CN112222671A - Fuzzy algorithm-based composite solder component design method and system - Google Patents
Fuzzy algorithm-based composite solder component design method and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K35/00—Rods, electrodes, materials, or media, for use in soldering, welding, or cutting
- B23K35/22—Rods, electrodes, materials, or media, for use in soldering, welding, or cutting characterised by the composition or nature of the material
- B23K35/24—Selection of soldering or welding materials proper
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- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K35/00—Rods, electrodes, materials, or media, for use in soldering, welding, or cutting
- B23K35/40—Making wire or rods for soldering or welding
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Abstract
The invention relates to a method and a system for designing components of a composite brazing filler metal based on a fuzzy algorithm, wherein the method comprises the following steps: setting the volume fraction change range and the initial volume fraction of each metal component in the composite solder, and setting the volume fraction threshold of the reinforced particles; calculating the coefficient of thermal expansion CTE1 of the composite solder under the state of each metal component volume fraction obtained in the previous step; obtaining an expansion coefficient CTE0 of a target high-silicon aluminum alloy base material, and obtaining a brazing joint quality judgment difference D based on a fuzzy algorithm; and increasing the volume fraction of the reinforcing particles, calculating the thermal expansion coefficient CTEC of the composite solder at the moment, judging whether the CTEC-CTE0< D is established, and if so, outputting the volume fractions of the metal components and the reinforcing particles at the moment as components. The invention can quickly obtain the composite brazing filler metal component matched with the thermal expansion coefficient of the target high-silicon aluminum alloy base metal, and avoids the problems that the joint after connection has higher residual stress and the joint strength is reduced; and screening out the most economical brazing filler metal component with the best effect.
Description
Technical Field
The invention relates to the technical field of brazing filler metal component design, in particular to a composite brazing filler metal component design method and system based on a fuzzy algorithm.
Background
The high-silicon aluminum alloy is a binary alloy consisting of silicon and aluminum, and is an alloy material mainly used for aerospace, space technology and portable electronic devices. The material has the advantages of good thermal conductivity, high strength and rigidity, good plating performance with gold, silver, copper and nickel, weldability with a base material, easy precision machining and the like, is an electronic packaging material with wide application prospect, and is particularly suitable for the high-tech fields of aerospace, space technology, portable electronic devices and the like.
Brazing is an important connection mode with better effect in the high-silicon aluminum alloy connection technology, and is a welding technology which adopts brazing filler metal with the melting temperature lower than that of a base metal and the welding temperature between the solidus line of the base metal and the liquidus line of the brazing filler metal.
In the soft solder suitable for welding the high-silicon aluminum alloy, the Sn-Ag-Cu series solder has the advantages of high soldering temperature, full diffusion of the solder to one side of a base metal, good connection strength and toughness and wide application range. Because the high-silicon aluminum alloy contains the hard silicon phase, the thermal expansion coefficient of the hard silicon phase is lower, the thermal expansion coefficient of the metal solder is higher, and the difference between the thermal expansion coefficients of the metal solder and the high-silicon aluminum alloy material is larger, the connected joint has higher residual stress, and the joint strength is reduced; during the service period of the packaging shell, multiple heating and cooling cycles can even cause thermal stress fatigue to cause the failure of the joint. Therefore, rapidly obtaining solder components matched with the thermal expansion coefficient of the high-silicon aluminum alloy material so as to improve the quality of the soldered joint is one of the most important research targets in the field.
Disclosure of Invention
The invention aims to provide a composite solder component design method for quickly obtaining a composite solder component matched with a high-silicon aluminum alloy thermal expansion coefficient.
In order to achieve the aim, the invention discloses a composite solder component design method based on a fuzzy algorithm, which comprises the following steps:
s1, setting the volume fraction change range and the initial volume fraction of each metal component in the composite solder, and setting the volume fraction threshold of reinforced particles in the composite solder;
s2, calculating the coefficient of thermal expansion CTE1 of the composite solder in the state of the volume fraction of each metal component obtained in the previous step;
s3, obtaining an expansion coefficient CTE0 of the target high-silicon aluminum alloy base material, and obtaining a brazing joint quality judgment difference D based on a fuzzy algorithm;
and S4, increasing the volume fraction of the reinforcing particles, calculating the thermal expansion coefficient CTEC of the composite solder at the moment, judging whether the CTEC-CTE0< D is true, and if so, outputting the volume fractions of the metal components and the reinforcing particles as components.
Further, the method also comprises the following steps:
s5, repeating the step S4 until the volume fraction of the enhanced particles is larger than a set threshold value;
and S6, adjusting the volume fraction of each metal component, and repeating the steps S2-S5 until the volume fraction of each metal component exceeds a set variation range.
Further, the method also comprises the following steps:
and S7, optimizing all the output components to obtain the optimal component of the composite solder.
Further, the method for obtaining the quality judgment difference D of the brazed joint by the fuzzy algorithm in the step S3 includes:
step S301, taking the difference value of the CTE0 and the CTE1 as a fuzzy input variable e;
step S302, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: the error is extremely small, medium and large, the error is large, and the error is extremely large; the triangular membership functions of which are respectively expressed as f1、f2、f3、f4、f5、f6、f7、f8The expression is as follows:
wherein, i is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when i is 1, a is-1, b is 0, and c is 1;
when i is 2, a is 1, b is 3, c is 5;
when i is 3, a is 3, b is 5, c is 7;
when i is 4, a is 5, b is 7, c is 9;
when i is 5, a is 7, b is 9, c is 11;
when i is 6, a is 9, b is 11, c is 13;
when i is 7, a is 11, b is 14, c is 17;
when i is 8, a is 13, b is 17, c is 25;
step S303, defining error threshold values T of 8 fuzzy subsetsiIs the barycentric coordinate of the triangular membership function image;
step S304, calculating a brazing joint quality judgment difference D,
further, the method for increasing the volume fraction of the reinforcing particles in step S4 is:
step S401, taking the difference value of the CTE0 and the CTE1 as a fuzzy input variable e;
step S402, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: extremely small, very small, medium, large, very large; the triangular membership functions are respectively expressed as g1、g2、g3、g4、g5、g6、g7、g8The expression is as follows:
wherein j is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when j is 1, a is-4, b is 0, c is 4;
when j is 2, a is-2, b is 2, and c is 6;
when j is 3, a is 0, b is 4, c is 8;
when j is 4, a is 2, b is 6, c is 10;
when j is 5, a is 4, b is 8, c is 12;
when j is 6, a is 6, b is 10, c is 14;
when j is 7, a is 8, b is 12, c is 16;
when j is 8, a is 10, b is 14, c is 21;
step S403. defining corresponding traversal step L of 8 fuzzy subsetsjSequentially comprises the following steps: 0.8%, 1.1%, 1.5%, 2%, 3%, 4%, 5%, 7%;
s404, calculating the volume fraction traversal step length L of the enhanced particlesrecycle，
Further, the method for adjusting the volume fraction of each metal component in step S6 is as follows:
nested loops are used to adjust the volume fraction of each metal component:
in the inner circulation, the volume fraction of the metal Sn is kept unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
in the outer layer circulation, the volume fraction of the metal Sn is reduced by one traversal step length L from the maximum valuerecycleAnd executing inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof.
Further, the optimization is carried out in all output components, and the method for obtaining the optimal component of the composite solder comprises the following steps:
the components of the composite solder are optimized through conditions of ternary phase diagram, soldering temperature, cost, wettability and the like.
The invention also provides a fuzzy algorithm-based composite solder component design system, which is characterized by comprising the following steps: the system comprises an initialization module, a nested loop module and an optimization module;
the initialization module is set to set the volume fraction change range and the initial volume fraction of each metal component in the composite solder, set the volume fraction threshold of the reinforced particles in the composite solder and obtain the expansion coefficient CTE0 of the target high-silicon aluminum alloy base material;
the nested circulation module is set to adopt nested circulation to adjust the volume fraction of each metal component, traverse the volume fraction change of the metal components and the reinforced particles and obtain the thermal expansion coefficient CTEC of the composite solder;
the optimization module is set to obtain a brazing joint quality judgment difference D based on a fuzzy algorithm, judge whether CTEC-CTE0< D is true, if yes, output the volume fractions of all metal components and reinforcing particles as components, and perform optimization in all output components to obtain the optimal component of the composite brazing filler metal.
Further, the nested loop module further comprises: the device comprises an internal circulation unit, an external circulation unit and a volume fraction traversal step length acquisition unit;
the internal circulation unit is set to keep the volume fraction of the metal Sn unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
the outer circulation unit is set to reduce the volume fraction of the metal Sn from the maximum value by one traversal step length L in sequencerecycleExecuting inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof;
the volume fraction traversal step obtaining unit is arranged to obtain a volume fraction traversal step L by using the steps of claim 5recycle。
Further, the optimization module further comprises: the device comprises a soldered joint quality judgment difference D acquisition unit, a component output unit and a composite solder optimal component output unit;
the brazing joint quality judgment difference D obtaining unit is configured to obtain a brazing joint quality judgment difference D by the steps of claim 4;
the component output unit is configured to determine whether CTEC-CTE0< D holds, and if so, output and store the volume fractions of the metal components and the reinforcing particles at that time as components;
the optimal component output unit of the composite solder is set to optimize the components of the composite solder through the conditions of ternary phase diagram, soldering temperature, cost, wettability and the like, so as to obtain the optimal components of the composite solder.
Compared with the prior art, the invention has the beneficial characteristics that:
1. the composite brazing filler metal component matched with the thermal expansion coefficient of the target high-silicon aluminum alloy base metal can be quickly obtained, and the problems that the joint after connection has higher residual stress and the joint strength is reduced due to larger difference of the thermal expansion coefficients between the metal brazing filler metal and the base metal are solved; and screening out the most economical brazing filler metal component with the best effect.
2. The fuzzy algorithm is applied to the field of materials, factors which cannot be clearly defined by large or small concepts are divided into a plurality of fuzzy subsets through a fuzzy membership function, and then a determined value is obtained through defuzzification processing and expected value calculation, so that the component design is more accurate and effective.
Drawings
FIG. 1 is a flow chart of a fuzzy algorithm based composite braze composition design method in a preferred embodiment of the present disclosure;
FIG. 2 is a triangular membership function image of a fuzzy input variable e in a preferred embodiment of the present disclosure;
FIG. 3 is a diagram illustrating a traversal step length L in a preferred embodiment of the present inventionjThe triangular membership function image;
FIG. 4 is a flow chart of a fuzzy algorithm based composite braze component design method in another preferred embodiment of the present disclosure;
FIG. 5 is a block diagram of a fuzzy algorithm based composite braze component design system in a preferred embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings. In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
As shown in FIG. 1, the invention discloses a composite solder component design method based on a fuzzy algorithm, which comprises the following steps:
s1, setting the volume fraction change range and the initial volume fraction of each metal component in the composite solder, and setting the volume fraction threshold of the reinforced particles in the composite solder.
Wherein, each metal component in the composite solder comprises: metallic Sn, metallic Ag and metallic Cu. The volume fraction variation ranges are respectively set as:
the metal Sn is used as a matrix, and the volume fraction variation range is 70-95%; the volume fraction change range of the metal Ag is 0-20%; the volume fraction of the metal Cu is within a range of 3-15%. In order to accommodate the difference in Coefficient of Thermal Expansion (CTE) between the base material and the brazing filler metal, it is necessary to add a certain proportion of reinforcing phase particles to the brazing filler metal. In some preferred embodiments, the reinforcing phase particles are SiC. The initial volume fraction of the above-mentioned constituent elements is generally set in accordance with the maximum value of the volume fraction of the base metal Sn and the minimum value of the metal Ag and the metal Cu, while the initial value of the strong phase particles is generally set to 0 and the threshold value is the maximum volume fraction which can be reached.
And S2, calculating the coefficient of thermal expansion CTE1 of the composite solder under the state of the volume fraction of each metal component obtained in the previous step.
In a preferred embodiment, the CTE1 of the composite filler metal can be calculated using a Turner model by:
linear expansion coefficient of composite solder
Wherein Cp and Cm are linear expansion coefficients of the components, Vp and Vm are volume fractions of the components, and Kp and Km are volume moduli of the components.
S3, obtaining the expansion coefficient CTE0 of the target high-silicon aluminum alloy base material, and obtaining a brazing joint quality judgment difference D based on a fuzzy algorithm.
In some preferred embodiments, the coefficient of expansion CTE0 for the target high silicon aluminum alloy parent material may be calculated using the Turner model described above. Generally, for high silicon aluminum alloy Al-50Si with metal Si accounting for 50% of volume fraction, the linear expansion coefficient is about 11X 10 at the working temperature-6/℃。
As can be understood by those skilled in the art, the quality of the soldered joint has a great relationship with the thermal expansion coefficient between the base metal and the metal solder, and when the difference between the thermal expansion coefficients of the metal solder and the high-silicon aluminum alloy material is large, the connected joint has high residual stress, so that the joint strength is reduced; multiple temperature rise and fall cycles during the service life of the package housing can also cause thermal stress fatigue and lead to failure of the joint. The invention adopts a method based on a fuzzy algorithm to obtain a brazing joint quality judgment difference D, and the method specifically comprises the following steps:
step S301, taking the difference between the CTE0 and the CTE1 as a fuzzy input variable e, namely e is CTEC-CTE 0;
it will be appreciated by those skilled in the art that whether the fuzzy input variable e is large or small is a fuzzy concept and the present invention contemplates the introduction of fuzzy algorithms to solve this problem. Wherein, the membership degree belongs to the concept in the fuzzy evaluation function: the fuzzy comprehensive evaluation is a very effective multi-factor decision method for comprehensively evaluating things influenced by various factors, and is characterized in that the evaluation result is not absolutely positive or negative, but is represented by a fuzzy set. Membership functions are the basis for the application of fuzzy control. At present, no mature method for determining the membership functions exists, and the method mainly stays on the basis of experience and experiments. The usual approach is to determine rough membership functions initially and then continually adjust and refine them by "learning" and practice.
The domain of discourse is considered to be a discrete domain of discourse, and the membership degree is directly given through analysis and reasoning according to subjective knowledge and combined with personal experience. Triangular membership functions are used in this application. The functional image is shown in fig. 2.
Step S302, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: the error is extremely small, medium and large, the error is large, and the error is extremely large; the triangular membership functions of which are respectively expressed as f1、f2、f3、f4、f5、f6、f7、f8The expression is as follows:
wherein, i is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when i is 1, a is-1, b is 0, and c is 1;
when i is 2, a is 1, b is 3, c is 5;
when i is 3, a is 3, b is 5, c is 7;
when i is 4, a is 5, b is 7, c is 9;
when i is 5, a is 7, b is 9, c is 11;
when i is 6, a is 9, b is 11, c is 13;
when i is 7, a is 11, b is 14, c is 17;
when i is 8, a is 13, b is 17, c is 25;
it should be understood that in the triangle membership function, the function image is equivalent to a simplification of a chi-square or gaussian distribution membership function obtained by using a triangle as a large amount of data statistics, in this case, the parameters a, b and c in the above formula are turning points of the membership degree, x ═ a and b are respectively turning points of the membership degree equal to 0 and the membership degree is greater than 0, and the membership degree is 1 when the error value x ═ c is included. One can artificially determine the "feet" of the triangle for parameters a and c, and the "peaks" of the triangle for parameter b.
Step S303, defining error threshold values T of 8 fuzzy subsetsiIs the barycentric coordinate of the triangular membership function image;
step S304, calculating a brazing joint quality judgment difference D,
and S4, increasing the volume fraction of the reinforcing particles, calculating the thermal expansion coefficient CTEC of the composite solder at the moment, judging whether the CTEC-CTE0< D is true, and if so, outputting the volume fractions of the metal components and the reinforcing particles as components.
The initial value of the initial volume fraction of the enhanced particles is 0, the volume fraction needs to be traversed, and when the initial value is traversed according to the fixed step length, the situation that if the step length is set too large, the traversal result is inaccurate, the error is large, and when the step length is set too small, the traversal times are too many, and the calculation amount of the whole design process is increased. In order to solve the problems, the method and the device consider a triangular membership function introduced into a fuzzy algorithm to optimize the traversal step length, so that the step length is not too large or too small, the traversal error is reduced, and the traversal times are reduced. The specific method comprises the following steps:
step S401, taking the difference value of the CTE0 and the CTE1 as a fuzzy input variable e;
step S402, dividing the function image into 8 fuzzy subsets according to the error size of the fuzzy input variable e as shown in FIG. 3: extremely small, very small, medium, large, very large; the triangular membership functions are respectively expressed as g1、g2、g3、g4、g5、g6、g7、g8Expression of whichThe formula is as follows:
wherein j is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when j is 1, a is-4, b is 0, c is 4;
when j is 2, a is-2, b is 2, and c is 6;
when j is 3, a is 0, b is 4, c is 8;
when j is 4, a is 2, b is 6, c is 10;
when j is 5, a is 4, b is 8, c is 12;
when j is 6, a is 6, b is 10, c is 14;
when j is 7, a is 8, b is 12, c is 16;
when j is 8, a is 10, b is 14, c is 21;
step S403. defining corresponding traversal step L of 8 fuzzy subsetsjSequentially comprises the following steps: 0.8%, 1.1%, 1.5%, 2%, 3%, 4%, 5%, 7%;
s404, calculating the volume fraction traversal step length L of the enhanced particlesrecycle，
So far, the calculation of the composite brazing filler metal component matched with the thermal expansion coefficient of the brazing base material, namely the high-silicon aluminum alloy under the condition that the metal component is the initial fixed value is completed. In some preferred embodiments, it is also desirable to traverse the volume fraction of the metal component to obtain a solder component that meets the parent material under various conditions. Therefore, as shown in fig. 4, the present application further includes the steps of:
s5, repeating the step S4 until the volume fraction of the enhanced particles is larger than a set threshold value;
and S6, adjusting the volume fraction of each metal component, and repeating the steps S2-S5 until the volume fraction of each metal component exceeds a set variation range.
In a preferred embodiment, the step S6 of adjusting the volume fraction of each metal component is performed by using a nested loop, which includes:
in the inner circulation, the volume fraction of the metal Sn is kept unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
in the outer layer circulation, the volume fraction of the metal Sn is reduced by one traversal step length L from the maximum valuerecycleAnd executing inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof.
In some preferred embodiments, the above steps are performed to obtain a qualified solder composition set, which is more data, and the following steps can be adopted:
and S7, optimizing all the output components to obtain the optimal component of the composite solder.
Further, the optimization may be to optimize the components of the composite filler metal by conditions of ternary phase diagram, brazing temperature, cost, wettability, and the like.
As shown in fig. 5, the present invention further provides a fuzzy algorithm based composite solder composition design system, which is characterized by comprising: the system comprises an initialization module, a nested loop module and an optimization module;
the initialization module is set to set the volume fraction change range and the initial volume fraction of each metal component in the composite solder, set the volume fraction threshold of the reinforced particles in the composite solder and obtain the expansion coefficient CTE0 of the target high-silicon aluminum alloy base material;
the nested circulation module is set to adopt nested circulation to adjust the volume fraction of each metal component, traverse the volume fraction change of the metal components and the reinforced particles and obtain the thermal expansion coefficient CTEC of the composite solder;
the optimization module is set to obtain a brazing joint quality judgment difference D based on a fuzzy algorithm, judge whether CTEC-CTE0< D is true, if yes, output the volume fractions of all metal components and reinforcing particles as components, and perform optimization in all output components to obtain the optimal component of the composite brazing filler metal.
Further, the nested loop module further comprises: the device comprises an internal circulation unit, an external circulation unit and a volume fraction traversal step length acquisition unit;
the internal circulation unit is set to keep the volume fraction of the metal Sn unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
the outer circulation unit is set to reduce the volume fraction of the metal Sn from the maximum value by one traversal step length L in sequencerecycleExecuting inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof;
the volume fraction traversal step obtaining unit is arranged to obtain a volume fraction traversal step L by using the steps of claim 5recycle。
Further, the optimization module further comprises: the device comprises a soldered joint quality judgment difference D acquisition unit, a component output unit and a composite solder optimal component output unit;
the brazing joint quality judgment difference D obtaining unit is configured to obtain a brazing joint quality judgment difference D by the steps of claim 4;
the component output unit is configured to determine whether CTEC-CTE0< D holds, and if so, output and store the volume fractions of the metal components and the reinforcing particles at that time as components;
the optimal component output unit of the composite solder is set to optimize the components of the composite solder through the conditions of ternary phase diagram, soldering temperature, cost, wettability and the like, so as to obtain the optimal components of the composite solder.
Examples
In order to better explain the technical solution of the present application, the following specific examples are provided.
In this embodiment, the target high-silicon aluminum alloy base material is high-silicon aluminum alloy Al-50Si in which the volume fraction of metal Si is 50%.
S1, setting the volume fraction variation range of matrix metal Sn to be 70% -95%, and setting the initial volume fraction to be 95%; the volume fraction change range of the metal Ag is 0-20%, and the initial volume fraction is 0%; the volume fraction variation range of the metal Cu is 3-15%, and the initial volume fraction is 0%; setting the volume fraction threshold value of the reinforced particles in the composite solder to be 27%;
s2, calculating and obtaining the coefficient of thermal expansion CTE1 of the composite solder in an initial state by using a Turner model;
s3, obtaining an expansion coefficient CTE0 of the target high-silicon aluminum alloy base material, and taking the difference value between the CTE0 and the CTE1 as a fuzzy input variable e;
s4, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: the error is extremely small, medium and large, the error is large, and the error is extremely large; the triangular membership functions of which are respectively expressed as f1、f2、f3、f4、f5、f6、f7、f8The expression is as follows:
wherein, i is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when i is 1, a is-1, b is 0, and c is 1;
when i is 2, a is 1, b is 3, c is 5;
when i is 3, a is 3, b is 5, c is 7;
when i is 4, a is 5, b is 7, c is 9;
when i is 5, a is 7, b is 9, c is 11;
when i is 6, a is 9, b is 11, c is 13;
when i is 7, a is 11, b is 14, c is 17;
when i is 8, a is 13, b is 17, c is 21;
s5, defining error thresholds of 8 fuzzy subsetsValue TiIs the barycentric coordinate of the triangular membership function image;
s6, calculating a brazing joint quality judgment difference D,
and S7, increasing the volume fraction of the reinforcing particles, calculating the thermal expansion coefficient CTEC of the composite solder at the moment, judging whether the CTEC-CTE0< D is true, and if so, outputting the volume fractions of the metal components and the reinforcing particles as components.
Wherein the method of increasing the volume fraction of reinforcing particles is:
step S701, taking the difference value of the CTE0 and the CTE1 as a fuzzy input variable e;
step S702, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: extremely small, very small, medium, large, very large; the triangular membership functions are respectively expressed as g1、g2、g3、g4、g5、g6、g7、g8The expression is as follows:
wherein j is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when j is 1, a is-4, b is 0, c is 4;
when j is 2, a is-2, b is 2, and c is 6;
when j is 3, a is 0, b is 4, c is 8;
when j is 4, a is 2, b is 6, c is 10;
when j is 5, a is 4, b is 8, c is 12;
when j is 6, a is 6, b is 10, c is 14;
when j is 7, a is 8, b is 12, c is 16;
when j is 8, a is 10, b is 14, c is 21;
step S703. define 8 modelsCorresponding traversal step L of the fuzzy subsetjSequentially comprises the following steps: 0.8%, 1.1%, 1.5%, 2%, 3%, 4%, 5%, 7%;
step S704, calculating volume fraction traversal step length L of enhanced particlesrecycle，
S8, repeating the step S7 until the volume fraction of the enhanced particles is larger than a set threshold value;
and S9, adjusting the volume fraction of each metal component, and repeating the steps S2-S8 until the volume fraction of each metal component exceeds a set variation range.
Wherein the method for adjusting the volume fraction of each metal component comprises the following steps:
nested loops are used to adjust the volume fraction of each metal component:
in the inner circulation, the volume fraction of the metal Sn is kept unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
in the outer layer circulation, the volume fraction of the metal Sn is reduced by one traversal step length L from the maximum valuerecycleAnd executing inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof.
And S10, optimizing all the output components to obtain the optimal component of the composite solder.
Through the steps S1 to S9, the qualified solder composition data 410 pieces are obtained. And then, continuously screening the component proportion through the conditions of ternary phase diagram, brazing temperature, cost, and the like. Firstly, considering the brazing temperature in a combined manner, if the lower brazing temperature is used as much as possible, the melting point of the composite brazing filler metal is required to be not too high, the ternary phase diagram shows that the melting point of the composite brazing filler metal is lower when the Sn content is higher, the data that the volume fraction of Sn is less than 85 percent are abandoned, and 110 data are left; secondly, from the perspective of reducing cost, the maximum content of Ag is reduced to 10%; and when the Ag content is more than 3% in terms of mechanical properties, the yield strength and the tensile rigidity are linearly increased along with the increase of the copper content, and the minimum content of Ag is 3%, and the rest data are 82; finally, the Sn content is accurate to 92% -95%, yielding 9 qualifying data as described in table 1:
TABLE 1 eligible data sheet
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Those skilled in the art will appreciate that any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and that the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Those skilled in the art will further appreciate that embodiments of the present invention can be implemented or realized in computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose. To clearly illustrate this interchangeability of hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Claims (10)
1. A composite solder component design method based on a fuzzy algorithm is characterized by comprising the following steps:
s1, setting the volume fraction change range and the initial volume fraction of each metal component in the composite solder, and setting the volume fraction threshold of reinforced particles in the composite solder;
s2, calculating the coefficient of thermal expansion CTE1 of the composite solder in the state of the volume fraction of each metal component obtained in the previous step;
s3, obtaining an expansion coefficient CTE0 of the target high-silicon aluminum alloy base material, and obtaining a brazing joint quality judgment difference D based on a fuzzy algorithm;
and S4, increasing the volume fraction of the reinforcing particles, calculating the thermal expansion coefficient CTEC of the composite solder at the moment, judging whether the CTEC-CTE0< D is true, and if so, outputting the volume fractions of the metal components and the reinforcing particles as components.
2. A composite braze component design method according to claim 1 further comprising the steps of:
s5, repeating the step S4 until the volume fraction of the enhanced particles is larger than a set threshold value;
and S6, adjusting the volume fraction of each metal component, and repeating the steps S2-S5 until the volume fraction of each metal component exceeds a set variation range.
3. A composite braze component design method according to claim 2 further comprising the steps of:
and S7, optimizing all the output components to obtain the optimal component of the composite solder.
4. The method for designing a composite solder composition according to claim 1, wherein the method for obtaining the difference D of judgment of the quality of the soldered joint by the fuzzy algorithm in step S3 is:
step S301, taking the difference value of the CTE0 and the CTE1 as a fuzzy input variable e;
step S302, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: the error is extremely small, medium and large, the error is large, and the error is extremely large; the triangular membership functions of which are respectively expressed as f1、f2、f3、f4、f5、f6、f7、f8The expression is as follows:
wherein, i is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when i is 1, a is-1, b is 0, and c is 1;
when i is 2, a is 1, b is 3, c is 5;
when i is 3, a is 3, b is 5, c is 7;
when i is 4, a is 5, b is 7, c is 9;
when i is 5, a is 7, b is 9, c is 11;
when i is 6, a is 9, b is 11, c is 13;
when i is 7, a is 11, b is 14, c is 17;
when i is 8, a is 13, b is 17, c is 25;
step S303, defining error threshold values T of 8 fuzzy subsetsiIs the barycentric coordinate of the triangular membership function image;
step S304, calculating a brazing joint quality judgment difference D,
5. the composite filler metal component design method according to claim 1, wherein the method of increasing the volume fraction of the reinforcing particles in step S4 is:
step S401, taking the difference value of the CTE0 and the CTE1 as a fuzzy input variable e;
step S402, dividing the following 8 fuzzy subsets according to the error size of the fuzzy input variable e: extremely small, very small, medium, large, very large; the triangular membership functions are respectively expressed as g1、g2、g3、g4、g5、g6、g7、g8The expression is as follows:
wherein j is 1, 2, 3 … … 8, x is the error value of CTE0 and CTE 1; the values of the parameters a, b and c are as follows:
when j is 1, a is-4, b is 0, c is 4;
when j is 2, a is-2, b is 2, and c is 6;
when j is 3, a is 0, b is 4, c is 8;
when j is 4, a is 2, b is 6, c is 10;
when j is 5, a is 4, b is 8, c is 12;
when j is 6, a is 6, b is 10, c is 14;
when j is 7, a is 8, b is 12, c is 16;
when j is 8, a is 10, b is 14, c is 21;
step S403. defining corresponding traversal step L of 8 fuzzy subsetsjSequentially comprises the following steps: 0.8%, 1.1%, 1.5%, 2%, 3%, 4%, 5%, 7%;
s404, calculating the volume fraction traversal step length L of the enhanced particlesrecycle，
6. The method of designing a composite filler metal component according to claim 2, wherein the method of adjusting the volume fraction of each of the metal components in step S6 is:
nested loops are used to adjust the volume fraction of each metal component:
in the inner circulation, the volume fraction of the metal Sn is kept unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
in the outer layer circulation, the volume fraction of the metal Sn is reduced by one traversal step length L from the maximum valuerecycleAnd executing inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof.
7. A method for designing a composite filler metal composition according to claim 3, wherein the optimization is performed among all the output compositions by a method of obtaining an optimal composition of the composite filler metal:
the components of the composite solder are optimized through conditions of ternary phase diagram, soldering temperature, cost, wettability and the like.
8. A composite solder component design system based on fuzzy algorithm is characterized by comprising: the system comprises an initialization module, a nested loop module and an optimization module;
the initialization module is set to set the volume fraction change range and the initial volume fraction of each metal component in the composite solder, set the volume fraction threshold of the reinforced particles in the composite solder and obtain the expansion coefficient CTE0 of the target high-silicon aluminum alloy base material;
the nested circulation module is set to adopt nested circulation to adjust the volume fraction of each metal component, traverse the volume fraction change of the metal components and the reinforced particles and obtain the thermal expansion coefficient CTEC of the composite solder;
the optimization module is set to obtain a brazing joint quality judgment difference D based on a fuzzy algorithm, judge whether CTEC-CTE0< D is true, if yes, output the volume fractions of all metal components and reinforcing particles as components, and perform optimization in all output components to obtain the optimal component of the composite brazing filler metal.
9. A composite braze component design system of claim 8 wherein the nested loop module further comprises: the device comprises an internal circulation unit, an external circulation unit and a volume fraction traversal step length acquisition unit;
the internal circulation unit is set to keep the volume fraction of the metal Sn unchanged, and the volume fraction of the metal Ag is sequentially increased by one traversal step length L from the minimum valuerecycleTraversing the volume fraction change of the metal Cu until the volume fraction of the metal Ag exceeds the volume fraction change range;
the outer circulation unit is set to reduce the volume fraction of the metal Sn from the maximum value by one traversal step length L in sequencerecycleExecuting inner layer circulation until the volume fraction of the metal Sn exceeds the volume fraction variation range thereof;
the volume fraction traversal step obtaining unit is arranged to obtain a volume fraction traversal step L by using the steps of claim 5recycle。
10. The composite braze composition design system of claim 8 wherein the optimization module further comprises: the device comprises a soldered joint quality judgment difference D acquisition unit, a component output unit and a composite solder optimal component output unit;
the brazing joint quality judgment difference D obtaining unit is configured to obtain a brazing joint quality judgment difference D by the steps of claim 4;
the component output unit is configured to determine whether CTEC-CTE0< D holds, and if so, output and store the volume fractions of the metal components and the reinforcing particles at that time as components;
the optimal component output unit of the composite solder is set to optimize the components of the composite solder through the conditions of ternary phase diagram, soldering temperature, cost, wettability and the like, so as to obtain the optimal components of the composite solder.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113579546A (en) * | 2021-08-23 | 2021-11-02 | 天津大学 | Method and system for predicting critical floating time of enhancement phase |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101537543A (en) * | 2008-11-12 | 2009-09-23 | 江苏科技大学 | Sn-Ag-Cu lead-free solder with low melting point and preparation method thereof |
| CN103273220A (en) * | 2013-06-06 | 2013-09-04 | 上海工程技术大学 | Welding materials for connection of low thermal expansion coefficient alloys |
| CN104142514A (en) * | 2013-10-29 | 2014-11-12 | 中国石油化工股份有限公司 | Three-dimensional earthquake observing system quantitative designing method |
| CN105436737A (en) * | 2015-12-02 | 2016-03-30 | 贵州理工学院 | Low-melting-point aluminum alloy brazing filler metal and manufacturing method thereof |
| CN110549032A (en) * | 2019-08-21 | 2019-12-10 | 河南机电职业学院 | copper-based brazing filler metal with gradient thermal expansion coefficient and preparation method thereof |
-
2020
- 2020-09-22 CN CN202011001484.3A patent/CN112222671B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101537543A (en) * | 2008-11-12 | 2009-09-23 | 江苏科技大学 | Sn-Ag-Cu lead-free solder with low melting point and preparation method thereof |
| CN103273220A (en) * | 2013-06-06 | 2013-09-04 | 上海工程技术大学 | Welding materials for connection of low thermal expansion coefficient alloys |
| CN104142514A (en) * | 2013-10-29 | 2014-11-12 | 中国石油化工股份有限公司 | Three-dimensional earthquake observing system quantitative designing method |
| CN105436737A (en) * | 2015-12-02 | 2016-03-30 | 贵州理工学院 | Low-melting-point aluminum alloy brazing filler metal and manufacturing method thereof |
| CN110549032A (en) * | 2019-08-21 | 2019-12-10 | 河南机电职业学院 | copper-based brazing filler metal with gradient thermal expansion coefficient and preparation method thereof |
Non-Patent Citations (2)
| Title |
|---|
| 吴京洧 等: "复合钎料的特点及研究现状", 《焊接》 * |
| 赵哲 等: "基于模糊算法自动控制生料磨喂料量", 《河南建材》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113579546A (en) * | 2021-08-23 | 2021-11-02 | 天津大学 | Method and system for predicting critical floating time of enhancement phase |
| CN113579546B (en) * | 2021-08-23 | 2022-06-14 | 天津大学 | Method and system for predicting critical floating time of enhancement phase |
| WO2023024363A1 (en) * | 2021-08-23 | 2023-03-02 | 天津大学 | Prediction method and system for critical floating time of reinforcing phase |
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