Robust parameter design of micro-injection molded gears using a LIGA-like fabricated mold insert

Dimensional deviation is a prerequisite for improving the manufacturing process of parts with free-form surfaces, for example, the reverse adjustment of the die cavity of turbine blade. Influenced by random noise of the manufacturing process, dimensional variation is inevitable for batch parts. Therefore, it may cause unacceptable error to estimate batch parts dimensional deviation by a single sample. Meanwhile, the optimum sample size for estimating dimensional deviation is difficult to determine. To overcome this problem, a practical method for estimating of dimensional deviation of parts with free-form surface is proposed. Firstly, displacements of the discrete points on part surface are employed to represent dimensional error of the part. Estimating dimensional deviation of parts is actually to estimate the simultaneous confidence intervals of the discrete point displacements. Secondly, Bonferroni simultaneous confidence intervals are adopted to estimate the confidence intervals of the part dimensional deviation. Moreover, the accuracy of the estimation will be continuously improved by increasing samples. Consequently, a practical dimensional deviation estimation method is presented. Finally, a compressor blade is adopted to illustrate the proposed method. The percentage of estimation error of the blade dimensional deviation that is less than 0.05mm, which is the estimation error limit, of all the 100 times sampling experiments is 99%, exceeding the given confidence level of 95%, while the percentage of the existing method is below the confidence level, only 87%.

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“…For constant systematic errors, the quality characteristics of the batch parts are regarded as approximately normal distribution [30,31].…”

Section: Interval Estimation Methods Of Dimensional Deviationmentioning

confidence: 99%

Dimensional Deviation Estimation for Parts with Free-Form Surfaces

Zhao

Wang

Zhou

et al. 2018

Mathematical Problems in Engineering

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“…However, now the stochastic manufacturing systems may result in more than one principal component and may have more than one eigenvalue greater than 1, and the determination of feasible solution corresponding to the each response is not guaranteed in such cases. Secondly, the derived CPC based on optimal set of variable cannot be assured to replace multi-response as the chosen principal component, in which fewer variations can be explained by total variation [30]. However, the PCA integrated with TM makes the methodology more practical and efficient for solving multi-response optimization problems [35].…”

Section: Taguchi Methods (Tm)mentioning

confidence: 99%

“…The present problem consists of five process quality measure and is found with correlation between them. As a result, the analysis of all these multiple quality characteristics for the optimal settings of the process is found to be a highly difficult task [30]. However, PCA can convert these multiple correlated responses data into several uncorrelated quality indices.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiCp MMC

Rao

Krishna

2013

Adv. Manuf.

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The compliance of an integrated approach, principal component analysis (PCA), coupled with Taguchi's robust theory for simultaneous optimization of correlated multiple responses of wire electrical discharge machining (WEDM) process for machining SiC P reinforced ZC63 metal matrix composites (MMCs) is investigated in this work. The WEDM is proven better for its efficiency to machine MMCs among others, while the particulate size and volume percentage of SiC p with the composite are the utmost important factors. These improve the mechanical properties enormously, however reduce the machining performance. Hence the WEDM experiments are conducted by varying the particulate size, volume fraction, pulse-on time, pulse-off time and wire tension. In the view of quality cut, the most important performance indicators of WEDM as surface roughness (R a), metal removal rate (MRR), wire wear ratio (WWR), kerf (K w) and white layer thickness (WLT) are measured as responses. PCA is used as multi-response optimization technique to derive the composite principal component (CPC) which acts as the overall quality index in the process. Consequently, Taguchi's S/N ratio analysis is applied to optimize the CPC. The derived optimal process responses are confirmed by the experimental validation tests results. The analysis of variance is conducted to find the effects of choosing process variables on the overall quality of the machined component. The practical possibility of the derived optimal process conditions is also presented using SEM. Keywords ZC63/SiC P metal matrix composites Á Wire electrical discharge machining (WEDM) Á Principal component analysis (PCA)-Taguchi method (TM) Á Analysis of variance (ANOVA)

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“…The different chosen responses of statistical studies can lead to different main results. Huang et al [54] applied the robust parameters design to the fabrication of a micro gear and found that the significant parameters for diameter dimensions are mould temperature, injection speed and holding pressure whereas for tooth thickness are holding pressure, cooling time and mould temperature.…”

Section: Process Parameters Influence On Components Quality and Theirmentioning

confidence: 99%