Product and Process Design Optimization by Design of Experiments Using Taguchi Methods

1988 ◽  
Author(s):  
Kailash C. Kapur
Keyword(s):  
Design Of Experiments ◽  
Design Optimization ◽  
Process Design ◽  
Taguchi Methods ◽  
Product And Process

scholarly journals Designing bioinspired green nanosilicas using statistical and machine learning approaches

2021 ◽  
Vol 6 (4) ◽  
pp. 293-307
Author(s):  
Luc Dewulf ◽  
Mauro Chiacchia ◽  
Aaron S. Yeardley ◽  
Robert A. Milton ◽  
Solomon F. Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Design Of Experiments ◽  
Process Design ◽  
Global Sensitivity Analysis ◽  
Sequential Design ◽  
Learning Approaches ◽  
Global Sensitivity ◽  
Sustainable Product ◽  
Product And Process

This is a first comparison of the sequential design of experiments strategy and global sensitivity analysis for nanomaterials, thus enabling sustainable product and process design in future.

“Non-Scientific” Experimentation in Design of Product and Process

2007 ◽  
Author(s):  
T. N. Goh ◽  
Liqing Guo
Keyword(s):  
Design Of Experiments ◽  
Process Design ◽  
Statistical Design ◽  
New Product ◽  
Statistical Design Of Experiments ◽  
Physical Sciences ◽  
Scientific Methods ◽  
Scientific Experimentation ◽  
Product And Process ◽  
Effective Design

College students in engineering and physical sciences are often educated in “scientific” methods of experimentation. For many years there have been advocates of statistical tools for research and development in design as well as prototyping and testing, yet their acceptance into engineering and technical curricula has been limited. The discussions in this paper explain the rationale for the use of statistical methods such as statistical design of experiments for superior product and process performance. Both educators and students in technical fields would realize that data-driven tools are indeed useful and important for designed-in quality and reliability. In fact, savings and opportunities for enhanced performance via virtually cost free techniques could often be discovered as well. The approach taken, largely based on statistical design of experiments, could be perceived to go against ingrained concepts of “scientific methods” but the efficacy of new product and process design can be readily explained and demonstrated. Such an understanding would be extremely useful in formulating effective design curricula at both graduate and undergraduate levels.

scholarly journals Design of experiments for non-manufacturing processes: benefits, challenges and some examples

2011 ◽  
Vol 225 (11) ◽  
pp. 2078-2087 ◽  
Author(s):  
J Antony ◽  
S Coleman ◽  
D C Montgomery ◽  
M J Anderson ◽  
R T Silvestrini
Keyword(s):  
Quality Improvement ◽  
Design Of Experiments ◽  
Process Design ◽  
Manufacturing Industries ◽  
Manufacturing Processes ◽  
Manufacturing Companies ◽  
Case Examples ◽  
Product And Process ◽  
Almost All ◽  
Manufacturing Environments

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments.

Integrating Product Design With Manufacturing Process Design Using the Robust Concept Exploration Method

1996 ◽  
Author(s):  
Jesse D. Peplinski ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Product Design ◽  
Process Design ◽  
Manufacturing Process ◽  
Response Surfaces ◽  
Production Costs ◽  
Early Stages ◽  
Trade Offs ◽  
Development Costs ◽  
Concept Exploration ◽  
Product And Process

Abstract How can the manufacturability of different product design alternatives be evaluated efficiently during the early stages of concept exploration? The benefits of such integrated product and manufacturing process design are widely recognized and include faster time to market, reduced development costs and production costs, and increased product quality. To reap these benefits fully, however, one must examine product/process trade-offs and cost/schedule/performance trade-offs in the early stages of design. Evaluating production cost and lead time requires detailed simulation or other analysis packages which 1) would be computationally expensive to run for every alternative, and 2) require detailed information that may or may not be available in these early design stages. Our approach is to generate response surfaces that serve as approximations to the analyses packages and use these approximations to identify robust regions of the design space for further exploration. In this paper we present a method for robust product and process exploration and illustrate this method using a simplified example of a machining center processing a single component. We close by discussing the implications of this work for manufacturing outsourcing, designing robust supplier chains, and ultimately designing the manufacturing enterprise itself.

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