In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) algorithm, is introduced for solving manufacturing optimization problems. This research is the first application of the CS to the optimization of machining parameters in the literature. In order to demonstrate the effectiveness of the CS, a milling optimization problem was solved and the results were compared with those obtained using other well-known optimization techniques like, ant colony algorithm, immune algorithm, hybrid immune algorithm, hybrid particle swarm algorithm, genetic algorithm, feasible direction method, and handbook recommendation. The results demonstrate that the CS is a very effective and robust approach for the optimization of machining optimization problems.

Cuckoo search algorithm for the selection of optimal machining parameters in milling operations

Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Application of soft computing techniques in machining performance prediction and optimization: a literature review

Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm

Highlights? Several evolutionary techniques are reviewed to optimize machining parameter. ? It was found that genetic algorithm was widely applied by researchers. ? The most employed machining operation was multipass-turning. ? The most considered machining performance was surface roughness. In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.

Evolutionary techniques in optimizing machining parameters

This paper presents a novel optimization approach that is a new hybrid optimization approach based on the particle swarm optimization algorithm and receptor editing property of immune system. The aim of the present research is to develop a new optimization approach and then to apply it in the solution of optimization problems in both the design and manufacturing areas. A single-objective test problem, tension spring problem, pressure vessel design optimization problem taken from the literature and two case studies for multi-pass turning operations are solved by the proposed new hybrid approach to evaluate performance of the approach. The results obtained by the proposed approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and integration of simulated annealing and Hooke-Jeeves pattern search.

https://rd.springer.com/content/pdf/10.1007%2Fs00170-008-1453-1.pdf

A novel particle swarm optimization approach for product design and manufacturing.

This paper proposes a new optimization technique based on the ant colony algorithm for solving multi-pass turning optimization problems. The cutting process has roughing and finishing stages. The machining parameters are determined by minimizing the unit production cost, subject to various practical machining constraints. The results indicate that the proposed ant colony framework is effective compared to other techniques carried out by different researchers.

Optimization of multi-pass turning operations using ant colony system

In this paper, optimization procedures based on the genetic algorithm, tabu search, ant colony algorithm and particle swarm optimization Algorithm were developed for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. An example has been presented at the end of the paper to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using the method of feasible directions and handbook recommendations.

Optimization of Machining Parameters for Milling Operations Using Non-conventional Methods

Design of fixture configuration (layout) is a procedure to establish the workpiece fixture contact through positioning of clamps and locating elements such that the workpiece elastic deformation is minimized. This work is done based on the assumption that the workpiece is an elastic body. Since the fixture layout influences the dimensional and form accuracy of the workpiece during the machining process, the fixture layout has to be optimized to minimize the workpiece deformation. In this paper, the workpiece deformation is modeled using finite element method (FEM), for the problem of fixture layout optimization problems with the objective of minimizing the dimensional and form errors. This paper presents a fixture layout optimization method that uses genetic algorithm (GA) and ant colony algorithm (ACA) separately. In this paper, three different number of node systems are defined on the same workpiece geometry to find the consistency in the performance of GA and ACA. The optimal solution, i.e., the most minimum value among the entire possible layout is determined separately for all three different number of node systems. The solution obtained for each node system using GA and ACA is compared with their respective optimal solution separately and ACA reports faster and accurate solutions.

Machining fixture layout optimization using FEM and evolutionary techniques

Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm

In an assembly, there are two ways to control the deviation of critical dimensions. One is by keeping the deviation of the critical dimension small by tightening manufacturing tolerances and controlling aging and environmental effects. This approach is traditional and expensive, as it requires tighter manufacturing tolerances and protection from aging and the environment. The second is by moving the nominal values of the non-critical dimensions to a less sensitive portion. This approach is very helpful in improving the quality with no additional cost. One can analyze any number of designs very early in the concept development stage of a project. After the concept design the cost-based optimal tolerances for the corresponding dimensions are allocated. The continuous ants colony algorithm, a kind of meta-heuristic approach, is used as an optimization tool for minimizing the critical dimension deviation and allocating the cost- based optimal tolerances.

G. Prabhaharan

Papers

Optimization of multi-pass turning operations using ant colony system

Optimization of Machining Parameters for Milling Operations Using Non-conventional Methods

Machining fixture layout optimization using FEM and evolutionary techniques

Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm

Sensitivity-based conceptual design and tolerance allocation using the continuous ants colony algorithm (CACO)