Emotional Optimized Design of Electro-hydraulic Actuators

Transcription

1 Sensors & Transducers, Vol. 77, Issue 8, Auust, pp. 9-9 Sensors & Transducers by IFSA Publishin, S. L. Emotional Optimized Desin of Electro-hydraulic Actuators Shi Boqian, Shan Chanzhen School of Mechanical Enineerin, University of Science and Technoloy, Beijin, 86, China Tel.: , fa: eceived: 9 June /Accepted: July /Published: Auust Abstract: This paper presents a mechanical product optimization desin method based on Kansei enineerin, which consider both the emotional needs of consumers, but also take into account the functional properties of the product itself. While established a relationship model between consumer preferences and product desin parameters. The model is applied to optimal desin of electro-hydraulic actuator product appearance to obtain a smaller volume electro-hydraulic actuator product which appearance and structure more reasonable, realized the unity of the perceptual and rational desin. Copyriht IFSA Publishin, S. L. Keywords: Kansei enineerin, Optimization method, Quantitative model, Parametric desin.. Introduction Traditional mechanical optimization desin methods often only consider the optimal technical parameters that maimize the technical function of the product. With the rapid development of economic and social as well as market demand to further epand and improve, the eistin desin for mechanical products have not only focus on internal factors such as technoloy, function, but also by means of industrial desin and stratey for a variety of eternal factors, mechanical desin and landscapin products, thereby increasin the added value of machinery products and artistry. Therefore, based on the perceptual enineerin mechanical desin method arises at the historic moment. "Kansei enineerin" to the past as hard to quantify, only qualitative, irrational emotional reaction without loic, quantified usin the modern computer technoloy, in order to develop a new eneration of technoloy and product desin. Domestic and forein scholars have applied "Kansei enineerin" to the practice of mechanical products. Kuniaki, Nakada et al. make "Kansei Enineerin" used in the desin of construction machinery products, he pointed out that "Kansei Enineerin" can create a database to quantify the relationship between consumer values and product desin elements, and its application to the product evaluation []. Kun-Chieh Wan, Jun-Chin Lian et al. Combined with the perceptual enineerin and support vector machine (SVM), to determine the best combination of desin elements of the machine tool product shape, so as to satisfy the consumer's perceptual demand []. Hsin-Hsi Laia, Yan Chen, and others use "Kansei enineerin", to eplore a kind of user conition into product desin element method, which uses "Kansei enineerin" concept and neural network to etract the eperimental sample data, and analyze the data to et the product manufacturers need to provide what kind of color, shape of product to attract users []. In a word, whether forein or domestic, "Kansei Enineerin" studies are radually deepened. 9

2 Sensors & Transducers, Vol. 77, Issue 8, Auust, pp. 9-9 However, in practical applications, "Kansei enineerin" does not make substantive application of the results, most of the studies only focus on "Kansei Enineerin" study of a unilateral (such as shape, appearance) in the process of implementation. Therefore, this paper presents a perceptual enineerin machinery products based optimization method, consider both the emotional needs of consumers, but also take into account the functional properties of the product itself, that is to create a shape with ecellent quality and keep the oriinal function of the mechanical efficiency of the product, achieve emotional needs and machinery products technical parameters of unity. (electro-hydraulic actuator structure shown in Fi. ), the oal is to meet the emotional needs while makin the smallest structure is more reasonable. There are five desin variables are the size and the variable parameters, namely the size of the variable in the lonitudinal direction X, X, X; variable width direction dimension X, X.. Established the Perceptual Quantization Emotional quantitative model is the establishment of consumer preferences (emotional needs) and the relational model of product desin parameters will soon be converted to perceptual evaluation scale enineerin scale. More and more studies show that the mappin between consumer preferences and product desin parameters, mainly throuh statistical methods to create, for eample, multiple linear reression analysis [], fuzzy reression analysis [], number theory class analysis [6], and artificial intellience technoloy neural network alorithm [7], enetic alorithm [8]. In this paper, multiple linear reression analysis is applied to establish emotional quantitative model. Compared with artificial intellience and fuzzy alorithm based on artificial intellience the use of multiple linear reression analysis to et more clear information. The latter in the form of a polynomial epression of the relationship between emotional needs and desin elements. The remarkable desin elements and the interaction between desin elements are obvious. Durin the analysis, the consumer preference value as a reference variable, the product desin parameters (desin elements) as the dependent variable, forecastin model is as follows; y α + α + α + + α i α, α,, α,,, p where p are the parameters to be estimated, is the independent variable of multiple linear reression models. Analysis of the models, which can be obtained desin variables affectin consumer demand as well as its emotional impact.. Applications We use quantitative models sensual electrohydraulic actuators for eterior desin is optimized p p Fi.. The electro-hydraulic actuator structure diaram... Established Objective Function The electro-hydraulic actuator volume mainly depends on the volume of a cylinder hydraulic power mechanism. Therefore, the optimization of objective function is minimum volume as follows, f [ + ( ] ( ) ) + We build electro-hydraulic actuator perceptual demand under the objective function. First, select the diaram electro-hydraulic actuator as sample, and make it into a questionnaire. Select volunteers scripts for testin, of which man-made mechanical desin major students, five were full-time teachers, professional desin staff, for ordinary users, let them make perceptual evaluation diaram for each sample, and played a certain score ( to points), as shown in Table for score sheets. Table. Perceptual scale. X X X X X The averae score

3 Sensors & Transducers, Vol. 77, Issue 8, Auust, pp. 9-9 Then the averae of each sample perceptual evaluation of independent variables, X, X, X, X, X as desin variables, usin SPSS statistical software for multiple reression analysis [9], the statistical results are as follows (Table ): Table. parameters. Square Adjusted Square Std. Error of the Estimate.97a a. Predictors: (Constant), X, X, X, X, and X As can be seen from Table, the correlation coefficient is.97, indicatin ood correlation between the independent and dependent variables. The coefficient of determination Square is.97 ( Square reression reflects the overall effect, the closer to the better.); the variation in the dependent variable, from 9.7 % could be eplained by the chane of variables []. Table Analysis of variance usin the reression equation for the entire test of sinificance, where F.9, P. <., the difference was statistically sinificant, that this reression equation can be established []. Table. eression analysis of variance. Sum of Mean df Squares Square F Si. eression 6.. esidual..6.9.a Total a. Predictors: (Constant), X, X, X, X, and X Table Analysis of variance partial reression analysis done for each dependent variable. In the table, a sinificant probability of the constant t is. >., which means that the constant term and no sinificant difference, the constant term in the equation should not occur []. Similarly, the probability of a sinificant value of the variable t of X.8 >., X represents a coefficient not sinificantly different from, X variable should not be interpreted as a variable in the equation; the variables X, X, X, X of sinificance probability value of less than. t, the X, X, X, X can be used as eplanatory variables in the equation. Table. eression analysis of variance. Unstandardized Standardized Coefficients Coefficients t Si. Std. B Beta Error (Constant) X X X X X The variable X removed from the eplanatory variables, usin SPSS software multiple linear reression analysis aain, the results are as follows (Table ). Table. eression analysis of variance. Unstandardized Coefficients Standardized Coefficients t Si. B Std. Error Beta (Constant) X X X X From the table, we see a sinificant probability remains constant term t is reater than., which means that the constant term should not appear in the equation, so we have to build multiple reression equation is, h( ) Because there are multiple objective optimization model in order to facilitate solvin multi-objective optimization problem we will into a sinle objective optimization problem, and the correspondin weihts assined to each optimization objectives, the size of the weihtin factor reflects the oals of the overall desin of the sub- proram the importance. Since the maimum value of the objective function in a request (perceptual objective function), one for the minimum (volume objective function), there are two different types of objective functions, in order to establish the overall objective function that will do the most for the minimum objective function into the objective function that, f ( ). +. h( ).6.6 To establish the overall objective function is as follows; min f( ) ω f( ) + ωf( ), where ω is the weiht coefficient of taret function f ( ), here take ω.6; ω is the weiht coefficient of taret function f ( ), here take ω.... Establish Emotional Constraint Function The electro-hydraulic actuator desin indees in the followin Table 6. 9

4 Sensors & Transducers, Vol. 77, Issue 8, Auust, pp. 9-9 Table 6. The electro-hydraulic servo actuator desin specifications. No. Inde Inde value Maimum output torque Nm Maimum output anle 9 The fastest runnin time of 9 s Control accuracy. Accordin to the result of market research, when the maimum output torque of Nm, preliminarily determine the scope of each desin variable, respectively is 9 6, 8,,, 7. In order to achieve the appearance of electrohydraulic actuators symmetry and coordination requirements, but also need to be met; ( ) ( ).. Established Technical Parameters Constraint Function Direct drive electro-hydraulic actuators for the main components of the rack and pinion hydraulic servo swin cylinder, so its main dimensions also its decision. Accordin to a hydraulic manual, reference eistin products work pressure, combined with the actual conditions, primary hydraulic cylinder workin pressure P is 6 MPa, cylinder force balance equation is: A PA T / P D, () where P is the oil chamber pressure; P is the back to the oil chamber pressure; A is the piston cylinder diameter; D is the Gear pitch diameter. Piston cylinder area: A πd /, () where D is the cylinder internal diameter of the cylinder. Take ear modulus m 6, number of teeth Z. Pitch circle diameter mm, the oil return chamber pressurep, the formula (), () a piston-cylinder diameter D 7. mm, round take D 8 mm. At this time, by the formula (), () the actual workin hydraulic cylinder pressure.8 MPa. Maimum anle of rotation of the ear actual work is 9, but considerin the percent marin, accordin to the rotation anle of the actual calculation. Thus the piston stroke can be obtained as follows. πd S. 6mm 6 Optimization of technical parameters can be established above the technical parameters of the model constraint functions, as follows.... Size Constraint. δ, S + L + where S is the piston stroke, here s6 mm; L is the rack lenth, here L mm; δ is the piston cylinder wall thickness, here δ mm. Therefore, we can conclude ( ) ( ) 6... Size Constraint D a + δ, where D is the GEA tip diameter, here a D a D + m 6mm ; δ is the includin wall thickness and ap size, here δ mm. Therefore, we can conclude ( ) ( ) 6... Size Constraint D +, δ where D is the diameter piston-cylinder, here D 8mm ; δ is the piston cylinder wall thickness, here δ mm. Therefore, we can conclude ( ) ( ) In summary, the optimization model is as follows: [,,, ] T, min f( ) ω f( ) + ωf( ) s. t i ( ) i,,,, 9

5 Sensors & Transducers, Vol. 77, Issue 8, Auust, pp Optimization and Analysis Select the foalattain function provided by MATLAB optimization toolbo to solve the multiobjective prorammin problems, optimization results is shown in Fi.. optimization desin method based on perceptual enineerin. Established the relationship between the consumer preferences and product desin parameter model, and applied to the electro-hydraulic actuator product appearance desin, et a smaller size, appearance, shape, structure more reasonable electrohydraulic actuator products. elative to conventional optimization method, in this paper the perceptual demand of consumers and the function of the product parameters toether, realize the unity of the perceptual and rational desin and provide references for other product desin. eferences Fi.. Multi-objective optimization proram run results. Before optimization desin parameters compared with the optimized results, shown in the Table 6 below. Table 6. The electro-hydraulic actuator perceptual optimization results. Parameter X X X X X The initial value Optimization esult Volume (m) As can be seen from Table 6, to meet the needs of the larest inductive premise compared to the oriinal desin parameters for most of the parameters has a reduced volume of m oriinal is reduced to. - m, a decrease of %.. Conclusion In this paper, based on the basic theory of "Kansei enineerin" and the basic method of mechanical desin, this paper proposes a mechanical []. K. Nakada, Kansei enineerin research on the desin of construction machinery, International Journal of Industrial Eronomics, Vol. 9, Issue, 997, pp []. K. Wan, J. Lian, Y. Lin, Form desin of CNC machine tools usin SVM-Kansei enineerin model, in Proceedins of the IEEE International Conference on Systems, Man and Cybernetics, (SMC 8), Sinapore, - October 8, pp. -9. []. H. Lai, Y. Lin, C. Yeh, et al, User-oriented desin for the optimal combination on product desin, International Journal of Production Economics, Vol., Issue, 6, pp []. C. Tanoue, K. Ishizaka, M. Naamachi, Kansei Enineerin: A study on perception of vehicle interior imae, International Journal of Industrial Eronomics, Vol. 9, Issue, 997, pp. -8. []. Y. Shimizu, T. Jindo, A fuzzy loic analysis method for evaluatin human sensitivities, International Journal of Industrial Eronomics, Vol., Issue, 99, pp [6] S. Hsiao, C. Chen, A semantic and shape rammar based approach for product desin, Desin Studies, Vol. 8, Issue, 997, pp [7] S. Hsiao, H. Huan, A neural network based approach for product form desin, Desin Studies, Vol., Issue,, pp [8]. Xu Jian, Sun Shouqian, Zhan Kejun, Optimization desin based on enetic alorithm modelin product imae, Mechanical Enineerin, Issue, 7, pp [9]. Li Yonfen, Zhu Lipin, esearch based product desin Kansei Enineerin, Packain Enineerin, Issue, 8, pp. -. []. SPSS statistical analysis advanced tutorial, Hiher Education Press, Beijin,. Copyriht, International Frequency Sensor Association (IFSA) Publishin, S. L. All rihts reserved. ( 9