CN108857600B - Cobalt-based alloy processing method and processing platform based on photocatalysis - Google Patents
Cobalt-based alloy processing method and processing platform based on photocatalysis Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B1/00—Processes of grinding or polishing; Use of auxiliary equipment in connection with such processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B41/00—Component parts such as frames, beds, carriages, headstocks
- B24B41/02—Frames; Beds; Carriages
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B41/00—Component parts such as frames, beds, carriages, headstocks
- B24B41/06—Work supports, e.g. adjustable steadies
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B57/00—Devices for feeding, applying, grading or recovering grinding, polishing or lapping agents
- B24B57/02—Devices for feeding, applying, grading or recovering grinding, polishing or lapping agents for feeding of fluid, sprayed, pulverised, or liquefied grinding, polishing or lapping agents
Abstract
The cobalt-based alloy processing method based on photocatalysis and the processing platform thereof comprise optimization adjustment of a chemical reaction process and optimization adjustment of a mechanical arm polishing path, wherein the optimization adjustment of the chemical reaction process adopts the conditions of establishing a linear regression model for different processing objects based on a linear regression algorithm to search for optimal illumination intensity, chemical additive proportion, temperature and the like; the mechanical arm polishing path optimization adjustment adopts a strategy network to adjust the polishing track; the processing platform includes: robot module, polishing workstation, polishing liquid interpolation device, industrial computer and memory. The invention has the beneficial effects that: the problems of poor ultraviolet intensity and poor amount of chemical additives in the chemical reaction process in the photocatalysis processing process are effectively solved, and the polishing quality can be effectively improved; the mechanical arm polishing paths are trained for different processing objects, so that the track error caused by the flexibility of the polishing disc can be effectively solved, and the polishing quality and efficiency are improved.
Description
Technical Field
The invention relates to a cobalt-based alloy processing method and a processing platform based on photocatalysis.
Background
Cobalt-based alloys can be used as self-fluxing alloy materials to improve the surface properties of metallic materials to form high hardness, high temperature and wear resistant coatings. The method is widely applied to the industries of mould manufacturing, automobile manufacturing industry, turbine manufacturing and the like. The cobalt-based alloy coating of the die not only has the complexity of a free curved surface, but also encounters new problems of high hardness, high wear resistance and the like, so that precision machining is more difficult, and a common machining method is difficult to quickly remove. Aiming at the material characteristics of cobalt element in cobalt-based alloy, an effective reaction solvent is configured to enable cobalt atoms on the surface layer to react to form a product which is easy to remove, and the relationship between photons and quantum yield is established by combining the ultraviolet light catalysis, so that the processes of cobalt element atomic bond breakage and energy release are regulated, and the reaction is controllable. Because the cobalt-based alloy is a multi-element alloy material, the material removal of insoluble products is realized by the cooperative mechanical action.
The process platform material removal process is a synergistic process of chemical reaction, optical control and mechanical removal. Therefore, the influence of the processing technology on the processing result is relatively large, in order to optimize the processing technology, the capability of the artificial intelligence technology in continuity decision and searching for the optimal point is utilized, the artificial intelligence technology is combined with a processing platform of the photocatalytic robot, and the processing efficiency and the processing quality of the polishing of the photocatalytic robot are improved.
Disclosure of Invention
The invention aims to provide a photocatalysis processing platform suitable for an artificial intelligence technology, which can select optimal chemical reaction conditions for different processing objects and solve the problem of polishing path errors caused by a flexible polishing disk when polishing different processing objects. The processing platform can adjust processing technology for different processing objects based on an artificial intelligence technology, and comprises the steps of optimizing and adjusting a chemical reaction process based on a linear regression model and optimizing and adjusting a mechanical arm polishing path based on a strategy network model.
The invention discloses a cobalt-based alloy processing method based on photocatalysis, which is characterized by comprising the following steps of:
(1) Optimizing and adjusting the chemical reaction process;
step 1), calling the number m of samples of the processing object stored in a database in a memory aiming at the operating system of the industrial personal computer, wherein the units are a number Q and a processing evaluation index Q, and the units are mm/s; wherein the number of processing object samples is m corresponding to the chemical reaction materialsAnd the conditions include a basic yellow 40 solvent concentration C1 in g/L, tiO 2 Concentration C2, the unit is g/L, ultraviolet illumination intensity I, the unit is lx and temperature T, the unit is DEG C; the machining evaluation index Q is in mm/s and comprises machining precision a, mm, surface roughness r, mu m and machining duration h, and s, wherein the machining quality is better when the Q value is larger, and the mathematical formula is as follows:
step 2) establishing a linear regression model function formula Q' as follows, w 0 、w 1 、w 2 、w 3 Randomly initializing weight parameters:
Q'＝w 0 +w 1 ×C1+w 2 ×C2+w 3 ×I+w 4 ×T (2)；
step 3) establishing a characteristic vector x, wherein x is obtained by the concentration C1 and TiO of an alkaline yellow 40 solvent 2 Concentration C2, intensity of ultraviolet illumination I, vector of temperature T, x= [ C1, C2, I, T]The method comprises the steps of carrying out a first treatment on the surface of the Establishing a loss function, and a loss value L 1 To measure the difference between the linear regression model output Q' obtained from the formula (2) and the actual process evaluation index Q obtained from the formula (1), the result formula is as follows, Q i And Q' i The i-th machining actual evaluation index value and the predicted evaluation index value are respectively:
step 4) iteratively updating the weight value by using a random batch gradient descent algorithm until reaching the designated iteration times (preset 1000 times), calculating the weight parameter value with the minimum loss value, updating the weight value by adopting an iteration formula, wherein alpha is the learning rate and is generally 0.001, t represents iteration rounds, t is set to 0 at the beginning of iteration, random number initialization of Gaussian distribution is generated in advance through a program,
a total of 1000 updates, i.e. 1000 calculations of equation (4), are needed, each time the last calculated +.>
The four weight parameters are substituted back into formula (4) to calculate the updated +.>
Wherein the method comprises the steps of
All represent iterative computations;
step 5) after the iteration is completed
Is the corresponding w 0 、w 1 、w 2 、w 3 The weight value is substituted into the formula (2) to establish a prediction model, and the values of the processing evaluation indexes under different alkaline yellow 40 solvent concentrations, tiO2 concentrations, ultraviolet illumination intensities and temperatures can be predicted through the prediction model;
step 6) solving the concentration C1, tiO of the alkaline yellow 40 solvent when the Q' value is maximum, namely, the processing evaluation index is highest according to the prediction model 2 Concentration C2, ultraviolet irradiation intensity L and temperature T respectively correspond to the values; the operating system sends the instructions of parameters to the ultraviolet lamp and the polishing solution adding device, so that the aim of optimizing and adjusting the chemical reaction process is fulfilled;
(2) Optimizing and adjusting the polishing path of the mechanical arm;
step (1) calling an expected track y stored in a cloud server database by the industrial personal computer operating system aiming at a processing object k Consists of k coordinate points, and the actual motion trail in the strategy network training process is y' k Establishing an error function e k The formula is as follows:
e k ＝y k -y' k (5)
training e through strategic network models k The value becomes as small as possible, i.e., the error of the polishing track is reduced;
setting a reward value R according to the error value, wherein the smaller the error is, the larger the reward value is, and in order to enable the strategy network to better understand future and potential rewards, the future attenuated rewards are used, and the action is assumed to be the next time, R k The formula for the K-th prize is as follows:
R＝R 1 +γR 2 +γ 2 R 3 +...+γ k-1 R k (gamma is the attenuation coefficient to be 0.99) (6);
step (3) a neural network model is built, the input is the current coordinate value of the mechanical arm, and the output is a probability value matrix p= [ p ] of the mechanical arm moving towards each azimuth 1 ,p 2 ,p 3 ,...,p n ](n is determined by the set number of orientations) and can be used to select actions;
step (4) establishing a loss function L 2 The cross entropy loss function is used, and the formula is as follows:
L 2 ＝-(r*log p)/n (7)
the probability of obtaining actions with more rewards is improved, and the probability of obtaining actions with lower rewards is reduced;
and (5) substituting the loss function, and iteratively updating the weight value in the neural network by using a gradient descent algorithm to finally obtain a strategy network capable of providing an optimal strategy, wherein the polishing path of the mechanical arm can be optimally adjusted based on the strategy network.
The processing platform constructed by the cobalt-based alloy processing method based on photocatalysis is characterized in that: comprising
The robot module comprises an industrial robot and a flexible pneumatic polishing device, wherein the flexible polishing device is arranged on a mechanical arm of the industrial robot and is used for adjusting the position of a workpiece;
the polishing workbench comprises a polishing disc for containing polishing liquid and an illumination device for illuminating the polishing liquid and a workpiece, wherein the illumination device is arranged in the polishing disc, and an illumination head of the illumination device is aligned to the polishing liquid;
the polishing liquid adding device is arranged beside the polishing workbench, and a liquid adding port of the polishing liquid adding device is positioned right above a polishing pool of the polishing workbench and is used for adding polishing liquid into the polishing workbench;
the industrial personal computer is electrically connected or in signal connection with the control ends of the robot module, the polishing workbench and the polishing liquid adding device and is used for controlling the robot module, the polishing workbench and the polishing liquid adding device to work so as to realize optimal adjustment of the chemical reaction process and optimal adjustment of the polishing path of the mechanical arm;
and the signal transmission end of the memory is in bidirectional signal connection or electric connection with the signal transmission port of the industrial personal computer through a wire or a wireless network and is used for storing processing information.
The bottom of the polishing disc is fixedly connected with a rotary workbench driven by a rotary motor, so that the polishing disc can rotate around a vertical central shaft of the polishing disc; the polishing device comprises a polishing pool, a polishing layer, an elastic film layer, a polishing layer and a polishing layer, wherein the elastic layer, the elastic film layer and the abrasive layer are sequentially arranged in the polishing pool from bottom to top and used for supporting abrasive particles, the abrasive layer is used for processing the surface of a die, the bottom end of the elastic layer is connected with a bottom plate in the polishing pool, the elastic film layer is paved on the upper surface of the elastic layer, and the edge of the elastic film layer is connected with the inner wall of the polishing pool in a sealing manner, so that a processing area for containing polishing liquid is formed by the elastic film layer and the inner wall of the polishing pool; the abrasive particle layer is paved on the surface of the elastic film layer, and polishing liquid for lubrication and heat dissipation is filled on the abrasive particle layer; the control end of the rotating motor is electrically connected with the control end corresponding to the master controller.
The elastic layer is a conical spiral spring array, the conical spiral spring array comprises a plurality of mutually independent conical spiral springs, the small-caliber end of each conical spiral spring is fixedly connected with the inner bottom surface of the polishing pool, the large-caliber end of each conical spiral spring is connected with a hemispherical support, and the arc-shaped curve of each hemispherical support is in contact with the lower surface of the elastic layer; the upper end of the conical spiral spring is abutted against the lower surface of the elastic film layer.
The illumination device is an ultraviolet lamp and is fixed at the center of the rotary workbench, wherein the ultraviolet lamp always irradiates the workpiece processing area.
The chemical reaction process optimization is adjusted by adopting a linear regression algorithm. The mechanical arm polishing path is optimized by adopting a strategy network to adjust the polishing track.
The data stored in the database of the memory is based on historical process data.
The model and the parameters of the neural network in the strategy network are selected and adjusted according to the actual processing conditions.
The invention has the beneficial effects that: according to the invention, an artificial intelligence technology is combined with a photocatalytic robot processing platform, so that the problem that the ultraviolet intensity and the amount of chemical additives in the chemical reaction process are not good in the photocatalytic processing process is effectively solved, and the polishing quality can be effectively improved; the mechanical arm polishing paths are trained for different processing objects, so that the track error caused by the flexibility of the polishing disc can be effectively solved, and the polishing quality and efficiency are improved.
Drawings
FIG. 1 is a schematic diagram of the chemical reaction process optimization adjustment flow of the invention.
Fig. 2 is a schematic diagram of a polishing path optimization adjustment process of a mechanical arm according to the present invention.
Fig. 3 is a view showing a platform structure of the photocatalytic machining robot according to the present invention.
Detailed Description
The invention is further described below with reference to the drawings.
Referring to the drawings:
(1) Optimizing parameters required by chemical reaction;
step 1) the work for the processing objectThe control computer operating system calls the number m (number) of the processing object samples and the processing evaluation index Q (mm/s) stored in the database in the memory; wherein the chemical reaction materials and conditions corresponding to the number m (m) of the processing object samples comprise the concentration C1 (g/L) of the alkaline yellow 40 solvent and TiO 2 Concentration C2 (g/L), ultraviolet illumination intensity I (lx) and temperature T (DEG C); the machining evaluation index Q (mm/s) comprises machining precision a (mm), surface roughness r (mu m) and machining duration h(s), wherein the machining quality is better as the Q value is larger, and the mathematical formula is as follows:
step 2) establishing a linear regression model function formula Q' as follows, w 0 、w 1 、w 2 、w 3 Randomly initializing weight parameters:
Q'＝w 0 +w 1 ×C1+w 2 ×C2+w 3 ×I+w 4 ×T (2)；
step 3) establishing a characteristic vector x, wherein x is obtained by the concentration C1 and TiO of an alkaline yellow 40 solvent 2 Concentration C2, intensity of ultraviolet illumination I, vector of temperature T, x= [ C1, C2, I, T]The method comprises the steps of carrying out a first treatment on the surface of the Establishing a loss function, and a loss value L 1 To measure the difference between the model output Q' (from equation (2)) and the actual Q (from equation (1)), the result equation is as follows, Q i And Q' i The i-th machining actual evaluation index value and the predicted evaluation index value are respectively.
Step 4) iteratively updating the weight value by using a random batch gradient descent algorithm until reaching the designated iteration times (preset 1000 times), calculating the weight parameter value with the minimum loss value, updating the weight value by adopting an iteration formula, wherein alpha is the learning rate and is generally 0.001, t represents iteration rounds, t is set to 0 at the beginning of iteration, random number initialization of Gaussian distribution is generated in advance through a program,
a total of 1000 updates, i.e. 1000 calculations of equation (4), are needed, each time the last calculated +.>
The four weight parameters are substituted back into formula (4) to calculate the updated +.>
Wherein the method comprises the steps of
All represent iterative computations;
step 5) after the iteration is completed
Is the corresponding w 0 、w 1 、w 2 、w 3 The weight value is substituted into the formula (2) to establish a prediction model, and the values of the processing evaluation indexes under different alkaline yellow 40 solvent concentrations, tiO2 concentrations, ultraviolet illumination intensities and temperatures can be predicted through the prediction model;
step 6), solving the corresponding values of the concentration C1 of the alkaline yellow 40 solvent, the concentration C2 of TiO2, the ultraviolet irradiation intensity L and the temperature T when the Q' value is maximum, namely when the processing evaluation index is highest, according to the prediction model; the operating system sends the instructions of parameters to the ultraviolet lamp and the polishing solution adding device, so that the aim of optimizing and adjusting the chemical reaction process is fulfilled;
(2) Optimizing and adjusting the polishing path of the mechanical arm;
step (1) calling an expected track y stored in a cloud server database by the industrial personal computer operating system aiming at a processing object k Consists of k coordinate points, and the actual motion trail in the strategy network training process is y' k Establishing an error function e k The formula is as follows:
e k ＝y k -y' k (5)
training e through strategic network models k The value becomes as small as possible, i.e., the error of the polishing track is reduced;
setting a reward value R according to the error value, wherein the smaller the error is, the larger the reward value is, and in order to enable the strategy network to better understand future and potential rewards, the future attenuated rewards are used, and the action is assumed to be the next time, R k The formula for the K-th prize is as follows:
R＝R 1 +γR 2 +γ 2 R 3 +...+γ k-1 R k (gamma is the attenuation coefficient to be 0.99) (6);
step (3) a neural network model is built, the input is the current coordinate value of the mechanical arm, and the output is a probability value matrix p= [ p ] of the mechanical arm moving towards each azimuth 1 ,p 2 ,p 3 ,...,p n ](n is determined by the set number of orientations) and can be used to select actions;
and (4) establishing a loss function, and adopting a cross entropy loss function, wherein the formula is as follows:
L 2 ＝-(r*log p)/n (7)
the probability of obtaining actions with more rewards is improved, and the probability of obtaining actions with lower rewards is reduced;
and (5) substituting the loss function, and iteratively updating the weight value in the neural network by using a gradient descent algorithm to finally obtain a strategy network capable of providing an optimal strategy, wherein the polishing path of the mechanical arm can be optimally adjusted based on the strategy network.
Example 2A processing platform constructed according to the photocatalytic cobalt-based alloy processing method of the present invention comprises
The robot module 1 comprises an industrial robot and a flexible pneumatic polishing device, wherein the flexible polishing device is arranged on a mechanical arm of the industrial robot and is used for adjusting the position of a workpiece;
the polishing workbench 2 comprises a polishing disc for containing polishing liquid and an illumination device for illuminating the polishing liquid and a workpiece, wherein the illumination device is arranged in the polishing disc, and an illumination head of the illumination device is aligned with the polishing liquid;
the polishing solution adding device 3 is arranged beside the polishing workbench, and a liquid adding port of the polishing solution adding device is positioned right above a polishing pool of the polishing workbench and is used for adding polishing solution into the polishing workbench;
the industrial personal computer 4 is electrically connected or in signal connection with the control ends of the robot module, the polishing workbench and the polishing liquid adding device and is used for controlling the robot module, the polishing workbench and the polishing liquid adding device to work so as to realize optimal adjustment of the chemical reaction process and optimal adjustment of the polishing path of the mechanical arm;
and the signal transmission end of the memory 5 is in bidirectional signal connection or electric connection with the signal transmission port of the industrial personal computer through a wire or a wireless network and is used for storing processing information.
The bottom of the polishing disc is fixedly connected with a rotary workbench driven by a rotary motor, so that the polishing disc can rotate around a vertical central shaft of the polishing disc; the polishing device comprises a polishing pool, a polishing layer, an elastic film layer, a polishing layer and a polishing layer, wherein the elastic layer, the elastic film layer and the abrasive layer are sequentially arranged in the polishing pool from bottom to top and used for supporting abrasive particles, the abrasive layer is used for processing the surface of a die, the bottom end of the elastic layer is connected with a bottom plate in the polishing pool, the elastic film layer is paved on the upper surface of the elastic layer, and the edge of the elastic film layer is connected with the inner wall of the polishing pool in a sealing manner, so that a processing area for containing polishing liquid is formed by the elastic film layer and the inner wall of the polishing pool; the abrasive particle layer is paved on the surface of the elastic film layer, and polishing liquid for lubrication and heat dissipation is filled on the abrasive particle layer; the control end of the rotating motor is electrically connected with the control end corresponding to the master controller.
The elastic layer is a conical spiral spring array, the conical spiral spring array comprises a plurality of mutually independent conical spiral springs, the small-caliber end of each conical spiral spring is fixedly connected with the inner bottom surface of the polishing pool, the large-caliber end of each conical spiral spring is connected with a hemispherical support, and the arc-shaped curve of each hemispherical support is in contact with the lower surface of the elastic layer; the upper end of the conical spiral spring is abutted against the lower surface of the elastic film layer.
The illumination device is an ultraviolet lamp and is fixed at the center of the rotary workbench, wherein the ultraviolet lamp always irradiates the workpiece processing area.
The chemical reaction process optimization is adjusted by adopting a linear regression algorithm. The mechanical arm polishing path is optimized by adopting a strategy network to adjust the polishing track.
The data stored in the database of the memory is based on historical process data.
The model and the parameters of the neural network in the strategy network are selected and adjusted according to the actual processing conditions.
The formula of the polishing solution is as follows: the polishing solution comprises alkaline yellow 40 solvent and TiO 2 Solvent and water, wherein the mass concentration of the alkaline yellow 40 is 80 g/L-100 g/L; the TiO 2 The mass concentration of (3) is 120 g/L-150 g/L. Each test uses different concentrations of basic yellow 40 and TiO 2 Preparing polishing solution.
Example 3
The polishing solution adding device stores alkaline yellow 40 and TiO with different concentrations 2 The solvent is controlled to flow out by an electromagnetic valve, the flow out of the solvent is randomly controlled in each processing test, the concentration of the solvent is calculated and recorded, the solvent is uniformly covered on the surface of a workpiece after being mixed, and then the workpiece is polished by a mechanical arm.
The solvent concentration data will be transmitted to a database of the server for storage for later recall.
The embodiments described in the present specification are merely examples of implementation forms of the inventive concept, and the scope of protection of the present invention should not be construed as being limited to the specific forms set forth in the embodiments, but also equivalent technical means that can be conceived by those skilled in the art according to the inventive concept.
Claims (5)
1. The cobalt-based alloy processing method based on photocatalysis is characterized by comprising the following steps of:
(1) Optimizing and adjusting the chemical reaction process;
step 1), calling a processing object sample number m stored in a database in a memory aiming at an industrial personal computer operating system of a processing object, wherein the unit is a processing evaluation index Q and the unit is mm/s; wherein the number m of corresponding chemically reactive materials and conditions of the processing object sample include the concentration C1 of the basic yellow 40 solvent, the unit is g/L, tiO 2 Concentration C2, the unit is g/L, ultraviolet illumination intensity I, the unit is lx and temperature T, the unit is DEG C; the machining evaluation index Q is in mm/s and comprises machining precision a, mm, surface roughness r, mu m and machining duration h, and s, wherein the machining quality is better when the Q value is larger, and the mathematical formula is as follows:
step 2) establishing a linear regression model function formula Q' as follows, w 0 、w 1 、w 2 、w 3 Randomly initializing weight parameters:
Q'＝w 0 +w 1 ×C1+w 2 ×C2+w 3 ×L+w 4 ×T (2)
step 3) establishing a characteristic vector x, wherein x is a vector consisting of an alkaline yellow 40 solvent concentration C1, a TiO2 concentration C2, an ultraviolet illumination intensity I and a temperature T, and x= [ C1, C2, I and T ]]The method comprises the steps of carrying out a first treatment on the surface of the Establishing a loss function, and a loss value L 1 To measure the difference between the linear regression model output Q' obtained from the formula (2) and the actual process evaluation index Q obtained from the formula (1), the result formula is as follows, Q i And Q' i The i-th machining actual evaluation index value and the predicted evaluation index value are respectively:
step 4) utilizing a random batchThe weight value is updated iteratively by a quantity gradient descent algorithm until reaching the specified iteration times, 1000 times are preset, the weight parameter value with the minimum loss value is calculated, the weight value is updated by adopting an iteration formula, alpha is the learning rate and is generally 0.001, t represents iteration rounds, t is set to 0 at the beginning of iteration, random number initialization of Gaussian distribution is generated in advance through a program,
a total of 1000 updates, i.e. 1000 calculations of equation (4), are needed, each time the last calculated +.>
The four weight parameters are substituted back into formula (4) to calculate the updated +.>
Wherein the method comprises the steps of
All represent iterative computations;
step 5) after the iteration is completed
Is the corresponding w 0 、w 1 、w 2 、w 3 The weight value is substituted into the formula (2) to establish a prediction model, and the values of the processing evaluation indexes under different alkaline yellow 40 solvent concentrations, tiO2 concentrations, ultraviolet illumination intensities and temperatures can be predicted through the prediction model;
step 6), solving the corresponding values of the concentration C1 of the alkaline yellow 40 solvent, the concentration C2 of TiO2, the ultraviolet irradiation intensity L and the temperature T when the Q' value is maximum, namely when the processing evaluation index is highest, according to the prediction model; the operating system sends the instructions of parameters to the ultraviolet lamp and the polishing solution adding device, so that the aim of optimizing and adjusting the chemical reaction process is fulfilled;
(2) Optimizing and adjusting the polishing path of the mechanical arm;
step (1) calling an expected track y stored in a cloud server database by the industrial personal computer operating system aiming at a processing object k Consists of k coordinate points, and the actual motion trail in the strategy network training process is y' k Establishing an error function e k The formula is as follows:
e k ＝y k -y' k (5)
training e through strategic network models k The value becomes as small as possible, i.e., the error of the polishing track is reduced;
setting a reward value R according to the error value, wherein the smaller the error is, the larger the reward value is, and in order to enable the strategy network to better understand future and potential rewards, the future attenuated rewards are used, and the action is assumed to be the next time, R k The formula for the K-th prize is as follows:
R＝R 1 +γR 2 +γ 2 R 3 +...+γ k-1 R k (gamma is the attenuation coefficient to be 0.99) (6);
step (3) a neural network model is built, the input is the current coordinate value of the mechanical arm, and the output is a probability value matrix p= [ p ] of the mechanical arm moving towards each azimuth 1 ,p 2 ,p 3 ,...,p n ](n is determined by the set number of orientations) and can be used to select actions;
step (4) establishing a loss function L 2 The cross entropy loss function is adopted, and the formula is as follows:
L 2 ＝-(r*logp)/n (7)
The probability of obtaining actions with more rewards is improved, and the probability of obtaining actions with lower rewards is reduced;
and (5) substituting the loss function, and iteratively updating the weight value in the neural network by using a gradient descent algorithm to finally obtain a strategy network capable of providing an optimal strategy, wherein the polishing path of the mechanical arm can be optimally adjusted based on the strategy network.
2. The processing platform constructed by the photocatalytic cobalt-based alloy processing method according to claim 1, wherein: comprising
The robot module comprises an industrial robot and a flexible pneumatic polishing device, wherein the flexible polishing device is arranged on a mechanical arm of the industrial robot and is used for adjusting the position of a workpiece;
the polishing workbench comprises a polishing disc for containing polishing liquid and an illumination device for illuminating the polishing liquid and a workpiece, wherein the illumination device is arranged in the polishing disc, and an illumination head of the illumination device is aligned to the polishing liquid;
the polishing liquid adding device is arranged beside the polishing workbench, and a liquid adding port of the polishing liquid adding device is positioned right above a polishing pool of the polishing workbench and is used for adding polishing liquid into the polishing workbench;
the industrial personal computer is electrically connected or in signal connection with the control ends of the robot module, the polishing workbench and the polishing liquid adding device and is used for controlling the robot module, the polishing workbench and the polishing liquid adding device to work so as to realize optimal adjustment of the chemical reaction process and optimal adjustment of the polishing path of the mechanical arm;
and the signal transmission end of the memory is in bidirectional signal connection or electric connection with the signal transmission port of the industrial personal computer through a wire or a wireless network and is used for storing processing information.
3. The processing platform of claim 2, wherein: the bottom of the polishing disc is fixedly connected with a rotary workbench driven by a rotary motor, so that the polishing disc can rotate around a vertical central shaft of the polishing disc; the polishing device comprises a polishing pool, a polishing layer, an elastic film layer, a polishing layer and a polishing layer, wherein the elastic layer, the elastic film layer and the abrasive layer are sequentially arranged in the polishing pool from bottom to top and used for supporting abrasive particles, the abrasive layer is used for processing the surface of a die, the bottom end of the elastic layer is connected with a bottom plate in the polishing pool, the elastic film layer is paved on the upper surface of the elastic layer, and the edge of the elastic film layer is connected with the inner wall of the polishing pool in a sealing manner, so that a processing area for containing polishing liquid is formed by the elastic film layer and the inner wall of the polishing pool; the abrasive particle layer is paved on the surface of the elastic film layer, and polishing liquid for lubrication and heat dissipation is filled on the abrasive particle layer; the control end of the rotating motor is electrically connected with the control end corresponding to the master controller.
4. A processing platform as claimed in claim 3, wherein: the elastic layer is a conical spiral spring array, the conical spiral spring array comprises a plurality of mutually independent conical spiral springs, the small-caliber end of each conical spiral spring is fixedly connected with the inner bottom surface of the polishing pool, the large-caliber end of each conical spiral spring is connected with a hemispherical support, and the arc-shaped curve of each hemispherical support is in contact with the lower surface of the elastic layer; the upper end of the conical spiral spring is abutted against the lower surface of the elastic film layer.
5. A processing platform as claimed in claim 3, wherein: the illumination device is an ultraviolet lamp and is fixed at the center of the rotary workbench, wherein the ultraviolet lamp always irradiates the workpiece processing area.
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