Improving Deep Drawing Quality of DD13 Sheet Metal: Optimization of Process Parameters Using Box–Behnken Design


ÇELİK İ., ŞENSOY A. T., Seven G., Cicek D.

Materials, cilt.18, sa.7, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 7
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/ma18071424
  • Dergi Adı: Materials
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, CAB Abstracts, Communication Abstracts, Compendex, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: Box–Behnken design, deep drawing, Particle Swarm Optimization, response surface method
  • Samsun Üniversitesi Adresli: Evet

Özet

This study presents a comprehensive analysis of the earing and thinning ratios in the deep drawing process of DD13-grade sheet metal. The variable parameters investigated include press descent speed (x1), blank holder pressure (x2) and punch pressure (x3). These parameters have been carefully selected based on production experience and preliminary experiments, and appropriate ranges of values have been established. A distinctive feature of this research is its focus on specimens with a deep drawing ratio greater than 2. The relationships between the input parameters and the response parameters were determined using the Box–Behnken design (BBD). Analysis of variance (ANOVA) for the regression models developed for earing and thinning ratios gave R2 values of 0.98 and 0.97, respectively, indicating robust predictive ability of the models. For the earing ratio, the optimal values of x1, x2 and x3 were found to be 27.38, 10 and 22, respectively. When evaluated using the same algorithm for uniform wall thickness, the optimum values were found to be 26.55, 10 and 22, respectively. The final earing and thinning ratios for all experimental runs were 3.79 and 21.19, respectively. The results of this study show that by optimizing certain parameters in the deep drawing process, the disadvantages associated with this manufacturing method can be significantly reduced.