An innovative approach for planning and execution of pre-experimental runs for Design of Experiments

This paper addresses the study of the pre-experimental planning phase of the Design of Experiments (DoE) in order to improve the final product quality. The pre-experimental planning phase includes a clear identification of the problem statement, selection of control factors and their respective levels and ranges. To improve production quality based on the DoE a new approach for the pre-experimental planning phase, called Non-Conformity Matrix (NCM), is presented. This article also addresses the key steps of the pre-experimental runs considering a consumer goods manufacturing process. Results of the application for an industrial case show thatthis methodology can support a clear definition ofthe problem and also a correct identification of the factor ranges in particular situations. The proposed new approach allows modeling the entire manufacturing system holistically and correctly defining the factor ranges and respective levels for a more effective application of DoE. This new approach can be a useful resource for both research and industrial practitioners who are dedicated to large DoE projects with unknown factor interactions, when the operational levels and ranges are not completely defined. / El presente ensayo aborda el estudio de la fase pre-experimental de planificacion del Diseno de Experimentos (DoE) con el fin de mejorar la calidad del producto final. En dicha fase pre-experimental de planificacion se incluye una identificacion clara del planteamiento del problema, de la seleccion de los factores de control y de sus correspondientes niveles y rangos. Para mejorar la calidad de produccion basada en el DoE se presenta un nuevo enfoque para la fase pre-experimental de planificacion llamado Matriz de No Conformidades (NCM). Este ensayo aborda tambien los pasos clave de las series experimentales teniendo en cuenta el proceso de fabricacion de articulos de consumo. Los resultados de la puesta en marcha para un caso industrial demuestran que esta metodologia puede respaldar una definicion clara del problema y tambien la correcta identificacion de los rangos de factores para situaciones concretas. Este nuevo enfoque propuesto permite modelar todo el sistema de fabricacion de manera holistica y definir correctamente los rangos de factores y niveles correspondientes para una puesta en marcha eficaz del DoE. Dicho enfoque puede ser un recurso util tanto para profesionales de la investigacion como de la industria que se dedican a grandes proyectos de DoE cuyas interacciones de factores son desconocidas, cuando los niveles y rangos operativos no estan definidos del todo

[1]  Mehmet Cakmakci,et al.  A feasibility study using simulation-based optimization and Taguchi experimental design method for material handling—transfer system in the automobile industry , 2012 .

[2]  Jean-Yves Dantan,et al.  Conceptual process planning - an improvement approach using QFD, FMEA, and ABC methods , 2010 .

[3]  Douglas C. Montgomery,et al.  Using statistically designed experiments for process development and improvement: an application in electronics manufacturing , 2000 .

[4]  Matthew Franchetti,et al.  Determining the optimal parameters of bonding polyvinylchloride to stainless steel in automotive applications with the use of full factorial design of experiment , 2014 .

[5]  Ravikanth Kona,et al.  Quality by Design I: Application of Failure Mode Effect Analysis (FMEA) and Plackett–Burman Design of Experiments in the Identification of “Main Factors” in the Formulation and Process Design Space for Roller-Compacted Ciprofloxacin Hydrochloride Immediate-Release Tablets , 2012, AAPS PharmSciTech.

[6]  Henriqueta Nóvoa,et al.  Evaluation of a Non-Conformity Matrix Complexity using Components Modularity Metrics , 2013 .

[7]  Tyson R. Browning,et al.  Design Structure Matrix Methods and Applications , 2012 .

[8]  Henriqueta Nóvoa,et al.  An application of Knowledge Management in Design Structure Matrix for a process improvement phase , 2014 .

[9]  Jiju Antony,et al.  Improving the wire bonding process quality using statistically designed experiments , 1999 .

[10]  Ramaratnam R Bishu,et al.  Design of experiments to study and optimize process performance , 1999 .

[11]  Veronica A. Czitrom.  Ch. 1. Guidelines for selecting factors and factor levels for an industrial designed experiment , 2003 .

[12]  Douglas C. Montgomery,et al.  A systematic approach to planning for a designed industrial experiment , 1993 .

[13]  Genichi Taguchi,et al.  Taguchi's Quality Engineering Handbook , 2004 .

[14]  William Y. Fowlkes,et al.  Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development , 1995 .

[15]  Henriqueta Nóvoa,et al.  Application of Non-conformity Matrix to Predict System Interactions in Complex Quality Problems , 2013 .

[16]  Tyson R. Browning,et al.  Applying the design structure matrix to system decomposition and integration problems: a review and new directions , 2001, IEEE Trans. Engineering Management.