Method for optimizing furniture structure based on grey Taguchi method

Abstract

Optimizing furniture structure based on a grey Taguchi method is provided. Relevant design parameters and evaluation indexes are determined, and orthogonal tests using a Taguchi method are designed an

Description

TECHNICAL FIELD

[0001] The invention relates to a field of furniture, and in particular to a method for optimizing furniture structure based on a grey Taguchi method. BACKGROUND

[0002] Taguchi method is a low-cost and high-efficiency quality engineering method, which emphasizes that improvement of product quality is not through inspection, but through design. The purpose of Taguchi method is to make quality of the designed products stable and has less fluctuating, and to make the production process insensitive to various noises. In a process of product design, high-quality products are developed under a low-cost condition by using a functional relationship of quality, cost and benefit.

[0003] Grey correlation analysis is a very active branch of grey system theory. The basic idea of the grey correlation analysis is to determine whether the relationship between different sequences is close based on the similarity between geometric shapes of sequence curves. The basic thought of the grey correlation analysis is to convert observation values of discrete behaviors of system factors into piecewise continuous polylines by a linear interpolation method, and then construct a model for measuring correlation degree based on geometric characteristics of the polylines. The closer the geometric shapes of the polylines are, the greater the correlation degree between the corresponding sequences, and vice versa.

[0004] Panel furniture is furniture made up of all surface-decorated man-made panels and hardware. It has basic features such as detachable, changeable shape, stylish appearance, not easy to deform, stable quality, and affordable. The optimization of furniture structure is now mostly aimed at extreme value optimization, for example, the size of structure is optimized as far as possible under the premise of satisfying the structural strength, so that the lightest-weight products are obtained and the material cost is reduced. The comer joint strength for the panel furniture is affected by multiple factors, so evaluation of the corner joint strength should not be performed only on a single index, but the traditional test design can only solve the single-index problem.

[0005] However, the above technology has the following problems when optimizing the furniture structure: (1) Optimized parts are manufactured in a limiting design scheme under a condition of complying with structural strength requirements. The structural quality is often distributed near a tolerance limit. And the structural strength of the product is inevitably affected by processing errors, environmental changes and an increase of service life, which easily causes the structural strength of the product to exceed the tolerance limit, making the product defective. (2) The traditional test design can only solve the single-index problem and cannot optimize the multi-index problem, so that the problems solved by the traditional test design are relatively simple, and additional steps are required to solve other problems, which wastes a lot of time and reduces the efficiency of the test.

[0006] Therefore, it is necessary to improve the test method in conventional art to solve the above problems. SUMMARY

[0007] The invention overcomes shortcomings of conventional art and provides a method for optimizing furniture structure based on a grey Taguchi method. The technical problems to be solved include: providing the method for optimizing the furniture structure based on the grey Taguchi method. In the present invention, based on a grey correlation analysis method, a single-index optimization problem is converted into a multiple index optimization problem to solve, and on the basis of the grey correlation analysis method combined with a Taguchi method, a design goal of panel furniture is converted into reducing a fluctuation of product quality and enhancing anti-interference ability for various factors, thus realizing multi-index robust optimization design of corner joint strength of a T-shaped plate structure. It solves a problem that traditional test design can only solve the single-index problem, changes a previous limiting design scheme, and avoids a phenomenon that structural strength of a product exceeds a tolerance limit, making the product defective.

[0008] In order to achieve the above effects, the present invention provides following solutions. The method for optimizing the furniture structure based on the grey Taguchi method includes:

[0009] SI, determining evaluation indexes and relevant design parameters affecting structure quality, and designing and performing orthogonal tests using a Taguchi method to collect original test data;

[0010] S2, converting the original test data in SI into signal-to-noise ratio to construct a signal-to-noise ratio matrix; where a calculation model of the signal-to-noise ratio is selected from nominal-the-type characteristic, larger-the-better characteristic and smaller-the-better characteristic based on a type of a selected target quality characteristic;

[0011] S3, nondimensionalizing the signal-to-noise ratio of the original test data in S2 based on the evaluation indexes of different orders of magnitude to obtain nondimensionalized data and construct a normalized signal-to-noise ratio matrix;

[0012] S4, processing the nondimensionalized data in S3 by means of a Deng’s correlation model to construct a grey correlation coefficient matrix, and taking an average value of correlation coefficients for each test as a value of grey correlation degree of the test;

[0013] S5, ranking the values of grey correlation degree of tests in S4 to select the best process parameters, and performing analysis of variable on the values of grey correlation degree of the tests to determine the best combination parameter in the tests; and

[0014] S6, carrying out a contrast test between the best combination parameter in S5 and an initial combination parameter to obtain an optimization conclusion.

[0015] In an embodiment of the invention, in S2, converting the original test data in SI into the signal-to-noise ratio to construct the signal-to-noise ratio matrix may include: —2

[0016] S2.1, taking 7 = 10lg(X) as a calculation model of the nominal-the-type 1 q characteristic, where y is test data, and a is a noise factor; taking 77 = 101g(— V yf) QTi as a calculation model of the larger-the-better characteristic , where yt is a test result, 1 q and q is the number of repetitions of each test; and taking 77 = — 101g(— \yf) as a 7 m calculation model of the smaller-the-better characteristic; and

[0017] S2.2, constructing the signal-to-noise ratio matrix as follows: XI 1^(1) 71(2) ••• 7,(0’ N2 72(1) 72(2) ••• 7:(0 M' N' 7X1) 7,(2) ••• 7,(0

[0018] In an embodiment of the invention, in S3, the nondimensionalized data Xg VtJ - mm 7^ may be obtained by nondimensionalization: x, =--------1:— , where Xg is a max7.-min7,, i v i y normalized value of test data, / / ,.- is the signal to noise ratio of the original test data, i is the number of test evaluation indexes, j is the number of tests in a orthogonal table, min 77. is the minimum value of the signal to noise ratio of the original test data for the ith evaluation index, and max 77. is the maximum value of the signal to noise i J ratio of the original test data for the i* evaluation index.

[0019] In an embodiment of the invention, in S3, the normalized signal-to-noise ratio matrix may be constructed as follows: M = 2

[0020] In an embodiment of the invention, in S4, the Deng’s correlation model is: minmin|x°-x | + ^maxmax|x°-x I 0 . - _ * j ' 1 ‘ >1 7| where x, is a normalized ideal dimensionless x,° -x,, +4 maxmax.v -x ; i a 1 '+ value of the ith index, generally xf = 1, and is a resolution coefficient, £ e (0,1], and Q = 0.5 in above formula.

[0021] In an embodiment of the invention, in S5, performing analysis of variable on the values of grey correlation degree of the tests may include:

[0022] S5.1, taking ^^+^+^+--- + ^ ~ s m as a formula for calculating a sum of squared deviations, where K is a sum of test results at each level of a design factor, m is the number of levels of each design factor, and n is the number of tests;

[0023] S5.2, taking fs = m -1 as a formula for calculating degrees of freedom; ,0024] S53, taking M as a fonnula for eale^

[0025] S5.4, taking F = as a formula for calculating a value of F; and

[0026] S5.5, taking Cs = — as a formula for calculating a contribution rate of each Qt design factor.

[0027] In an embodiment of the invention, in SI, an influence of dowel diameter, dowel length and connector length on comer joint strength of a shelf may be studied by the orthogonal tests.

[0028] In an embodiment of the invention, in S6, the initial combination parameter may be a middle value among values of grey correlation degree of factors.

[0029] In an embodiment of the invention, in SI, the structure may be a T-shaped plate structure, and comer joint strength of the T-shaped plate structure may be optimized.

[0030] In an embodiment of the invention, the T-shaped plate structure may be a simplified connection structure between a cabinet-type shelf and a side plate, and a base material may be solid-wood multi-layer plate.

[0031] The invention solves defects in background, and has following beneficial effects: (1) in the invention, on the basis of the grey correlation analysis method combined with the Taguchi method, the design goal of the panel furniture is converted into reducing the fluctuation of the product quality and enhancing the anti-interference ability of various factors, thus realizing the multi-index robust optimization design of the comer joint strength of the T-shaped plate structure, strengthening the structural strength of the product, increasing service life of the product, and reducing probability of defective products; and (2) in the invention, based on the grey correlation analysis method, the single index optimization problem is converted into the multiple index optimization problem to solve, and deformation quantity of a whole structure, and the maximum equivalent stress of connectors and dowels are all taken as measurement indexes for analysis, which increase the number of optimization problems and consider the impact of multiple measurement indexes on the furniture structure, so as to ensure the possible problems of the furniture optimization and improve the quality of the furniture structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to illustrate the embodiments of the present invention or the technical solutions of the conventional art more clearly, the accompanying drawing used in the embodiments or the conventional art will be briefly described below. Apparently, the accompanying drawings described below show merely some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the accompanying drawings without creative efforts.

[0033] FIG. 1 is a flow chart of a method for optimizing furniture structure of a preferred embodiment of the invention;

[0034] FIG. 2 is a connection diagram of a T-shaped structure of a preferred embodiment of the invention; and

[0035] FIG. 3 shows a relationship between grey correlation degree of signal-to-noise ratio and range of a preferred embodiment of the invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are merely a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by the ordinary skilled in the art based on the embodiment of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0037] In the following description, many specific details are set forth to better understand the invention, but the invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited by the specific embodiments disclosed below.

[0038] In the description of the present application, it should be understand that the orientation or position relations indicated by the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like are based on the orientation or position relations shown in the accompanying drawings, only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the scope of protection of this application. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features limited to "first", "second", etc. may explicitly or implicitly include one or more of these features. In the description of the invention, unless otherwise stated, "a plurality of means two or more.

[0039] In the description of this application, it should be noted that unless specified and limited otherwise, the terms "mount", "connect" and "couple" should be understood in a broad sense, for example, they may be fixed connection, removable connection, or integrated connection; be mechanical connection or electrical connection; be direct connection or indirect connection via intervening structures; and also be inner communication of two elements. The specific meaning of the above terms in this application may be understood by those skilled in the art based on specific situations.

[0040] As shown in FIG. 1, the present invention provides a method for optimizing furniture structure based on a grey Taguchi method, including SI, S2, S3, S4, S5 and S6.

[0041] In SI, evaluation indexes and relevant design parameters affecting structure quality are determined, and orthogonal tests are designed and performed using a Taguchi method to collect original test data.

[0042] In S2, the original test data in SI are converted into signal-to-noise ratio to construct a signal-to-noise ratio matrix; where a calculation model of the signal-to-noise ratio is selected from nominal-the-type characteristic, larger-the-better characteristic and smaller-the-better characteristic based on a type of a selected target quality characteristic.

[0043] In S3, the signal-to-noise ratio of the original test data in S2 is nondimensionalized based on the evaluation indexes of different orders of magnitude to obtain nondimensionalized data and construct a normalized signal-to-noise ratio matrix.

[0044] In S4, the nondimensionalized data in S3 is processed by means of a Deng’s correlation model to construct a grey correlation coefficient matrix, and an average value of correlation coefficients for each test is taken as a value of grey correlation degree of the test.

[0045] In S5, the values of grey correlation degree of tests in S4 is ranked to select the best process parameters, and analysis of variable on the values of grey correlation degree of the tests is performed to determine the best combination parameter in the tests.

[0046] In S6, a contrast test between the best combination parameter in S5 and an initial combination parameter is carried out to obtain an optimization conclusion.

[0047] In the invention, on the basis of the grey correlation analysis method combined with the Taguchi method, the design goal of panel furniture is converted into reducing fluctuation of product quality and enhancing anti-interference ability of various factors, thus realizing the multi-index robust optimization design of comer joint strength of the T-shaped plate structure, strengthening structural strength of the product, increasing service life of the product, and reducing probability of defective products.

[0048] In an embodiment of the invention, S2 may include S2.1 and S2.2.

[0049] In S2.1, a calculation model of the nominal-the-type characteristic is taken —2 as i] = 101g(Ar), where y is test data, and cr is a noise factor; a calculation model of g" 1 q the larger-the-better characteristic is taken as 77 = 101g(—V jf), where yt is a test result, and q is the number of repetitions of each test; and a calculation model of the 1 q smaller-the-better characteristic is taken as 77 = —10lg(— V yf).

[0050] In S2.2, the signal-to-noise ratio matrix is constructed as follows: >(1) 7i(2) ••• 7) (O’ 72(1) 72 (2) ••• 72 (0 7j (1) 7 / 2) ••• 77^( / )

[0051] In an embodiment of the invention, in S3, the nondimensionalized data Xg is 7y -min / 7y obtained by nondimensionalization: x„ =--------!------, where Xy is a normalized max rig - min ty value of test data, q- is the signal to noise ratio of the original test data, i is the number of test evaluation indexes, j is the number of tests in a orthogonal table, min rig is the minimum value of the signal to noise ratio of the original test data for the ith evaluation index, and max 77^ is the maximum value of the signal to noise ratio of the original test data for the ith evaluation index.

[0052] In an embodiment of the invention, in S3, the normalized signal-to-noise ratio X1) ^,(2) ■ •• *1 matrix is constructed as follows: M = N. = -^2(0 x,(2) • •’ X2 ■ - Y

[0053] In an embodiment of the invention, in S4, the Deng’s correlation model is: minminx®-x.+^maxmaxx°-x 0 . -, / - _ - j 1 1 ■ 2 1 . where x, is a normalized ideal dimensionless x® -x +<^maxmaxx® -x J I * j I 1 value of the ith index, generally xf = 1, and is a resolution coefficient, £ e (0,1], and = 0.5 in above formula.

[0054] In an embodiment of the invention, in S5, performing analysis of variable on the values of grey correlation degree of the tests includes S5.1, S5.2, S5.3, S5.4 and S5.5.

[0055] In S5.1, a formula for calculating a sum of squared deviations is taken as <« Y >y. Kl+Kl+K:+--- + K2m HY Y Q, =—1------------------, where a is a sum ot test results at each m n level of a design factor, m is the number of levels of each design factor, and n is the number of tests.

[0056] In S5.2, a formula for calculating degrees of freedom is taken as fs = m-l.

[0057] In S5.3, a formula for calculating a mean square is taken as M = —. Js

[0058] In S5.4, a formula for calculating a value of F is taken as F = .

[0059] S5.5, a formula for calculating a contribution rate of each design factor is O taken as C. = . Qt

[0060] In an embodiment of the invention, in SI, an influence of dowel diameter, dowel length and connector length on comer joint strength of a shelf is studied by the orthogonal tests.

[0061] In an embodiment of the invention, in S6, the initial combination parameter is a middle value among values of grey correlation degree of factors.

[0062] In an embodiment of the invention, in SI, the structure is a T-shaped plate structure, and comer joint strength of the T-shaped plate structure is optimized.

[0063] In an embodiment of the invention, the T-shaped plate structure is a simplified connection structure between a cabinet-type shelf and a side plate, and a base material is solid-wood multi-layer plate.

[0064] In the invention, based on the grey correlation analysis method, the single index optimization problem is converted into the multiple index optimization problem to solve, and deformation quantity of a whole structure, and the maximum equivalent stress of connectors and dowels are all taken as measurement indexes for analysis, which increase the number of optimization problems and consider the impact of multiple measurement indexes on the furniture structure, so as to ensure the possible problems of the furniture optimization and improve the quality of the furniture structure.

[0065] In the invention, firstly, the evaluation indexes and the relevant design parameters affecting the structure quality are determined, and the orthogonal tests are designed and performed using the Taguchi method to collect the original test data. Then the original test data is converted into the signal-to-noise ratio to construct the signal-to-noise ratio matrix; where the calculation model of the signal-to-noise ratio is selected from the nominal-the-type characteristic, the larger-the-better characteristic and the smaller-the-better characteristic based on the type of the selected target quality characteristic. The signal-to-noise ratio of the original test data is nondimensionalized based on the evaluation indexes of different orders of magnitude to obtain nondimensionalized data and construct the normalized signal-to-noise ratio matrix. Then, the nondimensionalized signal-to-noise ratio data is processed by means of the Deng’s correlation model to construct the grey correlation coefficient matrix, and the average value of the correlation coefficients for each test is taken as the value of the grey correlation degree of the test. Then the values of grey correlation degree of tests are ranked to select the best process parameters, and analysis of variable on the values of grey correlation degree of the tests is performed to determine the best combination parameter in the tests. Finally, the contrast test between the best combination parameter and the initial combination parameter is carried out to obtain the optimization conclusion.

[0066] Example 1

[0067] Step 1, Orthogonal test design

[0068] FIG. 2 shows a connection diagram of the T-shaped structure of a preferred embodiment of the invention. The T-shaped structure is the simplified connection structure between the cabinet-type shelf and the side plate. The base material is the solid-wood multi-layer plate. A horizontal plate has a size of 200mm x 100mm x 18mm (length x width x thickness), and a longitudinal plate has a size of 150mm x 100mm x 18mm (length x width x thickness). The two plates are connected by using eccentric connectors and dowels, and the spacing between the two connectors is 32mm. In the invention, the influence of the dowel diameter, the dowel length and the connector length on the comer joint strength of the shelf is studied by the orthogonal tests. A constant load of 150N is applied to an end of the horizontal plate of the structure, and the amount of structural deformation is recorded while also taking into account the maximum equivalent stresses experienced by the connectors and the dowels. Values of each factor are shown in Table 1, test design schemes are shown in Table 2, and test results are shown in Table 3. Table 1 Design Factors and Levels Factor A dowel diameter B dowel length C connector length 1 6 30 35 2 8 40 40 3 10 50 45 Table 2 Orthogonal Test Design test 1 2 3 4 5 6 7 8 9 A 1 1 1 2 2 2 3 3 3 B 1 2 3 1 2 3 1 2 3 C 1 2 3 2 3 1 3 1 2 Table 3 Target Quality Characteristic Values of Test Piece serial number deformation quantity / mm equivalent stress of connector / MPa equivalent stress of dowel / MPa 1 33.30 669.03 36.06 2 33.30 658.33 52.93 3 33.26 640.72 67.06 4 30.77 495.76 98.12 5 31.59 603.10 99.54 6 32.09 708.70 72.16 7 32.21 628.42 40.05 8 32.22 661.79 13.37 9 32.20 650.66 42.21

[0069] Step 2, Data dimensionless processing

[0070] Because original values of factors have different units of measurement in different dimensions, and there are differences in dimensions and magnitude between data, thus comparison cannot be performed. Before correlation degree is calculated, the signal-to-noise ratio of the original data needs to be nondimensionalized, so as to normalize the original data. The data is dimensionless based on the formula of 77,,-min x„ =--------1-------, and the processed data is recorded in Table 4. max 77. - min 77, i J i J Table 4 Nondimensionalized Signal to Noise Ratio serial number normalized signal to noise ratio of deformation quantity normalized signal to noise ratio of equivalent stress of connector normalized signal to noise ratio of equivalent stress of dowel 1 0 0.16 0.51 2 0 0.21 0.31 3 0.02 0.28 0.20 4 1 1 0.01 5 0.67 0.45 0 6 0.47 0 0.16 7 0.42 0.34 0.45 8 0.42 0.19 1 9 0.43 0.24 0.43

[0071] Step 3, Calculation of grey correlation degree

[0072] To determine the influences of parameters on the test results, the grey correlation coefficient is calculated based on the formula of minminxf ^x.+^maxmaxx° ^x. . j 1 1 ■ J 1 1 . In this test, m is the number of target quality x,° -x, +4niaxmasx,0 -x. 1 iJ\^ i j \ 1 y | characteristic indexes, m=3, and data processing results are shown in Table 5. Table 5 Grey Correlation Coefficients and Correlation Degree of Signal to Noise Ratio serial number correlation coefficient of deformation quantity correlation coefficient of equivalent stress of connector correlation coefficient of equivalent stress of dowel grey correlation degree 1 0.33 0.37 0.50 0.40 2 0.33 0.39 0.42 0.38 3 0.34 0.41 0.38 0.38 4 1 1 0.33 0.78 5 0.60 0.48 0.33 0.47 6 0.48 0.33 0.37 0.39 7 0.46 0.43 0.47 0.46 8 0.46 0.38 1 0.61 9 0.47 0.40 0.47 0.44

[0073] The greater the grey correlation degree is, the better the test result. It can be seen from the visual analysis of table 5 that the grey correlation degree of Test 4 (A2B1C2) in 9 groups of tests is the highest, i.e., has a better comprehensive performance. The correlation degree of average signal-to-noise ratio of each factor level is calculated. It can be seen From Table 6 that A2B1C2 is the optimal scheme designed for a multi-factor robust optimization.

[0074] FIG. 3 shows a relationship between the grey correlation degree of signal-to-noise ratio and range of the preferred embodiment of the invention. By the calculation and comparison of range of 3 levels for each factor, it can be seen that the dowel diameter has the largest influence on the results of the multi-target robust optimization test, the second is the dowel length, and the connector length has the smallest influence. Table 6 Grey Correlation Degree of Signal-to-noise Ratio of Each Factor Level and Range A B C kl 0.39 0.55 0.47 k2 0.55 0.49 0.53 k3 0.50 0.41 0.43 range(R) 0.16 0.14 0.10

[0075] Step 4, Variance analysis

[0076] To determine the degree of influence of each design factor on the overall system, the analysis of variable is performed on each design factor. When the deformation quantity of structure, the equivalent stress of connectors and the equivalent stress of dowel are integrated by the grey correlation method, and the overall system is evaluated by multiple indexes, it can be seen from table 7 that contribution rate of the dowel diameter to the overall system is the largest, 48%, the influence of the dowel length is the second, 34%; and contribution rate of the connector length is the smallest, 17%. Table 7 Analysis of Variable of Target Quality Characteristics with Multiple indexes design factor sum of squared deviations degree of freedom mean square Value of F contribution rate / % A 0.04 2 0.02 0.29 48 B 0.03 2 0.01 0.21 34 C 0.02 2 0.01 0.11 17 0.06 2 0.03 error 0.15 8 0.07 total 0.09

[0077] Step 5, Contrast test

[0078] Middle levels of factors are selected as an initial group. It can be seen from table 7 that the middle levels of factors are A3, B2, and Ci respectively, thus the initial control group A3B2C1 is selected to perform the contrast test with an optimized group A2B1C2 designed by multi-target robust optimization. The two groups of target quality characteristic values and values of grey correlation degree are calculated and the calculated results as shown in Table 8. After optimization, the deformation quantity of the structure is reduced by 1.45mm, the deformation quantity of the dowel in direction of force application is reduced by 0.1mm, and the deformation quantity of the connector in the same direction is reduced by 0.01mm. The equivalent stress of the connector is reduced by 166.03MPa. Although the equivalent stress of the dowel is increased, equivalent stress distribution between the connector and the dowel after optimization is more balanced than that of the initial group, and comprehensive performance is improved. The grey correlation degree is increased by 27.9% from 0.61 to 0.78. Table 8 Values of Contrast Tests initial group optimized group combination scheme A3B2C1 A2B1C2 deformation quantity / mm 32.22 30.77 deformation quantity of connector in Y axis / mm 0.22 0.21 deformation quantity of dowel in Y axis / mm 0.21 0.11 equivalent stress of connector / MPa 661.79 495.76 equivalent stress of dowel / MPa 13.37 98.12 grey correlation degree 0.61 0.78

[0079] In the invention, on the basis of the grey correlation analysis method combined with the Taguchi method, the design goal of the panel furniture is converted into reducing the fluctuation of the product quality and enhancing the anti-interference ability of various factors, thus realizing the multi-index robust optimization design of the comer joint strength of the T-shaped plate structure, strengthening the structural strength of the product, increasing service life of the product, and reducing probability of defective products.

[0080] In the invention, based on the grey correlation analysis method, the single index optimization problem is converted into the multiple index optimization problem to solve, and deformation quantity of a whole structure, and the maximum equivalent stress of connectors and dowels are all taken as measurement indexes for analysis, which increases the number of optimization problems and considers the impact of multiple measurement indexes on the furniture structure, so as to ensure the possible problems of the furniture optimization and improve the quality of the furniture structure.

[0081] According to the ideal embodiments of the invention above, the above -mentioned description can be completely changed and modified within the scope of the technical ideas of the invention without deviating from the scope of the technical ideas of this invention. The technical scope of the invention is not limited to the content in the specification. The technical scope must be determined according to the scope of the claims.

Claims

1. A method for optimizing furniture structure based on a grey Taguchi method, characterized in that the method comprises:SI, determining evaluation indexes and relevant design parameters affecting structure quality, and designing and performing orthogonal tests using a Taguchi method to collect original test data;S2, converting the original test data in SI into signal-to-noise ratio to construct a signal-to-noise ratio matrix; wherein a calculation model of the signal-to-noise ratio is selected from nominal-the-type characteristic, larger-the-better characteristic and smaller-the-better characteristic based on a type of a selected target quality characteristic;S3, nondimensionalizing the signal-to-noise ratio of the original test data in S2 based on the evaluation indexes of different orders of magnitude to obtain nondimensionalized data and construct a normalized signal-to-noise ratio matrix;S4, processing the nondimensionalized data in S3 by means of a Deng’s correlation model to construct a grey correlation coefficient matrix, and taking an average value of correlation coefficients for each test as a value of grey correlation degree of the test;S5, ranking the values of grey correlation degree of tests in S4 to select best process parameters, and performing analysis of variable on the values of grey correlation degree of the tests to determine a best combination parameter in the tests; andS6, carrying out a contrast test between the best combination parameter in S5 and an initial combination parameter to obtain an optimization conclusion.

2. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in S2, converting the original test data in SI into the signal-to-noise ratio to construct the signal-to-noise ratio matrix comprises:S2.1, taking rj = 10lg(-^-) as a calculation model of the nominal-the-type cr~characteristic, wherein y is test data, and cr is a noise factor; taking 1 q77 = 101g(—as a calculation model of the larger-the-better characteristic, wherein yt is a test result, and q is a number of repetitions of each test; and taking 1 q77 = -101g(— as a calculation model of the smaller-the-better characteristic;<1%andS2.2, constructing the sig XT 7i(l) 7i(2) ••• ?7i GT M'- = ^2(1) X2) ••• ^2 G) XJ JI j® X2) ( / )al-to-noise ratio matrix as follows:

3. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in S3, the nondimensionalized data Xy is “min^obtained by nondimensionalization: ~--------2------ , wherein xy is amax 77.. min 77.i J 1 Jnormalized value of test data, rfy is the signal to noise ratio of the original test data, / is a number of test evaluation indexes, j is a number of tests in a orthogonal table, min rr is a minimum value of the signal to noise ratio of the original test data for an ith evaluation index, and max 77, is a maximum value of the signal to noise ratio of i Jthe original test data for the i* evaluation index.

4. The method for optimizing the furniture structure based on the grey Taguchimethod according to claim 3, wherein in s: 1, the normalized signal -to-noise ratio matrix is constructed as follows M = XI M %i(l) X) S S ... X) N. L j J X; X)j5. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in S4, the Deng’s correlation model is: minminxf-x +^maxmaxLy°-x . 0 .a _ - j ' 1 ■ j 1 1 , wherein x, is a normalized ideal- . 1 . !x,° -X, +u maxmasx,0 -x Idimensionless value of an ith index, generally x^ = 1, and £ is a resolution coefficient, £ e (0,1], and Q = 0.5 in above formula.

6. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in S5, performing analysis of variable on the values of grey correlation degree of the tests comprises:If i । P” 2 , 1^2 ____+K1S5.1, taking Qs =—!------------------—Z_ as a formula forcalculating a sum of squared deviations, wherein K is a sum of test results at each level of a design factor, m is a number of levels of each design factor, and n is a number of tests;S5.2, taking fs=m-\ as a formula for calculating degrees of freedom ;S5.3, taking M = — as a formula for calculating a mean square;JsS5.4, taking F = as a formula for calculating a value of F; andS5.5, taking Csas a formula for calculating a contribution rate of eachdesign factor.

7. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in SI, an influence of dowel diameter, dowel length and connector length on comer joint strength of a shelf is studied by the orthogonal tests.

8. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in S6, the initial combination parameter is a middle value among values of grey correlation degree of factors.

9. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 1, wherein in SI, the structure is a T-shaped plate structure, and comer joint strength of the T-shaped plate structure is optimized.

10. The method for optimizing the furniture structure based on the grey Taguchi method according to claim 9, wherein the T-shaped plate structure is a simplified connection structure between a cabinet-type shelf and a side plate, and a base material is solid-wood multi-layer plate.

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