Graphite electrode threaded intelligent automatic processing method

Abstract

The application relates to the technical field of mechanical manufacturing, and specifically discloses a graphite electrode thread intelligent automatic processing method. The method comprises the following steps: S1, performing reference calibration and stress release treatment on a graphite electrode blank; S2, performing conical surface bottom hole forming processing on the end part of the electrode to be processed; S3, performing thread rough machining on a guide base surface; S4, adjusting a finishing machining path and cutting parameters in real time, and performing thread finishing machining; S5, collecting graphite powder scraps, compounding the graphite powder scraps with functional additives, and preparing protective slurry; S6, coating the protective slurry on the thread surface by utilizing processing waste heat to form a composite protective layer; and S7, marking a two-dimensional code on the end surface non-working area of the finished product. The processing method can be used for precision machining and strengthening of a thread connection part of a graphite electrode used for ultra-high power electric furnace steelmaking, has the advantages that the heat shock resistance, wear resistance and connection reliability of the electrode are improved, and the product whole life cycle tracing is realized by means of the two-dimensional code.

Description

Technical Field

[0001] This application relates to the field of mechanical manufacturing technology, and more specifically, it relates to an intelligent automated machining method for graphite electrode threads. Background Technology

[0002] In the manufacturing of graphite electrodes, mechanical manufacturing precisely controls aggregate particle size through raw material crushing and screening equipment. A large-tonnage molding machine presses the mixed paste into high-density green bodies. Automated furnace feeding and unloading systems and CNC machine tools are used for high-precision thread machining during the calcination and graphitization stages, thereby improving the electrode's thermal shock resistance, electrical conductivity, and joint strength. Its advantages lie in the precise reproduction of raw material proportions, real-time adjustment of molding pressure, and precision machining of tapered threads at both ends of the electrode through digital control. This not only improves production efficiency and product consistency but also reduces quality fluctuations caused by manual intervention through mechanical automation, ultimately producing high-temperature resistant, low-resistance graphite electrodes that meet the requirements of ultra-high-power electric arc furnace steelmaking.

[0003] The relevant graphite electrode threads are machined by CNC machine tools, relying on preset fixed programs and empirical parameters. However, this open-loop control method cannot sense and respond in real time to the instantaneous thermal load fluctuations caused by local unevenness of graphite material, tool micro-wear, or vibration during the machining process. This results in random defects such as micro-cracks and chipping on the thread surface, ultimately affecting the long-term service reliability and fatigue resistance of the electrode thread connection pair. Summary of the Invention

[0004] To address the problem that random defects such as microcracks and chipping are easily generated on the thread surface due to the machining of graphite electrode threads by CNC machine tools, this application provides an intelligent automated machining method for graphite electrode threads.

[0005] This application provides an intelligent automated machining method for graphite electrode threads, employing the following technical solution: A method for intelligent automated machining of graphite electrode threads includes the following steps: S1. Perform benchmark calibration and stress relief treatment on the graphite electrode blank to obtain the electrode to be processed; S2. Perform tapered bottom hole forming on the end of the electrode to be processed obtained in S1 to obtain a guide base surface with a preset taper and a textured surface. S3. Perform rough thread machining on the guide base surface obtained in S2, and simultaneously collect thermal radiation signals and acoustic emission signals in the cutting area; S4. Adjust the finishing path and cutting parameters in real time based on the thermal radiation signal and acoustic emission signal obtained in S3 to perform thread finishing and obtain a semi-finished electrode with a gradient microstructure on the thread surface. S5. Collect the graphite powder generated in S3 and S4, and combine it with functional additives to prepare a protective slurry. S6. Using the residual heat from processing the semi-finished electrode obtained in S4, the protective slurry obtained in S5 is coated onto the thread surface and cured to form a self-healing composite protective layer, thus obtaining the finished graphite electrode. S7. On the non-working area of ​​the end face of the finished graphite electrode, a QR code containing the characteristic parameters of the processing process is formed by laser marking.

[0006] By adopting the above technical solution, intelligent sensing, adaptive processing, and resource recycling are integrated. The thermal signals during processing are monitored in real time and process parameters are dynamically adjusted to ensure the controllable preparation of the microstructure of the thread gradient. At the same time, the by-product graphite powder is recycled to prepare functional slurry, and the residual heat of the workpiece is used to achieve in-situ curing of the protective layer, thereby improving the mechanical strength and damage resistance of the thread. Digital traceability of the processing process and high-value utilization of by-products are achieved. Therefore, the results are stable processing quality, excellent comprehensive thread performance, environmentally friendly and efficient process, and traceability.

[0007] Preferably, before step S1, a pretreatment of the graphite electrode blank is further included: forming a micron-level groove array on the end face of the graphite electrode blank to be processed by laser induction, wherein the orientation of the groove array is at an angle of 45° to 60° with the helical direction of the subsequent thread.

[0008] By adopting the above technical solution, since a microgroove array at a certain angle is pre-fabricated on the end face of the blank by laser in the initial stage of processing, the groove structure can serve as a guide for subsequent thread processing. When the tool performs conical bottom hole and thread processing, the cutting force will be decomposed along the direction of the groove prefabrication, guiding the removal of graphite material and texture formation, thereby reducing the risk of random chipping during the cutting process and providing a structural basis for forming a textured guide base surface. Therefore, the effect of enhancing the stability of the processing process, improving the initial surface quality, and laying the foundation for subsequent structure preparation is achieved.

[0009] Preferably, in step S2, the tapered bottom hole forming process adopts a variable feed helical milling method. The feed rate in the roughing stage is 0.15-0.25 mm / r, and the feed rate decreases to 0.04-0.06 mm / r in the finishing stage. At the same time, ultrasonic vibration is used in the finishing stage to form a periodic micro-pit structure on the machined surface. The depth of the micro-pit is 5-15 μm and the areal density is 30%-50%.

[0010] By adopting the above technical solution, the variable parameter process of first using a large feed for roughing and then a small feed for finishing ensures material removal efficiency, while the smaller feed in the finishing stage ensures the dimensional accuracy and surface finish of the conical bottom hole. The superimposed ultrasonic vibration causes the tool to generate high-frequency micro-impact, thereby etching a uniformly distributed micro-pit texture on the finishing surface. This texture serves two purposes: firstly, it accommodates some chips and reduces friction in the thread roughing process of step S3; secondly, its specific depth and surface density provide an optimized interface for the mechanical anchoring of the protective slurry in step S6, enhancing the adhesion of the protective layer. Therefore, the process achieves a balance between processing efficiency and accuracy, and constructs a functional surface that is beneficial to subsequent processes.

[0011] Preferably, in step S3, the wavelength of the thermal radiation signal is 8–14 μm and the sampling frequency is not less than 100 Hz; the frequency band of the acoustic emission signal is 50–400 kHz.

[0012] By adopting the above technical solution, since the thermal radiation generated by graphite material during cutting is concentrated in the mid-infrared band, the temperature field changes in the processing area can be captured by selecting a specific wavelength range for monitoring. At the same time, the sampling frequency is limited to track the rapid temperature rise during the cutting process. Meanwhile, controlling the acquisition of acoustic emission signals in a specific frequency band can capture elastic waves generated by processes such as micro-fracture, friction, and tool wear in the material. These two types of signals provide a real-time data source reflecting the thermo-mechanical coupling characteristics of the processing state for real-time process adjustment in step S4. Therefore, the effect of being able to comprehensively, in real time, and perceive the physical state of the processing process is achieved.

[0013] Preferably, in step S4, when the temperature gradient in the cutting area exceeds 20°C / mm or the acoustic emission energy rate suddenly increases beyond a set threshold, the spindle speed is reduced by 15% to 25%, and the cooling medium flow rate is increased simultaneously until the thermal characteristics return to the preset safe range.

[0014] By adopting the above technical solution, since the temperature gradient exceeding the standard signal collected in step S3 indicates the risk of local overheating, which can easily lead to graphite oxidation or microcracks; the sudden increase in acoustic emission energy indicates brittleness or abnormal wear; at this time, reducing the spindle speed can reduce heat input and cutting impact, while increasing the cooling flow to enhance heat dissipation. This coordinated adjustment process can quickly suppress the development of abnormal conditions and ensure that the finishing process is carried out in a stable thermal environment. This quantitative feedback control mechanism is the premise for the final formation of a dense layer and a porous buffer layer at the thread crest and root, respectively, to avoid structural inhomogeneity or defects caused by overheating or excessive vibration during processing. Therefore, the effect of maintaining the stability of the process window and ensuring high-quality forming of the gradient microstructure through closed-loop control is achieved.

[0015] Preferably, in step S4, the gradient microstructure formed after thread finishing is as follows: the thread crest region has a densified layer with a thickness of 50-150 μm and a porosity of ≤5%; the thread root region has a porous buffer layer with a porosity of 15%-25%.

[0016] By adopting the above technical solution, during the finishing process in step S4, the material at the top of the thread tooth, which bears the main contact stress and wear, undergoes plastic flow and compaction by controlling the cutting force and thermal effect, forming a dense layer with low porosity, thereby improving the hardness and wear resistance of this area. At the bottom of the thread tooth, which bears shear stress and needs to absorb deformation, a buffer layer with high porosity is formed. This structure can passivate cracks and absorb impact energy. This gradient transition from dense to porous from top to bottom matches the service stress characteristics of different parts of the thread. The thickness of the dense layer and the porosity range of the porous layer are designed to achieve fatigue resistance and anti-detachment performance while ensuring the overall connection strength. Therefore, the thread is made to have both high surface resistance and high overall toughness.

[0017] Preferably, in step S5, the collected graphite powder has a particle size of 1–50 μm; the functional additives include modified nano-alumina, zirconium boride precursor, and thermally activated curing agent.

[0018] By adopting the above technical solution, the graphite powder of a specific particle size generated in steps S3 and S4 is used as the slurry matrix. This particle size range ensures that the powder has high dispersibility and sintering activity. The addition of modified nano-alumina plays a role in reinforcement and wear resistance. At the same time, the zirconium boride precursor can be transformed into a high-hardness zirconium boride ceramic phase during the residual heat curing process in the subsequent step S6, thereby improving the hardness of the protective layer. The thermally activated curing agent triggers the cross-linking and curing of the slurry within a specific temperature range. These functional additives, combined with recycled graphite powder, transform processing waste into functional slurry raw materials with specific properties. This slurry is ultimately used to form the composite protective layer in step S6, achieving the goal of waste resource utilization and product functionalization improvement. Therefore, the effects of reducing raw material costs, achieving green recycling, and endowing the protective layer with reinforcing phase and curing ability are achieved.

[0019] Preferably, in step S5, the solid content of the protective slurry is 35% to 55%, and the viscosity is 200 to 800 mPa·s.

[0020] By adopting the above technical solution, the protective slurry needs to have high coatability and formability. The solid content is controlled within a certain range to ensure that there are enough solid functional components in the slurry to form an effective protective layer. At the same time, the liquid carrier is sufficient to make it highly fluid. Controlling the viscosity range ensures that in step S6, the slurry can be evenly covered on the surface of the workpiece with complex thread morphology and surface micro-pits by spraying or dipping, without flowing due to being too thin or unevenly filled due to being too thick. This range can ensure that the slurry fully wets the microstructure of the thread surface and forms a coating of uniform thickness. Therefore, the effect of adapting the protective slurry to the coating process requirements of complex workpiece morphology is achieved.

[0021] Preferably, in step S6, the temperature for in-situ curing using residual heat from processing is 120–180°C, and the time is 15–30 min; the thickness of the self-healing composite protective layer is 30–80 μm.

[0022] By adopting the above technical solution, since the semi-finished electrode itself carries residual heat after fine processing, its temperature falls within the effective range of the thermally activated curing agent in the slurry prepared in step S5; in-situ curing is carried out using residual heat, eliminating the need for additional heating equipment to achieve energy-saving effects; and sufficient curing time ensures that the organic components in the slurry are fully cross-linked, and the inorganic precursors complete thermal decomposition and preliminary sintering, forming a dense composite coating; finally, a protective layer is formed, the thickness of which covers the surface microtexture and provides effective protection, while not being too thick to affect thread accuracy or easily peel off; this process utilizes the energy output of the preceding process to achieve efficient and low-energy preparation of the protective layer, thus achieving the effects of reducing energy consumption, simplifying the process, and forming a protective layer of appropriate thickness that is firmly bonded to the substrate.

[0023] Preferably, in step S7, the marking depth of the QR code is 20-50 μm, and the marking area is simultaneously covered by the composite protective layer.

[0024] By adopting the above technical solution, the QR code is marked with a depth of 20 to 50 μm in the non-working area of ​​the electrode end face using laser. This depth ensures both the clarity and wear resistance of the marking, while avoiding excessive marking that could affect the strength of the electrode body. After marking, this area is also covered by the self-healing composite protective layer formed in step S6, which physically protects the QR code information and resists friction and corrosion during handling and use. The covering layer also ensures the overall performance consistency of the electrode end face. Furthermore, the QR code containing feature information such as processing parameters and batch number is bound to the product entity, providing a reliable data carrier for product quality traceability and full life cycle management. Therefore, the effect of achieving permanent and anti-counterfeiting marking of product information and integration with the overall protection system is achieved.

[0025] In summary, this application has the following beneficial effects: 1. Because this application adopts an integrated intelligent sensing and adaptive machining process, it simultaneously collects thermal radiation and acoustic emission signals from the cutting area, and dynamically adjusts the finishing path and cutting parameters based on real-time signals in subsequent processes, thus constructing an online monitoring-feedback control process. This process can sense thermal anomalies during machining and compensate by adjusting the spindle speed and cooling conditions, thereby ensuring that the machining process is always under suitable machining conditions. Therefore, it achieves stable thread machining quality, high repeatability, and the formation of a high-performance thread surface with a gradient microstructure, thereby improving the reliability and lifespan of the product.

[0026] 2. This application preferably employs a synergistic process of internal resource recycling and in-situ energy utilization. Graphite dust generated during processing is collected and compounded with functional additives to form a protective slurry. Subsequently, the residual heat carried by the semi-finished electrode after processing drives the slurry to cure in-situ on the thread surface, forming a composite protective layer. The recovered dust has good compatibility with the substrate material, enabling high-value utilization of waste as the slurry matrix. Simultaneously, the functional additives enhance, harden, and trigger curing. Utilizing the residual heat of the workpiece for curing achieves energy transfer and efficient utilization between processes, avoiding additional heating energy consumption. Therefore, the process achieves the effect of converting processing byproducts into functional coating raw materials, realizing low-energy coating and curing, reducing production costs, and ensuring environmentally friendly processes.

[0027] 3. In the method of this application, the guide groove formed by laser pretreatment lays the foundation for the formation of high-quality conical bottom holes; while the micro-pit surface texture formed by ultrasonic vibration provides a mechanical anchoring interface for the protective slurry and enhances the coating adhesion; the gradient microstructure thread formed by intelligent processing works synergistically with the composite protective layer to jointly bear mechanical loads and thermal shocks; finally, laser marking and protective layer coverage realize the protection and traceability of product information; thus, the product achieves a comprehensive performance improvement in terms of mechanics, wear resistance, thermal shock resistance and traceability. Attached Figure Description

[0028] Figure 1 This is a flowchart of an intelligent automated machining method for graphite electrode threads proposed in this application. Detailed Implementation

[0029] The present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0030] Technical concept: The relevant graphite electrode threads are machined by CNC machine tools, relying on preset fixed programs and empirical parameters. However, this open-loop control method cannot sense and respond in real time to the instantaneous thermal load fluctuations caused by local unevenness of graphite material, tool micro-wear, or vibration during the machining process. This results in random defects such as micro-cracks and chipping on the thread surface, ultimately affecting the long-term service reliability and fatigue resistance of the electrode thread connection pair.

[0031] This application discloses an intelligent automated machining method for graphite electrode threads. The method includes the following steps: S1, performing benchmark calibration and stress relief treatment on the graphite electrode blank; S2, forming a tapered bottom hole at the end of the electrode to be machined; S3, rough machining the thread on the guide surface; S4, adjusting the finishing path and cutting parameters in real time to perform thread finishing; S5, collecting graphite powder and combining it with functional additives to prepare a protective slurry; S6, using residual heat from machining to coat the protective slurry onto the thread surface, forming a composite protective layer; S7, marking a QR code on the non-working area of ​​the finished product's end face.

[0032] This application employs an integrated intelligent sensing and adaptive machining process, simultaneously collecting thermal radiation and acoustic emission signals from the cutting area. In subsequent processes, the finishing path and cutting parameters are dynamically adjusted based on real-time signals, constructing an online monitoring-feedback control process. This process can sense thermal anomalies during machining and compensate by adjusting the spindle speed and cooling conditions, thereby ensuring that the machining process is always under suitable processing conditions. As a result, stable and highly repeatable thread machining quality is achieved, and a high-performance thread surface with a gradient microstructure is formed, improving product reliability and lifespan.

[0033] Example 1: This example provides an intelligent automated machining method for graphite electrode threads, comprising the following steps: S1. Perform benchmark calibration and stress relief treatment on the graphite electrode blank to obtain the electrode to be processed; The process includes a pretreatment of the graphite electrode blank before step S1: a micron-level groove array is formed on the end face of the graphite electrode blank to be processed by laser induction, and the orientation of the groove array is at a 45° angle to the helical direction of the subsequent thread.

[0034] S2. Perform tapered bottom hole forming on the end of the electrode to be processed obtained in S1 to obtain a guide base surface with a preset taper and a textured surface. Among them, the tapered bottom hole forming process adopts the variable feed spiral milling method. The feed rate in the roughing stage is 0.15mm / r, and the feed rate in the finishing stage is reduced to 0.04mm / r. At the same time, ultrasonic vibration is used in the finishing stage to form a periodic micro-pit structure on the machined surface. The depth of the micro-pit is 5μm and the surface density is 30%.

[0035] S3. Perform rough thread machining on the guide base surface obtained in S2, and simultaneously collect thermal radiation signals and acoustic emission signals in the cutting area; The thermal radiation signal was acquired at a wavelength of 8 μm and a sampling frequency of 100 Hz; the acoustic emission signal was acquired in the frequency band of 50–400 kHz.

[0036] S4. Adjust the finishing path and cutting parameters in real time based on the thermal radiation signal and acoustic emission signal obtained in S3 to perform thread finishing and obtain a semi-finished electrode with a gradient microstructure on the thread surface. Specifically, when the temperature gradient in the cutting zone exceeds 20℃ / mm or the acoustic emission energy rate suddenly increases beyond a set threshold, the spindle speed is reduced by 15%, and the cooling medium flow rate is increased simultaneously until the thermal characteristics return to the preset safe range. The gradient microstructure formed after thread finishing is as follows: the thread crest region has a densified layer with a thickness of 50μm and a porosity ≤5%; the thread root region has a porous buffer layer with a porosity of 15%.

[0037] S5. Collect the graphite powder generated in S3 and S4, and combine it with functional additives to prepare a protective slurry. The collected graphite powder has a particle size of 1 μm; the functional additives include modified nano-alumina, zirconium boride precursor, and thermally activated curing agent. The protective slurry has a solid content of 35% and a viscosity of 200 mPa·s.

[0038] S6. Using the residual heat from processing the semi-finished electrode obtained in S4, the protective slurry obtained in S5 is coated onto the thread surface and cured to form a self-healing composite protective layer, thus obtaining the finished graphite electrode. The temperature for in-situ curing using residual heat from processing is 120℃, and the time is 15min; the thickness of the self-healing composite protective layer is 30μm.

[0039] S7. On the non-working area of ​​the end face of the finished graphite electrode, a QR code containing the characteristic parameters of the processing process is formed by laser marking.

[0040] The QR code is marked with a depth of 20μm, and the marked area is also covered by a composite protective layer.

[0041] Example 2: This example provides an intelligent automated machining method for graphite electrode threads, comprising the following steps: S1. Perform benchmark calibration and stress relief treatment on the graphite electrode blank to obtain the electrode to be processed; The process before step S1 includes a pretreatment of the graphite electrode blank: a micron-level groove array is formed on the end face of the graphite electrode blank to be processed by laser induction, and the orientation of the groove array is at a 52.5° angle with the helical direction of the subsequent thread.

[0042] S2. Perform tapered bottom hole forming on the end of the electrode to be processed obtained in S1 to obtain a guide base surface with a preset taper and a textured surface. Among them, the tapered bottom hole forming process adopts the variable feed spiral milling method. The feed rate in the roughing stage is 0.20 mm / r, and the feed rate in the finishing stage is reduced to 0.05 mm / r. At the same time, ultrasonic vibration is used in the finishing stage to form a periodic micro-pit structure on the machined surface. The depth of the micro-pit is 10 μm and the surface density is 40%.

[0043] S3. Perform rough thread machining on the guide base surface obtained in S2, and simultaneously collect thermal radiation signals and acoustic emission signals in the cutting area; The thermal radiation signal was acquired at a wavelength of 11 μm and a sampling frequency of 200 Hz; the acoustic emission signal was acquired in the frequency band of 50–400 kHz.

[0044] S4. Adjust the finishing path and cutting parameters in real time based on the thermal radiation signal and acoustic emission signal obtained in S3 to perform thread finishing and obtain a semi-finished electrode with a gradient microstructure on the thread surface. Specifically, when the temperature gradient in the cutting zone exceeds 20℃ / mm or the acoustic emission energy rate suddenly increases beyond a set threshold, the spindle speed is reduced by 20%, and the cooling medium flow rate is increased simultaneously until the thermal characteristics return to the preset safe range. The gradient microstructure formed after thread finishing is as follows: the thread crest region has a densified layer with a thickness of 100μm and a porosity ≤5%; the thread root region has a porous buffer layer with a porosity of 20%.

[0045] S5. Collect the graphite powder generated in S3 and S4, and combine it with functional additives to prepare a protective slurry. The collected graphite powder had a particle size of 25 μm; the functional additives included modified nano-alumina, zirconium boride precursor, and a thermally activated curing agent. The protective slurry had a solid content of 45% and a viscosity of 500 mPa·s.

[0046] S6. Using the residual heat from processing the semi-finished electrode obtained in S4, the protective slurry obtained in S5 is coated onto the thread surface and cured to form a self-healing composite protective layer, thus obtaining the finished graphite electrode. The temperature for in-situ curing using residual heat from processing is 150℃, and the time is 22 minutes; the thickness of the self-healing composite protective layer is 55μm.

[0047] S7. On the non-working area of ​​the end face of the finished graphite electrode, a QR code containing the characteristic parameters of the processing process is formed by laser marking.

[0048] The QR code is marked with a depth of 35μm, and the marked area is also covered by a composite protective layer.

[0049] Example 3: This example provides an intelligent automated machining method for graphite electrode threads, comprising the following steps: S1. Perform benchmark calibration and stress relief treatment on the graphite electrode blank to obtain the electrode to be processed; The process includes a pretreatment of the graphite electrode blank before step S1: a micron-level groove array is formed on the end face of the graphite electrode blank to be processed by laser induction, and the orientation of the groove array is at a 60° angle to the helical direction of the subsequent thread.

[0050] S2. Perform tapered bottom hole forming on the end of the electrode to be processed obtained in S1 to obtain a guide base surface with a preset taper and a textured surface. Among them, the tapered bottom hole forming process adopts the variable feed spiral milling method. The feed rate in the roughing stage is 0.25mm / r, and the feed rate decreases to 0.06mm / r in the finishing stage. At the same time, ultrasonic vibration is used in the finishing stage to form a periodic micro-pit structure on the machined surface. The depth of the micro-pit is 15μm and the surface density is 50%.

[0051] S3. Perform rough thread machining on the guide base surface obtained in S2, and simultaneously collect thermal radiation signals and acoustic emission signals in the cutting area; The thermal radiation signal was acquired at a wavelength of 14 μm and a sampling frequency of 300 Hz; the acoustic emission signal was acquired in the frequency band of 50–400 kHz.

[0052] S4. Adjust the finishing path and cutting parameters in real time based on the thermal radiation signal and acoustic emission signal obtained in S3 to perform thread finishing and obtain a semi-finished electrode with a gradient microstructure on the thread surface. Specifically, when the temperature gradient in the cutting zone exceeds 20℃ / mm or the acoustic emission energy rate suddenly increases beyond a set threshold, the spindle speed is reduced by 25%, and the cooling medium flow rate is increased simultaneously until the thermal characteristics return to the preset safe range. The gradient microstructure formed after thread finishing is as follows: the thread crest region has a densified layer with a thickness of 150μm and a porosity ≤5%; the thread root region has a porous buffer layer with a porosity of 25%.

[0053] S5. Collect the graphite powder generated in S3 and S4, and combine it with functional additives to prepare a protective slurry. The collected graphite powder has a particle size of 50 μm; the functional additives include modified nano-alumina, zirconium boride precursor, and thermally activated curing agent. The protective slurry has a solid content of 55% and a viscosity of 800 mPa·s.

[0054] S6. Using the residual heat from processing the semi-finished electrode obtained in S4, the protective slurry obtained in S5 is coated onto the thread surface and cured to form a self-healing composite protective layer, thus obtaining the finished graphite electrode. The temperature for in-situ curing using residual heat from processing is 180℃, and the time is 30 minutes; the thickness of the self-healing composite protective layer is 80μm.

[0055] S7. On the non-working area of ​​the end face of the finished graphite electrode, a QR code containing the characteristic parameters of the processing process is formed by laser marking.

[0056] The QR code is marked with a depth of 50μm, and the marked area is also covered by a composite protective layer.

[0057] Comparative Example 1: This comparative example refers to the content of Example 1, except that in the pretreatment before step S1, the orientation of the micron-scale groove array formed by laser induction is at a 30° angle to the helical direction of the subsequent thread; the rest is the same as Example 1.

[0058] Comparative Example 2: This comparative example refers to Example 1, except that in step S2, the feed rate for the finishing stage of the conical bottom hole forming is 0.08 mm / r. All other aspects are the same as in Example 1.

[0059] Comparative Example 3: This comparative example refers to the content of Example 1, except that in step S2, after ultrasonic vibration is applied during the finishing stage, the depth of the periodic micro-pit structure formed on the processed surface is 3 μm; the rest of the content is the same as Example 1.

[0060] Comparative Example 4: This comparative example refers to the content of Example 1, except that in step S5, the graphite powder collected and used to prepare the protective slurry has a particle size of 80 μm; the rest is the same as Example 1.

[0061] Comparative Example 5: This comparative example refers to Example 1, except that in step S5, the solid content of the prepared protective slurry is 25%. The rest of the contents are the same as in Example 1.

[0062] Comparative Example 6: This comparative example refers to Example 1, except that in step S6, the thickness of the self-healing composite protective layer is 100 μm. The rest of the content is the same as in Example 1.

[0063] Performance testing Sample preparation: The samples used in this experiment were prepared according to the processing methods described in Examples 1-3 and Comparative Examples 1-6.

[0064] Thread machining accuracy and surface integrity inspection: A high-precision coordinate measuring machine is used to measure the thread pitch diameter, thread angle, and tooth profile angle of the finished electrode, and its dimensional tolerances are calculated. At the same time, a laser confocal microscope is used to observe the surface morphology of the thread crest, flank, and root areas, and the number of defects such as chipping and microcracks per unit area is counted. The data of the example and the comparative example at the same measurement position are compared to evaluate the effect of online monitoring feedback control on improving the stability of the machining process and the consistency of the finished product. Test standards: Thread geometric accuracy inspection follows GB / T28703-2012 "Methods for testing cylindrical threads"; surface defect statistics refer to ISO25178-2 "Surface texture: Area measurement standard".

[0065] Table 1: Results of Thread Machining Accuracy and Surface Integrity Inspection Microstructure and Mechanical Property Testing of Threaded Gradient Structure: First, the sample was cut open along the thread axis to prepare a metallographic specimen. A scanning electron microscope was used to observe the thread cross-section, particularly the thickness, porosity, and transition of the densified layer at the crest and the porous buffer layer at the root, and compared with the process target. Subsequently, a nanoindenter was used to perform multi-point tests at the thread crest, flank, and root regions to obtain the nanohardness and elastic modulus distribution curves for each region, visually reflecting the gradient characteristics from high hardness at the crest to high toughness at the root. Testing standards: Microstructure analysis was performed according to GB / T13298-2015 "Metallic materials - Microstructure testing methods"; nanoindentation testing followed ISO14577-1 "Metallic materials - Hardness and material parameters - Instrumented indentation testing".

[0066] Table 2: Detection Results of Threaded Gradient Structure Characteristics and Mechanical Properties Composite protective layer bonding strength and wear resistance testing: Cross-cut adhesion test is performed on the threaded surface coated with protective layer to evaluate the coating's anti-peeling ability; wear resistance test is performed using a tribological testing machine, with steel balls at specific loads and speeds as the grinding pair, and reciprocating friction test is performed on the threaded working surface, measuring the amount of wear of the coating after a certain period or observing the wear morphology; Test standards: Coating adhesion test follows GB / T9286-2021 "Cross-cut test for paints and varnishes"; wear resistance test refers to GB / T3960-2016 "Plastics sliding friction and wear test method".

[0067] Table 3: Performance Test Results of Composite Protective Layer Test on the cyclic loading and unloading performance and conductivity stability of electrode threaded connection pairs: Connect the machined electrode thread to the standard electrode connector and install it on a special test bench; conduct a specified number of cyclic loading and unloading tests, and record the torque changes during each screwing in and out; after the test, disassemble the connection pair and check the wear, coating peeling and thread damage on the thread surface; at the same time, under the condition of applying a constant clamping force, use a micro-ohmmeter to measure the contact resistance at the threaded connection to evaluate its conductivity stability; test standards: the cyclic loading and unloading test refers to the relevant fixture test methods in JB / T13042-2017 "Test Method for Machining Performance of Electric Spindles for CNC Machine Tools"; the contact resistance measurement refers to GB / T5585.2-2005 "Electrical Copper, Aluminum and Their Alloy Busbars Part 2: Test Methods".

[0068] Table 4: Service Performance Test Results of Electrode Threaded Connection Pairs QR code readability and environmental tolerance testing: First, the QR code on the electrode end face was read using a standard QR code scanning device, and the initial read success rate and reading speed were recorded. Then, environmental tolerance tests were conducted, including high-temperature baking, simulated coolant immersion, and slight sandpaper friction. After each environmental test, the QR code information was read again to examine whether its readability was effectively protected by the protective layer. Testing standards: QR code readability testing refers to GB / T18284-2000 "Fast Response Matrix Code"; environmental testing refers to the relevant sections of GB / T2423 series "Environmental Testing of Electrical and Electronic Products".

[0069] Table 5: Results of QR Code Information Performance Testing Example Conclusion: As can be seen from Examples 1-3 and Comparative Example 1, and in conjunction with Tables 1 and 2, when the angle between the laser-prefabricated guide groove array and the thread helical direction is too small, its guiding and dispersing effect on the cutting force is weakened. This leads to a decrease in the stability of the cutting process and an increase in the randomness of material removal, which not only damages the geometric accuracy and surface integrity of the thread, but also destroys the stable thermal environment required to form a controllable gradient microstructure. Therefore, controlling the groove orientation within a specific angle range is a condition for obtaining high-precision, low-defect threads and successfully constructing a performance gradient structure.

[0070] As can be seen from Examples 1-3 and Comparative Example 2, and from Tables 1 and 2, the feed rate during the finishing stage of the conical bottom hole is neither better the smaller it is, nor better the larger it is. An excessively large feed rate will introduce excessive cutting force and heat, which will damage the texture quality of the machined surface and interfere with the stability of subsequent processing, making it difficult to control the energy input in the finishing stage to form the required gradient structure. Therefore, limiting the finishing feed rate to a low and appropriate range is a factor in achieving high surface quality and providing a stable foundation for subsequent gradient forming.

[0071] As can be seen from Examples 1-3 and Comparative Example 3, and from Tables 1 and 3, the depth of the micro-pit texture formed by ultrasonic vibration on the guide substrate affects its effectiveness as a mechanical anchor point. If the micro-pits are too shallow, they cannot provide sufficient wetting depth and locking structure for the subsequent protective slurry, thus weakening the mechanical bonding strength between the coating and the substrate. This indicates that a surface texture of a specific depth is a bridge connecting precision machining and functional coating processes, ensuring that the protective layer can adhere firmly and thus provide long-term protection.

[0072] As can be seen from Examples 1-3 and Comparative Example 4, and Table 3, the particle size of the recycled graphite powder has an impact on the quality of the protective slurry preparation. Excessively large particle size can impair the uniformity and suspension stability of the slurry, resulting in defects and a lack of density in the protective layer after curing, as well as poor interface quality with the substrate. This proves that controlling the particle size of waste powder within a fine range is a condition for realizing its high-value utilization and successfully transforming it into a high-performance protective coating raw material.

[0073] As can be seen from Examples 1-3 and Comparative Example 5 and Table 3, the solid content of the protective slurry is related to the density and mechanical properties of the cured coating. Too low a solid content means that there are not enough solid functional components in the slurry that play a reinforcing and wear-resistant role, resulting in a loose structure and low strength of the coating itself, which is quickly consumed under friction and wear conditions.

[0074] As can be seen from Examples 1-3 and Comparative Example 6, and Tables 3 and 4, the thickness of the composite protective layer needs to be balanced within a moderate range. Although an excessively thick coating provides complete coverage, it weakens the overall bonding force with the substrate due to factors such as increased internal stress and decreased thermal expansion matching with the substrate. This can easily lead to local peeling during cyclic stress, which in turn accelerates wear and affects the stability of the conductive connection. Therefore, controlling the coating thickness within a reasonable range is a key factor in achieving a balance between protective performance, bonding force, and long-term service reliability.

[0075] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. A method for intelligent automated machining of graphite electrode threads, characterized in that, Includes the following steps: S1. Perform benchmark calibration and stress relief treatment on the graphite electrode blank to obtain the electrode to be processed; S2. Perform tapered bottom hole forming on the end of the electrode to be processed obtained in S1 to obtain a guide base surface with a preset taper and a textured surface. S3. Perform rough thread machining on the guide base surface obtained in S2, and simultaneously collect thermal radiation signals and acoustic emission signals in the cutting area; S4. Adjust the finishing path and cutting parameters in real time based on the thermal radiation signal and acoustic emission signal obtained in S3 to perform thread finishing and obtain a semi-finished electrode with a gradient microstructure on the thread surface. S5. Collect the graphite powder generated in S3 and S4, and combine it with functional additives to prepare a protective slurry. S6. Using the residual heat from processing the semi-finished electrode obtained in S4, the protective slurry obtained in S5 is coated onto the thread surface and cured to form a self-healing composite protective layer, thus obtaining the finished graphite electrode. S7. On the non-working area of ​​the end face of the finished graphite electrode, a QR code containing the characteristic parameters of the processing process is formed by laser marking.

2. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, Before step S1, a pretreatment of the graphite electrode blank is also included: a micron-level groove array is formed on the end face of the graphite electrode blank to be processed by laser induction, and the orientation of the groove array is at an angle of 45° to 60° with the helical direction of the subsequent thread.

3. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S2, the tapered bottom hole forming process adopts a variable feed helical milling method. The feed rate in the roughing stage is 0.15-0.25 mm / r, and the feed rate decreases to 0.04-0.06 mm / r in the finishing stage. At the same time, ultrasonic vibration is used in the finishing stage to form a periodic micro-pit structure on the machined surface. The depth of the micro-pit is 5-15 μm and the areal density is 30%-50%.

4. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S3, the wavelength of the thermal radiation signal is 8–14 μm and the sampling frequency is not less than 100 Hz; the frequency band of the acoustic emission signal is 50–400 kHz.

5. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S4, when the temperature gradient in the cutting area exceeds 20°C / mm or the acoustic emission energy rate suddenly increases beyond the set threshold, the spindle speed is reduced by 15% to 25%, and the cooling medium flow rate is increased simultaneously until the thermal characteristics return to the preset safe range.

6. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S4, the gradient microstructure formed after thread finishing is as follows: the thread crest region has a densified layer with a thickness of 50-150 μm and a porosity of ≤5%; the thread root region has a porous buffer layer with a porosity of 15%-25%.

7. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S5, the collected graphite powder has a particle size of 1–50 μm; the functional additives include modified nano-alumina, zirconium boride precursor, and thermally activated curing agent.

8. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S5, the solid content of the protective slurry is 35% to 55%, and the viscosity is 200 to 800 mPa·s.

9. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S6, the temperature for in-situ curing using residual heat from processing is 120–180°C, and the time is 15–30 min; the thickness of the self-healing composite protective layer is 30–80 μm.

10. The intelligent automated machining method for graphite electrode threads according to claim 1, characterized in that, In step S7, the marking depth of the QR code is 20-50 μm, and the marking area is simultaneously covered by the composite protective layer.

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