Optimization of multi-pass turning operations using hybrid teaching learning-based approach

Yildiz A. R.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, cilt.66, sa.9-12, ss.1319-1326, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 9-12
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s00170-012-4410-y
  • Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1319-1326
  • Anahtar Kelimeler: Hybrid optimization, Teaching-learning based optimization algorithm, Taguchi method, Manufacturing, Turning, PARTICLE SWARM OPTIMIZATION, DESIGN OPTIMIZATION, MULTIOBJECTIVE OPTIMIZATION, MACHINING CONDITIONS, GLOBAL OPTIMIZATION, PARAMETER SELECTION, GENETIC ALGORITHM, IMMUNE ALGORITHM, CONSTRAINTS, SYSTEM
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This paper presents a novel hybrid optimization approach based on teaching-learning based optimization (TLBO) algorithm and Taguchi's method. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing area. This research is the first application of the TLBO to the optimization of turning operations in the literature The proposed hybrid approach is applied to two case studies for multi-pass turning operations to show its effectiveness in machining operations. The results obtained by the proposed approach for the case studies are compared with those of particle swarm optimization algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing, and Hooke-Jeeves patter search.