CIRP Encyclopedia of Production Engineering

2014 Edition
| Editors: The International Academy for Production Engineering, Luc Laperrière, Gunther Reinhart

Twist Drill Geometry Optimization

  • Eberhard Abele
Reference work entry



Optimization problems arise in many areas of industry, business, and research. They can be applied to a wide range of subjects from simple applications such as better usage of existing resources to difficult subjects such as improving construction designs. In this context the optimization tools can be described as search methods for solving a maximization or minimization problem. The optimization method is not to be considered as a closed mathematical solution, but as a method to find a good solution with a certain probability. In general, a metaheuristic optimization problem is given by an amount of solutions into a solution space and one or more evaluation functions. The evaluation function creates a statement about the quality of each solution as a function of its decision variables. Each solution thus represents a possible combination of the decision variables. The valid solutions are limited by one or more constraints...

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  1. Abele E, Fujara M, Schäfer D (2011) Holistic approach for a simulation-based twist drill geometry optimization. Proceedings of the ASME 2011 International Manufacturing Science and Engineering Conference, MSEC2011, Corvallis, USACrossRefGoogle Scholar
  2. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, BostonMATHGoogle Scholar
  3. Goldberg D, Holland J (1988) Genetic algorithms and machine learning. Kluwer, DordrechtGoogle Scholar
  4. Holland J (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2(2):88–105CrossRefMathSciNetMATHGoogle Scholar
  5. Muschalla D (2006) Evolutionäre und multikriterielle Optimierung komplexer wasserwirtschaftlicher Systeme [Evolutionary and multi-objective optimization of complex water management systems]. Dissertation. Fachbereich für Bauingenieurwesen und Geodäsie, TU DarmstadtGoogle Scholar
  6. Reynolds CW (1987) Flocks, herds and schools: a distributed behavioral model. Computer Graphics, ACM SIGGRAPH’87 Conference ProceedingsGoogle Scholar
  7. Treanor G, Hinds BK (2000) Analysis of stresses in micro-drills using the finite element method. Int J Mach Tools Manufact 40(10):1443–1456CrossRefGoogle Scholar

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© CIRP 2014

Authors and Affiliations

  1. 1.Institut Für Produktionsmanagement, Technologie Und WerkzeugmaschinenTechnische Universität DarmstadtDarmstadtGermany