the trajectory arc length parameter. The evaluation data distribution of the second robot is better than that of the first robot. Therefore, it can be determined that the robot with the best overall evaluation data is the industrial robot that meets the set conditions, and the second robot is selected to perform the orthokeratology cutting.

[0325] In one embodiment, the distribution state of the evaluation data or the set of evaluation data can be defined as the overall evaluation data.

[0326] In one specific embodiment, the evaluation data is arranged according to the corresponding position or trajectory arc length parameter on the cutting line, or according to the corresponding position or trajectory arc length parameter on the processing trajectory.

[0327] Step S02 can be set independently of steps S1 to S3. For example, it can be set before step S1 to form a control architecture of "first determining the industrial robot, then adjusting the motion parameters".

[0328] Step S02 can be set in step S1. After obtaining the cutting line data of the orthodontist in step S1, the industrial robot is first determined by implementing step S02, and then the corresponding evaluation data is determined based on the determined industrial robot.

[0329] In one embodiment, selecting or adjusting equipment layout parameters based on evaluation data helps to maximize the cutting quality of the industrial robot. In another embodiment, the evaluation data is used to determine the equipment layout associated with the industrial robot performing orthodontic cutting.

[0330] Step S03: Obtain the machining trajectory and overall evaluation data corresponding to several sets of workpiece coordinates, workpiece angles or redundant degrees of freedom of industrial robots, and determine the workpiece coordinates, workpiece angles or redundant degrees of freedom of industrial robots that meet the set conditions.

[0331] Equipment layout parameters, such as workpiece coordinates, workpiece angles, and redundant degrees of freedom of the industrial robot, also affect the cutting quality or motion accuracy along the cutting line of the industrial robot. Therefore, based on evaluation data, equipment layout parameters can be optimized to improve the cutting quality of the industrial robot.

[0332] For example, a scheme for determining process angles (ranging from 0 to 180°) using evaluation data may include at least one of the following steps: setting multiple workpiece angles; controlling or simulating an industrial robot to cut along a cutting line at multiple workpiece angles; determining several overall evaluation data points for the industrial robot corresponding to different workpiece angles; and determining the workpiece angle corresponding to the cutting line based on the several overall evaluation data points.

[0333] Figure 9 The figure shows the relationship between the evaluation data coefficient and the workpiece angle. The evaluation data coefficient has its maximum value when the workpiece angle is between 100 and 125 degrees. Therefore, it can be determined that the workpiece angle corresponding to the maximum evaluation data coefficient is the workpiece angle that meets the set conditions, and this workpiece angle is used to configure the industrial robot.

[0334] In one embodiment, the evaluation data coefficient can be defined as the overall evaluation data.

[0335] In one specific embodiment, the evaluation data coefficient can be a statistical quantity such as the average, weighted average, or median of several evaluation data corresponding to the same workpiece angle or different positions.

[0336] Step S03 includes at least the following three embodiments.

[0337] In one embodiment, step S03 includes step S031, obtaining the machining trajectory and overall evaluation data corresponding to several workpiece coordinate data, and determining the workpiece coordinates whose overall evaluation data meets the set conditions.

[0338] The overall evaluation data can be evaluation data coefficients. The evaluation data can be statistical quantities such as the average, weighted average, or median of several evaluation data corresponding to the same workpiece coordinates but different positions.

[0339] In one embodiment, step S03 includes step S032, obtaining the machining trajectory and overall evaluation data corresponding to several workpiece angle data, and determining the workpiece angles whose overall evaluation data meet the set conditions.

[0340] In one embodiment, step S03 includes step S033, obtaining the processing trajectory and overall evaluation data corresponding to a plurality of redundant degrees of freedom data, and determining the redundant degrees of freedom that satisfy the set conditions of the overall evaluation data.

[0341] The overall evaluation data can be evaluation data coefficients. The evaluation data can be statistical quantities such as the average, weighted average, or median of several evaluation data corresponding to the same redundant degree of freedom but at different positions.

[0342] The above three embodiments can be combined in pairs, or all three can be implemented simultaneously or sequentially to form new embodiments.

[0343] Step S03 can be set independently of steps S1 to S3. For example, it can be set before step S1 to form a control architecture of "first determining the equipment layout parameters, then adjusting the motion parameters".

[0344] Step S03 can be set in step S1. After obtaining the cutting line data of the orthodontist in step S1, the equipment layout parameters are first determined by implementing step S03, and then the corresponding evaluation data is determined based on the industrial robot equipped with the equipment layout parameters.

[0345] In one embodiment, determining the motion parameters of a specific joint of an industrial robot based on evaluation data helps to maximize the cutting quality of the industrial robot. In one embodiment, at least one set of evaluation data corresponding to at least one position in the cutting line is used to determine the motion parameters of the joint of the industrial robot.

[0346] Adjusting the motion parameters of an industrial robot can affect properties such as feedback control, stiffness, and resonant frequency. Industrial robots typically have multiple joints, and iteratively adjusting these joints would incur significant experimental costs. Therefore, the motion parameters of an industrial robot's joints can be determined based on at least one evaluation data point at at least one location, thus reducing experimental costs.

[0347] In one specific embodiment, the joint with the greatest influence at a given position can be determined based on the degree of influence of different joints on the evaluation data of different positions. By adjusting this joint, the range of joints to be adjusted can be narrowed down, thereby improving the cutting quality at the corresponding position at the lowest cost.

[0348] In one specific embodiment, a scheme for determining the motion parameters of an industrial robot joint using evaluation data corresponding to at least one position may include at least one of the following steps: determining the joint contribution rate of the industrial robot joint corresponding to the evaluation data at different positions along the cutting line; determining the joint corresponding to the influence weight that meets preset conditions, as well as the cutting line position or machining trajectory position; adjusting the motion parameters of the determined joint to optimize the cutting quality or motion accuracy corresponding to the determined cutting line position or machining trajectory position.

[0349] Step S04: Obtain the contribution rate of the industrial robot joint position to the evaluation data of the machining trajectory, determine the arc length range of the machining trajectory corresponding to the industrial robot joint with a contribution rate higher than the preset value, and use the evaluation data of the arc length range as the optimization target to adjust the industrial robot joint position, joint speed and acceleration with a contribution rate higher than the preset value until the machining trajectory that meets the set conditions is determined.

[0350] Figure 10 The figure shows the relationship between the joint contribution rates of joint 1 (solid line) and joint 5 (dashed line) and the trajectory arc length parameter. In most cases of trajectory arc length parameters, the joint contribution rate of joint 1 is greater than that of joint 5. Therefore, the motion parameters of joint 1 can be adjusted with emphasis.

[0351] Figure 10 In particular, when the trajectory arc length parameter is in the range of 2400 to 2700, joint 1 has a large joint contribution rate (the absolute value of the joint contribution rate is used to judge the impact on the cutting quality). Based on this, the motion parameters of joint 1 in the range of 2400 to 2700 can be adjusted in detail.

[0352] In one embodiment, an orthogonal experiment on the motion parameters of joint 1 can be designed, and the experimental range can be narrowed to the position corresponding to the trajectory arc length parameter in the range of 2400 to 2700. The evaluation data in the experiment is recorded and the optimal motion parameter configuration is compared. The motion parameters of the joint include at least one of joint position, joint velocity and acceleration.

[0353] In one embodiment, the evaluation data in step S04 is the evaluation data of the position on the corresponding cutting line, the position on the processing trajectory, or the trajectory arc length parameter.

[0354] Step S04 includes at least the following three embodiments.

[0355] In one embodiment, step S04 includes step S041, obtaining the contribution rate of the industrial robot joint position to the evaluation data of the machining trajectory, determining the arc length interval of the machining trajectory corresponding to the industrial robot joint with a contribution rate higher than a preset value, using the evaluation data of the arc length interval as the optimization target, adjusting the industrial robot joint position with a contribution rate higher than the preset value, until a machining trajectory whose overall evaluation data meets the set conditions is determined.

[0356] In one embodiment, step S04 includes step S042, obtaining the contribution rate of the industrial robot joint position to the evaluation data of the machining trajectory, determining the arc length interval of the machining trajectory corresponding to the industrial robot joint with a contribution rate higher than a preset value, using the evaluation data of the arc length interval as the optimization target, adjusting the speed of the industrial robot joint with a contribution rate higher than the preset value, until a machining trajectory whose overall evaluation data meets the set conditions is determined.

[0357] In one embodiment, step S04 includes step S043, obtaining the contribution rate of the industrial robot joint position to the evaluation data of the machining trajectory, determining the arc length interval of the machining trajectory corresponding to the industrial robot joint with a contribution rate higher than a preset value, using the evaluation data of the arc length interval as the optimization target, adjusting the acceleration of the industrial robot joint with a contribution rate higher than the preset value, until a machining trajectory whose overall evaluation data meets the set conditions is determined.

[0358] The machining trajectory determined above corresponds to the cutting line data and cutting line in step S1.

[0359] The above three embodiments can be combined in pairs, or all three can be implemented simultaneously or sequentially to form new embodiments.

[0360] Step S04 can be set independently of steps S1 to S3. For example, it can be set before step S1 to form a control architecture of "first determining the machining trajectory, then adjusting the motion parameters", with both corresponding to the same cutting line.

[0361] Step S04 can be set in step S1. After obtaining the cutting line data of the orthodontic appliance in step S1, the processing trajectory is first determined by implementing step S03, and then the corresponding evaluation data is determined according to the correspondence between the processing trajectory and the cutting line.

[0362] Step S04 can be set in step S3. After controlling the motion parameters of the industrial robot in step S3, the machining trajectory is first determined by implementing step S04, and then the straightener is cut according to the joint-optimized machining trajectory.

[0363] like Figure 11 One embodiment of this application provides a method for preparing an orthodontic appliance, which includes at least one of the following steps.

[0364] Step S11: Establish a quality evaluation function based on the motion parameters and cutting line data of the industrial robot.

[0365] The quality evaluation function is used to characterize the deviation that may occur when the industrial robot moves along at least one position of the cutting line.

[0366] The quality evaluation function is related to the motion accuracy of at least one joint of the industrial robot.

[0367] The relationship between motion parameters and cutting line data can be solved based on forward kinematics or inverse kinematics.

[0368] In one embodiment, the motion parameters of the industrial robot include the positions of each joint of the industrial robot.

[0369] In one specific embodiment, the target trajectory is defined as T, and the positions of each joint of the industrial robot are defined as Θ. The relationship between the two, T = T(Θ), can be obtained by inverse kinematics.

[0370] The quality evaluation function can be used to evaluate whether there are positions in the cutting line or cutting line data that are prone to deviation relative to the industrial robot; or it can be used to evaluate whether at least one position in the cutting line or cutting line data is prone to deviation relative to the industrial robot.

[0371] The quality evaluation function is related to the motion accuracy of the industrial robot.

[0372] In one embodiment, the quality evaluation function is related to the motion accuracy of at least one joint of the industrial robot.

[0373] In one embodiment, the quality evaluation function relates to the motion accuracy of at least one end effector of the industrial robot.

[0374] The quality evaluation function incorporates considerations for the joints or end effectors of the industrial robot, enabling it to evaluate the impact of local components such as joints or end effectors.

[0375] Step S12: Determine at least one evaluation data point for the industrial robot on the cutting line based on the quality evaluation function.

[0376] For the same cutting line as a target, different industrial robots or industrial robots under different conditions can generate multiple trajectories. Based on multiple trajectories and quality evaluation functions, the industrial robot generates multiple evaluation data for the cutting line.

[0377] By taking different cutting lines as targets, industrial robots can generate multiple trajectories. Based on these multiple trajectories and quality evaluation functions, the industrial robot generates multiple evaluation data points for the cutting lines.

[0378] By implementing step S11 or step S12, at least one evaluation data point for the cutting line can be provided by combining the cutting line and motion accuracy. In particular, it is possible to identify areas prone to deviation before the actual execution trajectory, allowing the operator to take appropriate optimization measures based on different quality evaluation conditions (e.g., optimizing the trajectory of relevant positions, adjusting the motion parameters of the industrial robot, or implementing accuracy compensation, etc.), thereby helping to achieve higher processing accuracy.

[0379] Step S11 may be included in step S1; step S12 may be included in step S1.

[0380] Steps S11 and S12 can be executed sequentially.

[0381] In one specific embodiment, such as Figure 12 The method for preparing the orthodontic appliance includes at least one of the following steps.

[0382] Step S111: Determine the target parameters of the objective function for evaluating the cutting quality of the industrial robot based on the motion type of the industrial robot moving along the cutting line.

[0383] In one embodiment, the objective function is used to determine the quality evaluation function. In a specific embodiment, the quality evaluation function can be viewed as an optimization problem determined based on the objective function; the parameters in the objective function or its expansion can be all or part of the parameters of the quality evaluation function.

[0384] In one embodiment, the target parameter is used to determine the quality evaluation function. In a specific embodiment, the parameters in the quality evaluation function or its expansion may include the target parameter or at least information related to it.

[0385] In one embodiment, target parameters or related information are included in the objective function. The target parameters or related information are used to determine the objective function.

[0386] In one embodiment, the target parameter may be a motion parameter related to the process or machining task to be performed. The target parameter has at least an impact on the motion process along the cutting line; in particular, the target parameter may be a parameter that affects the motion accuracy along the cutting line. The target parameter may be independent of the selection or operating status of the industrial robot. The target parameter can be used to reflect the inherent machining accuracy characteristics of the process or machining task.

[0387] In one embodiment, the parameter that has a significant impact on the motion process along the cutting line can be identified as the target parameter. The target parameter may be the same or different depending on the type of motion.

[0388] The motion type refers to the type of orientation, position, velocity, acceleration, rate of change of acceleration, or force required when moving along the cutting line.

[0389] The motion type can be determined based on the process or processing task to be performed. The motion type, process, or processing task may be independent of the selection or operating status of the industrial robot. The motion type can reflect the inherent attributes of the process or processing task.

[0390] In processes such as conventional along-track cutting, the type of motion can include the movement of joints along the cutting line.

[0391] In operations such as milling a workpiece, the type of motion can include motion in the depth direction.

[0392] In some other processes, the type of motion can include rotational motion at the end effector. Processes that focus on rotational motion at the end effector can include laser cutting or welding, precision assembly, surface treatment, 3D printing, and other processes.

[0393] In other processes, motion types can include maintaining high stability or generating torque by holding a load at the end. Processes focusing on these motion types can include heavy-duty machining, object handling, precision manufacturing, and medical surgery.

[0394] The process of determining the objective function and quality evaluation function takes into account the type of motion or the procedures and tasks being performed, so that the objective function or quality evaluation function can be more adapted to the current working state.

[0395] In one embodiment, the type of motion can be used to determine the objective function. For example, an appropriate objective function can be selected based on the type of motion.

[0396] For scenarios involving joint movements, target parameters including joint velocity can be determined.

[0397] For scenarios where the motion type includes depth direction motion, the target parameter can be determined as the motion parameter in the depth direction.

[0398] For scenarios where the motion type includes end-effector rotation, the target parameters can be determined as the motion parameters of the end-effector rotation.

[0399] For scenarios where the motion type includes stable motion or torque generated by end-load holding, the target parameters can be determined as torque-related motion parameters.

[0400] Step S112: Determine the objective function based on the cutting line data and the target parameters.

[0401] In one embodiment, multiple target parameters can be used to determine a unified objective function.

[0402] In one embodiment, multiple target parameters can be used to determine the corresponding objective function.

[0403] In one specific embodiment, after the various target parameters have their respective corresponding target functions determined, the various target functions can also be combined (such as weighted combination) to form a unified target function.

[0404] The cutting line data used to determine the objective function can be the cutting line data itself, or it can be the transformed and processed cutting line data. The transformation and processing may include constructing the relationship between the cutting line and other parameters; the other parameters may be the motion parameters of the industrial robot, its joints, or its end effector.

[0405] In one embodiment, the objective function is used to represent the ability of an industrial robot to maintain motion accuracy.

[0406] In one embodiment, the objective function is used to represent the ability of the industrial robot to maintain motion accuracy by reflecting the fluctuations in the motion of the industrial robot as the cutting line or the trajectory of the industrial robot changes.

[0407] In one embodiment, the objective function is characterized by the arc length parameter of the trajectory of at least one joint of the industrial robot moving at at least one position.

[0408] The objective function can be used to determine how an industrial robot or its joints and end effectors change with the trajectory arc length parameter. Based on this fluctuation, the ability of the industrial robot to maintain motion accuracy can be represented.

[0409] In one embodiment, the objective function characterizes how the motion of the industrial robot's joints changes with the trajectory arc length parameter. In a specific embodiment, the smaller the change in the objective function with the trajectory arc length parameter, the smaller the range of change in the industrial robot's joint motion, indicating a stronger ability of the industrial robot to maintain motion accuracy.

[0410] Step S113: Based on the objective function, determine the variation trend of the trajectory arc length parameter of at least one joint, and determine the quality evaluation function.

[0411] In one embodiment, the objective function reflects the motion accuracy of the industrial robot by indicating the fluctuation of the target parameters. By processing the objective function, this fluctuation can be quantified as a trend, thereby constructing a quality evaluation function.

[0412] The methods for determining the trend of change through the objective function can include total differential expansion, gradient analysis, Taylor expansion, sensitivity analysis, etc.

[0413] In addition to determining the changing trend of the trajectory arc length parameter of the joint pair, it can also determine the changing trend of other parameters of the joint pair related to the target parameter. In some cases, it can also be used to measure the fluctuation of the target parameter or other control parameters.

[0414] In one embodiment, the determination of the changing trend can be achieved not only by utilizing the relationship between the joint and the trajectory arc length parameter, but also by utilizing the relationship between the cutting line and the joint, or the relationship between the cutting line and the trajectory arc length parameter.

[0415] Step S111 can be included in step S11 or step S1; step S112 can be included in step S11 or step S1; step S113 can be included in step S11 or step S1.

[0416] Steps S111, S112, and S113 can be executed sequentially.

[0417] In one embodiment, the target parameters include at least one of joint velocity parameters, joint acceleration parameters, end-effector position parameters, end-effector rotation parameters, or end-effector torque parameters.

[0418] In one specific embodiment, the method for preparing the orthodontic appliance includes at least one of the following steps.

[0419] Step S1121: Based on the relationship between the cutting line data and the position of at least one joint, the trajectory arc length of the joint position is parameterized.

[0420] Arc length parametrization is a method for describing curves or trajectories, where parameters directly correspond to the arc lengths on the curve.

[0421] By parameterizing the trajectory arc length for the joint position, the following functional relationship can be obtained:

[0422] Θ=Θ(s)=Θ(θ1(s),...,θ n (s)).

[0423] Where s represents the trajectory arc length parameter; θ1(s),...,θ n (s) represents the n dimensions of the joint position.

[0424] In one embodiment, the dimension of the joint position is equal to the number of joints in the industrial robot.

[0425] In one specific embodiment, the industrial robot can be a six-axis robot. n = 6.

[0426] Step S1122: Determine the objective function based on the joint position and target parameters after parameterizing the trajectory arc length.

[0427] In one embodiment, the objective function can be defined as a function relating the target trajectory T and the positions Θ of the joints of the industrial robot. It can be represented as:

[0428]

[0429] Step S1121 may be included in step S112, step S11 or step S1; step S1122 may be included in step S112, step S11 or step S1.

[0430] Steps S1121 and S1122 can be executed sequentially.

[0431] In one embodiment, the objective function includes at least one parameter for representing the trajectory of an industrial robot.

[0432] In one embodiment, the objective function includes at least one parameter characterized by joint position.

[0433] In one specific embodiment, the objective function includes a first matrix.

[0434] The first matrix is ​​used to represent the trajectory of the industrial robot.

[0435] The first matrix is ​​represented by a matrix with respect to the joint position. For example, the first matrix is ​​represented by a homogeneous transformation matrix with respect to the joint position.

[0436] In a specific instance, the first matrix is ​​represented as T(θ1(s),...,θ) n The 4×4 homogeneous transformation matrix of (s)).

[0437] In one embodiment, the objective function includes at least one parameter representing the type of motion.

[0438] In one embodiment, the objective function includes at least one parameter characterized by an objective parameter.

[0439] In one specific embodiment, the objective function includes a second matrix.

[0440] The second matrix is ​​used to represent the type of motion.

[0441] The second matrix varies with the trajectory arc length parameter.

[0442] The second matrix is ​​represented by a matrix with respect to the target parameters. For example, the second matrix is ​​represented by a linear matrix with respect to the target parameters.

[0443] In a specific instance, the second matrix is ​​represented as A(s)∈R m×4 A linear matrix.

[0444] Furthermore, the second matrix can be determined using a second formula based on the process, processing task, or target parameters. This second formula can reflect the accuracy characteristics of the trajectory relative to the process, processing task, or target parameters. These accuracy characteristics are independent of robot selection and operating status.

[0445] When the second formula expresses a linear relationship, the elements of the second matrix can be calculated and determined according to the second formula. In one embodiment, each element in the second matrix corresponds to a trajectory position or trajectory arc length parameter.

[0446] When the second formula expresses a nonlinear relationship, it can be linearly expanded according to different trajectory positions to determine the elements of the second matrix. In one embodiment, each element in the second matrix corresponds to a trajectory position or trajectory arc length parameter.

[0447] In one embodiment, the objective function is represented by the product of a first matrix and a second matrix.

[0448] In one specific embodiment, the objective function It can be represented as:

[0449]

[0450] In one embodiment, the objective function For a given trajectory arc length s, the machining accuracy at that position can be reflected by the linear mapping relationship of the transformation matrix.

[0451] In one specific embodiment, the objective function can be set... The objective function is determined by differentiating it with respect to the arc length *s*. For example:

[0452]

[0453] It reflects the effects produced by the joint movements of industrial robots.

[0454] This reflects the effect caused by changes in the second matrix (i.e., motion type, target parameters). In other embodiments, it can also reflect any other inherent machining accuracy characteristics related to robot selection and operating state.

[0455] In one embodiment, the quality evaluation function includes the contribution rate of the motion of the industrial robot joints to the objective function.

[0456] In one embodiment, the quality evaluation function includes the partial derivative of the objective function with respect to the joint position.

[0457] In one embodiment, the quality evaluation function includes parameters for representing the type of motion, or includes parameters characterized by target parameters.

[0458] In one embodiment, the quality evaluation function includes parameters for representing the trajectory, or parameters characterized by joint positions.

[0459] In one embodiment, the quality evaluation function includes partial derivatives of a parameter representing the trajectory with respect to joint positions.

[0460] In one embodiment, the quality evaluation function includes the rate of change of the joint parameters of the industrial robot as a function of trajectory arc length.

[0461] In one embodiment, the quality evaluation function includes the derivative of the joint position with respect to the trajectory arc length parameter.

[0462] In one specific embodiment, the method for preparing the orthodontic appliance includes at least one of the following steps.

[0463] Step S1131: Based on the objective function, determine the contribution rate of the industrial robot joint motion to the objective function, as well as the rate of change of the joint with the trajectory arc length parameter, and determine the quality evaluation function.

[0464] In one embodiment, the quality evaluation function can be represented by the product of the influence weight of joint position on the target trajectory, the measurement data of the partial derivative of the target function with respect to joint position, and the absolute value of the derivative of the joint position with respect to the trajectory arc length parameter.

[0465] In one embodiment, the quality evaluation function Q(s) can be expressed as:

[0466]

[0467] w i (s) represents the influence weight of the i-th joint position on the target trajectory.

[0468] For any s and i, w i (s) satisfies

[0469] The selection of the influence weights is influenced by the specific application and can be determined through experimental data or theoretical calculations. In one embodiment, it can be determined based on joint stiffness.

[0470] ||·|| indicates that the measurement data is determined using a measurement function.

[0471] |·| represents the absolute value of a scalar.

[0472] This represents the contribution rate of the motion of the i-th joint to the objective function. Describe the objective function θ for the i-th joint position i The partial derivatives of .

[0473] This represents the rate of change of the i-th joint with respect to the trajectory arc length parameter. This represents the derivative of the i-th joint position with respect to the trajectory arc length parameter s.

[0474] In one embodiment, As a scalar, It can also have the connotation of slope or rate of change. The larger the absolute value of , the greater the influence rate of joint movement on the objective function.

[0475] In another embodiment, Not a scalar The influence rate of joint motion on the objective function can be measured through the aforementioned metric function or by determining its metric data.

[0476] The objective function is expressed as In the embodiments, It can be expanded as:

[0477]

[0478] In summary, the orthodontic preparation method provided in this application controls an industrial robot to cut the orthodontic along a cutting line, and controls the motion parameters of the industrial robot according to motion correction parameters. This enables the industrial robot to perform motion correction control based on the actual cutting situation along the cutting line. Furthermore, since the motion parameters correspond to evaluation data of specific positions on the cutting line, not only can the motion control be dynamically adjusted according to the cutting quality of the industrial robot, but also, compared to adjusting the action based on a global evaluation of the trajectory, it can quickly and conveniently improve the processing accuracy.

[0479] It should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0480] The detailed descriptions listed above are merely specific descriptions of feasible implementation methods of this application and are not intended to limit the scope of protection of this application. All equivalent implementation methods or modifications made without departing from the spirit of the art of this application should be included within the scope of protection of this application.

Claims

1. A method for preparing an orthodontic appliance, characterized in that, include: Obtain the cutting line data of the orthodontic device, and the cutting quality evaluation data of the industrial robot at at least one position on the corresponding cutting line; the evaluation data is determined based on the deviation or matching degree between the processing state of the industrial robot at the corresponding position and the expected state. When the evaluation data meets the first correction condition, the motion correction parameters of the industrial robot at the first position are determined; Based on motion correction parameters, the motion parameters of the industrial robot are controlled so that the industrial robot cuts the straightener along the cutting line.

2. The preparation method according to claim 1, characterized in that, The method includes: Based on motion correction parameters, control the motion speed of at least one of the industrial robot, the end effector of the industrial robot, the cutting tool of the industrial robot, or the joint of the industrial robot at the corresponding position.

3. The preparation method according to claim 2, characterized in that, The method includes at least one of the following: When the evaluation data is higher than the preset value, the movement speed of the industrial robot at the corresponding position is increased; When the evaluation data is lower than the preset value, the movement speed of the industrial robot at the corresponding position is reduced; When the evaluation data at the current position is higher than the evaluation data at the previous position, increase the movement speed of the industrial robot at the corresponding position. When the evaluation data at the current position is lower than the evaluation data at the previous position, reduce the movement speed of the industrial robot at the corresponding position. Adjust the speed difference between the current position and the previous position to a preset range; Adjust the speed difference between the current position and the next position to a preset range.

4. The preparation method according to claim 2, characterized in that, The method includes: Using evaluation data as the independent variable and motion speed as the dependent variable, the motion speed of the industrial robot at the corresponding position is adjusted based on a first function; the first function is used to set the correlation between evaluation data and motion speed, as well as the threshold of the motion speed.

5. The preparation method according to claim 1, characterized in that, The method includes: A quality evaluation function is established based on the motion parameters of the industrial robot and the cutting line data. The quality evaluation function is used to characterize the deviation that may occur when the industrial robot moves along at least one position of the cutting line. The quality evaluation function is related to the motion accuracy of at least one joint of the industrial robot. Based on the quality evaluation function, at least one evaluation data point for the cutting line by the industrial robot is determined.

6. The preparation method according to claim 5, characterized in that, The method includes: Based on the motion type of the industrial robot moving along the cutting line, determine the target parameters of the objective function for evaluating the cutting quality of the industrial robot; Based on the cutting line data and target parameters, a target function is determined; the target function is characterized by the arc length parameter of the trajectory of at least one joint of the industrial robot moving at at least one position. Based on the objective function, determine the variation trend of the trajectory arc length parameter of at least one joint, and determine the quality evaluation function.

7. The preparation method according to claim 1, characterized in that, The method includes at least one of the following: Using the overall evaluation data of the corresponding cutting line as the optimization target, adjust the trajectory positions where the evaluation data does not meet the set conditions until the processing trajectory where the overall evaluation data meets the set conditions is determined. Obtain several processing trajectories and several sets of overall evaluation data corresponding to several industrial robots' cutting lines, and determine the industrial robots whose overall evaluation data meets the set conditions. Obtain machining trajectory and overall evaluation data corresponding to several sets of workpiece coordinates, workpiece angles or redundant degrees of freedom of industrial robot, and determine the workpiece coordinates, workpiece angles or redundant degrees of freedom of industrial robot that meet the set conditions of the overall evaluation data. The contribution rate of the industrial robot joint position to the evaluation data of the machining trajectory is obtained. The arc length interval of the machining trajectory corresponding to the industrial robot joint with the contribution rate higher than the preset value is determined. The evaluation data of the arc length interval is used as the optimization target. The position, joint speed and acceleration of the industrial robot joint with the contribution rate higher than the preset value are adjusted until the machining trajectory that meets the set conditions is determined.

8. An apparatus for preparing an orthodontic appliance, characterized in that, include: The first module is used to obtain the cutting line data of the orthodontic appliance, and the cutting quality evaluation data of the industrial robot at at least one position on the corresponding cutting line; the evaluation data is determined based on the deviation or matching degree between the processing state of the industrial robot at the corresponding position and the expected state. The second module is used to determine the motion correction parameters of the industrial robot at the first position when the evaluation data meets the first correction condition. The third module is used to control the motion parameters of the industrial robot based on motion correction parameters, so that the industrial robot cuts the straightener along the cutting line.

9. An electronic device, characterized in that, It includes a processor, a memory, and a communication bus, characterized in that the processor and the memory communicate with each other through the communication bus; The memory is used to store application programs; The processor is configured to implement the steps of the preparation method according to any one of claims 1-7 when executing an application stored in the memory.

10. A storage medium having an application program stored thereon, characterized in that, When the application is executed, it performs the steps of the preparation method according to any one of claims 1-7.

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