Ph.D. Tezi Görüntüleme

Student: Nurdan ÇETİN YERLİKAYA
Supervisor: Doç. Dr. Abdulkadir MALKOÇOĞLU
Department: Orman Endüstri Müh.
Institution: Graduate School of Natural and Applied Sciences
University: Karadeniz Technical University, Turkey
Title of the Thesis: Investigating for strength values and optimum drill plans on at the ready to assemble (RTA) cabinet type furniture corner joints
Level: Ph.D.
Acceptance Date: 19/2/2010
Number of Pages: 141
Registration Number: Di753
Summary:

      This study aims to investigate the optimum drill plans on ready to assemble (RTA) cabinet type furniture corner joints via determining moments and elasticity with the aid of Artificial Neural Networks (YSA) method. For this purpose, five different sample length of melamine overlaid particleboard (YKYL) and medium density fibreboard (YKLL) with 4 different stop distances for each was prepared as test samples. Eccentric (minifix) connectors and dowels were used as fastener components. Tests were conducted in accordance with ASTM 1037 standards. As a result; the highest moment and elasticity value for YKYL was obtained as 23,6 Nm and 140,87 Nm/rad at 600x50 mm (sample length x stop distance), whereas the lowest value was obtained at 390x80 as 7,67 Nm and 57,45 Nm/rad. As for YKLL; the highest moment value was 31,23 Nm at 600x50 mm, the highest elasticity value was 121,19 Nm/rad at 600x60 mm, whereas the lowest moment value was 12,43 Nm at 320x50 mm and the lowest elasticity value was 61,10 Nm/rad at 320x60 mm. Average moment values were determined as 13,96 Nm for YKYL and 19,54 Nm for YKLL. Values for YKYL were 40 % higher than values for YKLL. As for elasticity, values for YKYL (89,02 Nm/rad) and for YKLL (90,61 Nm/rad) were almost equal. The optimum drill plans were presented by determining the moment and elasticity values for the stop distances of different sample lengths with the aid of Artificial Neural Networks (YSA) Method.

      Key words: Melamine overlaid particleboard, melamine overlaid fibreboard, strength, elasticity, Artificial Neural Networks Method