Process Optimization of a Superfinishing Machine through Experimental Design and Mixed Response Surface Models

Abstract

This article deals with process optimization for a centrifugal compressor. More precisely, the technological problem concerns the reduction of the surface roughness of centrifugal compressor impellers through a new technology implemented by GE Oil & Gas called superfinishing. The new technology is studied through statistical methods in order to achieve a minimization of the final roughness according to the best set of levels for the abrasive component mixture and the time process. To this end, an experimental design is planned for three different materialsÀ Àfor example, three types of steelÀ Àand mixed response surface models are applied. The application of mixed models allows us to estimate random effects, useful for better controlling the process variance in a robust design approach. Within this framework, a random effect is the initial roughness, measured for each impeller vane before starting the superfinishing process. Furthermore, random effects are included in the final optimization step. The contribution of this article is the study of this new superfinishing process through mixed response surface models and robust design optimization, in order to set the best levels of the abrasive component mixture and time process to minimize the final roughness for a centrifugal compressor impeller.

Assessment and Optimization for Novel Gas Materials Through the Evaluation of Mixed Response Surface Models

“…The application of this method could be considered an attempt to improve the analysis of the variability sources; in [32], a comparison between mixed RS models and a Bayesian approach is performed to consider the relevance of variance components for the evaluation of process optimization. Another novelty is the role of variability sources in optimizing the process in this field [33].…”

Optimization of Gas Sensors Based on Advanced Nanomaterials through Split-Plot Designs and GLMMs

“…In addition, even though the gas concentration is a process variable, it is difficult to control with any degree of accuracy. In [ 32 ], the resolution of a similar problem was conducted analogously, albeit in a different context.…”

Optimization of ADC Channels of A Smart Energy Meter Including Random Noise Effects

“… The application of this method aims to improve the analysis of the variability sources and their role in optimizing the process. In this paper, the innovative contributions aim at addressing two issues are as follows: the inclusion of fixed as well as random effects in a multiresponse optimization context through mixed RS models and the evaluation of the variance‐covariance matrices for the random effects and error components; the case‐study, where an ADC channel is optimized involving several response variables, by controlling them with respect to noise random effects. …”

“…16,17 The application of this method aims to improve the analysis of the variability sources and their role in optimizing the process. 18 In this paper, the innovative contributions aim at addressing two issues are as follows:the inclusion of fixed as well as random effects in a multiresponse optimization context through mixed RS models and the evaluation of the variance-covariance matrices for the random effects and error components; the case-study, where an ADC channel is optimized involving several response variables, by controlling them with respect to noise random effects.The paper is organized as follows: the next section contains an extended literature review on response surface methodology and process optimization, focusing on the multiresponse case; the third section illustrates the statistical approach; the role of the smart metering in the smart grid framework and the proposed prototype for the energy meter is briefly introduced in Section 4 in which the collected measurement data are also illustrated; the modelling and optimization results are reported in Section 5, followed by the discussion and final remarks.…”

“…16,17 The application of this method aims to improve the analysis of the variability sources and their role in optimizing the process. 18 In this paper, the innovative contributions aim at addressing two issues are as follows:…”