A method for controlling plasma cladding parameters based on virtual simulation

Abstract

This invention discloses a method for controlling plasma deposition parameters based on virtual simulation, relating to the field of soldering process, including the following steps: Step 1: Receive the performance requirements and constraints of the soldering coating, and set initial process parameters to generate an initial parameter setting scheme; Step 2: Construct a three-dimensional deposition motion model based on the initial parameter setting scheme, and define the variable process parameters in the model and their preset variation ranges; Step 3: Evaluate the completion rate of the actual parameters of each variable item in the current cycle, and dynamically compensate and adjust the initial parameter setting scheme; Step 4: Based on the compensated parameters, divide the current and voltage into several level intervals according to preset rules; calculate the optimal powder injection point in real time based on the thermal gradient distribution, improve the coating density and bonding strength, form a precise layered control method for process parameters, improve the stability and reliability of the deposition process, and significantly reduce the cost of experimental parameter adjustment.

Description

Technical Field

[0001] This invention relates to the field of brazing technology, specifically to a method for controlling plasma deposition parameters based on virtual simulation. Background Technology

[0002] In the drilling industry, especially in the extraction of oil, gas and mineral resources, drill bits are subjected to extreme conditions such as high pressure, high temperature and wear. With the advancement of materials science and manufacturing technology, plasma cladding, as an important surface treatment technology, has the advantages of being fast, efficient and environmentally friendly. By melting powder materials and depositing them on the substrate through a plasma arc, it can not only improve material properties, but also achieve precise control of coating thickness and microstructure. The rapid development of virtual simulation technology has provided new means for optimizing complex manufacturing processes. Computer simulation can accurately simulate the cladding process before actual processing, helping engineers predict processing results, optimize process parameters, thereby reducing trial and error costs and improving production efficiency.

[0003] However, existing technologies often rely on fixed process parameters, making it difficult to respond to molten pool fluctuations and powder melting state shifts caused by heat accumulation. They lack real-time sensing capabilities for the thermodynamic instability of the molten pool caused by heat accumulation and lack compensation mechanisms for solid-state phase transitions induced by thermal cycling. This leads to uncontrolled coating microstructure. Parameters such as current, voltage, and powder feeding are often adjusted independently, and the scanning speed is fixed, making it impossible to dynamically match the powder melting rate according to the thermal field gradient. This results in molten width fluctuations or uneven coating thickness. The correlation effects between multiple parameters are ignored, such as the strong correlation between the thermal field gradient and the powder feeding position. Existing current and voltage levels use fixed ranges, making it difficult to adapt to changes in material properties and process fluctuations. For example, sudden changes in thermal efficiency can easily cause substrate deformation or substandard coating performance. When thermal efficiency drops sharply, the fixed voltage range exacerbates arc drift, inducing porosity defects in the coating. Conventional simulation models ignore actual process boundaries, making it difficult to drive real-time process decisions. The parameter sensitivity matrix is ​​not quantified, making it impossible to predict critical process windows and dynamically correct the powder feeding trajectory. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for adjusting plasma cladding parameters based on virtual simulation, which can effectively solve the problems of the existing technology.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] This invention discloses a method for adjusting plasma cladding parameters based on virtual simulation, comprising the following steps:

[0009] Step 1: Receive the performance requirements and constraints of the drill bit coating, quantitatively extract the coating characteristics, and set the initial process parameters based on the properties of the substrate material, the powder material, and the coating characteristics to generate an initial parameter setting scheme.

[0010] Step 2: Construct a three-dimensional welding motion model based on the initial parameter setting scheme, and define the variable process parameters in the model and their preset variation range;

[0011] Step 3: Evaluate the completion rate of the actual parameters of each variable item in the current period, and dynamically compensate and adjust the initial parameter setting scheme according to the evaluation results to form the compensated parameters;

[0012] Step 4: Based on the compensated parameters, the plasma arc current and plasma arc voltage are divided into several level intervals according to preset rules. The level division criteria are determined based on the statistical distribution of multiple types of arc welding data in the current cycle.

[0013] Step 5: Based on the compensated parameters, update the three-dimensional cladding motion model and run it to simulate the weld width data and the thermal field distribution of the molten pool in real time. Calculate the real-time offset of the optimal powder injection position based on the thermal field gradient distribution and optimize the powder feeding position.

[0014] Step 6: Trigger the corresponding plasma arc current level and plasma arc voltage level according to the optimized powder delivery position, dynamically set the scanning speed, and automatically adjust the scanning speed until the deviation is eliminated when the actual powder injection position is detected to deviate from the ideal area.

[0015] Step 7: Repeat steps 3 to 6 until the virtual welding simulation is completed and the final process parameter set is output.

[0016] Furthermore, the coating performance requirements in step 1 include thickness, hardness, wear resistance, corrosion resistance, and bonding strength; the constraint indicators include: maximum dilution rate, minimum heat-affected zone, workpiece deformation, and processing efficiency. The maximum dilution rate is quantified by the ratio of the substrate melt depth to the total coating thickness, and the minimum heat-affected zone range is determined by the isotherm width corresponding to the Ac3 phase transition point in the thermal cycling curve; the initial process parameters include: plasma arc current, plasma arc voltage, powder feed rate, powder feed position, and powder particle size; the coating characteristics include: heat input sensitivity coefficient, fusion ratio critical value, and heat accumulation tolerance.

[0017] Furthermore, in step 2, when defining the range of variation for the variable item, the boundaries are set in the following manner:

[0018] The variation range of the plasma arc current is dynamically adjusted in combination with the melting point difference between the matrix material and the powder material. The lower limit of the range is the minimum plasma arc current value allowed by the equipment plus the product of the melting point difference and the thermal compatibility coefficient. The upper limit of the range is the maximum plasma arc current value allowed by the equipment minus the product of the melting point difference and the thermal compatibility coefficient.

[0019] The range of variation in the powder feeding position is related to the real-time molten pool length. The allowable offset is controlled within the range of 20% to 80% of the molten pool length. When the powder particle size exceeds 50 micrometers, the lower limit of the offset needs to be increased by an additional 5% of the molten pool length.

[0020] Furthermore, the construction process of the three-dimensional cladding motion model in step 2 is as follows:

[0021] Based on the three-dimensional geometric model of the substrate workpiece, the surface of the substrate is established for fusion deposition. Combined with the mechanical structural parameters of the powder feeding nozzle and the plasma gun, a three-dimensional spatial coordinate system including the motion trajectory is constructed to establish the temperature field, phase transition field, simulate the flow behavior of the molten pool and the powder melting state.

[0022] The variable process parameters are set as edge condition variables, and a parameter sensitivity matrix is ​​generated according to the preset variation range to drive the geometric deformation and physical field evolution of the model in real time.

[0023] Furthermore, the completion assessment process in step 3 is as follows:

[0024] Obtain the actual operating parameter values ​​of each variable and collect the corresponding monitoring data;

[0025] Determine the upper and lower limits of the range of variation for each variable item, and use the center value of the range of variation as the benchmark reference value;

[0026] Obtain the actual operating parameter values ​​and calculate the absolute difference between them and the benchmark reference values. Divide the absolute difference by half of the total width of the variation range, and convert the resulting quotient into a percentage form as the percentage deviation of the actual parameter values ​​from the center value.

[0027] When the percentage exceeds the preset deviation threshold, it is determined that the parameter is not up to standard.

[0028] A list of parameters requiring compensation was compiled from all non-compliant items.

[0029] Furthermore, the dynamic compensation adjustment in step 3 includes the following steps:

[0030] For each non-compliant item in the list of parameters to be compensated, a compensation function is established, which includes the product relationship between the current offset amplitude factor and the period time decay factor;

[0031] The output value of the compensation function is superimposed onto the base value of the corresponding parameter in the initial parameter setting scheme;

[0032] Edge verification is performed on the compensated parameters. The compensated parameter values ​​are compared with the upper and lower thresholds of the preset variation range. When the compensated parameter value exceeds the upper threshold, it is forcibly corrected to the upper threshold. When the compensated parameter value is lower than the lower threshold, it is forcibly corrected to the lower threshold. When the compensated parameter value is between the upper and lower thresholds, the original compensated value is retained.

[0033] The parameters that pass the verification are output as the set of compensated parameters.

[0034] Furthermore, the process of classifying the ion arc current and voltage levels in step 4 is as follows:

[0035] Extract arc welding operation data under compensated parameters within the current simulation cycle, including: real-time plasma arc current fluctuation value, voltage sampling sequence, molten pool thermal field gradient distribution, and powder melting efficiency data;

[0036] Gaussian distribution fitting was performed on the collected current and voltage data respectively, and the mean μ and standard deviation σ were calculated. The level interval was generated with (μ±kσ) as the boundary, where the value of k was dynamically adjusted according to the location of the extreme point of the thermal field gradient.

[0037] Number of current ratings According to the formula: Sure, This is the upper limit of the allowable variation in current after compensation. This is the lower limit of the allowable variation in current after compensation. The standard deviation of the current data. The function to round up;

[0038] Voltage level boundaries are segmented according to the molten pool thermal efficiency threshold. When the thermal efficiency η < 65%, the width of each voltage range is set to 2. When η≥65%, it is compressed to 1.5. , This represents the standard deviation of the voltage data.

[0039] Furthermore, the calculation process for the real-time offset in step 5 is as follows:

[0040] The thermal field distribution data of the molten pool output by the three-dimensional molten pool motion model is acquired in real time. The temperature change rate in the direction of the central axis of the molten pool is extracted as the longitudinal thermal field gradient, and the radial temperature change rate in the section perpendicular to the central axis of the molten pool is extracted as the transverse thermal field gradient.

[0041] Using the minimum point of the longitudinal thermal gradient as the reference position, and combining the transverse thermal gradient distribution, the annular region with the most gradual temperature change is determined.

[0042] When the maximum value of the transverse thermal gradient exceeds the preset critical value, the powder injection point is shifted away from the direction of the maximum temperature change rate within the annular region. The shift distance is inversely proportional to the extreme value of the transverse thermal gradient.

[0043] When the longitudinal thermal gradient fluctuation amplitude exceeds the preset threshold, the injection point position is dynamically adjusted along the central axis of the molten pool to keep it always in the region of minimum longitudinal gradient.

[0044] The calculated spatial coordinate offset is mapped to the powder feeding nozzle's motion trajectory in real time, driving the powder injection position to dynamically track the most stable region of the thermal field.

[0045] Furthermore, step 6 matches a preset level range mapping rule with the optimized powder feeding position to establish a linear correspondence between the offset value range and the current and voltage levels. When the offset falls into a specific value range, the associated current and voltage combination level is automatically activated. This mapping rule is generated by training with historical welding quality data.

[0046] Furthermore, the process of dynamically setting the scanning speed in step 6 is as follows: by analyzing the molten pool image, the actual injection position of the powder is monitored in real time. When the actual position is detected to deviate from the ideal area determined in step 5, a speed adjustment coefficient positively correlated with the offset distance is generated. This coefficient is applied to the initial scanning speed reference value to achieve automatic speed reduction compensation, and the reference scanning speed is restored after the position deviation returns to zero within three consecutive monitoring cycles.

[0047] (III) Beneficial Effects

[0048] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0049] 1. By integrating temperature field and phase change field simulation into a three-dimensional cladding motion model, the optimal powder injection point is calculated in real time based on the thermal gradient distribution. The powder feeding position, plasma arc current, voltage level, and scanning speed are linked and mapped. Multi-parameter coordinated control is achieved through nozzle dynamic tracking, overcoming the defect of isolated parameter adjustment in the existing technology, improving coating density and bonding strength, effectively optimizing production efficiency, and ensuring coating performance consistency.

[0050] 2. By fitting Gaussian distributions to the current, voltage, and thermal efficiency operating data within the simulation cycle, calculating the mean and standard deviation, constructing an adaptive level interval, and dynamically adjusting the voltage interval width in conjunction with the real-time thermal efficiency threshold, a precise hierarchical control method for process parameters is formed. This reduces the reliance on experience settings and improves the accuracy of parameter adjustment and the repeatability and stability of the welding process.

[0051] 3. By collecting the molten pool status in real time and dynamically adjusting the plasma current, voltage, powder feeding amount, powder feeding position and scanning speed, defects such as uneven coating thickness, uncontrolled dilution rate and pore cracks caused by static parameter settings are effectively eliminated, reducing the reliance on manual experience intervention, improving the stability and reliability of the molten deposition process, and significantly reducing the cost of test parameter adjustment. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0053] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0055] The present invention will be further described below with reference to embodiments.

[0056] This embodiment presents a method for adjusting plasma cladding parameters based on virtual simulation, such as... Figure 1 As shown, it includes the following steps:

[0057] Step 1: Receive the performance requirements for the drill bit coating, including thickness, hardness, wear resistance, corrosion resistance, and bonding strength, as well as constraint indicators including maximum dilution rate, minimum heat-affected zone, workpiece deformation, and processing efficiency. Quantify and extract coating characteristics. Based on the substrate material properties, powder material properties, and coating characteristics, set initial process parameters to generate an initial parameter setting scheme including plasma arc current, plasma arc voltage, powder feed rate, powder feed position, and powder particle size. The maximum dilution rate is quantified by the ratio of substrate melt depth to total coating thickness, and the minimum heat-affected zone range is determined by the isotherm width corresponding to the Ac3 phase transition point in the thermal cycling curve. Coating characteristics include: heat input sensitivity coefficient, calculated based on hardness and corrosion resistance requirements; fusion ratio critical value, derived from dilution rate constraints and bonding strength requirements; and heat accumulation tolerance, modeled based on the correlation between workpiece deformation threshold and heat-affected zone range.

[0058] Step 2: Construct a three-dimensional welding motion model based on the initial parameter setting scheme, and define the variable process parameters in the model and their preset variation ranges; when defining the variation range of the variable parameters, the boundaries are set in the following way:

[0059] The variation range of the plasma arc current is dynamically adjusted in combination with the melting point difference between the matrix material and the powder material. The lower limit of the range is the minimum plasma arc current value allowed by the equipment plus the product of the melting point difference and the thermal compatibility coefficient. The upper limit of the range is the maximum plasma arc current value allowed by the equipment minus the product of the melting point difference and the thermal compatibility coefficient.

[0060] The range of variation in the powder feeding position is related to the real-time molten pool length, and the allowable offset is controlled within the range of 20% to 80% of the molten pool length. When the powder particle size exceeds 50 micrometers, the lower limit of the offset needs to be increased by an additional 5% of the molten pool length.

[0061] The construction process of the three-dimensional cladding motion model is as follows:

[0062] Based on the three-dimensional geometric model of the substrate workpiece, the surface of the substrate is established for fusion deposition. Combined with the mechanical structural parameters of the powder feeding nozzle and the plasma gun, a three-dimensional spatial coordinate system including the motion trajectory is constructed to establish the temperature field, phase transition field, simulate the flow behavior of the molten pool and the powder melting state. The temperature field is calculated based on the Fourier heat conduction equation and the Gaussian distribution model of the plasma arc heat source. The VOF method is used to simulate the flow behavior of the molten pool and the powder melting state. The material thermophysical property database is linked to dynamically calculate the Ac3 phase transition point and the range of the heat-affected zone.

[0063] Variable process parameters are set as edge condition variables, and a parameter sensitivity matrix is ​​generated based on the preset variation range to drive the geometric deformation and physical field evolution of the model in real time. Dynamic boundaries are set for variable parameters such as plasma current and powder feeding position to take into account the influence of material melting point difference, nozzle structure and particle size, thereby improving the parameter space coverage.

[0064] Step 3: Evaluate the completion rate of the actual parameters of each variable item in the current cycle, and dynamically compensate and adjust the initial parameter setting scheme according to the evaluation results to form the compensated parameters; quantify the completion rate of the actual parameters in each simulation cycle and identify deviations in a timely manner.

[0065] Step 4: Based on the compensated parameters, the plasma arc current and plasma arc voltage are divided into several level intervals according to preset rules. The level division criteria are determined based on the statistical distribution of multiple types of arc welding data in the current cycle.

[0066] Step 5: Based on the compensated parameters, update and run the 3D molten metal deposition motion model to simulate the weld width data and the thermal field distribution of the molten pool in real time. Calculate the real-time offset of the optimal powder injection position based on the thermal field gradient distribution and optimize the powder feeding position. The calculation process for the real-time offset is as follows:

[0067] The thermal field distribution data of the molten pool output by the three-dimensional molten pool motion model is acquired in real time. The temperature change rate in the direction of the central axis of the molten pool is extracted as the longitudinal thermal field gradient, and the radial temperature change rate in the section perpendicular to the central axis of the molten pool is extracted as the transverse thermal field gradient.

[0068] Using the minimum point of the longitudinal thermal gradient as the reference position, and combining the transverse thermal gradient distribution, the annular region with the most gradual temperature change is determined.

[0069] When the maximum value of the transverse thermal gradient exceeds the preset critical value, the powder injection point is shifted away from the direction of the maximum temperature change rate within the annular region. The shift distance is inversely proportional to the extreme value of the transverse thermal gradient.

[0070] When the longitudinal thermal gradient fluctuation amplitude exceeds the preset threshold, the injection point position is dynamically adjusted along the central axis of the molten pool to keep it always in the region of minimum longitudinal gradient.

[0071] The calculated spatial coordinate offset is mapped to the powder feeding nozzle's motion trajectory in real time, driving the powder injection position to dynamically track the most stable region of the thermal field.

[0072] Step 6: Trigger the corresponding plasma arc current level and plasma arc voltage level according to the optimized powder feeding position, dynamically set the scanning speed, and automatically adjust the scanning speed to eliminate the offset when the actual powder injection position is detected to deviate from the ideal area; According to the optimized powder feeding position, match the preset level interval mapping rule to establish a linear correspondence between the offset value interval and the current and voltage levels. When the offset falls into a specific value range, automatically activate the associated current and voltage combination level. This mapping rule is generated by training through historical deposition quality data.

[0073] The process of dynamically setting the scanning speed is as follows: by analyzing the molten pool image, the actual injection position of the powder is monitored in real time. When the actual position is detected to deviate from the ideal area determined in step 5, a speed adjustment coefficient positively correlated with the offset distance is generated. This coefficient is applied to the initial scanning speed reference value to achieve automatic speed reduction compensation, and the reference scanning speed is restored after the position deviation returns to zero within three consecutive monitoring cycles.

[0074] Step 7: Repeat steps 3 to 6 until the virtual welding simulation is completed and the final process parameter set is output.

[0075] Compared with existing technologies, by connecting key technology nodes such as coating performance and constraint index quantification, initial parameter inversion, three-dimensional model construction, dynamic setting of multiple variable parameters, real-time evaluation and closed-loop compensation, data-driven layer control, thermal field gradient guided powder feeding optimization and adaptive adjustment of scanning speed, the traditional empirical and single parameter adjustment mode is broken.

[0076] The entire process is simulated and fed back online, avoiding a lot of experimental costs. Multi-parameter collaboration improves coating density and bonding strength. Data-driven grading and compensation significantly improve the accuracy of parameter adjustment. Adaptive powder feeding and speed control effectively suppress defect generation. A final process solution that meets performance requirements can be obtained in a single simulation.

[0077] At other levels, this embodiment provides a process for classifying plasma arc current and voltage levels, specifically as follows:

[0078] Extract arc welding operation data under compensated parameters within the current simulation cycle, including: real-time plasma arc current fluctuation value, voltage sampling sequence, molten pool thermal field gradient distribution, and powder melting efficiency data;

[0079] Gaussian distribution fitting was performed on the collected current and voltage data respectively, and the mean μ and standard deviation σ were calculated. The level interval was generated with (μ±kσ) as the boundary. The value of k was dynamically adjusted according to the location of the extreme point of the thermal gradient. The larger the thermal gradient, the smaller the value of k, and the more refined the interval division.

[0080] The k-value adjustment process includes:

[0081] The maximum gradient extremum point in the thermal field gradient distribution of the molten pool is located in real time, and the distance between this point and the geometric center of the molten pool is quantified as a relative offset ratio.

[0082] When the maximum gradient value exceeds the critical threshold, i.e., the maximum gradient value is ≥500°C / mm, and the extreme point is located in the first third of the molten pool, it is judged as a high-risk thermal shock state, and the k value is compressed to 50%-60% of the preset benchmark value;

[0083] When the maximum gradient value is in the middle range, i.e. 200°C / mm-500°C / mm, and the extreme point is located in one-third of the molten pool, it is determined to be a controllable transition state, and the k value is maintained as the preset baseline value.

[0084] When the maximum gradient value is lower than the stability threshold, i.e., the maximum gradient value is <200°C / mm, and the extreme point is located in the last third of the molten pool, it is determined to be in thermal equilibrium, and the k value is extended to 120%-150% of the preset reference value;

[0085] If the extreme point is detected to migrate towards the center of the molten pool three times consecutively within the same period, an additional k-value attenuation coefficient is applied, with the k-value decreasing by 0.8% for every 1% migration distance.

[0086] When the real-time offset of the powder delivery position triggers system adjustment, the k-value decay process is automatically reset.

[0087] Number of current ratings According to the formula: Sure, This is the upper limit of the allowable variation in current after compensation. This is the lower limit of the allowable variation in current after compensation. The standard deviation of the current data. The rounding function rounds up; for example, when the calculated value is 4.2, it rounds up to level 5.

[0088] Voltage level boundaries are segmented according to the molten pool thermal efficiency threshold. When the thermal efficiency η < 65%, the width of each voltage range is set to 2. When η≥65%, it is compressed to 1.5. , This represents the standard deviation of the voltage data.

[0089] Compared with existing technologies, the classification standard is determined by the real-time data distribution driven by the compensated parameters. It is not a fixed threshold, and the interval width is strongly correlated with the thermal field gradient and the thermal efficiency of the molten pool, ensuring that the classification matches the actual thermodynamic state. The parameter volatility is quantified by the standard deviation, and the interval boundary is self-converged. The greater the volatility, the wider the interval and the greater the control tolerance.

[0090] Furthermore, the embodiment also provides a strategy for evaluating the completion rate of existing setting operation indicators, and a strategy for dynamically compensating and adjusting the initial parameter setting scheme, the process of which is as follows:

[0091] Obtain the actual operating parameter values ​​of each variable and collect the corresponding monitoring data;

[0092] Determine the upper and lower limits of the range of variation for each variable item, and use the center value of the range of variation as the benchmark reference value;

[0093] Obtain the actual operating parameter values ​​and calculate the absolute difference between them and the benchmark reference values. Divide the absolute difference by half of the total width of the variation range, and convert the resulting quotient into a percentage form as the percentage deviation of the actual parameter values ​​from the center value.

[0094] When the percentage exceeds the preset deviation threshold, it is determined that the parameter is not up to standard.

[0095] A list of parameters requiring compensation was compiled from all non-compliant items.

[0096] Dynamic compensation adjustment includes the following steps:

[0097] For each non-compliant item in the list of parameters to be compensated, a compensation function is established, which includes the product relationship between the current offset amplitude factor and the period time decay factor;

[0098] The output value of the compensation function is superimposed onto the base value of the corresponding parameter in the initial parameter setting scheme;

[0099] Edge verification is performed on the compensated parameters. The compensated parameter values ​​are compared with the upper and lower thresholds of the preset variation range. When the compensated parameter value exceeds the upper threshold, it is forcibly corrected to the upper threshold. When the compensated parameter value is lower than the lower threshold, it is forcibly corrected to the lower threshold. When the compensated parameter value is between the upper and lower thresholds, the original compensated value is retained. Taking plasma arc current compensation as an example, let the preset variation range be [180A, 220A]: If the compensated current = 230A, it exceeds the upper limit and is forcibly corrected to 220A; if the compensated current = 170A, it is lower than the lower limit and is forcibly corrected to 180A; if the compensated current = 200A, it remains at 200A.

[0100] The parameters that pass the verification are output as the set of compensated parameters.

[0101] Compared with existing technologies, this method achieves a quantitative assessment of the degree of deviation by analyzing the percentage of actual parameter values ​​deviating from the center value of the variation range. It establishes a product-type compensation function containing an offset amplitude factor that reflects the current deviation and the compensation intensity decreasing with the number of iterations. This function can quickly respond to deviations and avoid overcompensation oscillations. An edge verification mechanism is added to force the out-of-bounds compensation value to be clamped to the preset variation range boundary, avoiding the risk of compensation value runaway in traditional methods and ensuring simulation stability.

[0102] In summary, this invention fully considers coating performance requirements and multiple constraints, and utilizes statistical distribution and thermal gradient to adjust the powder feeding position and scanning speed in real time, effectively improving the deposition quality and stability. Through grade classification and dynamic mapping, it achieves adaptive adjustment of plasma arc current and voltage, ensuring that process parameters fluctuate within a reasonable range, reducing the risks caused by parameter deviations, and significantly improving the intelligence and adaptability of the process.

[0103] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A virtual simulation-based method for controlling plasma deposition parameters, characterized in that, The method comprises the following steps: Step 1: receiving the bit coating performance requirements and constraint indicators, quantitatively extracting the coating characteristics, and setting the initial process parameters to generate an initial parameter setting scheme; Step 2: constructing a three-dimensional deposition motion model based on the initial parameter setting scheme, defining the variable process parameter items in the model and their preset variable ranges; Step 3: evaluating the completion degree of the actual parameters of each variable item in the current cycle, and dynamically compensating and adjusting the initial parameter setting scheme to form the compensated parameters; Step 4: based on the compensated parameters, the current and voltage are divided into several grade intervals according to the preset rules; Step 5: based on the compensated parameters, update the three-dimensional deposition motion model and run, calculate the real-time offset of the optimal powder injection position based on the thermal field gradient distribution and optimize the powder feeding position; Step 6: trigger the corresponding plasma arc current level and plasma arc voltage level according to the optimized powder feeding position, dynamically set the scanning speed, and automatically adjust the scanning speed to eliminate the offset when the actual powder injection position deviates from the ideal region; Step 7: Steps 3 to 6 are executed in a loop until the virtual deposition simulation is completed, and the final process parameter set is output.

2. The plasma deposition parameter control method based on virtual simulation according to claim 1, characterized in that: the coating performance requirements in step 1 include thickness, hardness, wear resistance, corrosion resistance and bonding strength; the constraint indicators include maximum dilution rate, minimum heat affected zone, workpiece deformation and processing efficiency, wherein the maximum dilution rate is quantified by the ratio of the base metal penetration depth to the total coating thickness, and the minimum heat affected zone range is determined by the isothermal line width corresponding to the Ac3 phase transition point in the thermal cycle curve; the initial process parameters include plasma arc current, plasma arc voltage, powder feeding amount, powder feeding position and powder particle size; the coating characteristics include thermal input sensitivity coefficient, critical value of fusion ratio and heat accumulation tolerance.

3. The virtual simulation-based plasma deposition parameter regulation method according to claim 1, wherein, When defining the variable range of the variable item in step 2, the boundary is set by the following method: The variable range of the plasma arc current is dynamically adjusted in combination with the melting point difference of the base material and the powder material; The variable range of the powder feeding position is associated with the real-time molten pool length, and the offset amount is controlled in the range of 20% to 80% of the molten pool length. When the powder particle size exceeds 50 microns, the lower limit value of the offset amount needs to be additionally increased by 5% of the molten pool length.

4. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, The construction process of the three-dimensional deposition motion model in step 2 is as follows: Based on the three-dimensional geometric model of the base workpiece, the deposition base surface is established, the three-dimensional space coordinate system including the motion trajectory is constructed combined with the mechanical structure parameters of the powder feeding nozzle and the plasma gun, and the temperature field, phase transition field, simulated molten pool flow behavior and powder melting state are established; Set the variable process parameter items as edge condition variables, generate a parameter sensitivity matrix according to the preset variable range, and drive the model geometry and physical field evolution in real time.

5. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, The process of completion degree evaluation in step 3 is as follows: Obtain the actual running parameter values of each variable item and collect the corresponding monitoring data; Determine the upper and lower limits of the variable range of each variable item, and take the center value of the variable range as the reference value; Obtain the percentage amplitude of the actual parameter value deviating from the center value; When the percentage amplitude exceeds the preset deviation threshold, it is determined that the parameter is not up to standard; All non-standard items are counted to form a list of parameters that need to be compensated.

6. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, The dynamic compensation adjustment in step 3 includes the following steps: For each non-standard item in the list of parameters that need to be compensated, a compensation function is established, which includes the product relationship between the current offset amplitude factor and the period time decay factor; The output value of the compensation function is superimposed on the base value of the corresponding parameter in the initial parameter setting scheme; The compensated parameters are edge checked; The parameters that pass the check are output as the compensated parameter set.

7. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, The classification process of the step 4 of the level of the plasma arc current and voltage is: Extract the arc welding operation data under the compensated parameters in the current simulation period, including: real-time plasma arc current fluctuation value, voltage sampling sequence, molten pool thermal field gradient distribution and powder melting efficiency data; The collected current and voltage data are respectively subjected to Gaussian distribution fitting, and the mean μ and standard deviation σ are calculated, and the level interval is generated with (μ±kσ) as the boundary, where the value of k is dynamically adjusted according to the position of the thermal field gradient extreme point; number of current classes According to the formula: determined, the upper limit value of the allowable variation of the compensated current, the lower limit value of the allowable variation of the compensated current, the standard deviation of the current data, the ceiling function; The voltage level boundary is segmented by the threshold of the heat efficiency of the molten pool, and the width of each voltage interval is set to 2 when the heat efficiency η < 65% ; and compressed to 1.5 when η ≥ 65% , is the standard deviation of the voltage data.

8. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, The calculation process of the real-time offset in step 5 is: Real-time acquisition of the molten pool thermal field distribution data output by the three-dimensional deposition motion model, extraction of the temperature change rate in the direction of the molten pool center axis as the longitudinal thermal field gradient, and extraction of the radial temperature change rate perpendicular to the molten pool center axis section as the transverse thermal field gradient; Taking the longitudinal thermal field gradient minimum point as the reference position, and combining the transverse thermal field gradient distribution to determine the annular region with the slowest temperature change; When the maximum value of the transverse thermal field gradient exceeds the preset threshold, the powder injection point is offset away from the direction with the maximum temperature change rate in the annular region, and the offset distance is inversely proportional to the transverse thermal field gradient extreme value; When the longitudinal thermal field gradient fluctuation amplitude is greater than the preset threshold, the injection point position is dynamically adjusted along the molten pool center axis direction, so that it is always in the longitudinal gradient minimum value region; Map the calculated spatial coordinate offset to the powder feeding nozzle motion trajectory in real time to drive the powder injection position to dynamically track the most stable thermal field region.

9. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, According to the optimized powder feeding position, the step 6 matches the preset level interval mapping rule to establish a linear correspondence between the offset value interval and the current voltage level. When the offset falls within a specific numerical range, the associated current voltage combination level is automatically activated. This mapping rule is generated by training historical deposition quality data.

10. The virtual simulation-based plasma deposition parameter regulation method of claim 1, wherein, The process of dynamically setting the scanning speed in step 6 is: through molten pool image analysis, the actual powder injection position is monitored in real time, and when the actual position deviates from the ideal region determined in step 5, a speed adjustment coefficient positively correlated with the offset distance is generated.

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