scholarly journals Robust parameter design and economical multi-objective optimization on characterizing rubber for shoe soles

DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 160-169
Author(s):  
Armando Mares Castro
Keyword(s):  
Quality Improvement ◽  
Loss Function ◽  
Quality Characteristics ◽  
Optimization Techniques ◽  
Taguchi Methods ◽  
Quality Characteristic ◽  
Parameter Design ◽  
Robust Parameter Design ◽  
Robust Parameter ◽  
Analytical Approaches

Within Taguchi methods, robust parameter design is a widely used tool for quality improvement in processes and products. The loss function is another quality improvement technique with a focus on cost reduction. Traditional Taguchi methods focus on process improvement or optimization with a unique quality characteristic. Analytical approaches for optimizing processes with multiple quality characteristics have been presented in the literature. In this investigation, a case of analysis for two quality characteristics in rubber for shoe sole is presented. A methodology supported in robust parameter design in combined array is used in order to obtain optimal levels in the vulcanization process. Optimization techniques based on the loss function and the use of restricted nonlinear optimization with genetic algorithms are proposed.

A semi-parametric approach to robust parameter design

2008 ◽  
Vol 138 (1) ◽  
pp. 114-131 ◽  
Author(s):  
Stephanie M. Pickle ◽  
Timothy J. Robinson ◽  
Jeffrey B. Birch ◽  
Christine M. Anderson-Cook
Keyword(s):  
Parameter Design ◽  
Robust Parameter Design ◽  
Parametric Approach ◽  
Robust Parameter

Robust Parameter Design of the Precision Injection Molding Process

1994 ◽  
Author(s):  
Sornkrit Leartcheongchowasak ◽  
Merwan Mehta ◽  
Hamid Al-Kadi ◽  
Keith Sequeira ◽  
Brian Snow ◽  
...  
Keyword(s):  
Injection Molding ◽  
Taguchi Methods ◽  
Injection Molding Process ◽  
Robust Parameter Design ◽  
Molding Process ◽  
Product Lines ◽  
Molded Parts ◽  
Robust Parameter ◽  
Independent Process ◽  
The Right

Abstract The most important problem, causing defective parts, in the injection molding process, is nonuniform shrinkage of molded parts. This leads to an iterative trial-and-error cycles of modification of mold cavity and core to arrive at the right dimensional size required which can occasionally to complete retooling. For this process, there are many factors that can be thrown out of control. Using the traditional scientific approach, engineers have longed to understand the mechanics of the process to control it, with limited success. In this paper, a design of experiments setup, using the Taguchi Methods, was done to reduce the nonuniform shrinkage. The company where the experiment was carried out is a precision parts molder for their own product lines. By using the internal experts from the company, a list of independent process parameters with no interactions which were thought the most responsible for dimensional size were listed. As there were 13 such parameters, it was decided to use the L27 orthogonal array. The optimum value that the company experts thought would produce the right part were used as the settings for the initial experiment. The 27 experiments were then performed, allowing sufficient time to let the machine stabilized between the experiments. The S/N ratio calculation for 27 experiments was explained. Next the calculations for the percentage that each parameter contributes to the dimension was determined. Finally, a confirmation experiment was performed to verify the results.

scholarly journals A Confidence Region for Zero-Gradient Solutions for Robust Parameter Design Experiments

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Aili Cheng ◽  
John Peterson ◽  
Pallavi Chitturi
Keyword(s):  
Confidence Region ◽  
Control Variable ◽  
Critical Values ◽  
Parameter Design ◽  
Feasible Region ◽  
Robust Parameter Design ◽  
Control Variables ◽  
Controllable Factors ◽  
Key Issues ◽  
Robust Parameter

One of the key issues in robust parameter design is to configure the controllable factors to minimize the variance due to noise variables. However, it can sometimes happen that the number of control variables is greater than the number of noise variables. When this occurs, two important situations arise. One is that the variance due to noise variables can be brought down to zero The second is that multiple optimal control variable settings become available to the experimenter. A simultaneous confidence region for such a locus of points not only provides a region of uncertainty about such a solution, but also provides a statistical test of whether or not such points lie within the region of experimentation or a feasible region of operation. However, this situation requires a confidence region for the multiple-solution factor levels that provides proper simultaneous coverage. This requirement has not been previously recognized in the literature. In the case where the number of control variables is greater than the number of noise variables, we show how to construct critical values needed to maintain the simultaneous coverage rate. Two examples are provided as a demonstration of the practical need to adjust the critical values for simultaneous coverage.

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