A method for optimizing a coil of a planar coil type ferrous metal detection system

Abstract

The present application relates to the technical field of magnetic metal detection, and particularly relates to a coil optimization method of a planar coil type ferromagnetic metal detection system; the present scheme is based on virtual simulation and algorithm optimization, and a more convenient and reliable coil optimization method of the ferromagnetic metal detection system is designed; the coil parameters can be optimized according to actual conditions, and more suitable coil performance is obtained; the present scheme adopts a virtual simulation test method to simulate and analyze the coil, and a multi-objective genetic algorithm is combined to optimize the inner diameter and the outer diameter, and other coil parameters are optimized in combination with the simulation results; the multi-objective genetic algorithm is adopted to optimize the inner diameter and the outer diameter of the coil; since there are many influencing factors and evaluation indexes of the coil, it is difficult to find an optimal solution; the method can consider more evaluation indexes, the optimization result is more practical, and the research and development time and the labor cost are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of magnetic metal detection technology, and more specifically to a coil optimization method for a planar coil-type ferromagnetic metal detection system. Background Technology

[0002] Silage is a type of feed obtained by controlling the fermentation process of corn stalks, grass, etc., under sealed, anaerobic conditions using silage technology. This technology allows green forage to retain its green and juicy characteristics and to be stored for a long time, saving feed costs and adjusting the forage supply cycle to alleviate forage shortages. With the expansion of silage planting areas, silage harvesters are being used more and more widely. During operation, ferromagnetic metal objects such as wires left in the field can easily mix into the forage, causing damage to machine parts. Furthermore, consuming feed containing metal can threaten the health of cattle and sheep. Therefore, more and more silage harvester manufacturers are installing metal detectors on their machines.

[0003] Most metal detectors for silage harvesters utilize eddy current detection technology, with the detection coil being the core component that directly affects its performance. Factors influencing the detection coil include its shape, inner diameter, outer diameter, number of layers, wire diameter, and conductor material. Key performance indicators (KPIs) include magnetic flux density, Q-value (the coil's quality factor), inductance, and resistance. It's fair to say that with current technology, it's difficult to optimize these indicators simultaneously. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a coil optimization method for a planar coil-type ferromagnetic metal detection system, which can select the optimal coil parameters and improve coil performance.

[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: the coil optimization method of the planar coil type ferromagnetic metal detection system, characterized in that the method includes the following steps:

[0006] (1) Determine the initial coil parameters according to the actual installation requirements and establish a planar single-layer coil model in COMSOL;

[0007] (2) Set the coil material to copper, the measured object material to iron, the solution domain to air, and set the excitation voltage / current and excitation frequency according to the actual installation requirements;

[0008] (3) Keep other parameters of the coil unchanged, change the wire diameter, extract the change in the magnetic flux density mode of the coil, calculate the inductance, resistance, Q value and eddy current loss of the test object, and determine the wire diameter by comparison and cost.

[0009] Different wire materials are selected based on the actual situation, and simulation calculations are performed to extract the change in the magnetic flux density mode of the coil. The inductance, resistance, Q value, resistance sensitivity, inductance sensitivity, and eddy current loss of the tested object are calculated. By comparison and in combination with material cost, the coil material is determined.

[0010] (4) Keep other coil parameters constant, change the excitation frequency, extract the change in magnetic flux density mode, calculate the inductance, resistance, Q value, inductance sensitivity, resistance sensitivity and eddy current loss of the test object, and obtain the law of influence of frequency on coil performance; the optimal excitation frequency under the coil parameters can be obtained through the Q value change curve or resistance sensitivity change curve.

[0011] (5) Establish a multi-objective genetic algorithm to optimize the inner and outer diameter parameters of the coil, select the reciprocal of the Q value and the reciprocal of the magnetic induction intensity as the fitness function, and calculate the minimum fitness function, that is, the inner and outer diameter of the coil when the Q value and the magnetic induction intensity are maximized;

[0012] (6) Based on the determination of the inner and outer diameters of the coil, change the coil thickness, extract the change in magnetic flux density mode, calculate the inductance, resistance, Q value, inductance sensitivity, resistance sensitivity and eddy current loss of the object under test, and determine the number of coil layers.

[0013] (7) Based on the determined coil parameters, repeat step (4) to determine the optimal coil frequency.

[0014] Further, step (5) includes

[0015] (5.1) Establish a multi-objective optimization genetic algorithm in MATLAB, define the population size, set the crossover probability, and perform iteration;

[0016] (5.2) Establish the fitness function, derive the formula for the magnetic induction intensity and Q value of the coil, take its reciprocal, define the outer diameter of the coil as R1 and the inner diameter as R2, and find the inner and outer diameters of the coil when the fitness function reaches the optimal solution and the magnetic induction intensity and Q value are maximized through algorithm iteration calculation.

[0017] (5.3) Determine the constraints and determine the range of values ​​for the inner and outer diameters based on actual installation requirements.

[0018] Further, in step (1), a two-dimensional axisymmetric model is adopted, and an AC / DC module is used for solution analysis. The physical field is selected as magnetic field, the solver is selected as frequency domain solver, the frequency is set to 10kHz, the initial value of the model magnetic displacement is 0, the outer boundary of the solution domain is magnetically insulated by default, the coil is selected as a single wire model, the excitation method is selected as voltage excitation, the direction is perpendicular to the coil cross-section, and the magnitude is 5V.

[0019] Compared with existing technologies, the significant advantages of this solution are:

[0020] 1. Based on virtual simulation and algorithm optimization, this solution designs a more convenient and reliable coil optimization method for ferromagnetic metal detection systems. The coil parameters can be optimized according to actual needs to obtain more suitable coil performance.

[0021] 2. This scheme uses virtual simulation testing to simulate and analyze the coil, and combines it with a multi-objective genetic algorithm to optimize the inner and outer diameters, and optimizes other coil parameters based on the simulation results;

[0022] 3. This scheme uses a multi-objective genetic algorithm to optimize the inner and outer diameters of the coil. Since there are many influencing factors and evaluation indicators for the coil, this method can consider more evaluation indicators, making the optimization results more practical and effectively reducing R&D time and manpower costs. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 This is a flowchart of the optimization method described in this invention;

[0025] Figure 2 This is the coil simulation model in the embodiment;

[0026] Figure 3 This illustrates the changes in resistance and inductance of coils with different wire diameters in this embodiment;

[0027] Figure 4 This illustrates the Q-value variation for coils with different wire diameters in this embodiment.

[0028] Figure 5 This example illustrates the variation of magnetic flux density modulus in coils made of different materials.

[0029] Figure 6 This example illustrates the variation in coil resistance sensitivity at different frequencies.

[0030] Figure 7 This example illustrates the variation of Q-values ​​in coils at different frequencies.

[0031] Figure 8 This is the algorithm flowchart of NSGA-II in this embodiment;

[0032] Figure 9 This is the Pareto solution set in this embodiment;

[0033] Figure 10 This is to verify the optimization results of this embodiment;

[0034] Figure 11 The calculation results for different numbers of coil layers in this embodiment are shown.

[0035] Figure 12 To optimize the results, we studied the changes in coil resistance sensitivity at different frequencies.

[0036] Figure 13 This example compares the coil parameters of different conductor materials.

[0037] Figure 14 This is the final optimized result of this embodiment. Detailed Implementation

[0038] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0039] like Figure 1 As shown, the coil optimization design method of the planar coil type ferromagnetic metal detection system of the present invention, based on finite element simulation analysis and algorithm optimization, has completed the determination of coil parameters and ensured that the coil achieves optimal performance.

[0040] In this scheme, the signal processing module uses an LDC1000. The LDC1000 mainly operates by monitoring the power of the injected system. Therefore, when designing the coil, the resistance should be low to reduce the coil's own losses, while the eddy current losses of the measured object should be high. The Q value is the coil's quality factor; the higher the Q value, the lower the coil losses. Therefore, a high quality factor is desired when designing the coil. Sufficient inductance is required to ensure detection accuracy. In addition, a large magnetic flux density modulus and a smooth change in radial magnetic flux density modulus are desired. Inductive sensitivity and resistive sensitivity (the rate of change of inductance and resistance with and without the measured object) are defined, and both inductive and resistive sensitivities are desired to be high.

[0041] like Figure 1 As shown, specifically, this solution includes the following steps:

[0042] (1) Determine the initial coil parameters based on actual installation requirements, such as... Figure 2 As shown, a planar single-layer coil model is created in COMSOL.

[0043] (2) Set the coil material to copper, the test object material to iron, the solution domain to air, use two-dimensional axisymmetric modeling, use AC / DC module for solution analysis, select magnetic field as the physical field, select frequency domain solver, set the frequency to 10kHz, the initial value of the model magnetic displacement is 0, the outer boundary of the solution domain is magnetically insulated by default, the coil is selected as a single wire model, the excitation method is selected as voltage excitation, the direction is perpendicular to the coil cross-section, and the magnitude is 5V; to ensure that the calculation results are more realistic, the solution domain should be much larger than the coil model, and the excitation voltage or current and excitation frequency are set according to actual needs;

[0044] (3) Keeping other coil parameters constant, change the wire diameter, extract the change in coil flux density modulus, calculate inductance, resistance, Q value, and eddy current loss of the measured object, and determine the wire diameter by comparison and cost. To ensure coil performance, a certain inductance needs to be guaranteed. Due to the skin effect, the wire diameter cannot be too large. To avoid excessive coil resistance, the wire diameter cannot be too small either. Under the above principles, the wire diameter can be determined by combining simulation calculation results. The results are as follows: Figure 3 and Figure 4 As shown, in this embodiment, under the premise that other conditions remain unchanged, a parametric scan is performed with a wire diameter range of 0.1 to 1.0 mm and a step size of 0.1 mm to calculate the inductance, resistance, and Q value. Considering the influence of the skin effect, it is necessary to ensure that the coil has a certain inductance and low resistance; therefore, a wire diameter of 0.3 mm is selected.

[0045] Simulation calculation of conductor materials: In this embodiment, conventional materials silver, copper, aluminum, and platinum are selected for calculation. The change in magnetic flux density mode of the coil is extracted, and its inductance, resistance, Q value, resistance sensitivity, inductance sensitivity, and eddy current loss of the tested object are calculated. Through comparison, silver has the best performance, while copper is not significantly different from silver. Considering cost, copper can be selected as the conductor material. Figure 5 This indicates the variation of magnetic flux density modulus in coils made of different materials. Figure 13 This section compares the coil parameters of different conductor materials.

[0046] (4) Keep other parameters of the coil unchanged, change the excitation frequency, extract the change of magnetic flux density mode, calculate the inductance, resistance, Q value, inductance sensitivity, resistance sensitivity and eddy current loss of the object under test, and obtain the optimal excitation frequency under the coil parameters to obtain the law of influence of frequency on coil performance, and obtain the Q value change curve or resistance sensitivity change curve. Figure 6 and Figure 7 These are schematic diagrams showing the changes in coil resistance sensitivity and Q value at different frequencies.

[0047] In this embodiment, keeping other parameters constant, the frequency range is set to 0.1–10 MHz. Within the 0.1–1 MHz range, the step size is set to 0.1 MHz, and within the 1–10 MHz range, the step size is set to 1 MHz for parameterized scanning. Based on the changes in Q value and resistance sensitivity, the initial optimal frequency is determined to be 0.3 MHz.

[0048] (5) Establish a multi-objective genetic algorithm to optimize the inner and outer diameter parameters of the coil, select the reciprocal of the Q value and the reciprocal of the magnetic induction intensity as the fitness function, and calculate the minimum fitness function, that is, the inner and outer diameter of the coil when the Q value and the magnetic induction intensity are maximized;

[0049] Specifically, step (5) includes:

[0050] (5.1) Establish a multi-objective optimization genetic algorithm in MATLAB, define the population size, set the crossover probability, and perform iteration;

[0051] (5.2) Establish the fitness function, derive the formula for the magnetic induction intensity and Q value of the coil, take its reciprocal, define the outer diameter of the coil as R1 and the inner diameter as R2, and find the optimal solution of the fitness function through algorithm iteration, that is, the inner and outer diameters of the coil when the magnetic induction intensity and Q value are maximized;

[0052] (5.3) Determine the constraints and determine the range of values ​​for the inner and outer diameters based on actual installation requirements.

[0053] In the above steps, the Pareto solution set is calculated by the algorithm. Since the magnetic induction intensity has a greater impact on the inner and outer diameters when it is close to the end face of the coil, and the difference is smaller when it is far away from the end face of the coil, the metal is moved from far to near during the operation of the silage harvester. Therefore, the consideration of magnetic induction intensity should be reduced when selecting the optimal solution.

[0054] like Figure 8 As shown, in this embodiment, the inner and outer diameters of the coil are optimized using NSGA-II, and the fitness function is used as the reciprocal of the magnetic induction intensity and the Q value. The initial population size is set to 500, and the iteration stops after 200 generations. Figure 9 As shown, the Pareto solution set is finally obtained. Since the change in magnetic flux density is smaller when the distance from the coil end face is greater, the magnetic flux density is reduced accordingly. Figure 9 The central pentagram is used as the optimal inner and outer diameter. After rounding, six sets of inner and outer diameters are randomly selected, and simulation calculations are performed to verify the optimization results. Figure 10 The reference column filled with intersecting diagonal lines is the result of optimization.

[0055] (6) Based on the determined inner and outer diameters of the coil, the coil thickness is changed. Due to the increase in the number of coil layers, the optimal frequency will decrease due to the influence of the self-resonant frequency. Therefore, the simulation frequency should be reduced. The optimal frequency in step (4) is 0.3MHz. In this step, the frequency is selected as 0.1MHz. The Q value, inductive sensitivity, resistive sensitivity, and eddy current loss of the measured object are calculated, as well as the equivalent impedance after parallel connection with a 100pF capacitor. Since the detection module selected in this system is LDC1000, it is necessary to ensure that the equivalent impedance is within its measurement range. The calculation results are as follows: Figure 11 As shown, after comprehensive comparison, a coil layer count of 2 is selected.

[0056] (7) Based on the above optimization, repeat step (4). Since the change in Q value is the same as the change in resistance sensitivity, we only need to use the resistance sensitivity to obtain the optimal frequency. The final optimal frequency is determined to be 0.15MHz. Figure 12 To optimize the results, we studied the changes in coil resistance sensitivity at different frequencies.

[0057] (8) The final optimized result is as follows Figure 14 As shown.

[0058] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the coil of a planar coil-type ferromagnetic metal detection system, characterized in that, The method includes the following steps: (1) Determine the initial coil parameters according to the actual installation requirements and establish a planar single-layer coil model in COMSOL; (2) Set the coil material to copper, the measured object material to iron, the solution domain to air, and set the excitation voltage / current and excitation frequency according to the actual installation requirements; (3) Keep other parameters of the coil unchanged, change the wire diameter, extract the change in the magnetic flux density mode of the coil, calculate the inductance, resistance, Q value and eddy current loss of the test object, and determine the wire diameter by comparison and cost. Different wire materials are selected based on the actual situation, and simulation calculations are performed to extract the change in the magnetic flux density mode of the coil. The inductance, resistance, Q value, resistance sensitivity, inductance sensitivity, and eddy current loss of the tested object are calculated. By comparison and in combination with material cost, the coil material is determined. (4) Keep other coil parameters constant, change the excitation frequency, extract the change in magnetic flux density mode, calculate the inductance, resistance, Q value, inductance sensitivity, resistance sensitivity and eddy current loss of the test object, and obtain the law of influence of frequency on coil performance; the optimal excitation frequency under the coil parameters can be obtained through the Q value change curve or resistance sensitivity change curve. (5) Establish a multi-objective genetic algorithm to optimize the inner and outer diameter parameters of the coil, select the reciprocal of the Q value and the reciprocal of the magnetic induction intensity as the fitness function, and calculate the minimum fitness function, that is, the inner and outer diameter of the coil when the Q value and the magnetic induction intensity are maximized; (6) Based on the determination of the inner and outer diameters of the coil, change the coil thickness, extract the change in magnetic flux density mode, calculate the inductance, resistance, Q value, inductance sensitivity, resistance sensitivity and eddy current loss of the object under test, and determine the number of coil layers. (7) Based on the determined coil parameters, repeat step (4) to determine the optimal coil frequency.

2. The coil optimization method for a planar coil-type ferromagnetic metal detection system according to claim 1, characterized in that, Step (5) includes (5.1) Establish a multi-objective optimization genetic algorithm in MATLAB, define the population size, set the crossover probability, and perform iteration; (5.2) Establish the fitness function, derive the formula for the magnetic induction intensity and Q value of the coil, take its reciprocal, define the outer diameter of the coil as R1 and the inner diameter as R2, and find the optimal solution of the fitness function through algorithm iteration, that is, the inner and outer diameters of the coil when the magnetic induction intensity and Q value are maximized; (5.3) Determine the constraints and determine the range of values ​​for the inner and outer diameters based on actual installation requirements.

3. The coil optimization method for a planar coil-type ferromagnetic metal detection system according to claim 1, characterized in that, In steps (1) and (2), a two-dimensional axisymmetric model is used, and the AC / DC module is used for solution analysis. The physical field is selected as magnetic field, the solver is selected as frequency domain solver, the frequency is set to 10kHz, the initial value of the model magnetic displacement is 0, the outer boundary of the solution domain is magnetically insulated by default, the coil is selected as a single wire model, the excitation method is selected as voltage excitation, the direction is perpendicular to the cross-section of the coil, and the magnitude is 5V.

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