A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

Iván E. Villalón-Turrubiates, Rogelio López-Herrera, Jorge L. García-Alcaraz, José R. Díaz-Reza, Arturo Soto-Cabral, Iván González-Lazalde, Gerardo Grijalva-Avila and José L. Rodríguez-Álvarez ()
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Iván E. Villalón-Turrubiates: Department of Doctoral Program in Engineering Sciences, Western Institute of Technology and Higher Education, Tlaquepaque 45604, Mexico
Rogelio López-Herrera: Department of Doctoral Program in Engineering Sciences, Western Institute of Technology and Higher Education, Tlaquepaque 45604, Mexico
Jorge L. García-Alcaraz: Department of Industrial Engineering, Autonomous University of Ciudad Juárez, Ciudad Juárez 32310, Mexico
José R. Díaz-Reza: Division of Research and Postgraduate Studies, National Technology of Mexico, Technological Institute of Ciudad Juárez, Ciudad Juárez 32310, Mexico
Arturo Soto-Cabral: Department of Industrial Engineering, National Technology of México, Technological Institute of Durango, Durango 34080, Mexico
Iván González-Lazalde: Department of Industrial Engineering, National Technology of México, Technological Institute of Durango, Durango 34080, Mexico
Gerardo Grijalva-Avila: Department of Manufacturing Engineering, Polytechnic University of Durango, Durango 34306, Mexico
José L. Rodríguez-Álvarez: Department of Doctoral Program in Engineering Sciences, Western Institute of Technology and Higher Education, Tlaquepaque 45604, Mexico

Mathematics, 2022, vol. 10, issue 16, 1-27

Abstract: The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

Keywords: fuzzy process capability indices; fuzzy set theory; neural network model; graphic user interface; factorial experimental design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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