Online monitoring method for additive and subtractive composite manufacturing

Abstract

The invention discloses an additive and subtractive composite manufacturing online monitoring method, which comprises the following steps of: acquiring strain characteristics in an additive and subtractive process by utilizing a monitoring and feedback control system comprising a substrate, a strain gauge, a strain collector, a data processing computer, an additive and subtractive composite forming control system and a motion system; according to the method, real-time monitoring of substrate deformation in the additive and subtractive composite manufacturing process is achieved, whether process adjustment of additive and subtractive composite manufacturing needs to be carried out or not is judged by comparing strain signals with strain signal preset values in a process adjustment database, feedback control is achieved, the local large strain phenomenon in the additive and subtractive manufacturing process can be found in time, and the production efficiency is improved. The method has the advantages of preventing crack defects, monitoring the state of the cutter, optimizing the roughness and dimensional precision of the machined surface, solving the problem of part machining failure caused by hot cracks or low precision in additive and subtractive composite manufacturing, and having wide application prospects and remarkable technical advantages.

Description

Technical Field

[0001] This invention relates to the field of additive and subtractive composite manufacturing technology, specifically to an online monitoring method for additive and subtractive composite manufacturing. Background Technology

[0002] Additive-subtractive manufacturing is a branch of additive manufacturing, encompassing additive manufacturing based on Directed Energy Deposition (DED) and subtractive manufacturing processes based on milling. DED uses high-energy beams such as lasers, electric arcs, or plasmas as energy sources to melt metal powder or wires and stack them layer by layer to form parts. DED's advantage lies in its ability to rapidly and efficiently manufacture parts with arbitrarily complex structures; however, its relatively low surface and dimensional accuracy limits its further application. Introducing milling and subtractive manufacturing into DED helps improve surface accuracy, reduce dimensional errors, and ultimately leads to additive-subtractive manufacturing technology.

[0003] When using high-energy beam deposition of metallic materials, the molten pool and its heat-affected zone undergo rapid heat exchange. The resulting thermal stress may exceed the material's tensile strength, leading to part printing failure. Therefore, it is essential to monitor stress and strain in real time during the printing process and adjust the process strategy promptly to ensure stable additive manufacturing.

[0004] The applicant noted that tool vibration generated during milling and cutting can affect tool life and the surface morphology and accuracy of the machined parts, and that vibration can be transmitted to the substrate through the parts. Therefore, it is necessary to measure the deformation of the substrate to provide feedback on the cutting state, and to obtain strain signals in the additive-subtractive composite manufacturing process in a timely manner through online monitoring and real-time feedback technology, so as to monitor the stress and strain in the additive-subtractive composite manufacturing process in real time, and then adjust the manufacturing process in real time to ensure the smooth progress of additive-subtractive composite manufacturing. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an online monitoring method for additive-subtractive composite manufacturing. This method can promptly detect localized large strain phenomena during the additive-subtractive manufacturing process, prevent crack defects, monitor tool status, and optimize surface roughness and dimensional accuracy. It solves the problem of part processing failure caused by thermal cracks or low precision in additive-subtractive composite manufacturing.

[0007] (II) Technical Solution

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an online monitoring method for additive-subtractive composite manufacturing, wherein the online monitoring method utilizes a monitoring and feedback control system, wherein the monitoring and feedback control system includes a substrate, strain gauges, a strain acquisition device, a data processing computer, an additive-subtractive composite forming control system, and a motion system, wherein the strain gauges are disposed on the bottom of the substrate, the strain acquisition device is connected to the strain gauges, the data processing computer is connected to the strain acquisition device and the additive-subtractive composite forming control system, the additive-subtractive composite forming control system is connected to the motion system, and the motion system is connected to the additive module and the subtractive module;

[0009] The online monitoring method for additive and subtractive composite manufacturing includes the following steps:

[0010] a. Additive and subtractive composite manufacturing of parts is carried out on the upper surface of the substrate. During the additive and subtractive composite manufacturing process, strain signals are collected in real time by strain acquisition device and strain gauge. Furthermore, the strain acquisition device sends the strain signals to the data processing computer.

[0011] b. The data processing computer compares the strain signal with the preset strain signal value in the process adjustment database to determine whether process adjustments for additive and subtractive composite manufacturing are required.

[0012] c. If it is determined that process adjustment is required, the data processing computer sends the process adjustment command to the motion system through the additive or subtractive composite forming control system to adjust the motion state of the additive or subtractive module.

[0013] Preferably, the process adjustment database is constructed through the following steps:

[0014] S1. Set the process parameters for additive and subtractive composite manufacturing;

[0015] S2. Collect the background noise of the additive manufacturing module and the subtractive manufacturing module;

[0016] S3. Additive and subtractive composite manufacturing of parts is carried out on the upper surface of the substrate. During the additive and subtractive composite manufacturing process, strain signals are collected in real time by strain acquisition device and strain gauge. Furthermore, the strain acquisition device sends the strain signals to the data processing computer.

[0017] S4. Based on the equipment background noise in step S2 and the strain signal in step S3, obtain the strain signal after removing the equipment background noise.

[0018] S5. Obtain the structural features of the part under the process parameters in step S1;

[0019] S6. Match the structural features of step S5 with the strain signals of step S4;

[0020] S7. Set different process parameters for additive and subtractive composite manufacturing, and repeat the above steps S1-S6 to obtain the corresponding structural features and strain signals under different process parameters.

[0021] S8. Set the strain signal threshold corresponding to the structural features that meet the requirements to the preset value of the strain signal;

[0022] S9. Calculate the deviation between the strain signal in step S4 and the preset value of the strain signal, and construct a process adjustment database corresponding to the deviation.

[0023] Preferably, before step a, the method further includes: setting the process parameters for additive and subtractive manufacturing; and collecting the background noise of the additive module and the subtractive module.

[0024] Preferably, in step b, the data processing computer specifically compares the strain signal after removing equipment background noise with the preset value of the strain signal in the process adjustment database, and further calculates the deviation.

[0025] Preferably, in step b, the deviation is determined to be less than or equal to 0; if the deviation is ≤0, no process adjustment is required; otherwise, process adjustment is required.

[0026] Preferably, a first groove is formed on the bottom of the substrate, the first groove being used to accommodate the strain gauge.

[0027] Preferably, the strain acquisition device is connected to the strain gauge via a strain data acquisition line.

[0028] Preferably, a second groove is also provided at the bottom of the substrate, the second groove is used for the strain data acquisition line to pass through, and the second groove is connected to the first groove.

[0029] Preferably, the bottom of the substrate is rectangular, the first groove is located in the middle of the bottom of the substrate, and the second groove is formed by slotting outward along the symmetrical axis of the bottom of the substrate.

[0030] Preferably, additive and subtractive composite manufacturing is achieved using directional energy deposition and milling methods.

[0031] (III) Beneficial Effects

[0032] Compared with existing technologies, this invention provides an online monitoring method for additive and subtractive composite manufacturing, which has the following beneficial effects: This invention utilizes a monitoring and feedback control system including a substrate, strain gauges, strain acquisition devices, a data processing computer, an additive and subtractive composite forming control system, and a motion system. Through this monitoring and feedback control system, this invention can acquire strain characteristics in the additive and subtractive processes, achieving real-time monitoring of substrate deformation during additive and subtractive composite manufacturing. By comparing the strain signal with preset strain signal values ​​in the process adjustment database, it determines whether process adjustments for additive and subtractive composite manufacturing are necessary, thus achieving feedback control. In this way, this invention can promptly detect localized large strain phenomena during additive and subtractive manufacturing, prevent crack defects, monitor tool status, optimize surface roughness and dimensional accuracy, and solve the problem of part processing failures caused by thermal cracking or low precision in additive and subtractive composite manufacturing. It has broad application prospects and significant technical advantages. Attached Figure Description

[0033] Figure 1 This is a flowchart of the first step of an online monitoring method for additive and subtractive manufacturing according to the present invention;

[0034] Figure 2 This is a flowchart of the second step of the online monitoring method for additive and subtractive composite manufacturing according to the present invention;

[0035] Figure 3 A flowchart illustrating the steps involved in constructing the process adjustment database for this invention;

[0036] Figure 4 This is a schematic diagram of the additive module and substrate of the present invention;

[0037] Figure 5 This is a schematic diagram of the subtractive material module and substrate of the present invention;

[0038] Figure 6 This is a schematic diagram of the monitoring and feedback control system of the present invention;

[0039] Figure 7 This is a schematic diagram of the substrate structure of the present invention.

[0040] The labels in the diagram are as follows: 111-Deposition head; 112-High energy beam; 113-Deposition part; 121-Milling cutter holder; 122-Milling cutter; 123-Part to be milled; 13-Substrate; 131-First groove; 132-Second groove; 141-Strain gauge; 142-Strain acquisition device; 143-Data processing computer; 144-Strain data acquisition line; 145-Strain data transmission line; 151-Additive and subtractive composite forming control system; 152-Motion system; 153-Process adjustment command transmission line; 154-Motion command transmission line. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] This invention provides an online monitoring method for additive-subtractive composite manufacturing. This method utilizes a monitoring and feedback control system, which includes a substrate 13, strain gauges 141, a strain acquisition device 142, a data processing computer 143, an additive-subtractive composite forming control system 151, and a motion system 152. The strain gauges 141 are disposed on the bottom of the substrate 13. The strain acquisition device 142 is connected to the strain gauges 141. The data processing computer 143 is connected to the strain acquisition device 142 and the additive-subtractive composite forming control system 151. The additive-subtractive composite forming control system 151 is connected to the motion system 152, which is connected to both the additive module and the subtractive module. The motion system 152 is the motion system of the additive-subtractive composite manufacturing equipment.

[0043] The strain gauge 141 described above is an element for measuring strain, composed of a sensitive grid or the like. The strain gauge 141 is used to detect the deformation of the substrate 13. The strain gauge 141 of this invention is prior art, and its specific structure and working principle will not be elaborated here. The strain gauge 141 of this invention is disposed at the bottom of the substrate 13, that is, the deformation of the bottom of the substrate 13 is used to monitor the stress-strain process during the additive-subtractive composite manufacturing process. Compared with the method of attaching the strain gauge to the upper surface of the substrate, this method can effectively avoid the reduction in strain measurement accuracy caused by the falling of slag, splashing particles, powder, and chips, and effectively prevent the strain gauge, acquisition lines, etc., from being burned by high temperatures or interfering with the cutting tools.

[0044] Preferably, a first groove 131 is formed on the bottom of the substrate 13 to accommodate the strain gauge 141. The area of ​​the first groove 131 is flat and free of obvious defects to better fit the strain gauge 141. The strain acquisition device 142 is connected to the strain gauge 141 through a strain data acquisition line 144. A second groove 132 is also formed on the bottom of the substrate 13 for the strain data acquisition line 144 to pass through. The second groove 132 communicates with the first groove 131. Further, preferably, to reduce the influence of the substrate structure on the error of the acquired strain signal, the bottom of the substrate 13 is rectangular, the first groove 131 is located in the middle of the bottom of the substrate 13, and the second groove 132 is formed by slotting outward along the symmetrical axis of the bottom of the substrate 13.

[0045] The online monitoring method for additive and subtractive composite manufacturing of the present invention includes the following steps:

[0046] a. Additive and subtractive composite manufacturing of parts is carried out on the upper surface of the substrate. During the additive and subtractive composite manufacturing process, strain signals are collected in real time by strain acquisition device and strain gauge. Furthermore, the strain acquisition device sends the strain signals to the data processing computer.

[0047] Preferably, the above-mentioned additive-subtractive composite manufacturing is achieved using directional energy deposition and milling methods; such as Figure 4 As shown, directional energy deposition is achieved through an additive manufacturing module, and the corresponding part is the deposited part 113. The additive manufacturing module includes a deposition head 111. During the directional energy deposition process, a high-energy beam 112 is output from the deposition head 111 to melt the metal powder or metal wire on the upper surface of the substrate 13, thereby forming the deposited part 113. Additionally, as... Figure 5 As shown, the milling method is implemented through the subtraction module, and the corresponding part is the part to be milled 123. The subtraction module includes a milling shank 121 and a milling cutter 122. The milling method uses the milling shank 121 to hold the milling cutter 122 to mill the part to be milled on the upper surface of the substrate 13, so as to improve the surface accuracy of the part and reduce dimensional errors.

[0048] b. The data processing computer compares the strain signal with the preset strain signal value in the process adjustment database to determine whether process adjustments for additive and subtractive composite manufacturing are required.

[0049] c. If it is determined that process adjustment is required, the data processing computer sends the process adjustment command to the motion system through the additive or subtractive composite forming control system to adjust the motion state of the additive or subtractive module.

[0050] Specifically, the aforementioned process adjustment database is constructed through the following steps:

[0051] S1. Set the process parameters for additive and subtractive composite manufacturing.

[0052] It is understood that the aforementioned motion system is used to adjust the motion state of the additive or subtractive manufacturing module under the control of the additive-subtractive composite forming control system. The process parameters of additive-subtractive composite manufacturing correspond to the aforementioned motion state. Specific process parameters may include laser power, scanning speed, laser scanning path, moving speed of the subtractive module, rotation speed of the milling cutter, etc. Step S1 sets the process parameters of additive-subtractive composite manufacturing and forms a program that can be recognized by the additive and subtractive manufacturing equipment (i.e., the additive module and subtractive module), and imports it into the equipment control system (i.e., the additive-subtractive composite forming control system).

[0053] S2. Collect background noise of additive and subtractive manufacturing modules: that is, collect the strain signal of strain gauges when there is no additive or subtractive manufacturing process and the additive and subtractive modules are running. This signal is the background noise of the equipment; that is, collect the background noise signal of the mechanical motion of the additive and subtractive module system.

[0054] S3. Perform additive and subtractive composite manufacturing of parts on the upper surface of the substrate. During the additive and subtractive composite manufacturing process, strain signals are collected in real time by strain acquisition device and strain gauge. Furthermore, the strain acquisition device sends the strain signals to the data processing computer.

[0055] S4. Based on the equipment background noise in step S2 and the strain signal in step S3, obtain the strain signals of the additive and subtractive processes after removing the equipment background noise.

[0056] S5. Obtain the structural features of the part under the process parameters in step S1: Specifically, the structural features under this process can be obtained through destructive testing, metallographic analysis, roughness detection, precision measurement, etc., including the presence or absence of cracks, the location of cracks, roughness value, dimensional error, etc.

[0057] S6. Match the structural features of step S5 with the strain signals of step S4: Match the above structural features with the time-series signals of the strain signals, extract features such as strain abrupt changes and strain peaks in the corresponding strain signals, and classify and record them.

[0058] S7. Set different process parameters for additive and subtractive composite manufacturing, and repeat the above steps S1-S6 to obtain the corresponding structural features and strain signals under different process parameters, that is, obtain the structural features and strain signal features at different process parameters and different part heights, and match them.

[0059] S8. Set the strain signal threshold corresponding to the structural features that meet the requirements (no cracks and surface roughness of the parts, dimensional errors meet the requirements, etc.) as the strain signal preset value.

[0060] S9. Calculate the deviation between the strain signal in step S4 and the preset value of the strain signal, and construct a process adjustment database corresponding to the deviation. The above deviation is calculated as follows: deviation = actual strain value - preset value. It can be understood that the above actual strain value is the actual deformation of the substrate corresponding to the strain signal in step S4, while the preset value corresponds to the deformation of the substrate corresponding to the preset value of the strain signal. By analyzing and extracting the collected strain information, a process adjustment database corresponding to the deviation is formulated, that is, a feedback adjustment strategy for the process is formulated based on the calculated deviation value.

[0061] In addition, before step a above, the process parameters for additive and subtractive manufacturing are set, i.e., initial process parameters are set and additive and subtractive manufacturing process programs are generated. This step is the same as step S1 above. Background noise of the additive and subtractive modules is collected. This step is the same as step S2 above.

[0062] Preferably, in step b above, the data processing computer specifically compares the strain signal after removing equipment background noise with the preset strain signal value in the process adjustment database, and further calculates the deviation. This method can eliminate the influence of equipment background noise and improve the accuracy of substrate strain detection. It can be understood that the processing here is the same as steps S4 and S9 above. Furthermore, preferably, the strain signal can also undergo data processing, including noise reduction, feature extraction, and classification.

[0063] Further, in step b, specifically, it is determined whether the above deviation is less than or equal to 0; if the deviation is ≤ 0, no process adjustment is needed, i.e., no equipment program adjustment is required; otherwise, process adjustment is needed, i.e., the equipment program needs to be changed. It can be understood that the above process adjustment database stores the process parameter adjustment methods (i.e., corresponding process adjustment instructions) corresponding to each deviation value. For example, for the additive manufacturing process, when the calculated deviation is greater than 0 and less than -10με (where με represents micro-strain, and the negative sign represents compression, i.e., 0 < deviation < 10 micro-strains, i.e., process adjustment is required), the laser power of the additive manufacturing module in the process parameters is increased by 10% to avoid problems such as cracks in the parts; for example, for the subtractive manufacturing process, when the calculated deviation is in the range of 5 to 15με, i.e., process adjustment is required, the subtractive manufacturing process is simultaneously reduced... The module's spindle speed is reduced by 10% to 15% (2550 r / min to 2700 r / min), and the cutting depth is reduced by 0.02 to 0.1 mm (e.g., from 0.3 mm to 0.25 mm). The cutting load is reduced through dual adjustment, etc. When it is determined that process adjustment is required, the data processing computer 143 generates a process adjustment command corresponding to the deviation according to the process adjustment database, and sends it to the additive or subtractive composite forming control system 151 through the process adjustment command transmission line 153. The motion system 152 is then controlled through the motion command transmission line 154 to change and adjust the motion state of the additive or subtractive module.

[0064] The strain acquisition device and strain gauges mentioned above continuously collect strain signals throughout the additive and subtractive composite manufacturing process. The data processing computer continuously processes and analyzes the data throughout the additive and subtractive composite manufacturing process, and continuously decides whether and how to adjust the process adjustment instructions.

[0065] The following section uses additive manufacturing as an example to explain in detail the steps for constructing the above-mentioned process adjustment database and the corresponding process adjustments for deviations:

[0066] S1. Setting Additive Manufacturing Process Parameters: Taking the additive manufacturing of thin-walled parts made of high-temperature alloy GH3536 as an example, the core process parameters are set as follows: laser power 1200W, laser scanning rate 1000mm / min, scanning path bidirectional scanning, and layer thickness 350μm. These parameters are compiled into G-code that the equipment can recognize and imported into the additive-subtractive composite forming control system. The dimensions of the aforementioned thin-walled part made of high-temperature alloy GH3536 are X100mm×Z200mm, and the thickness Y is the width of a single melt channel of laser melting, which is 3mm.

[0067] S2. Background noise acquisition: With the laser off, only the equipment's table movement system, inert gas protection device, and water cooling device are activated. The equipment moves according to the G code in S1 (without laser output), and data is continuously acquired for 5 minutes using strain gauges (accuracy ±1με) attached to the bottom of the substrate. The acquired background noise signal mainly consists of strain fluctuations caused by mechanical vibration, ranging from -5με to +5με.

[0068] S3. Real-time acquisition of strain signals during additive manufacturing: The laser is turned on, and additive manufacturing of parts is performed on the substrate. During the forming process, the strain acquisition device records strain signal data at a sampling frequency of 512Hz. The strain value stabilizes at +80με during the forming process; however, the strain signal monitored from the 32s to 32.06s after the start of printing is +50με, and the strain signal monitored from the 600s to 630s after the start of printing is +100με to +110με.

[0069] S4. Strain signal after removing equipment background noise: The effective strain signal is obtained by subtracting the average equipment background noise (0με) from S2 from the original signal collected in S3 using data processing software. For example, the original signal is +80με, and after processing it becomes +80με (because the average equipment background noise is 0).

[0070] S5. Obtain the structural features of the part: After the part is formed, the structural features are detected by the following methods: Destructive test: The part is cut along the forming direction and a micro-crack (1 mm in length) is found at a height of 2.1 mm from the substrate; Roughness test: The surface roughness Ra at a height of <35 mm from the substrate is 12 μm;

[0071] The surface roughness Ra at a height of 35–36.75 mm from the substrate is 20 μm.

[0072] S6. Correspondence between strain signals and structural features: Time-series analysis of strain signals corresponding to crack locations revealed that during the formation of a 2.1mm high layer, the strain signal exhibited a sudden change lasting 0.06 seconds (from +80με to +50με); the strain peak values ​​corresponding to the surface roughness out-of-tolerance area (35-36.75mm height from the substrate) were concentrated in the range of +100με to +110με; the strain signal fluctuation frequency in the dimensional out-of-tolerance area was consistent with the scanning path period.

[0073] S7. Data accumulation under multiple parameters, setting different process parameters: Adjusting process parameters to repeat experiments, for example: when the laser power is increased to 1400W, the crack disappears and the corresponding strain peak increases to +90με~+95με; when the scanning rate is increased to 1200mm / min, the surface roughness Ra is reduced to 6mm and the corresponding strain signal drops to +60με.

[0074] S8. Set the preset value of strain signal: Select the process parameter combination that meets the requirements (such as laser power 1300W, scanning rate 1200mm / min). The corresponding strain signal characteristics are: no continuous sudden change exceeding +85με, strain average 70με, and fluctuation amplitude ≤10με. Set these indicators as the preset value of strain signal.

[0075] S9. Construct a process adjustment database: Calculate the strain deviation under different process parameters (deviation = actual strain value - preset value). For example, when the laser power is 1200W, the strain peak value in a certain area is 85με, and the deviation = 85-70 = +15με. The corresponding record is "When the deviation is +15με, the laser power needs to be increased by 100W".

[0076] The following section uses subtractive manufacturing as an example to explain in detail the steps for constructing the above-mentioned process adjustment database and the corresponding process adjustments for deviations:

[0077] S1. Set the subtractive manufacturing process parameters: Taking the side milling of a 45# steel cuboid part (100mm×80mm×50mm) as an example, the core process parameters are set as follows: spindle speed of the subtractive module 3000r / min, feed rate 1000mm / min, depth of cut 0.3mm, and tool type φ10mm carbide end mill (4-flute); these parameters are written into a CNC program (G code) and imported into the control system of the vertical machining center, i.e., the additive and subtractive composite forming control system.

[0078] S2. Collect background noise from the equipment: Start the spindle idling system, table feed system, and coolant pump of the machining center, and continuously collect data for 5 minutes using strain gauges (accuracy ±1με) attached to the side of the workpiece clamping table. The collected background noise signal mainly consists of strain fluctuations caused by spindle idling vibration and table movement friction, ranging from -3με to +3με.

[0079] S3. Real-time acquisition of strain signals during subtractive processing: Side milling was performed on a 45# steel workpiece. During the cutting process, the strain acquisition device recorded strain data at a sampling frequency of 512Hz. When the tool milled to the corner area of ​​the workpiece, the strain signal fluctuated violently, with the peak value reaching +40με.

[0080] S4. Strain signal after removing equipment background noise: The effective strain signal is obtained by subtracting the average equipment background noise (0με) from S2 from the original signal collected in S3 using data processing software. For example, the original signal +40με becomes +40με after processing (because the average equipment background noise is 0).

[0081] S5. Obtain the structural features of the part: After the part is machined, the structural features are inspected in the following ways: Surface quality inspection: obvious vibration marks are found in the corner area, and the surface roughness Ra is 3.2μm (design requirement Ra≤1.6μm); Dimensional accuracy measurement: the actual length dimension is 0.04mm smaller than the design dimension (tolerance requirement ±0.02mm); Tool wear inspection: the tool edge has micro-chipping, and the wear amount reaches 0.1mm.

[0082] S6. Correspondence between strain signals and structural features: Time-series analysis of strain signals corresponding to the corner vibration pattern area revealed that during cutting in this area, the strain signal exhibited a high-frequency oscillation lasting 0.8 seconds (frequency consistent with the spindle speed), with the peak value suddenly increasing from +15με to +40με; the strain signal corresponding to the dimensional deviation area showed a continuous positive shift (average +10με); the fluctuation amplitude of the strain signal in the tool wear area gradually increased with cutting time.

[0083] S7. Data accumulation under multiple parameters, setting different process parameters: Adjusting process parameters and repeating experiments, for example: when the spindle speed drops to 2000 r / min, the vibration marks disappear and the corresponding strain peak drops to +25με; when the feed speed drops to 800 mm / min, the dimensional error is reduced to 0.01 mm, but the processing efficiency is reduced by 30%, and the corresponding strain signal fluctuation amplitude is reduced to ±10με.

[0084] S8. Set the preset value of strain signal: Select the process parameter combination that meets the requirements (such as spindle speed 2500r / min, feed speed 900mm / min), and the corresponding strain signal characteristics are: no continuous oscillation exceeding +25με, peak value ≤30με, fluctuation amplitude ≤8με. Set these indicators as the preset value of strain signal.

[0085] S9. Construct a process adjustment database: Calculate the strain deviation under different process parameters (deviation = actual strain value - preset value). For example, when the spindle speed is 3000 r / min, the peak strain value in the corner area is 40 με, and the deviation = 40 - 30 = +10 με. The corresponding record is "When the deviation is +10 με, the spindle speed needs to be reduced by 500 r / min".

[0086] Example of process parameter adjustment logic when the above deviation > 0: When the strain signal deviation > 0 is detected (i.e. the actual strain value is greater than the preset value), it indicates that there is a risk of abnormal machining quality of the part (such as vibration marks, dimensional deviation, etc.). It is preferable to adjust the parameters according to the following rules: (1) Deviation in the range of 0 to 5 με: reduce the feed rate by 5% to 8% (e.g., from 1000 mm / min to 920 mm / min) and keep the spindle speed unchanged to reduce the cutting impact force. (2) Deviation in the range of 5 to 15 με: reduce the spindle speed by 10% to 15% (2550 r / min to 2700 r / min) and reduce the cutting depth by 0.02 to 0.1 mm (e.g., from 0.3 mm to 0.25 mm) to reduce the cutting load through dual adjustment. (3) Deviation > 15 με: in addition to adjusting the speed and cutting depth, replace the tool with a new tool (wear ≤ 0.05 mm) to avoid machining instability caused by tool overheating.

[0087] For example, when a batch of parts is cut at a corner and a strain deviation of +12με is detected, the system automatically calls the adjustment strategy in the process adjustment database: the spindle speed is reduced from 3000r / min to 2550r / min (a reduction of 15%), and the cutting depth is reduced from 0.3mm to 0.25mm. As a result, the vibration marks of the subsequently processed parts disappear after inspection, the surface roughness Ra reaches 1.2μm, and the dimensional error is controlled within 0.01mm.

[0088] Furthermore, preferably, the present invention may further include: using historical data of the collected strain signals as model training data, inputting it into an LSTM neural network model, allowing the model to learn the relationship between substrate deformation and time; adjusting parameters such as model weights and biases to enable the model to fit the historical data as accurately as possible and capture the pattern of substrate deformation; the trained LSTM neural network model can predict the substrate deformation trend in the future, for example, 500ms, based on current and historical strain signal data. When the substrate deformation value predicted by the LSTM neural network model exceeds a preset deformation threshold, the model triggers an audible and visual alarm to remind operators to take timely measures, such as adjusting the process parameters of additive and subtractive manufacturing or suspending the manufacturing process, to avoid excessive substrate deformation affecting the manufacturing quality of the parts.

[0089] Compared with existing technologies, this invention provides an online monitoring method for additive and subtractive composite manufacturing, which has the following beneficial effects: This invention utilizes a monitoring and feedback control system including a substrate, strain gauges, a strain acquisition device, a data processing computer, an additive and subtractive composite forming control system, and a motion system. Through this monitoring and feedback control system, this invention can acquire strain characteristics in the additive and subtractive processes, achieving real-time monitoring of substrate deformation during additive and subtractive composite manufacturing. By comparing the strain signal with preset strain signal values ​​in the process adjustment database, it determines whether process adjustments for additive and subtractive composite manufacturing are necessary, thus achieving feedback control. In this way, this invention can promptly detect localized large strain phenomena during additive and subtractive manufacturing, prevent crack defects, and help monitor tool status, optimize and homogenize surface roughness and dimensional accuracy. It solves the problem of part processing failure caused by thermal cracking or low precision in additive and subtractive composite manufacturing, and has broad application prospects and significant technical advantages. Furthermore, this invention uses strain gauges attached to the bottom surface of the substrate to measure deformation characteristics during manufacturing, avoiding problems such as strain gauge high-temperature failure, strain gauge temperature drift, and interference between wiring and tools.

[0090] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0091] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An additive-subtractive hybrid manufacturing online monitoring method, characterized in that: The additive-subtractive composite manufacturing online monitoring method is a monitoring and feedback control system, wherein the monitoring and feedback control system comprises a substrate, a strain gauge, a strain collector, a data processing computer, an additive-subtractive composite forming control system and a motion system, the strain gauge is arranged on the bottom of the substrate, the strain collector is connected to the strain gauge, the data processing computer is connected to the strain collector and the additive-subtractive composite forming control system, the additive-subtractive composite forming control system is connected to the motion system, and the motion system is connected to an additive module and a subtractive module. The additive-subtractive composite manufacturing online monitoring method comprises the following steps: a. The additive-subtractive composite manufacturing of a part is performed on the upper surface of the substrate, and strain signals are collected in real time by the strain collector and the strain gauge during the additive-subtractive composite manufacturing, and further, the strain collector sends the strain signals to the data processing computer; b. The data processing computer compares the strain signals with preset strain signals in a process adjustment database to determine whether process adjustment of the additive-subtractive composite manufacturing is needed; c. If it is determined that the process adjustment is needed, the data processing computer sends a process adjustment instruction to the motion system through the additive-subtractive composite forming control system to adjust the motion state of the additive module or the subtractive module.

2. The AM-CMM online monitoring method of claim 1, wherein: The process adjustment database is constructed by the following steps: S1. Setting process parameters of the additive-subtractive composite manufacturing; S2. Collecting device background noise of the additive module and the subtractive module; S3. The additive-subtractive composite manufacturing of a part is performed on the upper surface of the substrate, and strain signals are collected in real time by the strain collector and the strain gauge during the additive-subtractive composite manufacturing, and further, the strain collector sends the strain signals to the data processing computer; S4. Obtaining strain signals after removing the device background noise according to the device background noise of step S2 and the strain signals of step S3; S5. Obtaining structural features of a part under the process parameters of step S1; S6. Corresponding the structural features of step S5 with the strain signals of step S4; S7. Setting different process parameters of the additive-subtractive composite manufacturing, and repeating the above steps S1-S6 to obtain corresponding structural features and strain signals under different process parameters; S8. Setting a strain signal threshold corresponding to the structural features meeting the requirements as a preset strain signal; S9. Calculating the deviation between the strain signals of step S4 and the preset strain signal to construct the process adjustment database corresponding to the deviation.

3. The AM-CMM online monitoring method of claim 2, wherein, Before step a, the process parameters of the additive-subtractive composite manufacturing are set, and the device background noise of the additive module and the subtractive module is collected.

4. The AM-CMM online monitoring method of claim 3, wherein, In step b, the data processing computer specifically compares the strain signals after removing the device background noise with the preset strain signals in the process adjustment database, and further calculates the deviation.

5. The AM-CMM online monitoring method of claim 4, wherein: In the step b, it is judged whether the deviation is less than or equal to 0; if the deviation is less than or equal to 0, the process adjustment is not needed, otherwise the process adjustment is needed.

6. The AM-CMM online monitoring method of claim 1, wherein: The bottom of the substrate is provided with a first groove for accommodating the strain gauge.

7. The AM-CMM online monitoring method of claim 6, wherein: The strain collector is connected to the strain gauge through a strain data collection line.

8. The AM-CMM online monitoring method of claim 7, wherein: The bottom of the substrate is further provided with a second groove for the strain data collection line to pass through, and the second groove is in communication with the first groove.

9. The AM-CMM online monitoring method of claim 8, wherein: The bottom of the substrate is rectangular, the first groove is located at the middle position of the bottom of the substrate, and the second groove is formed by slotting outward along the symmetry axis of the bottom of the substrate.

10. The AM-CMM online monitoring method of claim 1, wherein: The additive and subtractive composite manufacturing is realized by using a directional energy deposition and a milling method.

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