Study and analysis on fuzzy quality control for the high-end manufacturing process based on Taguchi quality loss function

2019 ◽  
Vol 19 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Ying Bai ◽  
Hanyou Wang ◽  
Longkang Wang ◽  
Kangkang Tian
Keyword(s):  
Quality Control ◽  
Loss Function ◽  
Manufacturing Process ◽  
Quality Loss ◽  
Quality Loss Function

scholarly journals The Quality Cost Reduction in Hollow Glass Manufacturing by Taguchi Method

2011 ◽  
Vol 4 (1) ◽  
pp. 155 ◽  
Author(s):  
A. Adil ◽  
A. Moutawakil
Keyword(s):  
Taguchi Method ◽  
Loss Function ◽  
Manufacturing Process ◽  
Control Factor ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Signal To Noise ◽  
Quality Cost ◽  
Hollow Glass ◽  
Target Value

This paper presents the results of an experimental investigation carried out to optimize the weight of a glass manufacturing bottle of a product called CC35 by Taguchi method of parameter design. The experiments have been designed using an L16 orthogonal array with thirteen factors and two levels each. So the effects of each control factor level on the performance characteristic are analyzed using signal-to-noise ratios, mean response data and analysis of variance (ANOVA). Hence, an experimental setting is established; and to measure the performance of the Taguchi method on the manufacturing process before and after the experiment, three tools are used. The Xbar and range charts measure the stability of the process, the capability index estimates the process ability to deliver a good product in regard to the target value and customer specifications. The quality loss function (QLF) was used to determine the reduction in quality cost due to the elimination of deviation of the quality characteristic (weight) from the target value. Experiment shows that the Taguchi method applied on the glass bottle’s manufacturing process (CC35) ensures best stability and capability of the manufacturing process and reduces the weight which results in the quality cost down to 92%. Keywords: Hollow glass manufacturing; Taguchi; Quality loss function (QLF); Design of experiments; Optimization; Capability; ANOVA; Signal-to-noise ratio. © 2012 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. doi: http://dx.doi.org/10.3329/jsr.v4i1.7900J. Sci. Res. 4 (1), 155-172 (2012)  

Robust Tolerance Design Considering Process Capability and Quality Loss

1999 ◽  
Author(s):  
Chang-Xue (Jack) Feng ◽  
Ravi Balusu
Keyword(s):  
Manufacturing Process ◽  
Lead Time ◽  
Process Capability ◽  
Concurrent Design ◽  
Manufacturing Cost ◽  
Tolerance Design ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Capability Indices ◽  
Design And Manufacturing

Abstract Tolerance design bridges design and manufacturing. Concurrent design of tolerances and manufacturing processes may ensure the manufacturability, reduce the manufacturing and other related costs, decrease the number of fraction nonconforming (or defective rate), and shorten the production lead time. Since process capability indices relate tolerance specifications to manufacturing process capabilities, it is quite natural to apply them to concurrent design of tolerances and processes. As process shifts often exist in a manufacturing process, using Cp does not yield a good estimation of fraction nonconforming. Index Cpk does not precisely measure process shift either, but Cpm does. Therefore, this research compares the applications of Cp, Cpk and Cpm based on a numerical example of non-linear mechanical tolerance synthesis. In addition, the Taguchi quality loss function is used together with the manufacturing cost as the objective function.

Applying the Quality Loss Function in Healthcare

2008 ◽  
pp. 102-107
Author(s):  
Elizabeth Cudney ◽  
Bonnie Paris
Keyword(s):  
Health Care ◽  
Loss Function ◽  
Health Care Services ◽  
Quadratic Loss ◽  
Healthcare Industry ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Quadratic Loss Function ◽  
Care Services ◽  
Fundamental Value

Using the quadratic loss function is one way to quantify a fundamental value in the provision of health care services: we must provide the best care and best service to every patient, every time. Sole reliance on specification limits leads to a focus on “acceptable” performance rather than “ideal” performance. This paper presents the application of the quadratic loss function to quantify improvement opportunities in the healthcare industry.

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