Determination of optimal subdivision of depth of cut in multipass turning with constraints

In multipass turning of flexible workpieces, the selection of the sequence of cutting passes is as important as the selection of machining parameters (depth of cut, feed rate, and cutting speed). This is because the varying workpiece diameter has an effect on the optimization objective function and constraints. In general, there should be a total of five decision variables in multipass turning optimization problems, that is, the three fundamental machining parameters, the number of cutting passes, and the sequence of cutting passes. However, this issue has been paid little attention in literature. This study presents an effective method for optimizing the cutting sequence and the machining parameters to suppress the deformation and chatter during turning a flexible cantilever tube. The mathematical optimization model distinct from the simplified model used in many previous studies is formulated, where the deformation and chatter constraints are intensively deduced from the dynamic analysis. The optimization problem is solved in two phases. The first phase is to determine the minimum production time for each cutting pass for the predefined depths of cut. A particle swarm optimization is employed to accomplish this step. After that, a dynamic programming technique is adopted to achieve the optimal sequential subdivision of the total depth of cut. A computational experiment elaborates the methodology. The results demonstrate that the proposed method is of generality and more efficient in comparison with other algorithms.

Section: Introductionmentioning

confidence: 99%

Dynamic optimization of multipass turning of a flexible workpiece considering the effect of cutting sequence

Zhang

Jing

et al. 2015

“…Multi-pass turning optimizations with optimal sub-division of depth of cut was developed by Gupta et al [14]. The above machining models were developed for straight turning only in which the constant diameter of stock is removed.…”

Section: Introductionmentioning

confidence: 99%

Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques

Saravanan¹,

Sankar

Asokan

et al. 2005

Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for turning operations with constant diameters only. All Computer Numerical Control (CNC) machines produce the finished components from the bar stock. Finished profiles consist of straight turning, facing, taper and circular machining.This research work concentrates on optimizing the machining parameters for turning cylindrical stocks into continuous finished profiles. The machining parameters in multi-pass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost.In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint, tool tip temperature, etc. Due to high complexity of this machining optimization problem, six non-traditional algorithms, the genetic algorithm (GA), simulated annealing algorithm (SA), Tabu search algorithm (TS), memetic algorithm (MA), ants colony algorithm (ACO) and the particle swarm optimization (PSO) have been employed to resolve this problem. The results obtained from GA, SA,TS, ACO, MA and PSO are compared for various profiles. Also, a comprehensive user-friendly software package has been developed to input the profile interactively and to obtain the optimal parameters using all six algorithms. New evolutionary PSO is explained with an illustration.

“…Most of the research works concentrate on finding a suitable optimization technique and testing the performance of proposed techniques by means of hypothetical examples [2][3][4][5][6][7][8][9][10][11][12]. In all the papers cited here, authors have assumed the existence of a reliable tool life equation, with known values of exponents.…”

Section: Introductionmentioning

confidence: 99%

An economic and reliable tool life estimation procedure for turning

Ojha

Dixit

2005

The conventional methods of tool life estimation take a long time and consume a lot of work piece material. In this paper, a quicker method for the estimation of tool life is proposed, which requires less consumption of work piece material and tools. In this method the tool life is estimated by fitting a best-fit line on the data falling in the steady wear zone and finding the time till tool failure by extrapolation. Neural networks are used to predict lower, upper and most likely estimates of the tool life. Comparison between neural networks and multiple regression shows the superiority of the former. The paper also proposes a methodology for continuous monitoring of tool use in the shop floor and updating/obtaining the tool life estimates based on the shop floor feed back.