Hybrid Integration of Taguchi Parametric Design, Grey Relational Analysis, and Principal Component Analysis Optimization for Plastic Gear Production

Abstract

The identification of optimal processing parameters is an important practice in the plastic injection moulding industry because of the significant effect of such parameters on plastic part quality and cost. However, the optimization design of injection moulding process parameters can be difficult because more than one quality characteristic is used in the evaluation. This study systematically develops a hybrid optimization method for multiple quality characteristics by integrating the Taguchi parameter design, grey relational analysis, and principal component analysis. A plastic gear is used to demonstrate the efficiency and validity of the proposed hybrid optimization method in controlling all influential injection moulding processing parameters during plastic gear manufacturing. To minimize the shrinkage behaviour in tooth thickness, addendum circle, and dedendum circle of moulded gear, the optimal combination of different process parameters is determined. The case study demonstrates that the proposed optimization method can produce plastic-moulded gear with minimum shrinkage behaviour of 1.8%, 1.53%, and 2.42% in tooth thickness, addendum circle, and dedendum circle, respectively; these values are less than the values in the main experiment. Therefore, shrinkage-related defects that lead to severe failure in plastic gears can be effectively minimized while satisfying the demand of the global plastic gear industry.

Experimental Study and Development of an Empirical Model for Sr and MRR in CNC Wedm of Az31b Magnesium Alloy Based MMNC

“…The Taguchi method [11] is used to design experiments based on the orthogonal arrays (OA). Taguchi OA designs minimizes the number of experiments can be used to study the whole parameter space with a small number of experiments and it saves time and experimental cost.…”

“…GRA is a method [10,11] that measures the correlation degree among factors based on the similarity or difference among factors. Grey relational generation involves data pre-processing and calculation according to the quality characteristics.…”

“…Lu et al, [10] investigated the optimization design of the cutting parameters for rough cutting processes in high speed end milling of SKD61 tool steel. Mehat et al, [11] tried to optimized the process parameters in the plastic injection moulding process through hybrid optimization approach by integrating the Taguchi parameter design, grey relational analysis (GRA), and principal component analysis (PCA). Patil and Brahmankar [12] attempted to determine the metal removal rate (MRR) in WEDM of Al-SiC composites by trying to develop a semi empirical model of MRR by using dimensional analysis technique and compared the model developed by response surface methodology (RSM) and result shows that the there is significant agreement in the predictions.…”

Determination of Optimal Small Ball-Burnishing Parameters for Both Surface Roughness and Superficial Hardness Improvement of STAVAX

“…Step 3: Perform the principal component analysis (PCA) [21] If the null hypothesis at step 2 is rejected, the PCA is conducted to decide the weight of each quality characteristic. The eigenvalues and the eigenvectors are determined from the correlation coefficient array of step 2 using Eq.…”

“…However, in these studies, the coefficients of the interrelationship among quality characteristics were equally given [20] or determined by the analytic hierarchy process (AHP) [19], which strongly depends on the human's experiences and decisions. The combination of principal component analysis (PCA) [21] and grey entropy [22] to objectively assign the weighting coefficients was proposed by Chiang et al [23]. Hence, in our study, an optimization procedure (Fig.…”