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Reducing machining time in a milling process is one of the important criteria to improve the overall efficiency of the machining process. This paper presents a study on the reduction of machining time, focusing on contour parallel machining to increase the efficiency and performance during the machining process. One method to enhance the performance of contour parallel machining is by defining a tool path interval that is larger than the radius of the cutting tool in a roughing operation because of its capability of reducing the tool path length and machining time. However, this causes the occurrence of an uncut region at the corner and at the centre of a contour parallel. This uncut region can be removed through an additional tool path known as the clear tool path. Therefore, in this paper, a new method based on an optimisation technique is introduced to generate a clear tool path that removes the entire uncut region in contour parallel machining at minimum cutting time. Ant colony algorithm (ACO) is used to optimise the clear tool path length in contour parallel machining time by minimising the movement of cutting tool in removing the entire uncut regions. A new transition rule has been established from the conventional ACO, which adapted the uncut region occurring at the corner of the contour parallel. Then, to validate the optimisation result, a cutting experiment was carried out using computer numerical control (CNC) milling machine. It can be ascertained from this study that the optimisation of the clear tool path gives optimal tool path length whilst reducing the cutting time in the roughing process. [ABSTRACT FROM AUTHOR]

This paper presents an optimized path generation algorithm for direction parallel milling, which is commonly used in the roughing and finishing stages. First, a geometrically efficient tool path generation algorithm using an intersection points graph is introduced. Second, the generated tool path is modified as an optimized tool path that maintains a constant material removal rate to achieve a constant cutting force and avoid chatter vibration, and the results are verified. Additional tool path segments are appended to the basic tool path through a pixel-based simulation technique. The algorithm is implemented for two-dimensional contiguous end milling operations with flat end mills, and cutting tests are conducted by measuring the spindle current, which reflects the changing machining situations, to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In any machining fixture, the workpiece elastic deformation caused during machining influences the dimensional and form errors of the workpiece. Placing each locator and clamp in an optimal place can minimize the elastic deformation of the workpiece, which in turn minimizes the dimensional and form errors of the workpiece. Design of fixture configuration (layout) is a procedure to establish the workpiece–fixture contact through optimal positioning of clamping and locating elements. In this paper, an ant colony algorithm (ACA) based discrete and continuous optimization methods are applied for optimizing the machining fixture layout so that the workpiece elastic deformation is minimized. The finite element method (FEM) is used for determining the dynamic response of the workpiece caused due to machining and clamping forces. The dynamic response of the workpiece is simulated for all ACA runs. This paper proves that the ACA-based continuous fixture layout optimization method exhibits the better results than that of ACA-based discrete fixture layout optimization method. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of cutting parameters for constrained machining operations is reported. Modified genetic algorithm (MGA) is an evolutionary computation technique that has been proposed in this paper to solve the machining problem. Additional constraints have been incorporated to the multipass turning model. The optimization of drilling and facing parameters have also been carried out. To demonstrate the procedure and performance of the approach, an illustrative example is discussed. The results of the proposed algorithm are compared with other traditional and non-traditional techniques such as Newton’s method, hill climbing and ants colony technique. [ABSTRACT FROM AUTHOR]

An algorithm of tool-path generation from densely scattered measure points is presented in this paper based on constraint quadratic error metrics (CQEM). In the algorithm where the surface to be machined is represented directly with measured points, first, a mesh interpolating the points is dynamically constructed according to cutter sizes and slicing plane locations. Second, the initial C-C points are generated from the intersection points between the mesh and the slicing planes. Finally, the initial C-C points are optimized with CQEM. The algorithm shows capability to save memory usages and to achieve a higher precision in path location calculation. [ABSTRACT FROM AUTHOR]

This paper deals with the tool path optimisation procedure in free form die-cavity pocketing. Many researchers have investigated pocket machining problems, and most of them focused on the tool-path element generation problem, which has been regarded as the key issue in the pocketing tool-path generation. However, free form die-cavity pocketing involves many other problems, such as machining area extraction, uncut removal, local tool path linking and global tool path linking. All these problems should be solved to optimise pocketing tool path and each of them involves its own geometrical and technological issues. The objective of this paper is not to develop a new algorithm, but to achieve the global optimisation of pocket machining by considering all issues and solutions in pocketing. [ABSTRACT FROM AUTHOR]

In this paper, a tri-objective optimization problem related to sequencing in multi-model reconfigurable assembly systems (RAS) is addressed. RASs are also designed around product families similar to the more generic reconfigurable manufacturing systems (RMSs). A multi-model assembly line needs reconfiguration after assembling each batch of the products from the family. Further, the changeover of assembly line from one product type to another is also of varying degree of complexity and reconfiguration cost. Two novel indices for commonality and reconfiguration complexity are proposed here to incorporate this feature of RAS. Optimizing the reconfiguration time/cost is taken as the first objective while fulfilling the product due dates and the work load balance among the assembly stations as the second and third objective functions, respectively. This optimization problem is solved using a Multi-objective Self-Organizing Migrating Algorithm (MOSOMA) metaheuristic to find the non-dominated Pareto optimal solutions representing optimum sequencing of products in the family. An approach for identifying the best solution from the multiple Pareto optimal solutions using aggregate fitness is also proposed. A numerical illustration along with computational procedure is presented on a hypothetical RAS model. [ABSTRACT FROM AUTHOR]

Enhancement of the efficiency and quality of internal thread forming requires continuous improvements in the design of the tools used and optimization of their body geometry. A new design for cutting-tightening taps with enhanced strength and reliability for forming thread surfaces by combining the methods of cutting and plastic deformation are proposed in this paper. An experimental study of torque was carried out during thread forming employing the designed combined toot and the major factors having an effect on the tool serviceability were defined. An algorithm for designing cutting-tightening taps has been proposed, which guarantees a minimum torque in thread forming. [ABSTRACT FROM AUTHOR]

In machining large complex parts with critical stock allowance, even small deviations in the blank parts or slight inadequacy in the fixturing may result in local shortage of material (i.e., insufficient stock allowance). This paper presents an optimal localization algorithm that aligns the measured points from a blank part with the nominal model to assure the weakest allowance area with as much material to be cut as possible. Instead of exploiting extra constraints to force the allowance at each point larger than a specific value, which is a popular strategy for allowance assurance in the previous localization algorithms, we formulate the blank localization problem as an unconstrained max-min problem. To deal with the unsmoothness exhibiting in the max-min objective function, a method based on the entropy optimization principle is adopted to convert the non-differential objective function to an unconstrained differential one, which can be efficiently solved using the conventional Quasi-Newton algorithms. The unconstrained optimization result finally gives rise to localization with the maximum allowance margin. For the blank parts that the material shortage is inevitable, the method can still efficiently achieve reasonable localization results, which confine the material shortage to a least extent. The proposed method is easy to be implemented and works well for both sparse sample points and dense-scanned points. Case studies included justify the superiority of the proposed scheme over the existing non-linear constrained optimization solutions. [ABSTRACT FROM AUTHOR]

In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden and statistical significance of these metaheuristics to aid future developments. This study focuses on six recent metaheuristics, namely, ant lion optimization (ALO), arithmetic optimization algorithm (AOA), dragonfly algorithm (DA), grey wolf optimizer (GWO), salp swarm algorithm (SSA) and whale optimization algorithm (WOA). Optimization of an industrial machining application is tackled in this paper. The optimal machining parameters (peak current, duty factor, wire tension and water pressure) of WEDM are predicted using the six aforementioned metaheuristics. The objective functions of the optimization study are to maximize the material removal rate (MRR) and minimize the wear ratio (WR) and surface roughness (SR). All of the current algorithms have been seen to surpass existing results, thereby indicating their superiority over conventional optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost. [ABSTRACT FROM AUTHOR]

In manufacturing practice for each machining operation, transfer techniques are extensively used for the allocation of manufacturing specification tolerance type and value. This paper focuses more on tolerance type which is the basis for a coherent and complete tolerancing process. We developed an algorithmic method called “DISMP” in order to generate the necessary types of manufacturing specifications that guarantee the respect of the functional requirements. A geometric variation model, based on the invariants degrees of freedom (DOFs) of the datum reference frames (DRF) and the toleranced surfaces serves to specify the two extremities of the tolerance chain. The identification of the controlled DOFs (Cont-DOFs) of the positioning reference frames in each machining phases and also the constrained DOFs (Cons-DOFs) of each machined surface contributes to the development of the way linking between the functional toleranced surface and its functional DRF. To generate the appropriate manufacturing geometric specifications, our method can be divided in five steps. We started by the translation of the ISO functional specification using the topologically and technologically related surfaces rules “TTRS” (Step 1: M1). Mapping the manufacturing process (Step 2: M2 and M3) requires the identification of the Cons-DOFs of the machined surfaces and those controlled by the positioning surfaces “Cont-DOFs” in each phase. The search of all chain links, which constitute the tolerance chain, is realized on Step3 (M4 and M5). Then, in step 4 (M6), we generate ISO standardized manufacturing specification. The fifth step (M7) draft technological constraints related to the manufacturing process, i.e., the case where there are inversion in the order between the toleranced surfaces and its DRF. Finally, we present the manufacturing process phases with ISO standards requirements. [ABSTRACT FROM AUTHOR]

Using automatic localization to ensure sufficient machining allowance or derive reliable evaluation results of machining quality, it is possible to cope with the case where the coordinate system in a part design specification is different from that for machining or measuring the part. To achieve workpiece localization and machining quality evaluation, it is essential to find the optimum Euclidean transformation that aligns the nominal mode to the sample points from a workpiece. This paper describes a unified algorithm for workpiece localization and quality evaluation. The optimum alignment model is firstly established with minimax criteria, and then sequential quadratic programming (SQP) is proposed to solve the optimization problem. A computational algorithm of point-to-surface distance and a linear differential motion model of the objective function with respect to alignment parameters are subsequently provided. The results show that the SQP-based workpiece localization and quality evaluation method is computationally more efficient than those based on direct search algorithms. It is found that the proposed method is effective in machining localization and quality evaluation even with a large set of sample points. [ABSTRACT FROM AUTHOR]

Quality and performance of the products requiring higher precision and involving moving-parts mainly depends on the surface finish and dimensional accuracy. Generally, finishing operations use fine abrasive particles in different forms along with some carrier or binding medium. Finishing operations are crucial, expensive, uncontrollable, and a labor-intensive phase in the overall production, and contribute significantly to the total production time and cost. As surface finish requirement increases, the cost of finishing operations increases exponentially. Though progress has been made in automating the finishing operations to reduce the production time to some extent, it increases the initial investment and operating costs significantly. Quality, cost, time, and efficiency of finishing operations can be improved significantly by choosing the optimum values of the process parameters. This paper presents the details of process parameters optimization of two advanced fine-finishing processes namely abrasive flow machining (AFM) and magnetic abrasive finishing (MAF), which are capable of giving nano-level surface finish, using real-coded genetic algorithms (GA). It also describes the development of a surface roughness model that was developed to form the objective function for the optimization of AFM process. [ABSTRACT FROM AUTHOR]

In Mellal and Williams (2015) 'Cuckoo optimization algorithm for unit production cost in multi-pass turning operations' published in the International Journal of Advanced Manufacturing Technology, the authors state the proposed Cuckoo optimization algorithm, denoted as COA, outperforms the hybrid particle swarm optimization (HPSO) for a class of multi-pass turning problems. The HPSO was presented by Costa et al. (Int J Adv Manuf Technol 53:421-433, 2011). Notably, though HPSO achieves a lower unit production cost than COA, the authors point out HPSO violates a tool life-related constraint, thus resulting ineffective. In this paper, we demonstrate that the HPSO does not violate any constraint pertaining to the multi-pass turning problem at hand and, as a result, the outperformance of COA over HPSO is incorrect. [ABSTRACT FROM AUTHOR]

Abstract: The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques. [Copyright &y& Elsevier]

Wire electrical discharge machining (WEDM) is a widely accepted non-traditional material removal process used to manufacture components with intricate shapes and profiles. Due to many parameters and the complex and stochastic nature of the process, achieving the optimal performance is a very difficult task. The objective of this present work is therefore to discover the relationship between the performance measures of the process and its controllable input parameters and subsequently to find the optimal combination of the input parameters to achieve the maximum process performance. A mathematical model using response surface modeling (RSM) approach is developed for correlating the inter-relationships of various wire electric discharge machining (WEDM) parameters with performance measures. A non-traditional optimization technique, known as particle swarm optimization (PSO), is then applied to find the optimal combination of process parameters with an objective to achieve maximum machining speed for a desired value of surface finish. It is observed that the results of optimization obtained by using PSO algorithm show significant improvement over those obtained using traditional optimization technique. Also the results obtained are in good agreement with those obtained through practical experimentation. The mathematical model developed in this work will help the practitioners to simulate the process performance on any WEDM machine. Moreover, the optimization using PSO algorithm will help EDM users to achieve significant improvement in process performance than by using traditional optimization algorithms and handbook recommendations. [ABSTRACT FROM AUTHOR]

Micro-wire electrical discharge machining (Micro-WEDM) process exhibits superior precision and greater relative accuracy for the efficient machining of difficult-to-machine materials. The micro-slit cutting operation using WEDM process has been experimentally investigated for the objective of analysing the average kerf-loss and responses pertaining to the economic viability of the process viz. average cutting rate and volumetric material removal rate (MRRv). The experiments are performed using a Tungsten wire of diameter 70 µm on titanium grade 5 alloy (Ti-6Al-4V). Three different controllable process variables (input parameters) associated with the Resistance-Capacitance (RC) based power generator namely discharge energy, wire feed-rate and wire travelling speed are varied to demonstrate their impacts on typical responses such as average kerf-loss, average cutting rate and MRRv. The experimental analysis revealed a close relationship that cutting rate bears with discharge energy, wire feed-rate and efficient flushing of molten liquid as well as fine debris particles. An advanced multi-objective optimization technique popularly known as Multi Objective-Jaya (MO-Jaya) algorithm has been adopted for the simultaneous optimization of average kerf-loss, average cutting rate and volumetric material removal rate. The best set of input parameters have been selected to suggest the most optimum responses for micro wire-cutting operations. [ABSTRACT FROM AUTHOR]