Publicación: A MACHINE LEARNING APPROACH FOR PLYWOOD QUALITY PREDICTION
Fecha
2023
Título de la revista
ISSN de la revista
Título del volumen
Editor
MADERAS: CIENCIA Y TECNOLOGIA
Resumen
BECAUSE OF THE IMPACT ON PRODUCTIVITY AND COST REDUCTION, DECISION MAKING IN INDUSTRIAL PROCESSES IS ONE OF THE MOST REQUIRED ASPECTS IN THE INDUSTRY. SPECIFICALLY IN THE PANEL INDUSTRIES, PRODUCT QUALITY DEPENDS ON MULTIPLE VARIABLES, ESPECIALLY WOOD VARIABILITY. AMONG OTHER FACTORS, QUALITY DEPENDS ON THE ADHESION OF VENEERS OR PERPENDICULAR TENSILE STRENGTH. THE MAIN OBJECTIVE OF THIS STUDY WAS TO EVALUATE A MACHINE LEARNING APPROACH TO PREDICT THE ADHESION UNDER INDUSTRIAL CONDITIONS IN THE GLUING AND PRE-PRESSING STAGE. THE CONTROL VARIABLES THAT DETERMINE THIS ADHESION ARE MAINLY: OPERATIONAL TIMES, AMOUNT OF ADHESIVE, ENVIRONMENTAL CONDITIONS, AND VENEER TEMPERATURE. USING KNOWLEDGE DISCOVERY IN DATABASES DATA ANALYTICS METHODOLOGY, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINE WERE EVALUATED. THE SIGMOID ACTIVATION FUNCTION WAS USED
WITH 3 HIDDEN LAYERS AND 245 NEURONS. IN ADDITION TO THE ADAM OPTIMIZER, MULTI-LAYERPERCEPTRON, ARTIFICIAL
NEURAL NETWORKS DELIVERED THE BEST ACCURACY LEVELS OF OVER 66 %. SIGMOID SHOWED AN ACCURACY OF OVER 66 %, PRECISION FIT GOOD TO FIND POSITIVE RESULTS (70 %). RELU FUNCTION OBTAINED THE BEST RECALL (OVER 74 %) SHOWING A GOOD CAPACITY TO IDENTIFY REALITY. RESULTS SHOW THAT IT IS NOT SUFFICIENT TO GENERATE A DATA SET USING THE AVERAGES OF EACH PROCESS VARIABLE, SINCE IT IS DIFFICULT TO OBTAIN BETTER RESULTS WITH THE ALGORITHMS EVALUATED. THIS WORK CONTRIBUTES TO DEFINING A METHODOLOGY TO BE USED IN PLYWOOD PLANTS USING INDUSTRIAL DATA TO TRAIN AND VALIDATE MACHINE LEARNING MODELS.
Descripción
Palabras clave
wood industry, supervised learning, predictive models, plywood, Ma- chine Learning, data engineering, artificial neural networks., Algorithms, tableros contrachapados, Redes Neuronales Artificiales., mode- los predictivos, Machine Learning, ingeniería de datos, industria de la madera, aprendizaje supervisado, Algoritmos
