A Hybrid Approach for Part Geometry Optimization through Engineering ..... supported by the Beijing Municipal Education Commission (Build a project). ... A New Motorcycle Helmet Liner Material: The Finite Element Simulation and Design of.
Advanced Materials Research Vols. 201-203 (2011) pp 1342-1347 Online available since 2011/Feb/21 at www.scientific.net © (2011) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMR.201-203.1342
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The scope of design optimization differs from one industry to another. In various situations, new part design is an instance of previously designed part i.e. it differs only in some dimension from the previous part. This can be due to change in functional parameters of the product, building part family or continual improvement in existing design etc. A hybrid approach for part geometry optimization of such parts is presented in this paper. It includes finding relationship between part geometric and functional parameters at assembly level. The designed part is simulated using finite element analysis (FEA) for validation and a set of feasible part geometry parameters along with their effect on objective function (functional parameters) are obtained through knowledge enabled design of experiments (DOE). The optimum solution is sought among them and validated through structural analysis of part / product. The process has been applied successfully in design optimization of electrode_holder in spot welding equipment for automotive industry.
Design optimization is very significant activity in any industry as its outcome is related with the overall performance of a company. A good optimized design leads to a better quality product satisfying customer to high extent as well as yielding higher profits, market share and goodwill for the company. Design optimization is carried out through part shape, geometry and topology optimization. Part geometric parameters are very important as they determine the part size and ensure part intended performance. During the design process, once the part material, shape, topology and configuration within the product has been defined, the part geometry can still have tendency for further design improvement / optimization. The driving aspects for product design process can be part geometry, function, material and process plan. A what-if question to these driving aspects can lead to change in design [1]. However, careful examination of these aspects leads to the fact that any change in part function, material or process plan may result in alteration of part geometry. Thus demanding need for geometry optimization for optimum design. Different approaches are used for design optimization like (DOE), and optimization. First two have been used mostly in literature for design optimization, but the optimization is a usual practice in industry [2]. It uses knowledge base (KB) or some computer aided engineering (CAE) simulation tools for the analysis, validation and optimization of a particular design [3,4]. It is useful because it works with designer’s existing skills, consumes less time and results continual improvement. The potential drawback is and its traditional form is believed to overlook some design space which might have optimum solution included [5]. Whereas, DOE approach comprises of a set of experiments to determine relationship among values of interest (e.g. design and functional parameters) through physical or computer based experiments (simulations). This approach is especially useful if mathematical relationship between input and output values is difficult to determine, nevertheless the weakness is large number of experiments [2,6]. It is worth mentioning All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 219.239.227.183-22/02/11,05:56:01)
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that FEA based CAE has been used widely with both expert and DOE based design optimization approaches [7,8]. Due to popularity of in industry and scope of ,a approach combining salient features of both these approaches has been developed in this research. The benefit of that is careful consideration of available design space and controlled number of experiments. These benefits are obtained by providing knowledge support to the designer in this research. The structural analysis of parts and assemblies has been performed through CAE simulations. The research work is applied to the geometry optimization of a product from automobile service sector industry.
Part geometric parameters / dimensions can be linked with the performance or functional parameters of that part with regard to its role in the product. From flexibility viewpoint, part geometric parameters can be divided into and dimensions. The dimensions are those whose value needs to be fixed to ensure required part functionality. For example, it can be a specific length or diameter which cannot be altered as doing that might disturb part function. On the other hand, dimensions are flexible and acceptable within a range of values until part intended function is not disturbed. The role of optimization is in fact finding the values of dimensions which yield optimum part design / performance. In this research, a has been developed for the optimization of part geometric parameters. Several FEA simulation based experiments (structural analysis) for a component are designed to determine behavioral relationships of different dimensions and functional parameters identified from part qualification criteria. The number of simulations is decided such that to cover the available design space and thus better visualize the result output. It even considers minute changes in geometric dimension value (e.g. less than 1 mm) if that have considerable effect on any of the functional parameter. The part qualification criteria include a specific value of stress, weight and / or volume etc under a specific loading, support and environmental conditions. Based on data analysis of the results from this stage, the expert (designer) decides to choose the best geometry for further processing. So the selected best geometry is made part of its higher assembly or product depending on the part under optimization. Thus the CAE analysis of resulting assembly / product is accomplished to examine the part (containing optimized geometric parameters) behavior in the whole product or assembly. The whole process is supported by KB support which plays a significant role in the process. Through data analysis and visual tools, it shows the trend of previous results which guides the designer for the rest of the experiments. It helps in determining the minimum and maximum number of experiments, thus saves time and computation cost as well as allow using available design geometry space optimally.
The optimization process formulated for geometry optimization is described in the following. The optimization process flow chart is shown in Fig. 1. From functionality viewpoint, it can be divided into four modules; A. B. , and C. and D. . In module A, part geometry-function relationship is established and as a result and dimensions are identified which are later used for optimization process as and respectively. The example of identification for these and dimensions is described later in this paper. Furthermore, part loading conditions, environment application and other physical constraints are identified. These may include applied force, moments, pressure, clamping position and supports, part environment etc.
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Fig. 1.
Geometry Optimization Process Flow Chart
The role of module B is to create part instance based on data from module A, calculate part inertia parameters (mass, weight, volume etc.), and perform CAE (structural) analysis based on part loading conditions identified in module A. The results are saved in knowledge database for every output. The module C is KB support to the designer through the provision of and . A very significant function of this module is to guide the designer while selecting new value for dimension by showing trend of the results from previous selected values. For example, if the trend shows some better results, then the designer may continue increasing or decreasing values in the same direction otherwise stop and go for next step. Another aspect is that if the trend shows that there is no significant increase or decrease in output, then the next selected value can be considerably higher or lower than the previous one. In short, module C guides the designer for choosing direction (increase or decrease) and value (how much higher or lower) of the selected free dimension, thus saving time and cost of unnecessary experiments (calculations and analysis simulations). The part instance based on the best solution (output after executing modules A, B and C) is validated in module D through CAE simulation of the part assembly to check part performance in the assembly. It starts from the best initial solution and stops if the first combined solution is qualified, if not, it continues until the qualified solution is obtained. The final output of this module is the optimum part geometric dimensions for the laid down functional criteria. The result of this module is saved in knowledge base for future use. The identification of and dimensions is demonstrated using ! of shown in Fig. 2. The machine is used at automotive assembly line for assembly of different structural parts through spot welding process. Electrode_holder was initially designed using (AD) theory principles which emphasizes determining relationships among customer requirements (CR) based functional requirements (FR), design parameters (DP) and process variables (PV) [9]. The and dimensions were identified from these already established relationships among FRs and DPs for this part as shown in Table 1. The derived dimensions in this case are D1 and L1 (Table 1).
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Table 1. Free and constrained dimensions !" # Support electrode specific point
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at 1. Height above support (L2, L3 = c) 2. Top transverse length (= c) Sufficient strength to 1. Diameter D1 withstand process (c ≥ D1 ≥ D2+c) dynamics 2. Clamping length L1 (c ≥ L1 ≥ L4) Facilitate space for 1. Hollow structure (D2, L4 = c) inner part Facilitate current flow 1. conductive material (choice of suitable material)
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Fig. 2. DPs of spot welding electrode holder $ The above mentioned hybrid approach was applied for the geometric parameter optimization of ! of spot welding machine (Fig. 2). The derived free dimensions from Table 1 (D1 and L1) were used to determine part functional behavior as per module B in Fig. 1. The values of these dimensions in existing part design were: D1 = 28mm, L1 = 75mm. The result of experiments is shown in Fig. 3 (only Von Mises stress). Appropriate values of D1 (26, 27 and 28mm) were simulated with different L1 values ≤L4, L4 = 45mm is the limiting value for L1 (according to DP in Table 1). It can be seen in the Fig. 3 that only D1=26mm free dimension was simulated for L4 = [48, 47 and 46 mm], the reason is that KB support guided the designer that according to the trend of the results so far, these are expected to be within limit. Therefore, the designer while working with D1 = [27 and 28mm] simulated for L1 = 45mm directly (yellow and blue dots on the graph), thus saving time and number of experiments. According to simulation results in Fig. 3, all values of dimension for which stress is below (horizontal line) is feasible design region. However, for finding optimal point, it is required to consider other design criteria i.e. mass saving, part deflection etc. Mass saving is very significant factor as it is directly related with overall weight of the product in focus. The data analysis in module C showed that the max mass saving within feasible design region occurs with D1 = 26mm and L1 = 47mm, about 24% as shown in Fig. 4. Therefore, the part design with the combination of these values of dimensions with already determined dimensions is the best geometric design from module B which can be further validated in module D for its behavior in the product. However, the benefit of using hybrid approach is that the designer at this step can himself decide if he / she wants to select 2nd or 3rd best geometry (sub-optimum from this stage) to proceed due to any other qualitative aspects as per requirement. Another option can be to proceed with more than one geometries one after another to check its behavior in module D.
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