Environment-friendly composite board processing system

Abstract

The invention relates to the technical field of plate processing, in particular to an environment-friendly composite plate processing system which comprises a spectral feature analysis module, a cutting parameter optimization module, a cutting quality optimization module, an excess material utilization optimization module and a plate coating adjustment module. According to the invention, by accurately measuring material characteristics before processing, finer material selection and utilization are realized, and spectral characteristic values and spectral data of the materials are compared, so that the system can ensure that each material is processed under the most suitable condition, processing errors caused by mismatching of the materials are reduced, and the processing precision is improved. Adjustment of real-time cutting parameters is dynamically optimized according to material characteristics and actual cutting conditions, the machining efficiency and precision are effectively improved, the comprehensive utilization rate of resources is further improved by intelligently analyzing cutting excess materials and conducting matched utilization, the production cost is reduced, and the production efficiency is improved. The fine control of the spraying process ensures the consistency of the appearance and quality of the plate, and meets the flexibility and sustainability of plate production.

Description

Technical Field

[0001] The present invention relates to the technical field of plate processing, and in particular to an environmentally friendly composite plate processing system. Background Art

[0002] Panel processing involves a variety of processes and technologies used to produce and process various panel materials such as wood, composite materials, metals and plastics. It includes multiple processing steps from raw material pretreatment to cutting, forming, bonding, painting, etc. With the advancement of technology, the field of panel processing is also continuing to develop, adopting more advanced machinery and automation technology to improve production efficiency and processing accuracy, while reducing material waste and environmental impact.

[0003] Among them, the environmentally friendly composite panel processing system adopts environmentally friendly technologies and materials in the process of processing composite panels, aiming to reduce the environmental impact during the production and processing process, such as by using fewer harmful chemicals, reducing waste generation and energy consumption. In addition, the system also uses efficient recycling technology to deal with residual materials in the production process. It has a wide range of uses and can be applied to furniture manufacturing, building materials, automotive interior panels and other fields. It not only improves the utilization rate of materials, but also supports the industry's green transformation and sustainable development goals.

[0004] Existing technologies mostly rely on traditional visual and physical inspection methods, which lack accurate scientific basis, resulting in inaccurate raw material classification, increasing the probability of material waste and processing errors. During the cutting and processing process, the parameter settings are fixed and lack flexibility, and cannot be adjusted according to material characteristics or real-time feedback, which limits the improvement of processing accuracy and energy efficiency. In addition, existing technologies lack support for the treatment of residual materials after cutting, fail to achieve efficient material recycling, cause large losses of plate raw materials, and limit the flexibility and sustainability of plate production. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an environmentally friendly composite plate processing system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an environmentally friendly composite plate processing system, the system comprising:

[0007] The spectral feature analysis module collects spectral reflectance data of composite board raw materials, identifies similar material characteristics, analyzes spectral absorptivity, reflectivity and peak distribution, classifies material categories, and obtains spectral feature index information;

[0008] The cutting parameter optimization module compares the power, cutting speed, and cutting angle of the current cutting device with the optimal cutting parameters based on the spectral characteristic index information, adjusts the operating parameters of the cutting device, and obtains the cutting parameter configuration;

[0009] The cutting quality optimization module monitors the energy input, cutting speed, cutting depth and edge flatness during the cutting process based on the cutting parameter configuration, recalculates the deviation correction value of the adjusted cutting parameters, and obtains the cutting accuracy correction index;

[0010] The waste material utilization optimization module screens the waste material size, shape and thickness of the plate based on the cutting accuracy correction index, calculates the matching degree between the waste material and the new required material, adjusts the material delivery order, and obtains the waste material matching index;

[0011] The plate coating adjustment module calls the residual material matching index, analyzes the surface quality of the plate to be sprayed, monitors the spectral reflectance and coating thickness uniformity, and adjusts the current spraying speed, nozzle angle and spraying pressure to obtain the plate spraying quality optimization result.

[0012] The improvements of the present invention are that the spectral characteristic index information includes wavelength characteristics, material classification, and absorption peak; the cutting parameter configuration includes power level, speed setting, and angle selection results; the cutting accuracy correction index includes error analysis results and quality rating; the residual material matching index includes size adaptability, shape compatibility, and material efficiency; the plate spraying quality optimization result includes spraying rate, nozzle adjustment result, and coating uniformity.

[0013] The present invention is improved in that the spectral feature analysis module includes:

[0014] The spectral reflectance data acquisition submodule collects the spectral reflectance data of the composite board raw materials. For different board types, it detects the spectral reflectance in multiple wavelength ranges, records the spectral reflectance intensity of each wavelength, and calculates the spectral absorbance to obtain the spectral reflectance and absorption data.

[0015] The spectral feature extraction submodule extracts the spectral features of each wavelength interval based on the spectral reflection and absorption data, calculates the spectral feature peaks and their distribution, and screens key spectral feature points using the formula:

[0016]

[0017] Get the spectral characteristic difference value T f , where f peak (k) represents the intensity of the kth characteristic peak, f total represents the sum of all peak intensities, and n represents the number of spectral feature points;

[0018] The material spectrum mapping construction submodule compares the spectral feature difference values ​​with the spectral data of known composite plate types, analyzes the spectral feature similarity between each category, screens matching known material categories, establishes spectral feature mapping relationships between material categories, and obtains spectral characteristic index information.

[0019] The present invention is improved in that the cutting parameter optimization module includes:

[0020] The optimal cutting parameter screening submodule obtains the spectral characteristics of the target plate according to the spectral characteristic index information, and screens the cutting effect of each type of material under different cutting conditions using the formula:

[0021]

[0022] Calculate the SE score of the cutting parameters c , and select the parameter with the highest score as the optimal cutting parameter, where PE i Represents the cutting power of the i-th matching material, PE max Represents the maximum cutting power in all matching materials, VE i Represents the cutting speed of the i-th matching material, VE max Represents the maximum cutting speed among all matching materials, θe i represents the cutting angle of the i-th matching material, θe max Represents the maximum cutting angle among all matching materials, n SE Represents the number of materials in the matching list of similar materials;

[0023] The cutting parameter adjustment submodule compares the deviation between the power, cutting speed and cutting angle of the current cutting equipment and the optimal cutting parameters based on the optimal cutting parameters, determines the parameter adjustment amount, and adjusts the operating parameters of the cutting equipment to obtain the cutting parameter configuration.

[0024] The present invention is improved in that the cutting quality optimization module includes:

[0025] The cutting energy monitoring submodule monitors the energy input, cutting speed, cutting depth and edge flatness data during the cutting process based on the cutting parameter configuration, calculates the ratio of energy input to cutting speed, analyzes the effect of unit energy on cutting depth, and compares the change in cutting depth with the standard cutting depth of the target plate to obtain the cutting energy input deviation value;

[0026] The cutting deviation analysis submodule analyzes the deviation between the current cutting parameters and the target plate cutting quality standard based on the cutting energy input deviation value, using the formula:

[0027]

[0028] Get the cutting parameter deviation correction value ΔFG, where Pg i Represents the value of the current i-th cutting parameter, Pg std Represents the standard value of the corresponding cutting parameter, n FG Represents the total number of cutting parameters;

[0029] The cutting accuracy correction submodule adjusts the cutting parameters based on the cutting parameter deviation correction value, recalculates the variation range of cutting speed, cutting depth and edge flatness, and compares the target plate cutting quality standard again to determine the corrected error range and obtain the cutting accuracy correction index.

[0030] The present invention is improved in that the waste material utilization optimization module includes:

[0031] The matching degree calculation submodule analyzes the material utilization data of the cutting process based on the cutting accuracy correction index, and screens the size, shape and thickness of the remaining material of the plate to be processed on the production line, using the formula:

[0032]

[0033] Calculate the matching degree MY between the remaining material and the new required material, where Dy i is the size of the i-th residual material, Dy t is the size of the new required material, Sy i is the shape of the i-th residual material, Sy t Is the shape of the new demand material, Ty i is the thickness of the i-th residual material, Ty t is the thickness of the new required material, n my Indicates the amount of remaining material;

[0034] The placing sequence adjustment submodule selects the remaining material with the best matching degree for the next processing based on the remaining material matching degree, and adjusts the material placing sequence to obtain the remaining material matching index.

[0035] The present invention is improved in that the plate coating adjustment module includes:

[0036] The surface quality analysis submodule uses the residual material matching index to analyze the surface quality of the plate to be sprayed, monitors the surface finish, coating adhesion and micro defect distribution, measures the surface uniformity based on the spectral reflectance, judges the surface quality difference of the plate, and obtains the surface spectral uniformity parameter;

[0037] The spraying deviation calculation submodule monitors the coating thickness uniformity during the spraying process based on the surface spectral uniformity parameter, using the formula:

[0038]

[0039] Calculate the spray thickness deviation at each measuring point to obtain the spray uniformity deviation value DX c , where TX i is the coating thickness at the i-th measurement point, TX avg is the average coating thickness of all measurement points, N dx is the total number of measurement points;

[0040] The spraying parameter optimization submodule adjusts the current spraying speed, nozzle angle and spraying pressure based on the spraying uniformity deviation value, determines the current spraying uniformity, and optimizes the nozzle movement trajectory and spraying flow distribution to obtain the plate spraying quality optimization result.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are:

[0042] In the present invention, by accurately measuring the material properties before processing, more refined material selection and utilization can be achieved. By comparing the spectral characteristic values ​​and the spectral data of the materials, the system can ensure that each material is processed under the most suitable conditions, reducing processing errors caused by material mismatch. The real-time adjustment of cutting parameters is dynamically optimized according to the material properties and actual cutting conditions, effectively improving processing efficiency and accuracy. By intelligently analyzing the cutting waste and matching it for utilization, the comprehensive utilization rate of resources is further improved and production costs are reduced. The fine control of the spraying process ensures the consistency of the appearance and quality of the board, meeting the flexibility and sustainability of board production. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a system flow chart of the present invention;

[0044] Figure 2 This is a flow chart of the spectral feature analysis module in the present invention;

[0045] Figure 3 This is a flow chart of the cutting parameter optimization module in the present invention;

[0046] Figure 4 This is a flow chart of the cutting quality optimization module in the present invention;

[0047] Figure 5 This is a flow chart of the surplus material utilization optimization module in the present invention;

[0048] Figure 6 This is a flow chart of the plate coating adjustment module in the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0051] Example

[0052] See also Figure 1 The present invention provides a technical solution: an environmentally friendly composite plate processing system comprising:

[0053] The spectral feature analysis module collects spectral reflectance data of composite sheet materials, extracts spectral feature values ​​from multiple wavelength intervals, compares spectral data of known composite sheet types, identifies similar material properties, and analyzes spectral absorptivity, reflectivity, and peak distribution to classify material types. It then establishes spectral feature mapping relationships between material types and obtains spectral feature index information.

[0054] The cutting parameter optimization module obtains the spectral characteristics of the target plate based on the spectral characteristic index information, selects the optimal cutting parameters for similar materials, compares the power, cutting speed, and cutting angle of the current cutting equipment with the optimal cutting parameters, adjusts the operating parameters of the cutting equipment, and obtains the cutting parameter configuration;

[0055] The cutting quality optimization module monitors the energy input, cutting speed, cutting depth and edge flatness during the cutting process based on the cutting parameter configuration. By comparing the cutting quality standards of the target plate, it adjusts the cutting parameters within the deviation range and recalculates the deviation correction value of the adjusted cutting parameters to obtain the cutting accuracy correction index.

[0056] The residual material utilization optimization module analyzes the material utilization data of the cutting process based on the cutting accuracy correction index, screens the residual material size, shape and thickness of the plates currently being processed on the production line, calculates the matching degree between the residual material and the new required material, selects the residual material with the best matching degree for the next processing step, and adjusts the material delivery sequence to obtain the residual material matching index;

[0057] The plate coating adjustment module calls the residual material matching index to analyze the surface quality of the plate to be sprayed, monitors the spectral reflectance and coating thickness uniformity during the spraying process, calculates the deviation from the target spraying uniformity, and adjusts the current spraying speed, nozzle angle and spraying pressure to obtain the optimized result of the plate spraying quality.

[0058] Spectral characteristic index information includes wavelength characteristics, material classification, and absorption peak; cutting parameter configuration includes power level, speed setting, and angle selection results; cutting accuracy correction indicators include error analysis results and quality rating; residual material matching index includes size adaptability, shape compatibility, and material efficiency; plate spraying quality optimization results include spraying rate, nozzle adjustment results, and coating uniformity.

[0059] See also Figure 2 , the spectral feature analysis module includes:

[0060] The spectral reflectance data acquisition submodule collects the spectral reflectance data of the composite board raw materials. For different board types, it detects the spectral reflectance in multiple wavelength ranges, records the spectral reflectance intensity of each wavelength, and calculates the spectral absorbance to obtain the spectral reflectance and absorption data.

[0061] Obtain the spectral reflectance data of the composite board raw materials. For different types of boards, it is necessary to accurately measure their spectral reflectance in different wavelength ranges. First, select a standard light source, such as a halogen lamp or a xenon lamp, to ensure the stability of the light source, and use the integrating sphere measurement method to collect the spectrum of the composite board. Set the incident light intensity to I0 and measure the reflected light intensity I on the surface of the composite board. r , and the reflectivity R=I r / I0, in order to improve the accuracy of the data, multiple scans are required during the measurement. The scanning interval Δλ is set to 5nm and the reflectivity data at each wavelength is recorded. For the calculation of the absorbance A, the spectral absorbance of each wavelength interval is calculated using the formula A=1-R. At the same time, the data of different wavelength intervals are normalized to reduce instrument noise interference. In the actual measurement process, different types of composite boards, such as wood-plastic composite materials and glass fiber reinforced composite materials, can be spectrally measured to obtain their spectral reflectivity curves respectively, and complete spectral reflection and absorption data can be established.

[0062] The spectral feature extraction submodule extracts the spectral features of each wavelength range based on the spectral reflection and absorption data, calculates the spectral feature peaks and their distribution, and screens the key spectral feature points using the formula:

[0063]

[0064] Get the spectral characteristic difference value T f , where f peak (k) represents the intensity of the kth characteristic peak, f total represents the sum of all peak intensities, and n represents the number of spectral feature points;

[0065] First, define the recognition criteria of the characteristic peak, for the local maximum point P in the reflectivity curvek , set the threshold condition, that is, if P k Satisfy R k >R k-1 , and R k >R k+1 , and R k If the value is greater than 0.8, it is defined as a characteristic peak point. Further statistical analysis is performed on the extracted characteristic peak points to calculate the normalized intensity f of each characteristic peak point. peak (k), defines its ratio f relative to all peak intensities peak (k) / f total , if a composite plate has characteristic peaks at five wavelength intervals (650nm, 720nm, 850nm, 900nm and 950nm), their normalized intensities are f peak =[0.12,0.08,0.30,0.25,0.25], calculate the total intensity:

[0066] f total =0.12+0.08+0.30+0.25+0.25=1.0;

[0067] Substitute into the formula to calculate:

[0068] T f =(0.12 / 1.0) 2 +(0.08 / 1.0) 2 +(0.30 / 1.0) 2 +(0.25 / 1.0) 2 +(0.25 / 1.0) 2 ;

[0069] =0.0144+0.0064+0.09+0.0625+0.0625=0.2358;

[0070] The results show that the spectral characteristic index of the composite plate is high and its spectral characteristic peak is relatively concentrated, indicating that the material belongs to the high absorption category and is suitable for the design of energy absorption layers in optical applications.

[0071] The material spectrum mapping construction submodule compares the spectral feature difference values ​​with the spectral data of known composite plate types, analyzes the spectral feature similarity between each category, screens matching known material categories, establishes spectral feature mapping relationships between material categories, and obtains spectral characteristic index information;

[0072] Call the spectral data of known composite board types and compare them with the spectral characteristic values ​​of the material to be tested. First, build a spectral characteristic database, which contains the spectral characteristic curves and known spectral characteristic indexes of different types of composite boards. Compare the spectral characteristic index of the board to be tested with the standard sample in the database, calculate the spectral characteristic similarity, and set the similarity calculation standard. If the characteristic index T of the board to be tested is f With a known material type T f,ref The difference between them is less than the set threshold ΔT f =0.05, then the two are considered to belong to similar categories, otherwise they are classified into independent categories. Further, by analyzing the characteristic peak distribution of the tested plate, matching it with the known material categories in the database, and screening the most consistent known material category. For example, if the spectral characteristic peak of a certain tested plate is concentrated in 800nm-900nm, and its spectral characteristic index T f =0.22, matching the reference spectrum characteristic index T of carbon fiber reinforced composite materials f,ref =0.23, it can be determined that the material has similar properties to the carbon fiber reinforced material, and the spectral feature mapping relationship of the material category is established to obtain the spectral feature index information.

[0073] See also Figure 3 , cutting parameter optimization module includes:

[0074] The optimal cutting parameter screening submodule obtains the spectral characteristics of the target plate based on the spectral characteristic index information, and screens the cutting effect of each type of material under different cutting conditions using the formula:

[0075]

[0076] Calculate the SE score of the cutting parameters c , and select the parameter with the highest score as the optimal cutting parameter, where PE i Represents the cutting power of the i-th matching material, PE max Represents the maximum cutting power in all matching materials, VE i Represents the cutting speed of the i-th matching material, VE max Represents the maximum cutting speed among all matching materials, θe i represents the cutting angle of the i-th matching material, θe max Represents the maximum cutting angle among all matching materials, n SE Represents the number of materials in the matching list of similar materials;

[0077] First, determine the source of the spectral data of the target plate. This can be detected directly through spectral measurement equipment or by calling spectral characteristic index information from an existing database. For different material types, such as glass fiber reinforced composites, carbon fiber composites, etc., extract the spectral characteristics within a specific wavelength range, select the key spectral peak interval, and then obtain the spectral reflectance or absorptivity of the target plate in the interval. After obtaining the spectral characteristics, it is necessary to match and screen the cutting parameters of different materials. In the cutting database, the experimental data of each material under different cutting conditions are stored, including the influence of power, speed and angle on cutting quality. Therefore, in order to quantitatively evaluate the cutting effect of different materials, It is necessary to define an evaluation standard and set a cutting quality scoring standard, such as the cutting surface roughness is less than 0.5μm, the incision verticality error is less than 2°, etc., and use the parameters as the reference benchmark value of the cutting quality. The experimental data of each type of material under different cutting conditions are normalized for unified comparison. After selecting the evaluation index, it is necessary to calculate the score under different cutting parameters. The scoring standard can be calculated based on factors such as the edge cleanliness of the material after cutting, the range of the heat-affected zone, and the cutting efficiency, so as to comprehensively evaluate the pros and cons of different parameters. If there are 3 similar materials in the matching list, their cutting powers are 200W, 250W, and 300W, respectively, where the maximum power PE max =300W, cutting speeds are 50mm / s, 55mm / s and 60mm / s respectively, with the maximum speed VE max =60mm / s, the cutting angles are 30°, 35° and 40° respectively, with the maximum angle θe max =40°, substitute into the formula to calculate:

[0078]

[0079] Calculate separately:

[0080]

[0081]

[0082] The results showed that SE c3 =3.0000 is the highest, so the corresponding optimal cutting parameter set is selected, that is, cutting power 300W, cutting speed 60mm / s, and cutting angle 40° as the optimal parameters, which can be used for parameter adjustment of subsequent equipment to optimize cutting quality and improve production efficiency.

[0083] The cutting parameter adjustment submodule compares the deviation between the power, cutting speed, and cutting angle of the current cutting equipment and the optimal cutting parameters based on the optimal cutting parameters, determines the parameter adjustment amount, and adjusts the operating parameters of the cutting equipment to obtain the cutting parameter configuration;

[0084] Compare the deviations between the current cutting equipment's power, cutting speed, and cutting angle and the optimal cutting parameters, and calculate the parameter adjustment amount. For example, if the current equipment's cutting power is 280W, and the power in the optimal cutting parameter set is 300W, the adjustment amount is 20W. If the current cutting speed is 50mm / s, and the optimal cutting parameter is 60mm / s, the adjustment amount is 10mm / s. After calculation, the complete parameter adjustment amount is obtained, and the equipment operating parameters are adjusted according to the calculation results to obtain the cutting parameter configuration.

[0085] See also Figure 4 , cutting quality optimization module includes:

[0086] The cutting energy monitoring submodule monitors the energy input, cutting speed, cutting depth and edge flatness data during the cutting process based on the cutting parameter configuration. It calculates the ratio of energy input to cutting speed, analyzes the effect of unit energy on cutting depth, and compares the change in cutting depth with the standard cutting depth of the target plate to obtain the cutting energy input deviation value.

[0087] Monitor the energy input, cutting speed, cutting depth and edge flatness data during the cutting process to obtain real-time cutting energy input. This parameter can be obtained through a laser power sensor or the energy consumption monitoring module of a plasma cutting machine. If the input power at a certain moment is 3000W, then obtain the cutting speed. This value can be read by the CNC system. For example, if the current cutting speed is set to 1.2m / min, then obtain the cutting depth. This can be measured by a laser rangefinder or a contact probe. If the current cutting depth is 8mm, the edge flatness can be captured by a camera and its deviation range can be calculated through image processing. The edge deviation is controlled within 0.2mm. Then, calculate the change in cutting depth per unit energy input. The calculation formula is as follows: Among them, E unit Represents the cutting energy input per unit length, E in is the cutting input power, S cut For cutting speed, substitute specific values: Then, compare the change in cutting depth under unit energy input with the standard cutting depth of the target plate. Set the standard cutting depth of the target plate to 7.5mm and calculate the change in cutting depth: ΔD = D cut -D std =8mm-7.5mm=0.5mm. The result shows that the cutting depth deviation caused by the current cutting energy input is 0.5mm. The cutting energy input deviation value is obtained.

[0088] The cutting deviation analysis submodule analyzes the deviation between the current cutting parameters and the target plate cutting quality standard based on the cutting energy input deviation value, using the formula:

[0089]

[0090] Get the cutting parameter deviation correction value ΔFG, where Pg i Represents the value of the current i-th cutting parameter, such as cutting depth, edge flatness, etc., Pg std Represents the standard value of the corresponding cutting parameter, n FG Represents the total number of cutting parameters;

[0091] Analyze the deviation between the current cutting parameters and the target plate cutting quality standard, obtain all key parameters that affect the cutting quality, such as cutting depth, edge flatness, etc. The data is obtained from the previous submodule. Secondly, calculate the deviation degree of all parameters, for example, cutting depth 8mm, edge flatness 0.2mm, Pg std Represents the target standard value, such as cutting depth 7.5mm, edge flatness 0.15mm, n FG Represents the total number of parameters, which is set to 2 here. Substitute the values ​​for calculation:

[0092]

[0093] The result shows that the overall deviation value of the current cutting parameters is 0.5, and the cutting parameter deviation correction value is obtained.

[0094] The cutting accuracy correction submodule adjusts the cutting parameters based on the cutting parameter deviation correction value, recalculates the variation range of cutting speed, cutting depth and edge flatness, and compares it again with the target plate cutting quality standard to determine the corrected error range and obtain the cutting accuracy correction index;

[0095] Based on the cutting parameter deviation correction value, adjust the cutting parameters, recalculate the variation range of cutting speed, cutting depth and edge flatness, and compare again with the target plate cutting quality standard. First, call the cutting parameter deviation correction value to determine the specific parameters that need to be adjusted. For example, if the cutting depth deviation is 0.5mm, adjust the cutting power or cutting speed to reduce the deviation. If the adjusted cutting power is 2900W and the cutting speed is adjusted to 1.25m / min, recalculate the unit energy input: Next, recalculate the cutting depth. Assuming that the influence factor of energy input on cutting depth is 0.003mm / (W·min / m), the adjusted cutting depth is:

[0096] D cut,new =D cut -(E unit -E unit,new )×0.003;

[0097] D cut,new=8mm-(2500-2320)×0.003;

[0098] D cut,new =8mm-0.54mm=7.46mm;

[0099] The results show that the adjusted cutting depth is 7.46 mm, and the deviation from the standard value of 7.5 mm is reduced to 0.04 mm, and the cutting accuracy correction index is finally obtained.

[0100] See also Figure 5 , the waste material utilization optimization module includes:

[0101] The matching degree calculation submodule analyzes the material utilization data of the cutting process based on the cutting accuracy correction index, and screens the size, shape and thickness of the remaining material of the plate to be processed on the production line. The formula is:

[0102]

[0103] Calculate the matching degree MY between the remaining material and the new required material, where Dy i is the size of the i-th remnant, which refers to the specific measurement value of the remnant such as length, width or area, Dy t is the size of the new required material, Sy i is the shape of the i-th residual material, which refers to the characteristic value of the geometric shape of the residual material (such as rectangle, triangle, etc.), Sy t Is the shape of the new demand material, Ty i is the thickness of the i-th residual material, Ty t is the thickness of the new required material, n my Indicates the amount of remaining material;

[0104] Obtain the cutting history data of the plate to be processed, including the actual cutting size, cutting deviation, residual size of the residual material, etc., and calculate the material utilization rate based on this. The material utilization rate can be obtained by (total input material area-residual residual area) / total input material area. For example, if the area of ​​an input plate is 10㎡ and the residual area after cutting is 2.5㎡, the utilization rate of the plate is calculated as (10-2.5) / 10=75%. When the utilization rate is lower than the preset threshold, such as the set threshold is 70%, it is necessary to optimize the residual material utilization method. Then, screen the size, shape and thickness of the residual material to be processed. For the size, measure the aspect ratio and area of ​​the residual material and compare them with the new required material. For example, the size of the new required material is 3 m×2m, and the size of the surplus material is 3.2m×2.1m, then the surplus material can preliminarily meet the requirements. For shape, the similarity between the surplus material and the target material is evaluated by shape matching calculation. Shape matching can be calculated based on morphological deviation. If the new required material is rectangular and the surplus material shape is a regular rectangle, the deviation value is 0. If the surplus material is approximately rectangular but has a small gap, the deviation value is 0.05-0.1. According to the set shape deviation threshold, such as 0.1, if the deviation exceeds this value, the surplus material is not applicable. For thickness, measure and obtain the surplus material thickness. For example, if the target material thickness is 5mm, the surplus material thickness range of 4.8mm-5.2mm is acceptable. Filter the surplus materials that meet the standards and obtain the filtered surplus material set.

[0105] Call the filter remnant set, analyze the size, shape, and thickness matching between each remnant and the new required material, and calculate the comprehensive matching degree. The matching degree is calculated based on the relative deviation between the remnant and the new required material in size, shape, and thickness. If there are currently 3 remnants, their parameters are as follows:

[0106] Residue 1: Dy1 = 3.1m × 2.05m, Sy1 = 0.08, Ty1 = 4.9mm;

[0107] Residue 2: Dy2 = 3.05m × 2.02m, Sy2 = 0.06, Ty2 = 5.1mm;

[0108] Residue 3: Dy3 = 3.2m × 2.1m, Sy3 = 0.09, Ty3 = 5.2mm;

[0109] Set new required material parameters:

[0110] Dy t =3m×2m, Sy t =0.05, Ty t =5mm;

[0111] Calculate the deviation of each residual material:

[0112]

[0113] The calculated matching index values ​​are ranked as follows: Residue 2 < Residue 1 < Residue 3, that is, the optimal matching residue is Residue 2. This result indicates that the residue with the lowest matching degree is given priority for placement to optimize material utilization.

[0114] The placement sequence adjustment submodule selects the best matching residual material for the next processing based on the residual material matching degree, and adjusts the material placement sequence to obtain the residual material matching index.

[0115] Call the surplus material matching data, sort based on the matching index value, select the surplus material with the best matching degree, and adjust the material delivery order. When sorting, give priority to the surplus material with the lowest matching degree MY value, and consider the actual processing needs. If the MY value of a piece of surplus material is the lowest, but the current process cannot process the surplus material, then select the second best surplus material and calculate the delivery order. If the matching degree of surplus material 1 is 0.02, the matching degree of surplus material 2 is 0.04, and the matching degree of surplus material 3 is 0.05, then after sorting, surplus material 1, surplus material 2, and surplus material 3 are selected in turn for delivery, and the optimized surplus material delivery sequence is obtained.

[0116] See also Figure 6 , the sheet coating adjustment module includes:

[0117] The surface quality analysis submodule uses the residual material matching index to analyze the surface quality of the sprayed plate, monitor the surface finish, coating adhesion and micro-defect distribution, measure the surface uniformity based on the spectral reflectance, judge the surface quality differences of the plate, and obtain the surface spectral uniformity parameters;

[0118] Monitor the surface finish of the plate to be sprayed, and use a laser scanner to measure the surface roughness. The roughness can be quantified by the Ra value (arithmetic mean roughness). If the surface Ra value is 0.5μm, it can be classified as a smooth surface. If it exceeds 2.0μm, pretreatment is required to reduce the roughness. Next, monitor the coating adhesion rate, and evaluate the bonding strength between the coating and the plate substrate through a pull-out test. If the pull-out force is less than 2MPa, the coating will peel off. Then analyze the distribution of microscopic defects on the plate surface, and use an electron microscope to detect surface cracks, bubbles and other defects, and calculate the defect area ratio. If the defect rate is greater than 5%, the pretreatment process needs to be adjusted. The surface uniformity is measured based on the spectral reflectance. The reflectance spectrum data in the 400nm to 700nm band is used to calculate the mean reflectance of each measurement point and the standard deviation. If the deviation exceeds 0.1, the surface uniformity is poor, which affects subsequent spraying. The surface quality difference of the plate is judged and the surface spectral uniformity parameters are obtained.

[0119] The spray deviation calculation submodule monitors the coating thickness uniformity during the spraying process based on the surface spectral uniformity parameter, using the formula:

[0120]

[0121] Calculate the spray thickness deviation at each measuring point to obtain the spray uniformity deviation value DX c , where TX i is the coating thickness at the i-th measurement point, TX avg is the average coating thickness of all measurement points, N dx is the total number of measurement points;

[0122] Call the surface spectrum uniformity parameter to monitor the coating thickness uniformity during the spraying process. During the spraying process, select 3 measurement points, measure the coating thickness data at each point, and calculate the average thickness. For example, the thickness measured on the plate after spraying is:

[0123] Measuring point 1 is 50 μm, measuring point 2 is 45 μm, and measuring point 3 is 48 μm;

[0124] Calculate the average thickness:

[0125]

[0126] Substitute the measured data for calculation:

[0127]

[0128] Calculated spray uniformity deviation value DX c =2.05 represents the discrete degree of spraying thickness. If DX c If the spraying thickness is lower than 2.0μm, it indicates that the spraying thickness uniformity is good and the spraying parameters can remain unchanged. c If the threshold is exceeded, it means that the coating thickness fluctuates greatly and the spraying parameters need to be optimized to reduce the local thickness deviation.

[0129] The spray parameter optimization submodule adjusts the current spray speed, nozzle angle, and spray pressure based on the spray uniformity deviation value, determines the current spray uniformity, and optimizes the nozzle movement trajectory and spray flow distribution to obtain the optimized result of the plate spray quality;

[0130] Adjust the current spraying speed, nozzle angle and spraying pressure, compare the thickness deviation data of each measuring point, and judge the current spraying uniformity. If the deviation exceeds 2.0μm, the spraying parameters need to be adjusted. When adjusting, first reduce the spraying speed. If the current speed is 1.5m / s, it can be reduced to 1.2m / s. Secondly, optimize the nozzle angle. If the current angle is 45°, it can be adjusted to 50° to optimize the spray coverage area. Finally, adjust the spraying pressure. If the current pressure is 2.8bar, it can be reduced to 2.5bar to reduce overspray. Finally, optimize the nozzle movement trajectory and spray flow distribution to obtain the plate spraying quality optimization result.

[0131] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An environmentally friendly composite plate processing system, characterized in that: The system comprises: The spectral feature analysis module collects spectral reflectance data of composite board raw materials, identifies similar material characteristics, analyzes spectral absorptivity, reflectivity and peak distribution, classifies material categories, and obtains spectral feature index information; The cutting parameter optimization module compares the power, cutting speed, and cutting angle of the current cutting device with the optimal cutting parameters based on the spectral characteristic index information, adjusts the operating parameters of the cutting device, and obtains the cutting parameter configuration; The cutting quality optimization module monitors the energy input, cutting speed, cutting depth and edge flatness during the cutting process based on the cutting parameter configuration, recalculates the deviation correction value of the adjusted cutting parameters, and obtains the cutting accuracy correction index; The waste material utilization optimization module screens the waste material size, shape and thickness of the plate based on the cutting accuracy correction index, calculates the matching degree between the waste material and the new required material, adjusts the material delivery order, and obtains the waste material matching index; The plate coating adjustment module calls the residual material matching index, analyzes the surface quality of the plate to be sprayed, monitors the spectral reflectance and coating thickness uniformity, and adjusts the current spraying speed, nozzle angle and spraying pressure to obtain the plate spraying quality optimization result.

2. The environmentally friendly composite plate processing system according to claim 1, characterized in that: The spectral characteristic index information includes wavelength characteristics, material classification, and absorption peak; the cutting parameter configuration includes power level, speed setting, and angle selection results; the cutting accuracy correction index includes error analysis results and quality rating; the residual material matching index includes size adaptability, shape compatibility, and material efficiency; the plate spraying quality optimization result includes spraying rate, nozzle adjustment result, and coating uniformity.

3. The environmentally friendly composite plate processing system according to claim 1, characterized in that: The spectral feature analysis module includes: The spectral reflectance data acquisition submodule collects the spectral reflectance data of the composite board raw materials. For different board types, it detects the spectral reflectance in multiple wavelength ranges, records the spectral reflectance intensity of each wavelength, and calculates the spectral absorbance to obtain the spectral reflectance and absorption data. The spectral feature extraction submodule extracts the spectral features of each wavelength interval based on the spectral reflection and absorption data, calculates the spectral feature peaks and their distribution, and screens key spectral feature points using the formula: Get the spectral characteristic difference value T f , where f peak (k) represents the intensity of the kth characteristic peak, f total represents the sum of all peak intensities, and n represents the number of spectral feature points; The material spectrum mapping construction submodule compares the spectral feature difference values ​​with the spectral data of known composite plate types, analyzes the spectral feature similarity between each category, screens matching known material categories, establishes spectral feature mapping relationships between material categories, and obtains spectral characteristic index information.

4. The environmentally friendly composite plate processing system according to claim 1, characterized in that: The cutting parameter optimization module includes: The optimal cutting parameter screening submodule obtains the spectral characteristics of the target plate according to the spectral characteristic index information, and screens the cutting effect of each type of material under different cutting conditions using the formula: Calculate the SE score of the cutting parameters c , and select the parameter with the highest score as the optimal cutting parameter, where PE i Represents the cutting power of the i-th matching material, PE max Represents the maximum cutting power in all matching materials, VE i Represents the cutting speed of the i-th matching material, VE max Represents the maximum cutting speed among all matching materials, θe i represents the cutting angle of the i-th matching material, θe max Represents the maximum cutting angle among all matching materials, n SE Represents the number of materials in the matching list of similar materials; The cutting parameter adjustment submodule compares the deviation between the power, cutting speed and cutting angle of the current cutting equipment and the optimal cutting parameters based on the optimal cutting parameters, determines the parameter adjustment amount, and adjusts the operating parameters of the cutting equipment to obtain the cutting parameter configuration.

5. The environmentally friendly composite plate processing system according to claim 1, characterized in that: The cutting quality optimization module includes: The cutting energy monitoring submodule monitors the energy input, cutting speed, cutting depth and edge flatness data during the cutting process based on the cutting parameter configuration, calculates the ratio of energy input to cutting speed, analyzes the effect of unit energy on cutting depth, and compares the change in cutting depth with the standard cutting depth of the target plate to obtain the cutting energy input deviation value; The cutting deviation analysis submodule analyzes the deviation between the current cutting parameters and the target plate cutting quality standard based on the cutting energy input deviation value, using the formula: Get the cutting parameter deviation correction value ΔFG, where Pg i Represents the value of the current i-th cutting parameter, Pg std Represents the standard value of the corresponding cutting parameter, n FG Represents the total number of cutting parameters; The cutting accuracy correction submodule adjusts the cutting parameters based on the cutting parameter deviation correction value, recalculates the variation range of cutting speed, cutting depth and edge flatness, and compares the target plate cutting quality standard again to determine the corrected error range and obtain the cutting accuracy correction index.

6. The environmentally friendly composite plate processing system according to claim 1, characterized in that: The waste material utilization optimization module includes: The matching degree calculation submodule analyzes the material utilization data of the cutting process based on the cutting accuracy correction index, and screens the size, shape and thickness of the remaining material of the plate to be processed on the production line, using the formula: Calculate the matching degree MY between the remaining material and the new required material, where Dy i is the size of the i-th residual material, Dy t is the size of the new required material, Sy i is the shape of the i-th residual material, Sy t Is the shape of the new demand material, Ty i is the thickness of the i-th residual material, Ty t is the thickness of the new required material, n my Indicates the amount of remaining material; The placing sequence adjustment submodule selects the remaining material with the best matching degree for the next processing based on the remaining material matching degree, and adjusts the material placing sequence to obtain the remaining material matching index.

7. The environmentally friendly composite plate processing system according to claim 1, characterized in that: The plate coating adjustment module includes: The surface quality analysis submodule uses the residual material matching index to analyze the surface quality of the plate to be sprayed, monitors the surface finish, coating adhesion and micro defect distribution, measures the surface uniformity based on the spectral reflectance, judges the surface quality difference of the plate, and obtains the surface spectral uniformity parameter; The spraying deviation calculation submodule monitors the coating thickness uniformity during the spraying process based on the surface spectral uniformity parameter, using the formula: Calculate the spray thickness deviation at each measuring point to obtain the spray uniformity deviation value DX c , where TX i is the coating thickness at the i-th measurement point, TX avg is the average coating thickness of all measurement points, N dx is the total number of measurement points; The spraying parameter optimization submodule adjusts the current spraying speed, nozzle angle and spraying pressure based on the spraying uniformity deviation value, determines the current spraying uniformity, and optimizes the nozzle movement trajectory and spraying flow distribution to obtain the plate spraying quality optimization result.

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