Vining and Myers  proposed the ....  G. G. Vining, R. H. Myers, Combining Taguchi and response ... optimization using designed experiments, John Wiley &.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 13 (2015) pp 33688-33690 © Research India Publications. http://www.ripublication.com
Development of a safe operating space on a green car side door beam manufacturing process Gyuhyo Choi, Tuan-Ho Le, Bogeun Lee, and Sangmun Shin Dept. of Industrial & Management Systems Engineering, Dong-A University, Busan 604-714, Republic of Korea Abstract- Design of experiment (DoE) method is usually utilized to identify the operating ranges of the input factors in the manufacturing processes. In practice, the overall operating ranges can be determined incorrectly in multivariate problems by using standard DoE methods. Therefore, the primary objective of this paper is to propose a systematic procedure in order to identify the safe operating space of input factors in multiple output responses problems. First of all, response surface methodology (RSM) is used to illustrate the relationship between the input factors and output responses. Secondly, the safe operating space can be established based on the process window obtained from the overlaid contour plot with the statistical confidence intervals. Finally, a case study in green car side door beam manufacturing process is performed for verification purposes. Keywords- Response surface methodology, Robust design, Design of experiment, Process window, Safe operating space
known or very complicated . The optimal factor settings can be obtained in an optimization step. Vining and Myers  proposed the dual response approach to find the optimal solutions. The optimization model is further extended by Lin and Tu , Shin and Cho , Shin and Cho , and Nha et al. . Therefore, the primary objective of this paper is to propose an alternative method in order to determine a safe operating space based on the process window concept for multivariate problems in a new manufacturing process development. Firstly, the information between the input factors and output responses can be exploited and estimated by using RSM. Secondly, the overlaid contour plot of multiple input factors and output responses can be generated in order to obtain a process window. The safe operating space can be identified by applying statistical confidence intervals of the obtained process window. Finally, a case study in green car side door beam manufacturing process is performed for verification purposes.
1. Introduction A number of quality issues (i.e., process efficiency, cycle times, and energy consumption in the manufacturing processes) have been widely recognized as a critical concept in maintaining a competitive advantage in the marketplace. Many manufacturing companies focused on continuously improving their products in order to satisfy customer demands as well as technical specifications. In addition, they are also to handle sensitive changes of materials and processes. Most of manufacturing companies have utilized DoE methods to identify a process window based on the relationships between technical specifications of materials and processes. DoE methods can be used to exploit systemically the information between the input-output variables based on statistical techniques. These standard DoE methods can also analyze both main and interaction effects of input factors to a quality characteristic with a small number of experimental runs. Among many DoE methods, the fractional factorial design can be applied as an alternative factor screening tool to determine important input control factors in an injection molding process  and . To the same end, Taguchi methods (i.e., the orthogonal array design) have been used in many manufacturing processes by many practitioners because of their practicability  and . Response surface methodology (RSM) and Neural network are a useful method to conduct the experiments in the early stage of the process as well as to estimate the functional relationship between the input factors and their associated output responses -. The main idea of RSM is to use a sequence of designed experiments to obtain optimal responses by estimating a response function when exact functional relationships are not
2. Method 2.1. Response surface methodology Many statistical estimation methods were proposed in order to identify this unidentified relationship. Among these methods, RSM is one of the most popular approaches to model the relationship function between input factors and output responses. The suitable approximation value for the true functional relationship can be established by a polynomial model  and . The estimated second-order regression model can also be identified as (1) The second-order model can identify specific statistical information (i.e., main, quadratic, and the interaction effects) as defined in equation (1). Therefore, the second-order model is usually used to determine the optimal solutions in practical problems.
3. Safe operating space In order to identify the satisfied ranges of input control factors associated quality requirements, a statistical representation tool, namely process window, can be used. In addition, this process window is usually called design space in the pharmaceutical field. The International Conference on Harmonization (ICH) defines process window as “multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” . As a process window is generated systematically, the quality requirements of product can be assured in a specific range. Based on the practical operating conditions of the
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 13 (2015) pp 33688-33690 © Research India Publications. http://www.ripublication.com manufacturing process, a safety operating space can be created in order to identify accurate process window. In order to generate the safe operating space, first of all, the functional relationship between input factors and output responses can be estimated by using RSM. Secondly, the contour plot of all estimated responses functions can be drawn in the same figure. The overlapped area of all contour plots is the process window of the manufacturing process. In opposition to the single response problem, the process window becomes more complicated when the problem considers multiple processes simultaneously. In addition, the process window can be drawn by a pilot scale instead of a production scale in many practical problems. This process window can be demonstrated by the slashed area as illustrated in Figure 1. Finally, by applying statistical confidence intervals of the process window, the safe operating space can then be obtained by the brown slashed rectangle in the right hand side of Figure 1.
manufacturing process, the time of a process must be more than 120 seconds in order to guarantee the heating conditions of the input steal materials. In addition, the temperature must be more than 870 0C in order to constitute the marten site condition and the limited temperature of the machine is 9800C. Therefore, the safe operating space can be identified as the red square in Figure 2 which is the overlap of the process window and these practical manufacturing conditions. The ranges of the time and temperature or the corners of the safe operating space are marked as the green points in Figure 2. By using the proposed safe operating space, the quality of the products of the automobile equipment manufacturing company can be significantly increased.
Fig 2. Process window and safe operating space
Fig 1. Overlay contour plot for drawing the process window
4. Case study In this paper, the data of green car side door beam manufacturing process is employed to demonstrate the applicability of the proposed method. The safe operating space of two input control factors consisting of temperature ( ) and time ( ) should be identified so that right tensile strength ( ), right hardness ( ), tensile strength ( ), and left hardness ( ) of the front door are in the ranges [1330; 1730], [44; 53], [1330; 1730], and [44; 53], respectively. In this paper, the second-order regression models are used to illustrate the functional relationship between the input control factors and the associated output responses. By using the Minitab software, the information about the regression as well as the error terms of these relationships can be obtained and illustrated in the analysis of variance (ANOVA) tables. All values of in four cases over 90 percentages showed that the relationship between input factors and output responses are well-fitting in the second-order models. In addition, the well-estimated model between the input factors and output responses can be obtained since the p-values of linear, quadratic, and interaction terms of four output responses are equal and smaller to the significant level (i.e., 0.05). The overlaid contour plot as well as the process window of the input factors can be depicted in Figure 2. In Figure 2, the dashed polygon (i.e. process window) is the area in which all product quality requirements can be satisfied simultaneously. Based on this process window, the range of input factors can be obtained. However, in the equipment
Quality engineers are often faced with problems associated with determination of the safe operating conditions in many industrial settings. In this paper, an operating space identification method was proposed to determine the safe ranges of the input factors for multiple output responses problems in a specific manufacturing process of a green car project. Based on the p-values from the ANOVA tables of all response, the significant terms of the regression models can be selected. The high values of coefficients ( ) showed that the well-estimated regression models of four responses can be obtained from the proposed method. In addition, a process window was also generated by using these wellestimated regression functions. By using a overlaid contour plot from these regression functions, the safe operating space could be established from the process window and the statistical confidence intervals. Based on the proposed safe operating space identification method, the input factor settings could be identified properly in order to increase the quality of the products and decrease the defective products in the manufacturing process. For further studies, a higher dimensionality problem of the input factors can be investigated.
Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (20120683). This work was supported by the Bio-Synergy Research Project (NRF-2013M3A9C4078156)
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 13 (2015) pp 33688-33690 © Research India Publications. http://www.ripublication.com of the Ministry of Science, ICT and Future Planning through the National Research Foundation.
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