Technical professionals in the pressure sensitive adhesives area are highly skilled and educated in tape-making processes. They have often taken course-work in the areas of PSA tape properties, characteristics of tape-making, web handling, coating and converting processes. Subject-matter knowledge is generally abundant. The purpose of this paper is to demonstrate a statistical methodology that builds on subject-matter knowledge to objectively fine-tune both processes and product formulations. The method will be illustrated with a case study where the experimenter was trying to optimize an adhesive to achieve specific test properties.
Standard factorial designs (one array) offer a cost effective and informational-efficient robust design alternative to parameter designs (two array) made popular by Taguchi. Consider a simulated case involving the development of a new method for attaching an elastomeric connector to a nylon tube, where the objective is to consistently deliver a specified pull-off force via three control factors.
This paper illustrates the use of design of experiments (DOE) and split-plot design to quickly and effectively determine the factor settings that maximize amplification in a polymerase chain reaction (PCR) experiment. As presented at the 2005 ASQ World Congress.
An innovative blend of hardware, software and the right training in statistical know-how supercharges research automation. This article was published online by Quality Digest. This article was originally published in ADVANCE for Medical Laboratory Professionals.
Questo articolo identifica otto soluzioni per avere successo nell’applicazione degli strumenti statistici del Design of Experiments (Doe). I responsabili della qualità che faranno proprie queste soluzioni potranno essere in grado di sostenere l’uso del Doe nel loro ambito organizzativo. Infine questo condurrà a miglioramenti progressivi nella qualità del prodotto e nell’efficienza del processo.
This is an Italian translation of Keys to Successful Designed Experiments.
Optimizing biological assay conditions is a demanding process that scientists face daily. The requirement is to develop high-quality, robust assays that work across a range of biological conditions. The demand is to do this within a short time frame. To overcome these obstacles, automated systems often are used to accommodate large numbers of samples.
We can improve experimentation results by studying organizations that have experienced both frustrations due to poor experimentation methodology and satisfaction from successful applications. This paper identifies eight factors essential to successful experimentation. A solid understanding of these key factors is the foundation to a successful design of experiments program.
This article explains why standard factorial designs (one array) offer a cost-effective alternative to parameter designs (two array) made popular by Taguchi. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE). Requires login to view.
Standard factorial designs (one array) offer a cost-effective and information-efficient robust design alternative to parameter designs (two -array) made popular by Taguchi. This paper compares these two methods (one-array versus two-array) in depth via an industrial case study. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE).