Manufacture of a spur tooth gear in Ti-6Al-4V alloy by electrical discharge

This paper proposes a method of manufacturing a spur tooth gear in Ti-6Al-4V alloy (grade 5) using a wire electrical discharge machine (Wire EDM). A geometrical model for the gear is drawn up and implemented using the program MATLAB.The electro-erosion parameters tested for this alloy are applied to an ONA PRIMA S-250. The parameters used (power, pause, voltage, …) are based on the ONA EDM charts. The Taguchi orthogonal array method was chosen to obtain the optimum values for cutting Titanium.The work presented follows established lines for manufacturing mechanical parts using general purpose machines and tools. In this case, the WEDM process was used. The MATLAB program was employed to obtain the interpolation points. This program simplifies the task of solving the equations originated by the mathematical model which allows the wire path to be calculated.The WEDM method used here is a commendable alternative for machining electrically conductible materials which are difficult to work with using conventional machine tools (milling, turning or boring). Furthermore, the WEDM process reduces or even eliminates the need for subsequent polishing processes due to the high-quality finish achieved.     

Parameter optimization of continuous sputtering process based on Taguchi methods,neural networks,desirability function,and genetic algorithms

To combat climate change, many industries have participated in the research on alternative energies. Industrial Technology Research Institute in Taiwan has developed techniques for the solar energy selective absorption film continuous sputtering process. For this extremely complicated process, plenty of parameters would influence the output quality. If parameters settings simply rely on the experience of engineers, the defect rate may increase due to instability. A more reliable approach is desirable to optimize the condition of manufacturing process parameters, thus improving the quality.The present study applies a systematic procedure for the parameter optimization of the absorption film continuous sputtering process. First, possible variables are determined based on collected data and engineering knowledge. Second, Taguchi methods are utilized to search for the significant factors and the optimal level combination of parameters. Finally, the integration of back-propagation neural network, desirability function, and genetic algorithms is used to obtain the optimal parameters setting. According to the experiment results, the performance of the integrated procedure is better than that of Taguchi methods and traditional approach. Furthermore, if applying the integrated method, the saving energy would achieve 9770.53 kiloliter of oil equivalent (kLOE) per year, which is 11.2 times the saving kLOE of the traditional paint process.     

制造企业供应商排序决策:基于SVM和TFN-RS的改进TOPSIS

考虑制造企业供应链的绿色内涵,构建供应商排序指标体系。为解决指标数量众多、计算方式复杂、协调困难等问题,构造一种基于SVM和TFN-RS的改进TOPSIS方法来进行供应商排序决策。首先根据候选供应商的主要数据信息,应用基于SVM的分类模型对其进行初步筛选,以缩小候选供应商数量;通过专家对准则下属指标进行考察和掌握,利用专家的智慧和经验对准则进行TFN评分,设计一种TFN-RS方法来计算供应商在准则上的分值;运用CRITIC方法对各准则进行赋权;最后通过相对熵替代欧氏距离的改进TOPSIS对各供应商进行排序。以某轴承制造企业的滚球供应商排序决策为例,验证了该方法的实用效果。     

Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach

The present study highlights application of Taguchi’s robust design coupled with fuzzy based desirability function approach for optimizing multiple bead geometry parameters of submerged arc weldment. Fuzzy inference system has been adapted to avoid uncertainly, imprecision and vagueness in experimentation as well as in data analysis by traditional Taguchi based optimization approach. Detailed methodology and unique features of the proposed method has been highlighted through a case study. The said approach can efficiently be used in off-line quality control of any production process as well as automation of the process.     

Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with α-cut level set

This study aims to improve the general flood vulnerability approach using fuzzy TOPSIS based on α-cut level sets which can reduce the uncertainty inherent in even fuzzy multi-criteria decision making process. Since fuzzy TOPSIS leads to a crisp closeness for each alternative, it is frequently argued that fuzzy weights and fuzzy ratings should be in fuzzy relative closeness. Therefore, this study used a modified α-cut level set based fuzzy TOPSIS to develop a spatial flood vulnerability approach for Han River in Korea, considering various uncertainties in weights derivation and crisp data aggregation. Two results from fuzzy TOPSIS and modified fuzzy TOPSIS were compared. Some regions which showed no or small ranking changes have their centro-symmetric distributions, while other regions whose rankings varied dynamically, have biased (anti-symmetric) distributions. It can be concluded that α-cut level set based fuzzy TOPSIS produce more robust prioritization since more uncertainties can be considered. This method can be applied to robust spatial vulnerability or decision making in water resources management.     

Design of a robust fuzzy controller for the arc stability ofCO2 welding process using the Taguchi method

CO(2) welding is a complex process. Weld quality is dependent on arc stability and minimizing the effects of disturbances or changes in the operating condition commonly occurring during the welding process. In order to minimize these effects, a controller can be used. In this study, a fuzzy controller was used in order to stabilize the arc during CO(2) welding. The input variable of the controller was the Mita index. This index estimates quantitatively the arc stability that is influenced by many welding process parameters. Because the welding process is complex, a mathematical model of the Mita index was difficult to derive. Therefore, the parameter settings of the fuzzy controller were determined by performing actual control experiments without using a mathematical model of the controlled process. The solution, the Taguchi method was used to determine the optimal control parameter settings of the fuzzy controller to make the control performance robust and insensitive to the changes in the operating conditions.     

Optimization and predictive modeling using S/N,RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization, respectively. In the last phase, regression analysis and artificial neural network (ANN) models for predicting the MRR, EWR, AOC and TA. As a result of experimental analysis, discharge current was the most important parameter for micro-drilling with EDM. It was found out that this parameter influenced positively MR, while it has negatively an effect on EWR, AOC and TA. Mathematical model based on ANNs exhibited a successful performance for predication of outputs. Optimum process parameters which were discharge current of 10.18 Å, dielectric liquid pressure of 58.78 bar and electrode tool rotational speed of 100 rpm for multi-objective optimization were determined through RSM with desirability function analysis in micro-deep hole EDM drilling of AISI 304 stainless steel.

     

Hybrid evolutionary optimization for nutraceutical manufacturing processes

In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN’s input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively.     

Development of expert decision model to monitor precision of solar silicon wafer machine line

In this study, we develop a two-stage decision model for managing uncertainty and imprecision of solar silicon wafer slicing evaluations during a wafer manufacturing process. Stage 1 is the evaluation process, which is performed by a procedure based on a combination of the fuzzy analytic hierarchy process (AHP) and the TOPSIS method. Stage 2 is the verification process, in which process capability indices are calculated to verify the feasibility and effectiveness of the proposed methods.     

Development of expert decision model to monitor precision of solar silicon wafer machine line

In this study, we develop a two-stage decision model for managing uncertainty and imprecision of solar silicon wafer slicing evaluations during a wafer manufacturing process. Stage 1 is the evaluation process, which is performed by a procedure based on a combination of the fuzzy analytic hierarchy process (AHP) and the TOPSIS method. Stage 2 is the verification process, in which process capability indices are calculated to verify the feasibility and effectiveness of the proposed methods.     

Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach

This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.     

An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence

The Taguchi robust parameter design has been widely used over the past decade to solve many single-response process parameter designs. However, the Taguchi method is unable to deal with multi-response problems that are of main interest today, owing to increasing complexity of manufacturing processes and products. Several recent studies have been conducted in order to solve this problem. But, they did not effectively treat situations where responses are correlated and situations in which control factors have continuous values. This study proposed an integrated model for experimental design of processes with multiple correlated responses, composed of three stages which (1) use expert system, designed for selecting an inner and an outer orthogonal array, to design an actual experiment, (2) use Taguchi’s quality loss function to present relative significance of responses, and multivariate statistical methods to uncorrelate and synthesise responses into a single performance measure, (3) use neural networks to construct the response function model and genetic algorithms to optimise parameter design. The effectiveness of the proposed model is illustrated with three examples. Results of analysis showed that the proposed approach could yield a better solution in terms of the optimal parameters setting that results in a higher process performance measure than the traditional experimental design.     

Artificial neural network based approach for dynamic parameter design

The Taguchi parameter design method has been recognized as an important tool for improving the quality of a product or a process. However, the statistical methods and optimization procedures proposed by Taguchi have much room for improvement. For instance, the two-step procedure proposed by Taguchi may fail to identify an optimum design condition if an adjustment parameter does not exist, the optimal setting of a design parameter is determined only among the levels included in the parameter design experiment, and, for the dynamic parameter design, the signal parameter is assumed to follow a uniform rather than a general distribution. This paper develops an artificial neural network based dynamic parameter design approach to overcome the shortcomings of the Taguchi and existing alternative approaches. First, an artificial neural network is trained to map the relationship between the characteristic, design, noise and signal parameters. Second, Latin hypercube samples of the signal and noise parameters are obtained and used to estimate the slope between the signal parameter and characteristic as well as the variance of the characteristic at each set of design parameter settings. Then, the dynamic parameter design problem is formulated as a nonlinear optimization problem and solved to find the optimal settings of the design parameters using sequential quadratic programming. The effectiveness of the proposed approach is illustrated with an example.     

Combining a neural network with a genetic algorithm for process parameter optimization

A neural-network model has been developed to predict the value of a critical strength parameter (internal bond) in a particleboard manufacturing process, based on process operating parameters and conditions. A genetic algorithm was then applied to the trained neural network model to determine the process parameter values that would result in desired levels of the strength parameter for given operating conditions. The integrated NN–GA system was successful in determining the process parameter values needed under different conditions, and at various stages in the process, to provide the desired level of internal bond. The NN–GA tool allows a manufacturer to quickly determine the values of critical process parameters needed to achieve acceptable levels of board strength, based on current operating conditions and the stage of manufacturing.     

Development of a quick credibility scoring decision support system using fuzzy TOPSIS

In this study, a quick credibility scoring decision support system is developed for the banks to determine the credibility of manufacturing firms in Turkey. The proposed decision support system is expected to be used by the banks when they want to determine whether an applicant firm is worth a detailed credit check or not. Using such a quick credit scoring decision model reduces the banks’ workload. The proposed credit scoring model is based on the financial ratios and fuzzy TOPSIS approach. It obtains two separate scores which reflect the attractiveness of manufacturing industries within the overall economy and manufacturing firms’ performance with respect to its competitors belonging to the same industry. These two scores are then used to determine the credibility of applicant manufacturing firms. The developed decision support system is tested with various real cases and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology

In this research, the effect of parameters in Resistance Spot Welding (RSW) on the weld zone development was first investigated using Taguchi Method. Further, the RSW parameters were to be optimized based on multiple quality features, focusing on weld nugget and Heat Affected Zone using multi-objective Taguchi Method (MTM). The optimum welding parameter for MTM was obtained using Multi Signal to Noise Ratio and the significant level was further analyzed using Analysis of Variance. Lastly, Response Surface Methodology was employed to develop the mathematical model for predicting the weld zone development. The experimental study was conducted under varied welding current, weld time and hold time. To validate the predicted model, experimental confirmation test was conducted for plate thickness of 1 and 1.5 mm. Based on the results, the developed model can be effectively used to predict the size of weld zone which can improve the welding quality and performance in RSW.     

Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Mobile application (app) design is an expanding research area, with user experience (UX) as its core. UX encompasses all aspects of human–computer interaction, and thus the optimization of UX has multiple objectives. Quality characteristics related to UX are subjective and even subconscious; moreover, there exists interdependence among UX quality characteristics. However, very little attention has been focused on these issues when optimizing UX based on multiple objectives. In this paper, a fuzzy analytic network process (ANP)-based Taguchi method is proposed for optimizing UX in mobile app design. First, design patterns and UX quality characteristics are determined. Subsequently, a Taguchi experiment is designed and carried out, and then signal-to-noise (S/N) ratios are calculated. A fuzzy ANP is adopted to derive the preference weights for the UX quality characteristics. Based on these weights, the S/N ratios are converted into a multiperformance characteristic index (MPCI) by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finally, according to the MPCI, the significant design patterns are identified by using the analysis of variance, and the optimal design is obtained by using the response table and response graph. A mobile health app design was presented to illustrate the proposed approach. The results suggest that the proposed approach can effectively manage the interdependence among the subjective and even subconscious UX quality characteristics in the optimization process, and be used as a universal robust design approach to optimize UX in mobile app design.     

Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method

The simulation model is a proven tool in solving nonlinear and stochastic problems and allows examination of the likely behavior of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. A practical problem often embodies many characteristics of a multiresponse optimization problem. The present paper proposes to solve the multiresponse simulation-optimization problem by a multiple-attribute decision-making method—a technique for order preference by similarity to ideal solution (TOPSIS). The method assumes that the control factors have discrete values and that each control factor has exactly three control levels. Taguchi quality-loss functions are adapted to model the factor mean and variance effects. TOPSIS is then used to find the surrogate objective function for the multiple responses. The present paper predicts the system performances for any combination of levels of the control factors by using the main effects of the control factors according to the principles of a robust design method. The optimal design can then be obtained. A practical case study from an integrated-circuit packaging company illustrates the efficiency and effectiveness of the proposed method. Finally, constraints of the proposed method are addressed.     

A new approach for optimizing automotive crashworthiness: concurrent usage of ANFIS and Taguchi method

Design optimization is presented for the crashworthiness improvement of an automotive body structure. The optimization objective was to improve automotive crashworthiness conditions according to the defined criterion (occupant chest deceleration) during a full frontal impact. The controllable factors used in this study consisted of six internal parts of the vehicle’s frontal structure in a condition that their thickness was the “design parameter”. First using the Taguchi method, this study analyzed the optimum conditions in discontinuous design area and impact factors and their optimal levels of design objectives were obtained by analyzing the experimental results. Next to model a precise understanding of the explicit mathematical input–output relationship, fuzzy logic is utilized which make use of full factorial design set of experimental test cases resulted from Taguchi predicting formulations. Interestingly, the optimum conditions for automotive crashworthiness occurred with 2.72 % improvement in the defined crashworthiness criterion in comparison with the baseline design while selected structural parts experienced mass reduction by 8.23 %.