Efficient additive manufacturing method based on cooperation of electric arc presetting and laser refining

Abstract

The invention relates to the technical field of additive manufacturing, and discloses an efficient additive manufacturing method based on cooperation of electric arc presetting and laser refining, which comprises the following steps: starting an electric arc welding gun according to an initial heat input parameter, and performing electric arc presetting cladding on a current layered area of a part to be manufactured according to an electric arc prefabrication forming path to form a preset cladding layer; initial heat input parameters of the arc welding gun are adjusted based on the temperature field distribution data and the molten pool form data, and adjusted heat input parameters are obtained; starting a high-power laser head according to the initial power parameter and the scanning speed parameter, and carrying out laser refining treatment on the preset cladding layer according to a laser refining scanning path; whether the power parameter and the scanning speed parameter are adjusted or not is judged based on the surface temperature; and completing laser refining treatment on the preset cladding layer of the current layered area according to the adjusted power parameter and scanning speed parameter. Through deep synergy of electric arc presetting and laser refining, the comprehensive performance and manufacturing stability of the formed part are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and more specifically, to a highly efficient additive manufacturing method based on the synergy of electric arc pre-setting and laser refining. Background Technology

[0002] Currently, additive manufacturing technology, with its advantages of not requiring molds, high material utilization, and the ability to form complex structures, has shown enormous application potential in aerospace, automotive, and medical fields. However, traditional single-heat-source additive manufacturing methods often face the challenge of balancing efficiency and quality. For example, while arc additive manufacturing boasts high deposition efficiency and relatively low cost, its formed parts have a large surface roughness and are prone to internal defects such as porosity and incomplete fusion, resulting in poor mechanical property stability. Laser additive manufacturing, on the other hand, can achieve high forming accuracy and good surface quality, but its deposition efficiency is typically low due to limitations in laser spot size and energy density distribution, making it difficult to meet the demands of rapid manufacturing of large components. Therefore, effectively combining the advantages of different heat sources to achieve a synergistic improvement in efficiency and quality during additive manufacturing has become a pressing technical challenge in this field. While existing technologies have attempted additive manufacturing methods that combine electric arcs and lasers, they mostly focus on preliminary explorations of simple superposition or energy coupling between heat sources. There are still many shortcomings in terms of the dynamic matching of the electric arc pre-set molten pool and the laser refining process, as well as how to achieve deep synergistic optimization of the two through real-time monitoring and feedback control to further improve the overall performance and manufacturing stability of the formed parts.

[0003] Therefore, it is necessary to design an efficient additive manufacturing method based on the synergy of electric arc pre-setting and laser refining to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes an efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining, which aims to solve the dynamic matching problem between the arc pre-positioned molten pool and the laser refining process, and to achieve deep synergistic optimization of the two through real-time monitoring and feedback control, so as to improve the overall performance and manufacturing stability of the formed parts.

[0005] This invention proposes a highly efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining, comprising: Based on the 3D model of the part to be manufactured, layered manufacturing data, arc preforming path and laser refining scanning path are generated, and arc welding gun and high-power laser head are respectively installed at the execution ends of the first robot and the second robot. The material type and layered manufacturing data of the part to be manufactured are collected, and the initial heat input parameters of the arc welding gun, the initial power parameters of the high-power laser head, and the scanning speed parameters are determined based on the material type and layered manufacturing data. The arc welding gun is started with the initial heat input parameters and the current layered area of ​​the part to be manufactured is pre-clad with arc according to the arc pre-forming path to form a pre-clad layer; The temperature field distribution data and molten pool morphology data of the pre-set cladding layer are collected in real time. Based on the temperature field distribution data and molten pool morphology data, the initial heat input parameters of the arc welding gun are adjusted to obtain the adjusted heat input parameters. After the arc pre-cladding of the current layered region is completed with the adjusted heat input parameters, the high-power laser head is started with the initial power parameters and scanning speed parameters and laser refining is performed on the pre-cladding layer according to the laser refining scanning path. The surface temperature and depth data of the pre-set cladding layer are collected in real time. Based on the surface temperature, it is determined whether the power parameters and scanning speed parameters need to be adjusted. If so, the initial power parameters and scanning speed parameters of the high-power laser head are adjusted according to the surface temperature and depth data to obtain the adjusted power parameters and scanning speed parameters. The laser refining process of the pre-set cladding layer in the current layered region is completed using the adjusted power and scanning speed parameters; Repeat the electric arc pre-setting cladding step and the laser refining process until the additive manufacturing of all layered areas of the part to be manufactured is completed, and the shaped part is obtained.

[0006] Furthermore, the electric arc preforming path and the laser refining scanning path correspond to each other in spatial position.

[0007] Furthermore, when determining the initial thermal input parameters of the arc welding torch, the initial power parameters of the laser head, and the scanning speed parameters based on the material type and layered manufacturing data, the following steps are included: The layered manufacturing data is analyzed to obtain the outline dimensions, layer height, and infill density of each layered region; The basic thermal input parameters of the arc welding torch, the basic power parameters of the high-power laser head, and the basic scanning speed are determined according to the material type. A layered coupling vector is constructed based on the outline dimensions, layer height, and fill density. The hierarchical coupling vector is compared with the historical hierarchical vector group, and the correction coefficients corresponding to the basic thermal input parameters, basic power parameters and basic scanning speed are determined based on the comparison results. The correction coefficient is multiplied by the corresponding basic thermal input parameter, basic power parameter and basic scan speed to obtain the initial thermal input parameter, initial power parameter and scan speed parameter.

[0008] Furthermore, when determining the basic thermal input parameters of the arc welding torch, the basic power parameters of the high-power laser head, and the basic scanning speed based on the material type, the following steps are included: The material types include metallic materials, alloy materials, or composite materials; The material type is compared with a preset parameter setting library, and the basic thermal input parameters, basic power parameters, and basic scanning speed are determined based on the comparison results.

[0009] Further, when comparing the hierarchical coupling vector with the historical hierarchical vector set, and determining the correction coefficients corresponding to the basic thermal input parameters, basic power parameters, and basic scan speed based on the comparison results, the following steps are included: When there is a historical layered coupling vector in the historical layered vector group that is the same as the layered coupling vector, the correction coefficient corresponding to the historical layered coupling vector is used as the correction coefficient; When there is no historical layer coupling vector in the historical layer vector group that is the same as the layer coupling vector, the Euclidean distance between the layer coupling vector and each historical layer vector in the historical layer vector group is calculated. The k historical layer vectors with the smallest Euclidean distance are selected, and the correction coefficients corresponding to the k historical layer vectors are fused and calculated using the weighted average method to obtain the correction coefficient corresponding to the layer coupling vector. Among them, the weight value is inversely proportional to the Euclidean distance.

[0010] Further, when adjusting the initial thermal input parameters of the arc welding torch based on the temperature field distribution data and molten pool morphology data to obtain the adjusted thermal input parameters, the process includes: Feature extraction is performed on the temperature field distribution data and molten pool morphology data to obtain temperature field distribution feature values ​​and molten pool morphology feature values. Obtain standard values ​​for temperature field distribution and molten pool morphology; Calculate the temperature field distribution deviation between the temperature field distribution characteristic value and the temperature field distribution standard value, and the molten pool morphology deviation between the molten pool morphology characteristic value and the molten pool morphology standard value; The adjustment amount of the initial heat input parameters is determined based on the temperature field distribution deviation value and the molten pool morphology deviation value; The adjusted amount is added to the initial thermal input parameters to obtain the adjusted thermal input parameters.

[0011] Furthermore, when determining the adjustment amount of the initial thermal input parameters based on the temperature field distribution deviation value and the molten pool morphology deviation value, the following steps are included: The temperature field distribution deviation value is compared with the temperature field distribution deviation threshold, and the molten pool morphology deviation value is compared with the molten pool morphology deviation threshold. The adjustment amount is determined based on the comparison results. When the temperature field distribution deviation value is greater than or equal to the temperature field distribution deviation threshold, and the molten pool shape deviation value is greater than or equal to the molten pool shape deviation threshold, the adjustment amount is determined to be the first adjustment amount; When the temperature field distribution deviation value is greater than or equal to the temperature field distribution deviation threshold, and the molten pool shape deviation value is less than the molten pool shape deviation threshold, the adjustment amount is determined to be the second adjustment amount; When the temperature field distribution deviation value is less than the temperature field distribution deviation threshold, and the molten pool shape deviation value is greater than or equal to the molten pool shape deviation threshold, the adjustment amount is determined to be the third adjustment amount; When the temperature field distribution deviation value is less than the temperature field distribution deviation threshold and the molten pool morphology deviation value is less than the molten pool morphology deviation threshold, the adjustment amount is determined to be the fourth adjustment amount.

[0012] Furthermore, when determining whether to adjust the power parameters and scanning speed parameters based on the surface temperature, the process includes: The surface temperature is analyzed to identify any abnormal surface temperature points. If present, it is determined that the power parameter and scan speed parameter should be adjusted. Otherwise, it is determined that the power parameters and scan speed parameters will not be adjusted.

[0013] Furthermore, when adjusting the initial power parameters and scanning speed parameters of the high-power laser head based on the surface temperature and melt depth data to obtain the adjusted power parameters and scanning speed parameters, the process includes: Collect the abnormal temperature value at each abnormal surface temperature point, and calculate the average value of all the abnormal temperature values, which is recorded as the average abnormal temperature value. The ratio of the average abnormal temperature value to the temperature threshold is determined and denoted as the average abnormal temperature ratio. The melting depth data is analyzed to obtain the difference between the actual melting depth value and the target melting depth value, which is recorded as the melting depth deviation value; The optimization coefficients of the initial power parameters are determined based on the average abnormal temperature ratio. The correction ratio of the scanning speed parameter is determined based on the penetration depth deviation value; Multiplying the initial power parameters by the optimization coefficients yields the adjusted power parameters; The scan speed parameter is multiplied by the correction ratio to obtain the adjusted scan speed parameter.

[0014] Further, when determining the optimization coefficient of the initial power parameter based on the average abnormal temperature ratio, the following steps are included: The average abnormal temperature ratio is compared with the first average abnormal temperature ratio and the second average abnormal temperature ratio, and the optimization coefficient is determined based on the comparison result; wherein the first average abnormal temperature ratio is less than the second average abnormal temperature ratio. When the average abnormal temperature ratio is less than or equal to the first average abnormal temperature ratio, the optimization coefficient is determined to be the first optimization coefficient. When the average abnormal temperature ratio is greater than the first average abnormal temperature ratio and less than or equal to the second average abnormal temperature ratio, the optimization coefficient is determined to be the second optimization coefficient. When the average abnormal temperature ratio is greater than the second average abnormal temperature ratio, the optimization coefficient is determined to be the third optimization coefficient.

[0015] Compared with existing technologies, the advantages of this invention are as follows: This invention's highly efficient additive manufacturing method, based on the synergy of arc pre-positioning and laser refining, significantly improves the overall performance and manufacturing stability of the formed parts through the deep synergy of arc pre-positioning and laser refining. Specifically, arc pre-positioning cladding enables efficient material deposition, providing a basic contour and sufficient density for forming; while the subsequent refining process using high-power lasers effectively improves the microstructure of the pre-positioned cladding layer, refines grains, and reduces defects such as porosity and cracks, thereby improving the mechanical properties of the formed parts, such as strength, hardness, and toughness. Simultaneously, this invention uses a first robot and a second robot respectively equipped with an arc welding torch and a laser head, ensuring that the arc pre-positioning path and the laser refining scanning path correspond spatially, providing a precise kinematic basis for their collaborative work. More importantly, this invention introduces a multi-layered real-time monitoring and feedback control mechanism: In the arc pre-setting stage, by collecting real-time temperature field distribution data and molten pool morphology data of the pre-set cladding layer, the heat input parameters of the arc welding torch are dynamically adjusted accordingly, ensuring the stability of the cladding process and the consistency of the cladding layer quality; in the laser refining stage, by collecting real-time surface temperature and melt depth data of the pre-set cladding layer, the power parameters and scanning speed parameters of the laser are judged and adjusted, achieving precise matching between laser energy input and the cladding layer state. This dual closed-loop control based on material type, layer data, and real-time process data not only solves the dynamic matching problem between the arc pre-setting and laser refining processes, but also, through historical data comparison and multi-parameter collaborative optimization, enables the entire additive manufacturing process to adapt to the manufacturing needs of parts with different materials and structural characteristics, effectively improving the dimensional accuracy, surface quality, and internal quality stability of the formed parts, reducing the scrap rate caused by process fluctuations, and providing strong technical support for efficient and high-quality additive manufacturing of complex metal parts. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an efficient additive manufacturing method based on the synergy of arc pre-setting and laser refining, provided in an embodiment of the present invention. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] See Figure 1 As shown in some embodiments of this application, this embodiment provides a highly efficient additive manufacturing method based on the synergy of arc pre-setting and laser refining, including: S100: Generate layered manufacturing data, arc preforming path and laser refining scanning path based on the three-dimensional model of the part to be manufactured, and install arc welding gun and high-power laser head at the execution ends of the first robot and the second robot respectively. S200: Collect the material type and layered manufacturing data of the part to be manufactured, and determine the initial heat input parameters of the arc welding gun, the initial power parameters of the high-power laser head, and the scanning speed parameters based on the material type and layered manufacturing data; S300: Start the arc welding gun with the initial heat input parameters and perform arc pre-cladding on the current layered area of ​​the part to be manufactured according to the arc pre-forming path to form a pre-cladding layer; S400: Real-time acquisition of temperature field distribution data and molten pool morphology data of the pre-set cladding layer; adjustment of the initial heat input parameters of the arc welding gun based on the temperature field distribution data and molten pool morphology data; and obtaining the adjusted heat input parameters. S500: After the arc pre-cladding of the current layered area is completed with the adjusted heat input parameters, the high-power laser head is started with the initial power parameters and scanning speed parameters and laser refining is performed on the pre-cladding layer according to the laser refining scanning path. S600: Real-time acquisition of surface temperature and fusion depth data of the preset cladding layer; based on the surface temperature, determine whether to adjust the power parameters and scanning speed parameters; if so, adjust the initial power parameters and scanning speed parameters of the high-power laser head according to the surface temperature and fusion depth data to obtain the adjusted power parameters and scanning speed parameters. S700: Completes laser refining of the pre-set cladding layer in the current layered region using adjusted power and scanning speed parameters; S800: Repeat the arc pre-setting cladding step and the laser refining process until the additive manufacturing of all layered areas of the part to be manufactured is completed, and the shaped part is obtained.

[0019] It is understood that this embodiment, based on the efficient additive manufacturing method of arc pre-positioning and laser refining synergy, significantly improves the overall performance and manufacturing stability of the formed parts through the deep synergy of arc pre-positioning and laser refining. Specifically, arc pre-positioning cladding enables efficient material deposition, providing a basic contour and sufficient density for forming; while the subsequent refining process of high-power laser can effectively improve the microstructure of the pre-positioned cladding layer, refine the grains, and reduce defects such as porosity and cracks, thereby improving the mechanical properties of the formed parts, such as strength, hardness, and toughness. Simultaneously, this invention uses a first robot and a second robot respectively equipped with an arc welding gun and a laser head, ensuring that the arc pre-positioning forming path and the laser refining scanning path correspond to each other in spatial position, providing a precise kinematic basis for their collaborative work. More importantly, this invention introduces a multi-layered real-time monitoring and feedback control mechanism: In the arc pre-setting stage, by collecting real-time temperature field distribution data and molten pool morphology data of the pre-set cladding layer, the heat input parameters of the arc welding torch are dynamically adjusted accordingly, ensuring the stability of the cladding process and the consistency of the cladding layer quality; in the laser refining stage, by collecting real-time surface temperature and melt depth data of the pre-set cladding layer, the power parameters and scanning speed parameters of the laser are judged and adjusted, achieving precise matching between laser energy input and the cladding layer state. This dual closed-loop control based on material type, layer data, and real-time process data not only solves the dynamic matching problem between the arc pre-setting and laser refining processes, but also, through historical data comparison and multi-parameter collaborative optimization, enables the entire additive manufacturing process to adapt to the manufacturing needs of parts with different materials and structural characteristics, effectively improving the dimensional accuracy, surface quality, and internal quality stability of the formed parts, reducing the scrap rate caused by process fluctuations, and providing strong technical support for efficient and high-quality additive manufacturing of complex metal parts.

[0020] Specifically, the electric arc preforming path and the laser refining scanning path correspond to each other in spatial position.

[0021] Understandably, this means that the laser refining scanning path is generated based on the arc pre-forming path, ensuring that the laser beam precisely targets the predetermined area of ​​the arc pre-formed cladding layer. For example, for the cladding track formed by the arc pre-forming path, the laser refining scanning path can coincide with or be offset parallel to the centerline of the cladding track by a certain distance, or a specific scanning strategy can be used to cover the cross-section of the cladding track and adjacent overlapping areas. This spatial correspondence is a prerequisite for ensuring that laser energy effectively acts on the refining area, achieving precise control over the microstructure and properties of the pre-formed cladding layer. It avoids ineffective laser energy loss, ensures the targeting and effectiveness of laser refining, allows for precise subsequent processing of the arc pre-formed basic contour, and ultimately guarantees the uniformity and consistency of the properties of each area of ​​the formed part.

[0022] Specifically, when determining the initial thermal input parameters of the arc welding torch, the initial power parameters of the laser head, and the scanning speed parameters based on the material type and layered manufacturing data, the following steps are included: The layered manufacturing data is analyzed to obtain the outline dimensions, layer height, and infill density of each layered region; The basic thermal input parameters of the arc welding torch, the basic power parameters of the high-power laser head, and the basic scanning speed are determined according to the material type. A layered coupling vector is constructed based on the outline dimensions, layer height, and fill density. The hierarchical coupling vector is compared with the historical hierarchical vector group, and the correction coefficients corresponding to the basic thermal input parameters, basic power parameters and basic scanning speed are determined based on the comparison results. The correction coefficient is multiplied by the corresponding basic thermal input parameter, basic power parameter and basic scan speed to obtain the initial thermal input parameter, initial power parameter and scan speed parameter.

[0023] Understandably, the historical layered vector group is a database collection consisting of multiple sets of layered manufacturing data and corresponding process parameters for completed additive manufacturing tasks. Each set of historical layered vectors includes the layered contour dimensions, layer height, fill density, and process-verified arc welding gun thermal input parameters, laser head power parameters, and scanning speed parameters for a specific material type.

[0024] Specifically, determining the basic thermal input parameters of the arc welding torch, the basic power parameters of the high-power laser head, and the basic scanning speed based on the material type includes: The material types include metallic materials, alloy materials, or composite materials; The material type is compared with a preset parameter setting library, and the basic thermal input parameters, basic power parameters, and basic scanning speed are determined based on the comparison results.

[0025] Understandably, the preset parameter setting library refers to a set of material-process parameter mapping relationships established based on a large amount of experimental data and engineering experience. This library covers the basic process parameter ranges for different material types (such as stainless steel, aluminum alloys, and titanium alloys in metallic materials; high-temperature alloys and magnesium alloys in alloy materials; and metal matrix composites in composite materials) in the collaborative manufacturing process of arc pre-setting and laser refining. Taking stainless steel as an example, its corresponding basic heat input parameters, basic power parameters, and basic scanning speed are 80-120 J / mm, 1500-2500 W, and 300-500 mm / s, respectively. For aluminum alloys, the basic heat input parameters can be set to 60-90 J / mm, the basic laser power parameters to 1200-2000 W, and the scanning speed parameters to 400-600 mm / s. For alloy materials such as titanium alloys, considering their high melting point and strong oxidation properties, the basic heat input parameters are typically controlled at 90-130 J / mm, the basic laser power parameter is increased to 2000-3000 W, and the scanning speed is relatively reduced to 250-400 mm / s to ensure that the laser energy fully acts on the cladding layer for refining. For high-temperature alloys, due to their complex composition and sensitivity to heat input, the basic heat input parameters are generally set at 100-140 J / mm, the basic laser power parameter is 2200-3200 W, and the scanning speed parameter is 200-350 mm / s. For composite materials such as metal matrix composites, the basic heat input parameters need to be adjusted according to the type and content of the reinforcing phase. For example, for iron matrix composites reinforced with tungsten carbide particles, the basic heat input parameters are typically 110-150 J / mm, the basic laser power parameter is 2500-3500 W, and the scanning speed parameter is 280-420 mm / s to avoid decomposition or agglomeration of the reinforcing phase at high temperatures. The preset parameter setting library will match reasonable basic process parameter ranges for different material types based on the material's physical properties such as thermal conductivity, melting point, specific heat capacity, and coefficient of linear expansion, as well as the material's metallurgical behavior under electric arc cladding and laser action (such as solidification shrinkage rate and phase transformation characteristics), serving as the initial basis for subsequent parameter correction in conjunction with layered manufacturing data.

[0026] Specifically, when comparing the hierarchical coupling vector with the historical hierarchical vector set, and determining the correction coefficients corresponding to the basic thermal input parameters, basic power parameters, and basic scan speed based on the comparison results, the following steps are included: When there is a historical layered coupling vector in the historical layered vector group that is the same as the layered coupling vector, the correction coefficient corresponding to the historical layered coupling vector is used as the correction coefficient; When there is no historical layer coupling vector in the historical layer vector group that is the same as the layer coupling vector, the Euclidean distance between the layer coupling vector and each historical layer vector in the historical layer vector group is calculated. The k historical layer vectors with the smallest Euclidean distance are selected, and the correction coefficients corresponding to the k historical layer vectors are fused and calculated using the weighted average method to obtain the correction coefficient corresponding to the layer coupling vector. Among them, the weight value is inversely proportional to the Euclidean distance.

[0027] Understandably, this means that during parameter correction, the system first prioritizes searching the historical layer vector set for cases where the current layer coupling vector is completely identical. If a perfect match exists, the correction coefficient corresponding to that historical vector is directly reused, as this indicates that the key features of the current layer, such as material type, contour size, layer height, and infill density, are completely consistent with historical successful cases. Using its correction coefficient ensures a high degree of adaptability of the initial parameters. When no perfectly matching historical layer vector exists, the system measures the similarity between the current layer coupling vector and each historical vector by calculating the Euclidean distance. The smaller the Euclidean distance, the closer the two sets of vectors are in the multidimensional feature space, and the more similar the corresponding layer manufacturing conditions are. After selecting k historical vectors with the smallest distance (the value of k can be dynamically adjusted according to the amount of historical data and manufacturing accuracy requirements, usually 3-5), a weighted average method is used to fuse the correction coefficients. Historical vectors with smaller distances are given higher weights, making the fused correction coefficients more inclined to draw on the successful experience of similar layers. For example, if the Euclidean distance between the current layered coupling vector and a certain historical vector is d1, and the distance with another historical vector is d2 (d1 < d2), then the weight coefficient of the former will be greater than that of the latter. This ensures that the corrected parameters are closer to the process optimization direction of the historical cases with small distances, thereby achieving fine adjustment of the basic parameters and further improving the accuracy of the initial parameter settings.

[0028] Specifically, adjusting the initial heat input parameters of the arc welding torch based on the temperature field distribution data and molten pool morphology data to obtain the adjusted heat input parameters includes: Feature extraction is performed on the temperature field distribution data and molten pool morphology data to obtain temperature field distribution feature values ​​and molten pool morphology feature values. Obtain standard values ​​for temperature field distribution and molten pool morphology; Calculate the temperature field distribution deviation between the temperature field distribution characteristic value and the temperature field distribution standard value, and the molten pool morphology deviation between the molten pool morphology characteristic value and the molten pool morphology standard value; The adjustment amount of the initial heat input parameters is determined based on the temperature field distribution deviation value and the molten pool morphology deviation value; The adjusted amount is added to the initial thermal input parameters to obtain the adjusted thermal input parameters.

[0029] Understandably, the temperature field distribution characteristic value is preferably one of the highest temperature, average temperature gradient, and isotherm distribution density of the pre-set cladding layer cross-section, while the molten pool morphology characteristic value is preferably one of the molten pool length, width, depth, and solid-liquid interface curvature. These characteristic values ​​can intuitively reflect the heat conduction state and metal flow behavior during the arc cladding process and are key indicators for judging cladding quality. The standard values ​​for temperature field distribution and molten pool morphology are ideal ranges pre-set based on the material properties of the parts to be manufactured and the requirements of layered manufacturing. For example, for medium-speed cladding of low-carbon steel, the standard molten pool length is usually set to 8-12 mm and the width to 4-6 mm to ensure good metallurgical bonding between the cladding layer and the substrate and appropriate cladding efficiency. When calculating the deviation value, the system will compare the real-time extracted characteristic values ​​with the standard values ​​dimension by dimension. For example, the temperature field distribution deviation value can be obtained by calculating indicators such as the percentage deviation of the highest temperature and the deviation rate of the average temperature gradient, while the molten pool morphology deviation value includes the length deviation and the width deviation.

[0030] Specifically, when determining the adjustment amount of the initial heat input parameters based on the temperature field distribution deviation value and the molten pool morphology deviation value, it includes: The temperature field distribution deviation value is compared with the temperature field distribution deviation threshold, and the molten pool morphology deviation value is compared with the molten pool morphology deviation threshold. The adjustment amount is determined based on the comparison results. When the temperature field distribution deviation value is greater than or equal to the temperature field distribution deviation threshold, and the molten pool shape deviation value is greater than or equal to the molten pool shape deviation threshold, the adjustment amount is determined to be the first adjustment amount; When the temperature field distribution deviation value is greater than or equal to the temperature field distribution deviation threshold, and the molten pool shape deviation value is less than the molten pool shape deviation threshold, the adjustment amount is determined to be the second adjustment amount; When the temperature field distribution deviation value is less than the temperature field distribution deviation threshold, and the molten pool shape deviation value is greater than or equal to the molten pool shape deviation threshold, the adjustment amount is determined to be the third adjustment amount; When the temperature field distribution deviation value is less than the temperature field distribution deviation threshold and the molten pool morphology deviation value is less than the molten pool morphology deviation threshold, the adjustment amount is determined to be the fourth adjustment amount.

[0031] Understandably, the preferred value for the first adjustment is -8% to -12% of the initial heat input parameter. This is because when both the temperature field distribution and the molten pool morphology exceed the standard range, it indicates that the current heat input is too high or too low, causing the cladding process to deviate significantly from the ideal state. A substantial negative adjustment (i.e., reducing the heat input) can quickly suppress over-melting or improve the problem of incomplete fusion. The preferred value for the second adjustment is -3% to -5% of the initial heat input parameter. At this point, abnormal temperature field is the main problem. A slight reduction in heat input can alleviate the temperature field distribution deviation while avoiding excessive interference to the molten pool morphology that has not yet exceeded the threshold. The preferred value for the third adjustment is +2% to +4% of the initial heat input parameter. When only the molten pool morphology deviates from the threshold (such as when the molten pool size is too small), moderately increasing the heat input can promote molten pool expansion and bring its morphology back to the standard range. The preferred value for the fourth adjustment is 0, that is, keeping the initial heat input parameter unchanged. At this point, both the temperature field and the molten pool morphology are within an acceptable fluctuation range, and no additional adjustment is needed to ensure the stability of the cladding quality.

[0032] Specifically, determining whether to adjust the power parameters and scanning speed parameters based on the surface temperature includes: The surface temperature is analyzed to identify any abnormal surface temperature points. If present, it is determined that the power parameter and scan speed parameter should be adjusted. Otherwise, it is determined that the power parameters and scan speed parameters will not be adjusted.

[0033] Understandably, surface temperature anomalies refer to localized areas within the laser refining region where the surface temperature exceeds a preset temperature range. This preset temperature range is determined based on the material's phase transformation temperature, grain growth characteristics, and surface quality requirements. For example, for aluminum alloy laser refining, the preset temperature range is typically set to 550℃-620℃. This range ensures sufficient remelting of the pre-formed cladding layer to eliminate defects such as porosity and inclusions, while also preventing surface burn-off or excessive grain coarsening due to excessively high temperatures. When analyzing the surface temperature, the system acquires real-time temperature field images of the laser-scanned area using an infrared thermal imager. It then employs a deep learning-based image segmentation algorithm (such as the U-Net network) to perform pixel-level analysis of the temperature field images, identifying overheating anomalies where the temperature value exceeds the upper limit of the range (e.g., 620℃) or underheating anomalies where the temperature value falls below the lower limit of the range (e.g., 550℃). For example, when the laser scanning path passes through the overlap area of ​​the pre-set cladding layer, if the initial temperature of this area is too high due to the accumulated heat input during the arc pre-setting stage, local overheating is likely to occur during laser refining. In this case, isolated bright pixel clusters (temperature > 650℃) will appear in the infrared image, and the system will mark them as overheating anomalies. Conversely, when the temperature of an area with low filling density is below 500℃ due to insufficient heat conduction, dark-colored underheating anomalies will appear. If the analysis results show that the number of anomalies accounts for more than 3% of the total area of ​​the scanned area (this threshold can be adjusted according to the surface quality grade requirements of the part), or the area of ​​a single anomaly is greater than 0.5mm, the system will mark it as an overheating anomaly. 2 If the percentage of abnormal points is less than 1% and the area of ​​each abnormal point is less than 0.1 mm, then adjustments to the power and scanning speed parameters are necessary; conversely, if the percentage of abnormal points is less than 1% and the area of ​​each abnormal point is less than 0.1 mm, then adjustments to the power and scanning speed parameters are necessary. 2 If the fluctuation is normal, no parameter adjustment is required. This judgment mechanism based on anomaly point identification can accurately capture local temperature imbalances during the laser refining process, providing clear triggering conditions for subsequent parameter adjustments and avoiding over-adjustment or under-adjustment caused by overall temperature statistical errors.

[0034] Specifically, adjusting the initial power parameters and scanning speed parameters of the high-power laser head based on the surface temperature and melt depth data to obtain the adjusted power parameters and scanning speed parameters includes: Collect the abnormal temperature value at each abnormal surface temperature point, and calculate the average value of all the abnormal temperature values, which is recorded as the average abnormal temperature value. The ratio of the average abnormal temperature value to the temperature threshold is determined and denoted as the average abnormal temperature ratio. The melting depth data is analyzed to obtain the difference between the actual melting depth value and the target melting depth value, which is recorded as the melting depth deviation value; The optimization coefficients of the initial power parameters are determined based on the average abnormal temperature ratio. The correction ratio of the scanning speed parameter is determined based on the penetration depth deviation value; Multiplying the initial power parameters by the optimization coefficients yields the adjusted power parameters; The scan speed parameter is multiplied by the correction ratio to obtain the adjusted scan speed parameter.

[0035] It is understandable that the average abnormal temperature ratio is a quantitative indicator reflecting the overall deviation of the surface temperature. Its calculation formula is: average abnormal temperature ratio = average abnormal temperature value / upper limit of temperature threshold (when it is an overheating abnormality) or average abnormal temperature ratio = average abnormal temperature value / lower limit of temperature threshold (when it is an underheating abnormality).

[0036] When determining the correction ratio of the scanning speed parameter based on the penetration depth deviation value, the following are included: When the actual melt depth is greater than the target melt depth, it is determined to be too deep. In this case, the correction ratio of the scanning speed parameter is set to a positive value (i.e., the scanning speed is increased). The specific value is determined according to the absolute value of the melt depth deviation: if the melt depth deviation is +0.1-0.3mm (5%-15% relative to the target value), the correction ratio is +5%-+8%; if the deviation is greater than +0.3mm (deviation > 15%), the correction ratio is increased to +10%-+15%. This can reduce the actual melt depth by increasing the laser scanning speed and reducing the laser energy accumulation time of the material. When the actual melt depth is less than the target melt depth, it is determined to be insufficient. The correction ratio is set to a negative value (i.e., the scanning speed is reduced). For example, when the melt depth deviation is -0.1-0.3mm (deviation -5% to -15%), the correction ratio is -6%--3%; when the deviation is less than -0.3mm (deviation < -15%), the correction ratio is -12%--8%. This can increase the melt depth by extending the laser action time and increasing the heat input. The target penetration value is preset based on the layer thickness and material properties of the part. For example, for a thin-walled stainless steel part with a layer height of 0.5 mm, the target penetration value is usually set to 0.3-0.4 mm to ensure effective metallurgical bonding between adjacent layers while avoiding burning through the substrate.

[0037] Specifically, determining the optimization coefficient of the initial power parameter based on the average abnormal temperature ratio includes: The average abnormal temperature ratio is compared with the first average abnormal temperature ratio and the second average abnormal temperature ratio, and the optimization coefficient is determined based on the comparison result; wherein the first average abnormal temperature ratio is less than the second average abnormal temperature ratio. When the average abnormal temperature ratio is less than or equal to the first average abnormal temperature ratio, the optimization coefficient is determined to be the first optimization coefficient. When the average abnormal temperature ratio is greater than the first average abnormal temperature ratio and less than or equal to the second average abnormal temperature ratio, the optimization coefficient is determined to be the second optimization coefficient. When the average abnormal temperature ratio is greater than the second average abnormal temperature ratio, the optimization coefficient is determined to be the third optimization coefficient.

[0038] Understandably, the preferred value for the first optimization coefficient is 1.10-1.15. This range is suitable for underheating scenarios where the average abnormal temperature ratio is significantly lower than the first average abnormal temperature ratio. In this case, a significant increase in laser power is needed to quickly compensate for the insufficient heat. The preferred value for the second optimization coefficient is 1.00-1.05. This range corresponds to slightly underheating or near-normal temperature conditions where the average abnormal temperature ratio is between the first and second ratios. A slight increase in power is sufficient to meet the temperature requirements, avoiding the risk of overheating due to excessive power adjustment. The preferred value for the third optimization coefficient is 0.85-0.95. This value is suitable for overheating scenarios where the average abnormal temperature ratio exceeds the second average abnormal temperature ratio. In this case, laser power needs to be reduced to suppress the continued rise in temperature. The greater the excess of the ratio, the lower the optimization coefficient value. For example, when the average abnormal temperature ratio reaches 1.2 (far exceeding the second ratio), the optimization coefficient can be as low as 0.85 to achieve a strong cooling effect. If the ratio is only slightly higher than the second ratio (e.g., 1.08), the optimization coefficient can be set to 0.93 for a gentle adjustment. The first average abnormal temperature ratio and the second average abnormal temperature ratio are also set according to the material properties. For high-temperature alloy materials, the first ratio is usually set to 0.92 and the second ratio is set to 1.05 to strictly control temperature fluctuations; while for structural steel materials, the first ratio can be set to 0.90 and the second ratio can be set to 1.10 to allow for a larger temperature adjustment range.

[0039] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A highly efficient additive manufacturing method based on the synergy of electric arc pre-positioning and laser refining, characterized in that, include: Based on the 3D model of the part to be manufactured, layered manufacturing data, arc preforming path and laser refining scanning path are generated, and arc welding gun and high-power laser head are respectively installed at the execution ends of the first robot and the second robot. The material type and layered manufacturing data of the part to be manufactured are collected, and the initial heat input parameters of the arc welding gun, the initial power parameters of the high-power laser head, and the scanning speed parameters are determined based on the material type and layered manufacturing data. The arc welding gun is started with the initial heat input parameters and the current layered area of ​​the part to be manufactured is pre-clad with arc according to the arc pre-forming path to form a pre-clad layer; The temperature field distribution data and molten pool morphology data of the pre-set cladding layer are collected in real time. Based on the temperature field distribution data and molten pool morphology data, the initial heat input parameters of the arc welding gun are adjusted to obtain the adjusted heat input parameters. After the arc pre-cladding of the current layered region is completed with the adjusted heat input parameters, the high-power laser head is started with the initial power parameters and scanning speed parameters and laser refining is performed on the pre-cladding layer according to the laser refining scanning path. The surface temperature and cladding depth data of the preset cladding layer are collected in real time, and the power parameters and scanning speed parameters are adjusted based on the surface temperature. If so, the initial power parameters and scanning speed parameters of the high-power laser head are adjusted according to the surface temperature and melting depth data to obtain the adjusted power parameters and scanning speed parameters; The laser refining process of the pre-set cladding layer in the current layered region is completed using the adjusted power and scanning speed parameters; Repeat the electric arc pre-setting cladding step and the laser refining process until the additive manufacturing of all layered areas of the part to be manufactured is completed, and the shaped part is obtained.

2. The efficient additive manufacturing method based on the synergy of arc pre-setting and laser refining according to claim 1, characterized in that, The electric arc preforming path and the laser refining scanning path correspond to each other in spatial position.

3. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 2, characterized in that, When determining the initial thermal input parameters of the arc welding torch, the initial power parameters of the laser head, and the scanning speed parameters based on the material type and layered manufacturing data, the following steps are included: The layered manufacturing data is analyzed to obtain the outline dimensions, layer height, and infill density of each layered region; The basic thermal input parameters of the arc welding torch, the basic power parameters of the high-power laser head, and the basic scanning speed are determined according to the material type. A layered coupling vector is constructed based on the outline dimensions, layer height, and fill density. The hierarchical coupling vector is compared with the historical hierarchical vector group, and the correction coefficients corresponding to the basic thermal input parameters, basic power parameters and basic scanning speed are determined based on the comparison results. The initial thermal input parameters, initial power parameters, and scan speed parameters are obtained by multiplying the correction coefficient by the corresponding basic thermal input parameters, basic power parameters, and basic scan speed.

4. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 3, characterized in that, When determining the basic thermal input parameters of the arc welding torch, the basic power parameters of the high-power laser head, and the basic scanning speed based on the material type, the following are included: The material types include metallic materials, alloy materials, or composite materials; The material type is compared with a preset parameter setting library, and the basic thermal input parameters, basic power parameters, and basic scanning speed are determined based on the comparison results.

5. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 4, characterized in that, When comparing the hierarchical coupling vector with the historical hierarchical vector set, and determining the correction coefficients corresponding to the basic thermal input parameters, basic power parameters, and basic scan speed based on the comparison results, the process includes: When there is a historical layered coupling vector in the historical layered vector group that is the same as the layered coupling vector, the correction coefficient corresponding to the historical layered coupling vector is used as the correction coefficient; When there is no historical layer coupling vector in the historical layer vector group that is the same as the layer coupling vector, the Euclidean distance between the layer coupling vector and each historical layer vector in the historical layer vector group is calculated. The k historical layer vectors with the smallest Euclidean distance are selected, and the correction coefficients corresponding to the k historical layer vectors are fused and calculated using the weighted average method to obtain the correction coefficient corresponding to the layer coupling vector. Among them, the weight value is inversely proportional to the Euclidean distance.

6. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 5, characterized in that, When adjusting the initial heat input parameters of the arc welding torch based on the temperature field distribution data and molten pool morphology data to obtain the adjusted heat input parameters, the following steps are included: Feature extraction is performed on the temperature field distribution data and molten pool morphology data to obtain temperature field distribution feature values ​​and molten pool morphology feature values. Obtain standard values ​​for temperature field distribution and molten pool morphology; Calculate the temperature field distribution deviation between the temperature field distribution characteristic value and the temperature field distribution standard value, and the molten pool morphology deviation between the molten pool morphology characteristic value and the molten pool morphology standard value; The adjustment amount of the initial heat input parameters is determined based on the temperature field distribution deviation value and the molten pool morphology deviation value; The adjusted amount is added to the initial thermal input parameter to obtain the adjusted thermal input parameter.

7. The efficient additive manufacturing method based on the synergy of arc pre-setting and laser refining according to claim 6, characterized in that, When determining the adjustment amount of the initial heat input parameters based on the temperature field distribution deviation value and the molten pool morphology deviation value, the following are included: The temperature field distribution deviation value is compared with the temperature field distribution deviation threshold, and the molten pool morphology deviation value is compared with the molten pool morphology deviation threshold. The adjustment amount is determined based on the comparison results. When the temperature field distribution deviation value is greater than or equal to the temperature field distribution deviation threshold, and the molten pool shape deviation value is greater than or equal to the molten pool shape deviation threshold, the adjustment amount is determined to be the first adjustment amount; When the temperature field distribution deviation value is greater than or equal to the temperature field distribution deviation threshold, and the molten pool shape deviation value is less than the molten pool shape deviation threshold, the adjustment amount is determined to be the second adjustment amount; When the temperature field distribution deviation value is less than the temperature field distribution deviation threshold, and the molten pool shape deviation value is greater than or equal to the molten pool shape deviation threshold, the adjustment amount is determined to be the third adjustment amount; When the temperature field distribution deviation value is less than the temperature field distribution deviation threshold and the molten pool morphology deviation value is less than the molten pool morphology deviation threshold, the adjustment amount is determined to be the fourth adjustment amount.

8. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 7, characterized in that, When determining whether to adjust the power parameters and scanning speed parameters based on the surface temperature, the following steps are included: The surface temperature is analyzed to identify any abnormal surface temperature points. If present, it is determined that the power parameter and scan speed parameter should be adjusted. Otherwise, it is determined that the power parameters and scan speed parameters will not be adjusted.

9. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 8, characterized in that, When adjusting the initial power parameters and scanning speed parameters of the high-power laser head based on the surface temperature and melting depth data, and obtaining the adjusted power parameters and scanning speed parameters, the following steps are included: Collect the abnormal temperature value at each abnormal surface temperature point, and calculate the average value of all the abnormal temperature values, which is recorded as the average abnormal temperature value. The ratio of the average abnormal temperature value to the temperature threshold is determined and denoted as the average abnormal temperature ratio. The melting depth data is analyzed to obtain the difference between the actual melting depth value and the target melting depth value, which is recorded as the melting depth deviation value; The optimization coefficients of the initial power parameters are determined based on the average abnormal temperature ratio. The correction ratio of the scanning speed parameter is determined based on the penetration depth deviation value; Multiplying the initial power parameters by the optimization coefficients yields the adjusted power parameters; The scan speed parameter is multiplied by the correction ratio to obtain the adjusted scan speed parameter.

10. The efficient additive manufacturing method based on the synergy of arc pre-positioning and laser refining according to claim 9, characterized in that, When determining the optimization coefficient of the initial power parameters based on the average abnormal temperature ratio, the following steps are included: The average abnormal temperature ratio is compared with the first average abnormal temperature ratio and the second average abnormal temperature ratio, and the optimization coefficient is determined based on the comparison result; wherein the first average abnormal temperature ratio is less than the second average abnormal temperature ratio. When the average abnormal temperature ratio is less than or equal to the first average abnormal temperature ratio, the optimization coefficient is determined to be the first optimization coefficient. When the average abnormal temperature ratio is greater than the first average abnormal temperature ratio and less than or equal to the second average abnormal temperature ratio, the optimization coefficient is determined to be the second optimization coefficient. When the average abnormal temperature ratio is greater than the second average abnormal temperature ratio, the optimization coefficient is determined to be the third optimization coefficient.

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