Optimization of multipass turning operations with genetic algorithms

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in multipass machining operations. The cutting process simultaneously considers multipass rough machining and finish machining. The optimum machining parameters are determined by minimizing the unit production cost subject to practical machining constraints. The cutting model formulated is a non-linear-constrained programming (NCP) problem with 20 machining parameter constraints. Experimental results show that the proposed genetic algorithm-based procedure for solving the NCP problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.     

Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process

Multi-pass milling is a common manufacturing process in practical production. Parameter optimisation is of great significance since the parameters largely affect the production time, quality, cost and some other process performance measures. However, the parameter optimisation of the multi-pass milling process is a nonlinear constrained optimisation problem. It is very difficult to obtain satisfactory results by the traditional optimisation methods. Therefore, in this paper, a new optimisation technique based on the electromagnetism-like mechanism (EM) algorithm is proposed to solve the parameter optimisation problem in a multi-pass milling process. The EM algorithm is a population based meta-heuristic algorithm for unconstrained optimisation problems. As the parameter optimisation problem is a constrained problem, the proposed approach handles the constraints of the problem by improving the charge calculation formula combined with the feasibility and dominance rules at the same time. This paper also puts forward flexible cutting strategies to simultaneously optimise the depth of cut for each pass, cutting speed and feed to improve solutions. A case study is presented to verify the effectiveness of the proposed approach. The results show that the proposed method is better than other algorithms and achieves significant improvement.     

A simulated annealing approach for optimization of multi-pass turning operations

In this paper, an optimization algorithm based on the simulated annealing (SA) algorithm and the Hooke-Jeeves pattern search (PS) is developed for optimization of multi-pass turning operations. The cutting process is divided into multi-pass rough machining and finish machining. Machining parameters are determined to optimize the cutting conditions in the sense of the minimum unit production cost under a set of practical machining constraints. Experimental results indicate that the proposed nonlinear constrained optimization algorithm, named SA/PS, is effective for solving complex machining optimization problems. The SA/PS algorithm can be integrated into a CAPP system for generating optimal machining parameters.     

Generating collision-free tool orientations for 5-axis NC machining with a short ball-end cutter

A novel tool orientation optimisation algorithm is proposed for 5-axis NC machining with a short ball-end cutter. It can generate collision-free and smooth tool orientations along with a safe and shortest tool length (SSTL). The use of shorter cutters without collision is a key advantage of 5-axis machining because the magnitude of tool deflection and the stability of cutting process are greatly affected by the slenderness ratio of the cutter. Existing methods can calculate the SSTL in the NC simulation process. However, the SSTL is essentially determined by the tool orientations and should be considered in the process of tool path generation. To overcome this limitation, a new tool orientation optimisation algorithm is proposed. The SSTL is determined by optimising the tool orientations under the constraints of global collision avoidance and tool orientation smoothness. The algorithm first computes the global accessibility cone and the SSTL along each accessible tool orientation. Then the tool orientations are optimised based on the discrete dynamic programming with the SSTL along the whole tool path being the optimisation objective. Finally, the tool path is generated by globally smoothing the tool orientations. Computational examples and cutting experiment are given to illustrate the validity and efficiency of the proposed algorithm.     

Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral?derivative controller in automatic voltage regulator system

Optimal tuning of proportional?integral?derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.     

Experimental investigation of cutting parameters in machining of 100Cr6 with tangential turn-milling method

The turn-milling methods for machining operation have been developed to increase efficiency of conventional machines recently. These methods are used especially by coupling some apparatuses on the computer numerical control (CNC) machine to decrease the production time and machine costs, ensure the maximum production and increase the quality of machining. In this study, 100Cr6 bearing steel extensively used in industry has been machined by tangential turn-milling method. This paper presents an approach for optimization of the effects of the cutting parameters including cutter speed, workpiece speed, axial feed rate, and depth of cut on the surface roughness in the machining of 100Cr6 steel with tangential turn-milling method by using genetic algorithm (GA). Tangential turning-milling method has been determined to have optimum effects of cutting parameters on the machining of 100Cr6 steel. The experimental results show that the surface roughness quality is close to that of grinding process.     

Design,evaluation and optimisation of cutter path strategies when high speed machining hardened mould and die materials

The use of high speed milling (HSM) for the production of moulds and dies is becoming more widespread. Critical aspects of the technology include cutting tools, machinability data, cutter path generation and technology. Much published information exists on cutting tools and related data (cutting speeds, feed rates, depths of cut, etc.). However, relatively little information has been published on the optimisation of cutter paths for this application. Most of the research work is mainly focused on cutter path generation with the main aim on reducing production time. Work with regards to cutter path evaluation and optimisation on tool wear, tool life, surface integrity and relevant workpiece machinability characteristics are scant. Therefore, a detailed knowledge on the evaluation of cutter path when high speed rough and finish milling is essential in order to improve productivity and surface quality. The paper details techniques used to reduce machining times and improve workpiece surface roughness/accuracy when HSM hardened mould and die materials. Optimisation routines are considered for the roughing and finishing of cavities. The effects of machining parameters notably feed rate adaptation techniques and cutting tools are presented.     

Optimisation of machining parameters by criterion of maximum productivity

The optimisation of machining parameters for machine tools using the criterion of maximum productivity rate is not a new problem – many unresolved issues remain. The intensification of machining processes leads to changes in the productivity rate; analytically defining optimal machining parameters for the maximum productivity rate in real-world manufacturing processes is, therefore, an important problem that needs to be addressed. This paper aims to formulate a mathematical model for the optimisation of cutting processes on machine tools based on the criterion of maximum productivity rate. The mathematical model is based on technological data, machining and reliability parameters of the machine tool units. Several practical applications are discussed.     

Transient stability constrained optimal power flow using particle swarm optimisation

A novel approach based on the particle swarm optimisation (PSO) technique is proposed for the transient-stability constrained optimal power flow (TSCOPF) problem. Optimal power flow (OPF) with transient-stability constraints considered is formulated as an extended OPF with additional rotor angle inequality constraints. For this nonlinear optimisation problem, the objective function is defined as minimising the total fuel cost of the system. The proposed PSO-based approach is demonstrated and compared with conventional OPF as well as a genetic algorithm based counterpart on the IEEE 30-bus system. Furthermore, the effectiveness of the PSO-based TSCOPF in handling multiple contingencies is illustrated using the New England 39-bus system. Test results show that the proposed approach is capable of obtaining higher quality solutions efficiently in the TSCOPF problem     

Globally optimal tool paths for sculptured surfaces with emphasis to machining error and cutting posture smoothness

Global optimisation for manufacturing problems is mandatory for obtaining versatile benefits to facilitate modern industry. This paper deals with an original approach of globally optimising tool paths to CNC-machine sculptured surfaces. The approach entails the development of a fully automated manufacturing software interface integrated by a non-conventional genetic/evolutionary algorithm to enable intelligent machining. These attributes have been built using already existing practical machining modelling tools such as CAM systems so as to deliver a truly viable computer-aided manufacturing solution. Since global optimisation is heavily based on the formulation of the problem, emphasis has been given to the definition of optimisation criteria as crucial elements for representing performance. The criteria involve the machining error as a combined effect of chord error and scallop height, the tool path smoothness and productivity. Experiments have been designed considering several benchmark sculptured surfaces as well as tool path parameters to validate the aforementioned criteria. The new approach was implemented to another sculptured surface which has been extensively tested by previous research works. Results were compared to those available in the literature and it was found that the proposed approach can indeed constitute a promising and trustworthy technique for the global optimisation of sculptured surface CNC tool paths.     

Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites

Metal matrix composites (MMCs) are difficult to machine due to their abrasive properties. With the projected widespread application of MMCs, it is necessary to develop an appropriate technology for their effective machining. The present investigation focuses on finding the optimal machining parameters setting in drilling of hybrid aluminium metal matrix composites using the grey-fuzzy algorithm. This proposed algorithm, coupling the grey relational analysis with the fuzzy logic, obtains a grey-fuzzy reasoning grade to evaluate the multiple performance characteristics according to the grey relational coefficient of each performance characteristics. The Taguchi method of experimental design is a widely accepted technique used for producing high quality products at low cost, therefore a L₂₇ 3-level orthogonal array is used for the experiments. The optimisation of multiple responses in complex processes is common; therefore, to reduce the degree of uncertainty during the decision making, fuzzy rule-based reasoning is integrated with the Taguchi’s method. The response table, response graph and analysis of variance (ANOVA) are used to find the optimal setting and the influence of machining parameters on the multiple performance characteristics. Experimental results have shown that the required performance characteristics in the drilling process are improved by using this approach.     

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70–80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a _p), radial immersion (a _e), feed rate (f _t) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine–tool–part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.     

Optimization of a machining economics model with fuzzy exponents and coefficients

The primary objective of a machining economics model is to determine the optimal cutting parameters that minimize production costs while satisfying some design constraints. This paper develops a solution method that can derive the fuzzy unit production cost of a fuzzy machining economic model when the exponents of decision variables in the objective function, the cost and the constraint coefficients are fuzzy numbers. A pair of two-level machining economics problems is formulated to calculate the upper and lower bounds of the fuzzy unit production cost at possibility level α. Based on the duality theorem and by using a variable substitution technique, the two-level machining economics problem is transformed into the one-level conventional geometric program. Solving the corresponding pair of geometric programs produces the interval of the unit production cost. The examples show that the interval of unit production cost contain more information when the parameters in machining economics problems are fuzzy numbers.     

Shape optimisation of a two-phase ejector for CO2 refrigeration systems

The shape optimisation of four CO₂ ejectors is presented in this study. The considered ejectors were originally designed for a multi-ejector supermarket CO₂ refrigeration system. The objective function was formulated to consider the multiple operating regimes, where the goal of the optimisation was to maximise the device efficiency. Six geometrical parameters were considered in the optimisation procedure. The applied optimisation scheme was a combination of a genetic algorithm coupled with the effective and validated CFD tool, ejectorPL. The optimisation results showed that the ejector efficiency improved by 6%. The shape modification trends were similar for all of the considered ejectors. All of the shape modifications resulted in a smoother expansion inside the motive nozzle, less intense turbulence inside the mixing section and a more uniform velocity field inside the mixing section. The obtained results showed that the presented methodology can be effectively used for ejector design for numerous applications.     

Optimal fixture parameters considering locator errors

A machining fixture consists of elements such as locators, clamps and supports. Fixture design aims at ensuring workpiece quality by restraining the workpiece in the desired position throughout machining thereby minimising the overall machining error. Workpiece elastic deformation and geometric error of locators are major components of the overall machining error. The effect of geometric error is considerable in certain cases and hence cannot be ignored. For a given error in locators, the geometry related machining error is manifest in the locator layout whereas the workpiece deformation depends on both the layout and the external forces such as clamping and machining forces. Layout of fixturing elements and the applied clamping forces are collectively called fixture parameters. The objective of minimising the total machining error can be achieved by optimising either one or both of these parameters. In this research work both of these parameters are simultaneously optimised using a genetic algorithm (GA). A finite element model of the workpiece fixture system is developed and analysed using commercial finite element software ANSYS®. Elastic deformation of workpiece under machining loads is obtained from the finite element model. MATLAB® based GA is interfaced with ANSYS® for the determination of total machining error and subsequent optimisation with the objective of complying with tolerance requirements on the critical machining feature. Results indicate that the error sources can contribute to the final machining error in varying degrees. The results also underscore the need to consider the entire machining path for optimisation of the fixture parameters.     

Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

This paper investigates a multi-module reconfigurable manufacturing system for multi-product manufacturing. The system consists of a rotary table and multiple machining modules (turrets and spindles). The production plan of the system is divided into the system design phase and the manufacturing phase, where the installation cost and the energy consumption cost correspond to the two phases, respectively. A mixed-integer programming model for a more general problem is presented. The objectives are to minimise the total cost and minimise the cycle time simultaneously. To solve the optimisation problem, the ε-constraint method is adopted to obtain the Pareto front for small size problems. Since the ε-constraint method is time consuming when problem size increases, we develop a multi-objective simulated annealing algorithm for practical size problems. To demonstrate the efficiency of the proposed algorithm, we compare it with a classic non-dominated sorting genetic algorithm. Experimental results demonstrate the efficiency of the multi-objective simulated annealing algorithm in terms of solution quality and computation time.     

Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool

There are many advanced tooling approaches in metal cutting to enhance the cutting tool performance for machining hard-to-cut materials.The self propelled rotary tool(SPRT) is one of the novel approaches to improve the cutting tool performance by providing cutting edge in the form of a disk,which rotates about its principal axis and provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge with the work piece.This paper aimed to present the cutting performance of SPRT while turning hardened EN24 steel and optimize the machining conditions.Surface roughness(R_a) and metal removal rate(r_(MMR)) are considered as machining performance parameters to evaluate,while the horizontal inclination angle of the SPRT,depth of cut,feed rate and spindle speed are considered as process variables.Initially,design of experiments(DOEs) is employed to minimize the number of experiments.For each set of chosen process variables,the machining experiments are conducted on computer numerical control(CNC) lathe to measure the machining responses.Then,the response surface methodology(RSM) is used to establish quantitative relationships for the output responses in terms of the input variables.Analysis of variance(ANOVA) is used to check the adequacy of the model.The influence of input variables on the output responses is also determined.Consequently,these models are formulated as a multi-response optimization problem to minimize the R_a and maximize the r_(MMR)simultaneously.Non-dominated sorting genetic algorithmII(NSGA-II) is used to derive the set of Pareto-optimal solutions.The optimal results obtained through the proposed methodology are also compared with the results of validation experimental runs and good correlation is found between them.     

An improved approach for determination of index positions on CNC magazines with cutting tool duplications by integrating shortest path algorithm

Optimisation of automatic tool changer (ATC) indexing problem, where cutting tools are allocated to the stations on a turret magazine of a CNC machine, is one of the challenging problems in machining. The aim of the problem is to minimise the total indexing time of ATC. This problem becomes even more challenging if duplication of cutting tools is allowed and a bidirectional ATC is used. The problem has a unique feature which has not been stressed yet by other researchers, that is, although ATC indexing (master problem) is the main optimisation problem, objective function evaluation of this problem is a standalone optimisation problem (sub problem) indeed. Although an approximation algorithm does not guarantee optimality for the master problem, the subproblem must be solved optimally; otherwise, deficiencies arising from ill-defined objective function might be encountered. Considering this interesting future, a novel methodology, which employs a shortest path algorithm, is developed. Thus, the subproblem of this complicated problem can be optimally solved. Moreover, two metaheuristics, based on threshold accepting and descent first improvement greedy methodologies, are proposed for generating efficient solutions. Finally, several benchmarking instances are generated and solved to test the proposed algorithms.     

Prediction and optimisation models for turning operations

Selection of process parameters has very significant impact on product quality, production costs and production times. The quality and cost are much related to tool life, surface roughness and cutting forces which they are functions of process parameters (cutting speed, feed rate, depth of cut and tool nose radius). In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. The process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machineability models. Two data mining techniques are used; response surface methodology and neural networks. The data of 28 experiments have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the selected tool/material combination. The resulting models are utilized to formulate an optimisation model and solved to find optimal process parameters, when the objective is minimising production cost per workpiece, taking into account the related boundaries and limitation of this multi-pass turning operations. Numerical examples are given to demonstrate the suggested optimisation models.     

Heuristic optimization system for the determination of index positions on CNC magazines with the consideration of cutting tool duplications

This paper presents a meta-heuristic optimization system developed for the determination of optimal index positions of cutting tools on the tool magazines (automatic tool changer (ATC) or turret magazine, etc.) of CNC (computerized numerical control) machine tools. The selection of index positions is performed using a simulated annealing (SA) algorithm that takes the following as the input: (1) a list of cutting tools assigned to certain machining operations; (2) the number of copies of each cutting tool available in the workshop; (3) total number of index positions on the tool magazines; (4) the indexing time of a tool magazine of a CNC machine tool specified by manufacturer. Then, the SA algorithm determines index locations of cutting tools on the tool magazines. Dereli et al. and Dereli and Filiz previously studied the present problem by using genetic algorithms (GAs). However, the duplication of cutting tools was not taken into account in their works, although it can reduce the total tool-indexing time and therefore improve the productivity considerably. Nevertheless, the consideration of the ‘tool duplications’ makes the problem much harder to model and to solve. In this paper, a novel solution representation-scheme based on the SA, which enables easy manipulation during feasible neighbourhood solution generation, is proposed for the determination of the index positions of cutting tools on the CNC magazines with the consideration of ‘tool duplications’. Example problems are solved to present the implementation and merits of the proposed optimisation system. It is shown that it is possible to allocate the cutting tools in an efficient manner on the CNC magazines with the developed system.