Publicación:
MACHINE LEARNING TO PREDICT VENEER DRYING QUALITY IN THE PINUS RADIATA PLYWOOD INDUSTRY

Vista sencilla de ítem
dc.creatorDIEGO FERNANDO VENEGAS VASCONEZ
dc.creatorFRANCISCO EDUARDO GATICA NEIRA
dc.creatorMARIO ALEJANDRO RAMOS MALDONADO
dc.date2024
dc.date.accessioned2025-01-10T15:51:26Z
dc.date.available2025-01-10T15:51:26Z
dc.date.issued2024
dc.description.abstractMACHINE LEARNING IS A TOOL THAT IS BEING USED TO OPTIMIZE HIGHLY COMPLEX INDUSTRIAL PROCESSES. IN THE PLYWOOD PANEL PRODUCTION INDUSTRY, VENEER DRYING IS ONE OF THE MOST IMPORTANT PROCESSES AS IT ALLOWS TO OBTAIN HIGH QUALITY PRODUCTS. THE BIOLOGICAL NATURE AND HIGH STRUCTURAL VARIABILITY OF WOOD TURNS ITS INDUSTRIAL PROCESSING MULTIVARIATE AND DIFFICULT TO CONTROL. THE LARGE NUMBER OF VARIABLES PRESENT AND THE INCREASING POSSIBILITY TO MEASURE THEM IN REAL TIME ARE ENABLING THE AVAILABILITY OF A LARGE AMOUNT OF DATA. NOWADAYS, DATA-DRIVEN APPROACH AND INTELLIGENCE ARTIFICIAL TECHNIQUES, SPECIFICALLY MACHINE LEARNING CAN ENABLE ROBUST PREDICTION AND CONTROL APPROACHES. IN THE PROCESS INDUSTRY, WITH HIGH LEVELS OF AUTOMATION, IT IS POSSIBLE TO ENABLE DECISION MAKING TO PREDICT PRODUCT QUALITY BY MONITORING EXPLANATORY CONTROL VARIABLES. THE OBJECTIVE OF THIS WORK WAS TO EVALUATE A MACHINE LEARNING ALGORITHM CAPABLE OF PREDICTING THE QUALITY OF THE VENEER DRYING PROCESS FROM A CONSIDERABLE NUMBER OF INPUT VARIABLES CAPTURED FROM A REAL INDUSTRIAL PROCESS. THE WEKA PLATFORM AND PYTHON CODE WERE USED. THREE ALGORITHMS WERE EVALUATED: K-NEAREST-NEIGHBOR, EXTREME GRADIENT BOOSTING AND SUPPORT VECTOR MACHINE. VARIABLE AND DIMENSIONALITY REDUCTION, CORRELATION ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS WERE PERFORMED. THE RESULTS SHOWED THAT EXTREME GRADIENT BOOSTING ACHIEVED AN ACCURACY OF 76 % IN PREDICTING QUALITY SCORES. FINALLY, IT IS CONCLUDED THAT BOTH THE DATA ENGINEERING METHODOLOGY AND THE EVALUATED ALGORITHMS WERE EFFICIENT IN PREDICTING INDUSTRIAL DATA.
dc.formatapplication/pdf
dc.identifier.doi10.22320/s0718221x/2024.46
dc.identifier.issn0717-3644
dc.identifier.issn0718-221X
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/13974
dc.languagespa
dc.publisherMADERAS: CIENCIA Y TECNOLOGIA
dc.relation.uri10.22320/s0718221x/2024.46
dc.rightsPUBLICADA
dc.subjectwood quality
dc.subjectveneer drying
dc.subjectprocess optimization.
dc.subjectpredictive model
dc.subjectMachine learning algorithms
dc.titleMACHINE LEARNING TO PREDICT VENEER DRYING QUALITY IN THE PINUS RADIATA PLYWOOD INDUSTRY
dc.typeARTÍCULO
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE ECONOMIA Y FINANZAS
ubb.Otra ReparticionDEPARTAMENTO DE INGENIERIA EN MADERAS
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
Archivos
Colecciones
Artículos científicos