Modeling and optimization of tool wear and surface roughness in turning of austenitic stainless steel using response surface methodology

The wear of cutting tools remains a major obstacle. The effects of wear are not only antagonistic at the lifespan and productivity, but also harmful with the surface quality. The present work deals with some machinability studies on ?ank wear, surface roughness, and lifespan in ?nish turning of AISI304 stainless steel using multilayerTi(C,N)/Al2O3/TiN coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), and combined effects of two cutting parameters, namely cutting speed and feed rate on lifespan (T), are explored employing the analysis of variance (ANOVA).The relationship between the variables and the technological parameters is determined using a quadratic regression model and optimal cutting conditions for each performance level are established. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. In addition, it is indicated that the cutting time is the dominant factor affecting workpiece surface roughness followed by feed rate, while lifespan is influenced by cutting speed

Dynamic Scale Genetic Algorithm: An Enhanced Genetic Search for Discrete Optimization

The minimization of operations and support resources of reusable launch vehicles is a complex task, involving discrete optimization and the simulation domain. Genetic algorithms, offering a robust search strategy suitable for integer variables and the simulation domain, can be applied to minimize these resources. This research developed an enhanced genetic algorithm for problems with a linear objective function, the most common class of discrete optimization problems. The dynamic scale genetic algorithm developed here incorporates concepts of implicit enumeration to enhance search. This is achieved by utilizing problem specific information to refine the solution space over successive generations. The utility of the proposed algorithm was demonstrated by comparing its performance, in terms of quality of solutions produced, to that of the simple genetic algorithm. For all test problems, the dynamic scale genetic algorithm consistently produced better solutions in fewer generations. The proposed algorithm was successfully applied to optimize the operation and support resources of reusable launch vehicles, through a discrete event simulation model. The least cost solution so obtained represents an improvement over both the simple genetic algorithm, and the previous manual approach of minimizing operation and support resources

Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems

A new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is extended to a new problem setting in which objective function evaluations require sampling from a model of a stochastic system. The approach combines GPS with ranking and selection (R&S) statistical procedures to select new iterates. The derivative-free algorithms require only black-box simulation responses and are applicable over domains with mixed variables (continuous, discrete numeric, and discrete categorical) to include bound and linear constraints on the continuous variables. A convergence analysis for the general class of algorithms establishes almost sure convergence of an iteration subsequence to stationary points appropriately defined in the mixed-variable domain. Additionally, specific algorithm instances are implemented that provide computational enhancements to the basic algorithm. Implementation alternatives include the use modern R&S procedures designed to provide efficient sampling strategies and the use of surrogate functions that augment the search by approximating the unknown objective function with nonparametric response surfaces. In a computational evaluation, six variants of the algorithm are tested along with four competing methods on 26 standardized test problems. The numerical results validate the use of advanced implementations as a means to improve algorithm performance