Prediction of Optimum CNC Cutting Conditions Using Artificial Neural Network Models for the Best Wood Surface Quality, Low Energy Consumption, and Time Savings

Cakiroglu E. O., DEMİR A., AYDIN İ., Buyuksari U.

BIORESOURCES, cilt.17, sa.2, ss.2501-2524, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.15376/biores.17.2.2501-2524
  • Dergi Adı: BIORESOURCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Chemical Abstracts Core, Compendex, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.2501-2524
  • Anahtar Kelimeler: Cutting conditions, Artificial neural network, CNC machine, Surface quality, Energy consumption, Processing time, EDGE-GLUED PANELS, MACHINING PARAMETERS, ROUGHNESS CHARACTERISTICS, TAGUCHI DESIGN, WETTABILITY, DENSITY, OPTIMIZATION, METHODOLOGY, MDF, ADHESION
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

This study aimed to predict the CNC cutting conditions for the best wood surface quality, energy, and time savings using artificial neural network (ANN) models. In the CNC process, walnut, and ash wood were used as materials, while three different cutting tool diameters (3 mm, 6 mm, and 8 mm), spindle speed (12000 rpm, 15000 rpm, and 18000 rpm), and feed rate (3 m/min, 6 m/min, and 9 m/min) were determined as cutting conditions. After the cutting processes were completed with the CNC machine, energy consumption and processing time were determined for all groups. Surface roughness and wettability tests were performed on the processed wood samples, and their surface qualities were determined. The experimentally obtained data were analysed in ANN, and the models with the best performance were obtained. By using these prediction models, optimum cutting conditions were determined. Using the findings of the study, the optimum cutting condition values can be determined for walnut and ash wood with the smoothest and best wettable surface. Furthermore, in CNC processes using such materials, minimum energy consumption and shorter processing time can be obtained with optimum cutting conditions.