A multi-objective response evaluation method for laser welding systems considering carbon emissions and welding quality

Abstract

The present invention provides a multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality. The method includes obtaining data related to carbon emissions and welding quality during the welding process through experimental measurement and simulation, establishing a multi-objective response evaluation framework for laser welding systems targeting carbon emissions and welding quality, and conducting a multi-objective response evaluation of carbon emissions and welding quality using the TOPSIS evaluation method based on the expert evaluation-entropy weight method. The method obtains an optimal set of laser welding process parameters. This method provides a theoretical model and evaluation method for the multi-objective response evaluation of laser welding systems targeting carbon emissions and welding quality. The method is highly operational and practical, and can subsequently be extended and applied to other mechanical manufacturing fields.

Description

Technical Field

[0001] The present invention relates to the field of laser processing, and in particular to a multi-objective response evaluation method for a laser welding system oriented towards carbon emissions and welding quality. Background Art

[0002] Manufacturing is a pillar of the national economy, but it's also a major source of carbon emissions. Clearly, manufacturing will be one of the key battlegrounds for my country to achieve carbon peak and carbon neutrality. According to a report by the International Energy Agency (IEA), global carbon emissions reached 35 billion tons in 2020, of which manufacturing accounted for approximately 9 billion tons (nearly a quarter), making it the second-largest source of carbon emissions after electricity. Therefore, to achieve the dual carbon goals, promoting the green transformation and development of the manufacturing industry is imperative.

[0003] As an important connection method, welding technology is widely used in the field of mechanical manufacturing. Among them, laser welding, as an advanced connection process integrating light, heat, machinery and electricity, is a key technology for achieving green and low-carbon development in the manufacturing industry. Compared with traditional welding methods such as resistance welding, arc welding and gas welding, laser welding has the advantages of high processing efficiency, deep penetration, small welding stress and deformation, and easy automation. It has been widely used in manufacturing fields such as new energy vehicles, rail transportation, ships and aerospace. However, laser welding also has some problems and difficulties: on the one hand, the laser welding process has the characteristics of high energy consumption, low energy efficiency and serious carbon emissions; on the other hand, laser welded welds are prone to defects such as pores, cracks, and structural softening. The above factors affect the low-carbon development and application of laser welding technology.

[0004] In the context of carbon peak and carbon neutrality, research on multi-objective response evaluation methods for laser welding systems targeting carbon emissions and welding quality can reveal the combined impact of laser welding process parameters on carbon emissions and welding quality during the welding process, achieve accurate and scientific evaluation of laser welding process carbon emissions and welding quality, and lay a theoretical foundation for laser welding process parameter decision-making optimization and energy-saving selection of welding equipment. However, existing technologies have difficulty in fully revealing the combined impact of laser welding process parameters on carbon emissions and welding quality from the perspective of environmental attributes and quality attributes.

[0005] Therefore, it is of great significance to develop a multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-objective response evaluation method for a laser welding system for carbon emissions and welding quality, comprising the following steps:

[0007] 1) Based on preliminary experiments, a process parameter window that ensures welding quality is obtained. Laser welding experiments are conducted using an orthogonal experimental design based on this welding process parameter window. Based on the welding process parameters, intelligent sensors, welding equipment, and information about the workpiece to be welded, data related to carbon emissions and welding quality during the welding process are obtained through experimental measurement and simulation.

[0008] 2) Summarize the main evaluation indicators that affect carbon emissions and welding quality, and construct a multi-objective response evaluation framework for laser welding systems oriented towards carbon emissions and welding quality. The multi-objective response evaluation framework includes two primary indicators: carbon emission factors and welding quality factors, represented by B1 and B2 respectively. The carbon emission factor B1 has no secondary indicators. Its carbon emissions are composed of laser power consumption, robot power consumption, chiller power consumption, control computer power consumption, welder power consumption, shielding gas consumption, compressed air consumption, and filler wire material consumption. The welding quality factor B2 includes two secondary indicators: weld deformation and weld tensile strength, represented by C21 and C22 respectively.

[0009] 3) A multi-objective response evaluation of carbon emissions and welding quality was conducted using the TOPSIS evaluation method based on expert evaluation and entropy weighting to determine the optimal set of laser welding process parameters. The weight λ of the primary indicator was determined using the expert scoring method. The weight W of the secondary indicator was determined using the entropy weighting method. The combined weight β was calculated by combining the primary and secondary indicator weights. The process parameters were ranked using the TOPSIS (Ranking by Approximately Ideal Solutions) evaluation method.

[0010] Furthermore, in step 1), welding process parameters include laser welding power, welding speed, defocusing amount, shielding gas flow rate, compressed air flow rate, welding machine processing current and voltage, and wire feed speed. Intelligent sensors include smart meters and gas flow sensors. Welding equipment information includes basic operating energy consumption, equipment operating efficiency, equipment operating parameter ranges, and shielding gas and compressed air supply flow and pressure ranges for the laser, chiller, robot, control computer, and welding machine. Workpiece information includes material type, chemical composition, mechanical / physical properties, workpiece dimensions, and the type of joint to be welded.

[0011] Furthermore, step 2) specifically includes the following sub-steps:

[0012] 2.1) Based on the carbon emission and welding quality related data, calculate the carbon emission of the laser welding system manufacturing process and the welding quality index value of the weldment.

[0013] 2.2) Decompose the process components and analyze the impact of laser welding process parameters on carbon emissions and welding quality.

[0014] 2.3) Establish a multi-objective response evaluation framework for laser welding systems oriented towards carbon emissions and welding quality.

[0015] Furthermore, the deformation of the weldment is obtained by numerical simulation based on the workpiece to be welded and the welding process parameters.

[0016] Furthermore, the method for obtaining the weldment deformation specifically includes the following sub-steps:

[0017] a) Based on the geometric dimensions of the workpiece to be welded, a three-dimensional model of the workpiece to be welded is established using three-dimensional modeling software.

[0018] b) Use meshing software to mesh the three-dimensional model of the workpiece to be welded.

[0019] c) Use welding process simulation software to obtain the deformation of the weldment under given process conditions.

[0020] Furthermore, step 3) specifically includes the following sub-steps:

[0021] 3.1) Evaluation index data is positive.

[0022] 3.2) Normalize the evaluation indicators and construct the evaluation indicator matrix. The normalized evaluation indicator matrix can be expressed as follows:

[0023]

[0024] Where Z is the normalized evaluation index matrix. i,j It represents the jth evaluation index in the i-th group of evaluation objects after normalization.

[0025] 3.3) Determine the positive ideal solution Z max and negative ideal solution Z min .

[0026] Z max =[Z 1,max ,Z 2,max ,...,Z m,max ]

[0027] =[Max(z 1,1 ,z 2,1 ,…,z n,1 ),Max(z 1,2 ,z 2,2 ,…,z n,2 ),…,Max(z 1,m ,z 2,m ,…,z n,m )]

[0028] Where, Max(z 1,m ,z 2,m ,…,zn,m ) means getting the maximum value in the mth column of the array. m,max is the maximum value in the corresponding m-th evaluation index array.

[0029] Z min =[Z 1,min ,Z 2,min ,...,Z m,min ]

[0030] =[Min(z 1,1 ,z 2,1 ,…,z n,1 ),Min(z 1,2 ,z 2,2 ,…,z n,2 ),…,Min(z 1,m ,z 2,m ,…,z n,m )]

[0031] Where, Min(z 1,m ,z 2,m ,…,z n,m ) means getting the minimum value in the mth column of the array. m,min is the minimum value in the corresponding m-th evaluation index array.

[0032] 3.4) Calculate the distance between each evaluation object and the positive ideal solution and the negative ideal solution. The distance between the i-th evaluation object and the positive ideal solution is as follows:

[0033]

[0034] Where, The distance between the i-th evaluation object and the positive ideal solution. The weight β of the j-th evaluation index is determined based on the expert evaluation-entropy weight method. j As shown below:

[0035] β j =λ A ×w A,k

[0036] In the formula, assuming that the jth evaluation index belongs to the evaluation index of the Ath attribute dimension, then λ A is the weight of the A-type attribute range evaluation index (i.e., the first-level index) determined by the expert scoring method. A,k is the weight of the kth evaluation index (i.e., the secondary index) within the Ath attribute range determined by the entropy weight method.

[0037] According to the expert evaluation-entropy weight method, the weights of each evaluation index are assigned. It can be further expressed as the following formula:

[0038]

[0039] Similarly, the distance between the i-th evaluation object and the negative ideal solution is as follows:

[0040]

[0041] Where, is the distance between the i-th evaluation object and the negative ideal solution.

[0042] 3.5) Calculate the relative closeness between each evaluation object and the ideal solution

[0043]

[0044] Where S i is the relative closeness between the i-th evaluation object and the ideal solution.

[0045] 3.6) Comparison S i The size and sorting, S i The larger it is, the better the result of the corresponding evaluation object will be.

[0046] Furthermore, in step 3.1), the two indicators of carbon emission and weld deformation are positively oriented.

[0047] Furthermore, the expert scoring method is used to determine the weights of indicators in different attribute dimensions.

[0048] Furthermore, the entropy weight method is used to determine the weights of different evaluation indicators under the same attribute dimension.

[0049] The present invention also discloses a multi-objective response evaluation device for a laser welding system aimed at carbon emissions and welding quality, which includes a data input module, a microprocessor, a display, a keyboard and a memory.

[0050] The memory stores a computer program. When the computer program is executed by the microprocessor, it is used to implement the above method. The computer program stored in the memory can be adjusted by the keyboard.

[0051] During operation, the data input module transmits the input measured index values ​​and simulated index values ​​to the microprocessor. The microprocessor analyzes and evaluates the input index values ​​and outputs an evaluation report to the display.

[0052] The technical effects of the present invention are unquestionable:

[0053] A. Targeting the environmental emission characteristics of laser welding (high energy consumption, low energy efficiency, and severe carbon emissions) and process quality characteristics (welds are prone to porosity, cracks, and structural softening), a multi-objective response evaluation method for laser welding systems, focusing on carbon emissions and welding quality, was proposed. This method reveals the combined impact of laser welding process parameters on carbon emissions and welding quality, enabling a precise and scientific evaluation of laser welding carbon emissions and welding quality. This approach lays the foundation for subsequent optimization of laser welding process parameters and energy-saving selection of welding equipment, thereby promoting the green and low-carbon development of laser welding processes.

[0054] B. Carbon emission and welding quality indicators were constructed from the two dimensions of laser welding's environmental and quality attributes. A multi-objective response evaluation of carbon emissions and welding quality was conducted using the TOPSIS evaluation method based on expert evaluation and entropy weighting. This evaluation method not only considers the variability between indicators across different attribute dimensions using the expert scoring method, but also incorporates the entropy weighting method to account for the objectivity of different indicators within the same attribute dimension. This method is highly operational and practical, and can be subsequently extended to other mechanical manufacturing fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Flowchart of the multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality;

[0056] Figure 2 This is a schematic diagram of the intelligent monitoring platform for carbon emissions of laser welding systems;

[0057] Figure 3 This is a schematic diagram of the simulation of weld deformation;

[0058] Figure 4 Schematic diagram of the actual measurement of weld tensile strength. DETAILED DESCRIPTION

[0059] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all should be included in the scope of protection of the present invention.

[0060] Example 1

[0061] See also Figures 1 to 4 This embodiment provides a multi-objective response evaluation method for a laser welding system for carbon emissions and welding quality, comprising the following steps:

[0062] 1) Based on preliminary experiments, a process parameter window that ensures welding quality is obtained. Laser welding experiments are conducted using an orthogonal experimental design based on this welding process parameter window. Based on the welding process parameters, intelligent sensors, welding equipment, and workpiece information, experimental measurement and simulation methods are used to obtain carbon emissions and welding quality-related data during the welding process. The welding process parameters include laser welding power, welding speed, defocus, shielding gas flow rate, compressed air flow rate, welding machine processing current and voltage, and wire feed speed. The intelligent sensors include smart meters and gas flow sensors. The welding equipment information includes the basic operating energy consumption of the laser, chiller, robot, control computer, welding machine, equipment operating efficiency, equipment safe operating parameter range, and gas supply flow and pressure range of the shielding gas and compressed air facilities. The workpiece information includes the workpiece material model, chemical composition, mechanical / physical properties, dimensional information, and the type of workpiece joint to be welded. The sources of carbon emissions from the laser welding system include electricity consumption and material consumption (since the waste slag and exhaust gas during the welding process are of small magnitude in a single welding process, they are not considered here). The electricity consumption comes from the laser, chiller, robot, control computer and welding machine, and the material consumption comes from the shielding gas, compressed air and filling wire.

[0063] 2) Based on the above carbon emission and welding quality related data, the carbon emissions of the laser welding system manufacturing process and the welding quality index values ​​of the weldments are calculated, the influence of laser welding process parameters on the carbon emissions and welding quality of the welding process is analyzed, and a multi-objective response evaluation framework for laser welding systems oriented to carbon emissions and welding quality is established.

[0064] 3) Taking the carbon emission and welding quality of the laser welding system as the evaluation objects, the TOPSIS evaluation method based on expert evaluation-entropy weight method is used to carry out a multi-objective response evaluation of carbon emission and welding quality, so as to obtain the optimal process parameter set for laser welding.

[0065] 3.1) Evaluation index data is positive

[0066] Common evaluation indicators include extremely large indicators (i.e., the larger the indicator value, the better) and extremely small indicators (i.e., the smaller the indicator value, the better). In order to unify and facilitate calculation, indicator data is often normalized. The positive normalization of extremely small indicators can be achieved using the following formula:

[0067] y i,j =Max(x 1,j ,x 2,j ,...,x N,j )-x i,j (1)

[0068] Where, Max(x 1,j ,x 2,j ,...,x N,j) represents the maximum value of N sets of data of the jth extremely small index, x i,j Represents the i-th value of the j-th minimal indicator.

[0069] Suppose there are n groups of objects to be evaluated, and each group corresponds to m evaluation indicators. The evaluation indicator matrix after positive transformation can be expressed as follows:

[0070]

[0071] Where Y represents the evaluation index matrix after positive transformation. i,j It represents the jth evaluation index value of the i-th group of evaluation objects after positive transformation.

[0072] The evaluation indicators for laser welding in this invention are carbon emissions, weld deformation, and weld tensile strength. Weld tensile strength is a very large indicator, while carbon emissions and weld deformation are very small indicators. Therefore, these two very small indicators need to be positively evaluated.

[0073] 3.2) Evaluation index normalization and matrix construction

[0074] In order to eliminate the influence of data dimensions among the evaluation indicators, it is necessary to normalize the positive indicator data. The normalization formula for the jth indicator is as follows:

[0075]

[0076] Where z i,j Represents the jth evaluation index in the i-th group of evaluation objects after normalization. Max(y 1,j ,y 2,j ,...,y N,j ) represents the maximum value of the jth evaluation index after positive transformation. Min(y 1,j ,y 2,j ,...,y N,j ) represents the minimum value of the j-th evaluation index after positive transformation.

[0077] The normalized evaluation index matrix can be expressed as follows:

[0078]

[0079] Where Z is the normalized evaluation index matrix.

[0080] 3.3) Determine the positive ideal solution and the negative ideal solution

[0081] Positive ideal solution Z max The maximum value of each column element in the normalized matrix Z is formed as follows:

[0082]

[0083] Where, Max(z 1,m ,z 2,m ,…,z n,m ) means getting the maximum value in the mth column of the array. m,max is the maximum value in the corresponding m-th evaluation index array.

[0084] Negative ideal solution Z min The minimum value of each column element in the normalized matrix Z is formed as follows: min =[Z 1,min ,Z 2,min ,...,Z m,min ]

[0085] =[Min(z 1,1 ,z 2,1 ,…,z n,1 ),Min(z 1,2 ,z 2,2 ,…,z n,2 ),…,Min(z 1,m ,z 2,m ,…,z n,m )](6)

[0086] Where, Min(z 1,m ,z 2,m ,…,z n,m ) means getting the minimum value in the mth column of the array. m,min is the minimum value in the corresponding m-th evaluation index array.

[0087] 3.4) Calculate the distance between each evaluation object and the positive ideal solution and the negative ideal solution. The distance between the i-th evaluation object and the positive ideal solution is as follows:

[0088]

[0089] Where, is the distance between the i-th evaluation object and the positive ideal solution. j is the weight of the jth evaluation indicator, which is determined based on the expert evaluation-entropy weight method, as shown below:

[0090] β j =λ A ×w A,k (8)

[0091] In the formula, assuming that the jth evaluation index belongs to the evaluation index of the Ath attribute dimension, then λ A is the weight of the A-type attribute range evaluation index (i.e., the first-level index) determined by the expert scoring method. A,kis the weight of the kth evaluation index (i.e., the secondary index) within the Ath attribute range determined by the entropy weight method.

[0092] Then, according to the expert evaluation-entropy weight method, each evaluation index is given a weight. Formula (7) can be further expressed as follows:

[0093]

[0094] Similarly, the distance between the i-th evaluation object and the negative ideal solution is as follows:

[0095]

[0096] Where, is the distance between the i-th evaluation object and the negative ideal solution.

[0097] The weighting process of evaluation indicators based on expert evaluation-entropy weight method is as follows:

[0098] Considering the weak correlation and large differences in the categories of evaluation indicators of different attribute dimensions, the expert scoring method is used to determine the weights of indicators of different attribute dimensions. Specifically, for the laser welding system, the expert scoring method is used to assign overall weights to environmental attribute indicators (such as carbon emissions) and quality attribute indicators (such as weld deformation and weld tensile strength). The specific definitions are as follows:

[0099] λ A =[A1,A2,...,A k ] (11)

[0100] Σλ i =1 (12)

[0101] Where λ A is the overall weight of the evaluation indicators within the Ath attribute range determined by the expert scoring method.

[0102] At the same time, considering the correlation between different evaluation indicators under the same attribute dimension, in order to ensure the objectivity of the weighting of different evaluation indicators under the same attribute dimension, the entropy weight method is used to determine the weights of different evaluation indicators under the same attribute dimension according to the normalized evaluation indicator matrix shown in formula (4). For the laser welding system, the entropy weight method is used to assign weights to the weld deformation and weld tensile strength evaluation indicators in the welding quality attribute indicator system.

[0103] 3.5) Calculate the relative closeness between each evaluation object and the ideal solution

[0104]

[0105] Where S i is the relative closeness between the i-th evaluation object and the ideal solution.

[0106] 3.6) Comparison S i The size and sorting, S i The larger it is, the better the result of the corresponding evaluation object will be.

[0107] It's worth noting that weld deformation is calculated using numerical simulation based on the workpiece and welding process parameters. The specific process is as follows: First, a 3D model of the workpiece is created using 3D modeling software (such as SolidWorks) based on its geometric dimensions. This 3D model is then meshed using meshing software (such as Hypermesh). Finally, welding process simulation software (such as Simufact Welding) is used to calculate weld deformation under given process conditions. The tensile strength of the weld is measured using tensile testing equipment.

[0108] Example 2

[0109] This embodiment provides a multi-objective response evaluation method for a laser welding system for carbon emissions and welding quality, including the following steps:

[0110] 1) Based on preliminary experiments, a process parameter window that ensures welding quality is obtained. Laser welding experiments are conducted using an orthogonal experimental design based on this welding process parameter window. Based on the welding process parameters, intelligent sensors, welding equipment, and information about the workpiece to be welded, data related to carbon emissions and welding quality during the welding process are obtained through experimental measurement and simulation.

[0111] 2) Summarize the main evaluation indicators that affect carbon emissions and welding quality, and construct a multi-objective response evaluation framework for laser welding systems oriented towards carbon emissions and welding quality. The multi-objective response evaluation framework includes two primary indicators: carbon emission factors and welding quality factors, represented by B1 and B2 respectively. The carbon emission factor B1 has no secondary indicators. Its carbon emissions are composed of laser power consumption, robot power consumption, chiller power consumption, control computer power consumption, welder power consumption, shielding gas consumption, compressed air consumption, and filler wire material consumption. The welding quality factor B2 includes two secondary indicators: weld deformation and weld tensile strength, represented by C21 and C22 respectively.

[0112] 3) A multi-objective response evaluation of carbon emissions and welding quality was conducted using the TOPSIS evaluation method based on expert evaluation and entropy weighting to determine the optimal set of laser welding process parameters. The weight λ of the primary indicator was determined using the expert scoring method. The weight W of the secondary indicator was determined using the entropy weighting method. The combined weight β was calculated by combining the primary and secondary indicator weights. The process parameters were ranked using the TOPSIS (Ranking by Approximately Ideal Solutions) evaluation method.

[0113] Example 3

[0114] The main contents of this embodiment are the same as those of Example 2, wherein, in step 1), the welding process parameters include laser welding power, welding speed, defocusing amount, shielding gas flow rate, compressed air flow rate, welding machine processing current and voltage, and wire feed speed. Intelligent sensors include smart meters and gas flow sensors. Welding equipment information includes basic operating energy consumption of the laser, chiller, robot, control computer, and welding machine, equipment operating efficiency, equipment operating parameter range, and gas supply flow rate and pressure range of the shielding gas and compressed air facilities. Workpiece information to be welded includes the workpiece material model, chemical composition, mechanical / physical properties, workpiece dimensions, and the type of joint to be welded.

[0115] Example 4

[0116] The main contents of this embodiment are the same as those of embodiment 2 or 3, wherein step 2) specifically includes the following sub-steps:

[0117] 2.1) Based on the carbon emission and welding quality related data, calculate the carbon emission of the laser welding system manufacturing process and the welding quality index value of the weldment.

[0118] 2.2) Decompose the process components and analyze the impact of laser welding process parameters on carbon emissions and welding quality.

[0119] 2.3) Establish a multi-objective response evaluation framework for laser welding systems oriented towards carbon emissions and welding quality.

[0120] Example 5

[0121] The main content of this embodiment is the same as any one of embodiments 2 to 4, wherein the deformation of the weldment is obtained by numerical simulation based on the workpiece to be welded and the welding process parameters.

[0122] Example 6

[0123] The main content of this embodiment is the same as any one of embodiments 2 to 5, wherein the method for obtaining the deformation amount of the weldment specifically includes the following sub-steps:

[0124] a) Based on the geometric dimensions of the workpiece to be welded, a three-dimensional model of the workpiece to be welded is established using three-dimensional modeling software.

[0125] b) Use meshing software to mesh the three-dimensional model of the workpiece to be welded.

[0126] c) Use welding process simulation software to obtain the deformation of the weldment under given process conditions.

[0127] Example 7

[0128] The main contents of this embodiment are the same as any one of Embodiments 2 to 6, wherein step 3) specifically includes the following sub-steps:

[0129] 3.1) Evaluation index data is positive.

[0130] 3.2) Normalize the evaluation indicators and construct the evaluation indicator matrix. The normalized evaluation indicator matrix can be expressed as follows:

[0131]

[0132] Where Z is the normalized evaluation index matrix. i,j It represents the jth evaluation index in the i-th group of evaluation objects after normalization.

[0133] 3.3) Determine the positive ideal solution Z max and negative ideal solution Z min .

[0134]

[0135] Where, Max(z 1,m ,z 2,m ,…,z n,m ) means getting the maximum value in the mth column of the array. m,max is the maximum value in the corresponding m-th evaluation index array.

[0136]

[0137] Where, Min(z 1,m ,z 2,m ,…,z n,m ) means getting the minimum value in the mth column of the array. m,min is the minimum value in the corresponding m-th evaluation index array.

[0138] 3.4) Calculate the distance between each evaluation object and the positive ideal solution and the negative ideal solution. The distance between the i-th evaluation object and the positive ideal solution is as follows:

[0139]

[0140] Where, The distance between the i-th evaluation object and the positive ideal solution. The weight β of the j-th evaluation index is determined based on the expert evaluation-entropy weight method. j As shown below:

[0141] β j =λ A ×w A,k (5)

[0142] In the formula, assuming that the jth evaluation index belongs to the evaluation index of the Ath attribute dimension, then λ Ais the weight of the A-type attribute range evaluation index (i.e., the first-level index) determined by the expert scoring method. A,k is the weight of the kth evaluation index (i.e., the secondary index) within the Ath attribute range determined by the entropy weight method.

[0143] According to the weights of each evaluation index assigned by the expert evaluation-entropy weight method, formula (4) can be further expressed as follows:

[0144]

[0145] Similarly, the distance between the i-th evaluation object and the negative ideal solution is as follows:

[0146]

[0147] Where, is the distance between the i-th evaluation object and the negative ideal solution.

[0148] 3.5) Calculate the relative closeness between each evaluation object and the ideal solution

[0149]

[0150] Where S i is the relative closeness between the i-th evaluation object and the ideal solution.

[0151] 3.6) Comparison S i The size and sorting, S i The larger it is, the better the result of the corresponding evaluation object will be.

[0152] Example 8

[0153] The main content of this embodiment is the same as any one of Embodiments 2 to 7, wherein, in step 3.1), the two indicators of carbon emission and weld deformation are positively oriented.

[0154] Example 9

[0155] The main content of this embodiment is the same as any one of Embodiments 2 to 8, wherein the weights of different attribute dimension indicators are determined by using the expert scoring method and the entropy weight method is used to determine the weights of different evaluation indicators under the same attribute dimension.

[0156] Example 10

[0157] The main content of this embodiment is the same as any of Examples 1-9, except that this embodiment uses a laser welding system as an example to explain the invention in detail. Taking 6061-T6 aluminum alloy laser butt welding as an example, the workpiece dimensions to be welded are 150 mm × 100 mm × 2 mm. The welding system equipment includes an RFL-A3000D fiber laser, an ABB IRB 2600 robot, a CWFL-3000 chiller, a Lenovo control computer, and shielding gas and compressed air facilities. First, based on preliminary experiments, the process parameter window for butt welding of this aluminum alloy model and size was obtained. The relevant welding experiments were conducted using an orthogonal experimental design. The shielding gas in each experimental group was 99.9% argon at a flow rate of 12 L / min, and the compressed air flow rate was 200 L / min. The experimental results for each group were obtained using a constructed carbon emission monitoring system platform and welding quality testing equipment. The experimental parameters and results for the 6061-T6 aluminum alloy laser butt welding are shown in Table 1. Among them, the carbon emissions of the welding process of each experimental group were obtained based on the real-time intelligent monitoring platform for carbon emissions of the laser processing system (see Appendix Figure 2 ), the weldment deformation is obtained based on the welding simulation software (see Appendix Figure 3 ), the weld tensile strength of the weldment is obtained by actual measurement using a WANCE universal material testing machine at ambient temperature (298K) (see Appendix Figure 4 ).

[0158] Table 1

[0159]

[0160]

[0161] Based on the experimental parameters and results shown in Table 1, a multi-objective response evaluation of carbon emissions and welding quality was conducted using the TOPSIS evaluation method based on expert evaluation and entropy weighting. First, carbon emissions, as a key factor affecting the manufacturing and processing environment, are environmental attribute indicators. Weld deformation and weld tensile strength are key indicators of processing quality and are quality attribute indicators. Assuming that the environmental attribute indicators and quality attribute indicators of laser welding are equally important, based on the expert scoring method, the environmental attribute indicator system and the quality attribute indicator system are both assigned a weight of 0.5. Furthermore, to ensure objectivity when assigning weights to weld deformation and weld tensile strength in the quality attribute indicator system, the entropy weighting method is used to assign weights to weld deformation and weld tensile strength. Combining the experimental results for 6061-T6 aluminum alloy laser butt welding in Table 1 and using the expert evaluation-entropy weighting process, we calculated a carbon emission weight of 0.50, a weld deformation weight of 0.24 (i.e., 0.50 × 0.485), and a weld tensile strength weight of 0.26 (i.e., 0.50 × 0.515). The relevant solution process and results are shown in Tables 2, 3, and 4. The normalized evaluation indicators for the laser welding system are shown in Table 2. The weight calculation parameters for the quality attribute indicators (weld deformation and weld tensile strength) for each experimental group based on the entropy weighting method are shown in Table 3. The weighting results for weld deformation and weld tensile strength based on the entropy weighting method are shown in Table 4.

[0162] Table 2

[0163] Experiment number Normalization of negative carbon emission indicators Normalization of negative deformation index Normalization of positive tensile strength index 1 0.17 0.00 0.83 2 0.45 0.50 1.00 3 0.82 0.59 0.89 4 0.93 0.54 0.91 5 1.00 1.00 0.52 6 0.48 0.38 0.86 7 0.64 0.41 0.72 8 0.78 0.58 0.55 9 0.98 0.76 0.87 10 0.00 0.03 0.87 11 0.68 0.47 0.75 12 0.81 0.59 0.98 13 0.88 0.70 0.07 14 0.04 0.19 0.00 15 0.55 0.26 0.99 16 0.76 0.61 0.60 17 0.99 0.55 0.02 18 0.17 0.01 0.15 19 0.44 0.55 0.98 20 0.71 0.34 0.10 21 0.86 0.68 0.61 22 0.12 0.05 0.69 23 0.57 0.38 0.88 24 0.76 0.57 0.07 25 0.73 0.50 0.62

[0164] Table 3

[0165]

[0166]

[0167] Table 4

[0168] Parameter indicators Weldment deformation Weld tensile strength Indicator entropy 0.939 0.935 Information entropy redundancy 0.061 0.065 Indicator weight 0.485 0.515

[0169] Based on the weights of carbon emissions, weld deformation, and weld tensile strength obtained by the expert evaluation-entropy weight method, the TOPSIS evaluation method was used to calculate the evaluation results of the indicators of each experimental group, as shown in Table 5. According to the ranking results of relative proximity from large to small in Table 5, the experimental numbers corresponding to the welding process parameters from best to worst are: 9, 5, 4, 12, 3, 21, 16, 8, 11, 25, 7, 23, 19, 13, 2, 17, 15, 6, 24, 20, 1, 10, 22, 18, 14.

[0170] Table 5

[0171]

[0172]

[0173] Example 11

[0174] This embodiment discloses a multi-objective response evaluation device for a laser welding system targeting carbon emissions and welding quality, including a data input module, a microprocessor, a display, a keyboard, and a memory.

[0175] The memory stores a computer program. When the computer program is executed by the microprocessor, it is used to implement the methods described in Examples 1 to 10. The computer program stored in the memory can be adjusted via a keyboard.

[0176] During operation, the data input module transmits the input measured index values ​​and simulated index values ​​to the microprocessor. The microprocessor analyzes and evaluates the input index values ​​and outputs an evaluation report to the display.

Claims

1. A multi-objective response evaluation method for laser welding systems for carbon emissions and welding quality, characterized by: The following steps are involved: 1) Based on preliminary experiments, a process parameter window that ensures welding quality is obtained. Laser welding experiments are conducted using orthogonal experimental design based on the welding process parameter window. Based on the welding process parameters, intelligent sensors, welding equipment, and information about the workpiece to be welded, experimental measurement and simulation are used to obtain data related to carbon emissions and welding quality during the welding process. 2) Summarize the evaluation indicators that affect carbon emissions and welding quality, and construct a multi-objective response evaluation framework for laser welding systems oriented towards carbon emissions and welding quality. The multi-objective response evaluation framework includes two primary indicators: carbon emission factors and welding quality factors, represented by B1 and B2 respectively. The carbon emission factor B1 includes laser power consumption, robot power consumption, chiller power consumption, control computer power consumption, welder power consumption, shielding gas consumption, compressed air consumption, and filler wire material consumption. The welding quality factor B2 includes two secondary indicators: weldment deformation and weld tensile strength, represented by C21 and C22 respectively. 3) A TOPSIS evaluation method based on expert evaluation and entropy weight method is used to carry out a multi-objective response evaluation of carbon emissions and welding quality to obtain the optimal set of laser welding process parameters; the weight λ of the first-level indicator is determined by the expert scoring method; the weight W of the second-level indicator is determined by the entropy weight method; the combined weight β is obtained by combining the first-level indicator weight and the second-level indicator weight; and the ranking of the process parameters is obtained by the TOPSIS evaluation method. Step 3) specifically includes the following sub-steps: 3.1) Evaluation index data is positive; 3.2) The evaluation indicators are normalized and the evaluation indicator matrix is ​​constructed. The normalized evaluation indicator matrix can be expressed as follows: Where Z is the normalized evaluation index matrix; i,j represents the jth evaluation index in the i-th group of evaluation objects after normalization; 3.3) Determine the positive ideal solution Z max and negative ideal solution Z min ; Where, Max(z 1,m ,z 2,m ,…,z n,m ) means getting the maximum value in the mth column array; Z m,max is the maximum value in the corresponding m-th evaluation index array; Where, Min(z 1,m ,z 2,m ,…,z n,m ) means getting the minimum value in the mth column array; Z m,min is the minimum value in the corresponding m-th evaluation index array; 3.4) Calculate the distance between each evaluation object and the positive ideal solution and the negative ideal solution; the distance between the i-th evaluation object and the positive ideal solution is as follows: Where, is the distance between the i-th evaluation object and the positive ideal solution; the weight β of the j-th evaluation index is determined based on the expert evaluation-entropy weight method j As shown below: b j =λ A ×w A,k (5) In the formula, assuming that the jth evaluation index belongs to the evaluation index of the Ath attribute dimension, then λ A is the weight of the evaluation index of the Ath attribute range determined by the expert scoring method; w A,k is the weight of the kth evaluation index within the Ath attribute range determined by the entropy weight method; According to the weights of each evaluation index assigned by the expert evaluation-entropy weight method, formula (4) can be further expressed as follows: Similarly, the distance between the i-th evaluation object and the negative ideal solution is as follows: Where, is the distance between the i-th evaluation object and the negative ideal solution; 3.5) Calculate the relative closeness between each evaluation object and the ideal solution: Where S i is the relative closeness between the i-th evaluation object and the ideal solution; 3.6) Comparison S i The size and sorting, S i The larger it is, the better the result of the corresponding evaluation object will be.

2. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 1, characterized in that: In step 1), the welding process parameters include laser welding power, welding speed, defocusing amount, shielding gas flow rate, compressed air flow rate, electric welding machine processing current and voltage, and wire feed speed; the intelligent sensor includes an intelligent electric meter and a gas flow sensor; the welding equipment information includes the operating energy consumption of the laser, chiller, robot, control computer, and electric welding machine, equipment operating efficiency, equipment operating parameter range, and the gas supply flow rate and pressure range of the shielding gas and compressed air facilities; The information of the workpiece to be welded includes the material model, chemical composition, mechanical / physical properties, workpiece size and the type of joint of the workpiece to be welded.

3. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 1 is characterized in that: Step 2) specifically includes the following sub-steps: 2.1) Based on carbon emission and welding quality related data, calculate the carbon emission of the laser welding system manufacturing process and the welding quality index value of the weldment; 2.2) Decomposition of process components and analysis of the impact of laser welding process parameters on carbon emissions and welding quality; 2.3) Establish a multi-objective response evaluation framework for laser welding systems oriented towards carbon emissions and welding quality.

4. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 1, characterized in that: The deformation of the weldment is obtained by numerical simulation based on the workpiece to be welded and the welding process parameters.

5. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 4 is characterized in that: The method for obtaining the weldment deformation specifically includes the following sub-steps: a) Using 3D modeling software to build a 3D model of the workpiece to be welded based on its geometric dimensions; b) Use meshing software to mesh the three-dimensional model of the workpiece to be welded; c) Use welding process simulation software to obtain the deformation of the weldment under given process conditions.

6. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 1, characterized in that: In step 3.1), the two indicators of carbon emission and weld deformation are positively oriented.

7. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 1, characterized in that: The expert scoring method is used to determine the weights of indicators of different attribute dimensions.

8. The multi-objective response evaluation method for laser welding systems targeting carbon emissions and welding quality according to claim 1, characterized in that: The entropy weight method is used to determine the weights of different evaluation indicators under the same attribute dimension.

9. A multi-objective response evaluation device for a laser welding system targeting carbon emissions and welding quality, characterized by: including a data input module, a microprocessor, a display, a keyboard and a memory; The memory stores a computer program; when the computer program is executed by the microprocessor, it is used to implement the method according to any one of claims 1 to 8; The computer program stored in the memory can be adjusted via the keyboard; When working, the data input module transmits the input measured index values ​​and simulated index values ​​to the microprocessor; the microprocessor analyzes and evaluates the input index values ​​and outputs an evaluation report to the display.

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