Case Studies
This paper demonstrates a statistical methodology that builds on subject-matter knowledge to objectively fine-tune both processes and product formulations. The concept is illustrated with an adhesives case study.
A Sticky Situation
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.
Design of Experiments for Coatings
The traditional approach to experimentation changes only one process factor at a time (OFAT) or one component in a formulation. However, the OFAT approach does not provide data on interactions of factors (or components), a likely occurrence with coating formulations and processes. Statistically-based design of experiments (DOE) provides validated models, including any significant interactions, that allow you to confidently predict response measures as a function of inputs. The payoff is the identification of "sweet spots," where you can achieve all product specifications and processing objectives.
Benefits of Standard Factorial Designs
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.
Assay Automation
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.
Formulazione Ottimale Delle Miscele
Italian translation of "Find the Optimal Formulations with Mixtures."
La tecnica del DOE (Design of Experiments) fornisce un metodo efficiente per ottimizzare i processi e può portare a risultati interessanti se applicata alle formulazioni. Come accelerare l’esplorazione delle possibili alternative compositive di una miscela
Soluzioni per un Doe corretto
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.
Keys to Successful Designed Experiments
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.
Practical versus Statistical Aspects of Altering Central Composite Designs
Topic: Practical versus Statistical Aspects of Altering Central Composite Designs
1. Introduce ourselves: What we each do, how it involves use of DOE/RSM and the reason for coming to the roundtable (any specific questions that should be addressed?)
2. Host presents discussion issues on handout (prepared by Pat Whitcomb, Stat-Ease Inc.)
3. Open forum.
Come diagnosticare e gestire dati sperimentali scadenti
Italian translation of "How to Use Graphs to Diagnose and Deal with Bad Experimental Data."
Questo Articolo e relativo ad uno degli argomenti piu spinosi per ogni sperimentatore si troca ad affrontare: come trattare risultati individuali che non sembrano adattarsi con il resto dei dati. L'obiettivo dell'articolo e quello di fornire una serie di strumenti grafici per diagnosticare in modo esigenza di eventuale transformazione dei dati