Ants colony algorithm approach for multi-objective optimisation of surface grinding operations

Abstract

An ant colony based optimisation procedure has been developed to optimise grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using a multi-objective function model with a weighted approach for the surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. The results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) techniques. Nomenclature a p down feed of grinding (mm/pass) a w total thickness of cut (mm) A o initial wear flat-area percentage (%) b e empty width of grinding (mm) b s width of wheel (mm) b w width of workpiece (mm) B k positive definite approximation of the Hessian doc depth of dressing (mm) c d cost of dressing ($) c s cost of wheel per mm 3 ($/mm 3 ) CTtotal production cost ($/pc) CT * expected production cost limit ($/pc) d g grind size (mm) D e diameter of wheel (mm) f b cross feed rate (mm/pass) G grinding ratio k a constant dependent on coolant and wheel grind type k u wear constant (mm -1 ) k c cutting stiffness (N/mm) k m static machine stiffness (N/mm) k s wheel wear stiffness (N/mm) L lead of dressing (mm/rev) L e empty length of grinding (mm) L w length of workpiece (mm) M c cost per hour labour and administration ($/h) N d total number of pieces to be grouped during the life of dressing (pc) N t batch size of workpieces (pc) N td total number of workpieces to be grouped during the life of dressing (pc) P number of workpieces loaded on the table (pc) R a surface roughness (lm) R a * surface finish limit during rough grinding (lm) R c workpiece hardness (Rockwell hardness number) R em dynamic machine characteristics S d distance of wheel idling (mm) S p number of spark out grinding (pass) t sh time of adjusting machine tool (min) t i time of loading and unloading workpiece (min) T ave average chip thickness during grinding (lm) U specific grinding energy (J/mm) U * critical specific grinding energy (J/mm 3 ) V r speed of wheel idling (mm/min) V s wheel speed (m/min) V w workpiece speed (m/min) VOLwheel bond percentage (%) WRP workpiece removal parameter (mm 3 /min-N) WRP * workpiece removal parameter limit (mm 3 /min-N) WWP wheel wear parameter (mm 3 /min-N) W i weighting factor, 0 £ W i £ 1

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

“…(Mukherjee and Ray 2006;Yusup et al 2012;Wen et al 1992;Rowe et al 1994;Saravanan et al 2002;Dhavalikar et al 2003;Mitra and Gopinath 2004;Baskar et al 2004;Krishna 2007;Pawar et al 2010;Rao and Pawar 2010). Now, NSTLBO algorithm is applied to solve the mutiobjective optimization problem in surface grinding process.…”

Review of Multiresponse Process Optimisation Methods

“…Recently, in order to develop an effective global optimisation tool, a modified ACO algorithm was developed implying a bi-level search procedure called local and global search (Baskar et al 2004). In a local search, the initial solutions are classified as superior and inferior solutions based on their fitness values, and local updating is applied only on superior regions according to a probability function that depends on the pheromone trail on certain region at a certain time.…”

“…They concluded that ACO performs better than SA and GA in solving the observed optimisation problem. Baskar et al (2004) performed ACO-based optimisation of surface grinding process parameters with respect to multiple objectives. The results showed that ACO algorithm gave slightly better results than GA and significantly better results than quadratic programming.…”

Improved differential evolution approach for optimization of surface grinding process

“…However, the variables selected by such practice will usually be on the conservative side and they cannot satisfy any economic criterion. Therefore, recently, some researchers (Baskar, Saravanan, Asokan, & Prabhaharan, 2004;Krishna, 2007;Krishna & Rao, 2006;Lin & Li, 2008;Saravanan, Asokan, & Sachidanandam, 2002;Wen, Tay, & Nee, 1992) have applied various optimization techniques to optimize the grinding variables of wheel speed, workpiece speed, depth of dressing, and lead of dressing using a multi-objective function model with a weighted approach for the surface grinding process. The production cost, production rate and surface finish are evaluated for the optimal grinding conditions, subject to the constraints of thermal damage, wheel-wear parameters, machine-tool stiffness, and either workpiece removal parameter limitation or surface finish limitation.…”