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Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving. [ABSTRACT FROM AUTHOR]

In five-axis machining, tool orientation optimization is a critical problem that significantly affects machining quality, efficiency, and kinetic performance. In this paper, a tool orientation optimization method based on ruled surface is proposed. Through optimizing section selection algorithm, the region to be optimized is selected precisely, and a ruled surface is established at each cutter contact point (CC point). The tool orientation optimization range is limited to a certain level, and optimization objective function is set to minimize the vibration of rotary axes. The proposed method is of advantages over existing methods in three aspects. First, the influence on machining area and surface quality can be reduced to a lower level due to the tool orientation change is limited to a certain surface. Second, the efficiency of optimization algorithm is improved. Third, the nonlinear mapping between tool orientation and rotary axes position is avoided by setting the kinetic parameters of the rotary axes as optimization goal. Simulation and experiment are performed to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Components with complex arrays of multi-island structures are widely used in aerospace applications, but existing commonly used milling strategies usually have multiple feeds and retreats during machining, and the machined surfaces produce feed marks that do not meet the requirements for high-quality surfaces. In this paper, a machine path generation method for array multi-island machining regions is proposed, which divides the array multi-island region machining area into multiple double-connected regions from outside to inside, establishes a partial differential equation model in each double-connected machining subregion to compute isotherms, generates spiral machining trajectories in the double-connected subregions by identifying suitable isotherms in each double-connected machining subregion, and then connects each sub-region helix trajectory; the overall spiral machining trajectory in the array multi-island region can be obtained to avoid multiple tool feeds and feed marks on the machined surface. Meanwhile, in order to avoid the sudden change of cutting direction at the boundary of two sub-regions, an optimization algorithm for B-sample curve generation at the boundary connection of sub-regions is proposed. Finally, the superiority of the method in improving machining quality is verified by machining experiments. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) uses a direct current (DC) at high density of 0.5–5 A/mm2 which is passed through the electrolytic solution that fills the gap between an anodic workpiece and a pre-shaped cathodic tool. At the anodic surface, metal is dissolved into metallic ions and thus as the tool moves towards the workpiece at a constant feed proportional to the dissolution rate of the anodic surface, then its shape is copied into the workpiece. During ECM, the electrolyte is forced to flow through a narrow interelectrode gap at high velocity of more than 5 m/s to intensify the mass/charge transfer through the sublayer near the anodic surface. The electrolyte removes the dissolution by-products, e.g., hydroxide of metal, heat, and gas bubbles generated in the interelectrode gap. These machining by-products affect the process accuracy, efficiency, stability, and productivity. Ensuring the continuous flushing of these products is, therefore, essential. One of these methods is through the use of pulsed voltage. Introducing vibrational motion, at low or ultrasonic frequency, to the tool/workpiece or the machining medium became a viable alternative for the evacuation of the machining products during the vibration-assisted ECM (VA-ECM). Other attempts to further enhance VA-ECM performance include the proper tool design, addition of abrasive particles to the electrolyte medium, and use of magnetic flux assistance. This paper reviews the principles of VA-ECM, main research directions, process parameters, and performance indicators. Numerous fields of VA-ECM which include micro-slotting, micro-drilling, macro-drilling, electrochemical wire cutting (ECWC), polishing and finishing, and micro-tool fabrication have been covered. Several mathematical and statistical modeling and optimization techniques have been also examined. The current paper also outlines possible trends for future research work. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization algorithm of a computerized numerical controller (CNC) interpolator parameter using a simulation model to reduce trajectory error, which is the difference between the path of the part program and the actual trajectory. A tool path generation algorithm with a look-ahead of the CNC interpolator is proposed for high-precision machining. A feed drive model and control algorithm is modeled to describe the closed-loop dynamics of the CNC machine tool feed drive. The simulation model of machine tool is designed to estimate the trajectory error. To find the optimal parameter values of the CNC interpolator to improve machining accuracy, a simulation-based optimization method utilizing a genetic algorithm was used. A two-axis feed drive test rig was used to evaluate the estimation accuracy of the simulation model and investigate the effectiveness of the proposed method. Trajectory error was estimated using the simulation model, and compared with the experimental results, to evaluate the accuracy of estimation of the simulation model. The experimental results demonstrated that the trajectory error can be reduced without increasing the cycle time by applying the proposed optimization method. [ABSTRACT FROM AUTHOR]

The profile error is inevitable in the machining process of globoid cam. In order to tackle the problem of normal vector anisotropy in unequal diameter machining, the offset point optimization algorithm is served to optimize the tool position according to the error transfer relationship of equidistant surfaces and the characteristics of unequal diameter tool path generation. In this paper, a bidirectional search algorithm is devised to minimize the deviation between unequal diameter machining tool path surface and fitting ruled surface. The optimal numerical solution with high accuracy is obtained by genetic algorithm. And then the end points of the tool axis are regarded as the initial data. Inverse interpolation of the NURBS (Non-Uniform Rational B-Spline) interpolation is utilized to reconstruct the ruled surface of the flank milling tool path. The simulation results verify the effectiveness of the proposed approach. Compared with the existing methods, the presented one reduces the machining error greatly. [ABSTRACT FROM AUTHOR]

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 accurate surface error interpolation algorithm for five-axis machining of freeform surfaces. One of the most important steps in the interpolation process is to calculate the next cutter contact (CC) point according to the present one. In this paper, the next CC point is calculated by an accurate chord evaluation method. This method is developed based on the cutting simulation process, which can be vividly described as firstly planting dense grasses on the tool path curve and then cutting them when the tool moves by. The left lengths of the grasses either positive or negative are considered to be the machining error. The method is accurate also because the tool geometry and the tool orientation changes during five-axis machining are taken into consideration. With this method, the chord errors between CC points are controlled uniform along the tool path. The proposed interpolation algorithm is compared with the commercial CAM systems like PowerMILL and UG. The results show that the proposed algorithm can significantly reduce the number of cutter locations meanwhile confine the chord error. A real cutting experiment is implemented, and the result indicates its promising value in industrial applications. [ABSTRACT FROM AUTHOR]

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product. [ABSTRACT FROM AUTHOR]

A blank dimension is not only a premise of blank design and manufacture, but also plays a significant role to decide the machining process and the energy consumption of the machining process. The design of an appropriate blank dimension is important to ensure efficient energy usage. However, consideration of a workpiece machining process and energy consumption during the machining process in the blank dimension design phase is often neglected. To design a blank dimension scientifically and effectively, this paper proposes an optimization approach for blank dimension design, by considering energy consumption, machine cost, and process time. The three objectives are affected by three variables, cutting depth, feed rate, and cutting speed. The non-dominated sorting genetic algorithm-II (NSGA-II) algorithm was used to solve multi-objective optimization problem, and blank dimensions could be calculated from the cutting depth of minimum energy consumption and workpiece dimension. The results indicated that the proposed approach is effective in realizing energy savings due to the appropriate blank dimension design. The optimization approach not only offers some insights for designing a new blank dimension, but also provides a theoretical basis for existing blank dimension selection. [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]

This paper discusses a typical STEP-compliant manufacturing environment, which effectively integrates two systems. The first generates native data that retain the information needed to machine a part on a particular machine tool, whereas the second carries out optimization for machining parameters using the dispatched information from the first system. The related research work is divided into four areas, feature generation, macro process planning, micro process planning, and machining execution. The main part of the paper is devoted to reviewing the most recent research publications. The publications have been organized into the four areas as mentioned above. The discussion section that follows looks at the STEP-compliant research from the perspectives of industrial adoption, feature recognition for process planning, challenges in STEP-enabled inspection and STEP-NC controllers. [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]

This paper presents a method for the process planning optimization of hole-making operations, where the machining process of a hole consists of several individual operations with different machining tools. The optimization objective is to minimize the total auxiliary time of the machining process, including the airtime and the tool changing time. The machining process of a part is represented as a dynamic directed graph, where each graph node represents a single operation in the hole-making process. The objective of minimizing the total auxiliary time is to find the shortest path of the directed graph, and this objective has a structure similar to the traveling salesman problem. A mathematical model that describes the hole-making problem is proposed based on this graph structure, and an ant colony algorithm is applied to solve the optimization, since the problem is NP-complete. It is demonstrated with examples that the proposed method can significantly reduce the auxiliary time of hole-making process. [ABSTRACT FROM AUTHOR]

Stone machining by diamond tool is a widespread process to manufacture both standard products, such as tiles, slabs, kerbs, and so on, and design shapes. Cutting force and energy may be used to monitor stone machining. Empirical models are required to guide the selection of cutting conditions. In this paper, the effects of cutting conditions on cutting force and cutting energy are related to the shape of the idealized chip thickness. These effects are put into relationship with the diamond tool wear too. The empirical models developed in this paper can be used to predict the variation of the cutting energy. Therefore these models can be used to guide the selection of cutting conditions and to predict when it is needed to change the tool. The chip generation and removal process has been quantified with the intention of assisting both the toolmaker and the stonemason in optimizing the tool composition and cutting process parameters, respectively. [ABSTRACT FROM AUTHOR]

Selection of the optimal set of cutting tools is one of the most important steps in process planning for 2.5-D pocket machining. Conventional CAM software requires considerable input from the user in terms of selection of tool sizes and machining strategy. This trial-and-error procedure to determine the optimal process sequence tends to generate conservative and suboptimal results. This paper presents a methodology for optimal selection of a sequence of tools to minimize the total time required to end mill a non-convex polygonal pocket with or without islands using the staircase milling strategy. The algorithm decomposes the pocket geometry into convex regions and mills each region independently by selecting a sequence of tools based on the accessibility of various tools to the region. Strategies have been developed for machining the main pass and the subsequent leftover areas in order to obtain the final pocket geometry. Subsequently, the machining times for each decomposed area are aggregated while accounting for the need to use multiple passes, non-cutting time, and the tool change time. A dynamic programming approach is used to determine the optimal set of tools which minimizes the total processing time. The effect of varying the non-cutting speed and tool change time on the tool path length and number of tool selection is studied. [ABSTRACT FROM AUTHOR]

This paper presents the manufacturing process for complex parts in the aim of building functional prototype mechanisms. Functional prototypes are used during testing in order to validate new product design. Their layouts are very similar to the final product, wherein lies the interest of testing many modifications. The mechanism must respect the functional geometrical requirements and be capable of withstanding forces or, for example, ensuring a tight seal. The principle being proposed consists of decomposing the complex parts into several simple ones that can then be manufactured on a five-axis, high-speed milling machine from thick (approximately 40 mm) sheets made of resistant materials, notably aluminum. The problem at hand is threefold: the choice of slicing in order to avoid cutting functional areas; the choice of both positioning mode and sheet fastening mode; and lastly, the choice of machining process. This paper also presents a detailed application with a machining simulation, using CATIA (Dassault Systèmes) for a five-axis MIKRON UCP 710 milling machine. [ABSTRACT FROM AUTHOR]

The iso-parametric tool path is broadly used in CAM systems to machine sculptured surfaces. To satisfy tolerance constraints CAM systems usually adopt the smallest intervals across discrete tool paths, leading often to overlap and inefficient tool path. This paper presents a novel optimization scheme based on adaptive grid that can produce the optimal tool path on the basis of iso-parametric tool path and introduces the numerical method of generating the adaptive grid. When the generated optimal tool path has the same number of discrete tool path as the iso-parametric tool path, the optimal tool path will produce smaller machining errors. Or when the generated optimal tool path produces the same errors as the iso-parametric tool path, the optimal tool path will have fewer discrete tool paths and higher efficiency. This optimization scheme is robust, concise, and easy to be integrated into CAM systems to produce the optimal tool path automatically. [ABSTRACT FROM AUTHOR]

Formation of exit burr on part edges during drilling has several undesirable features with regard to product quality and functionality. Hence it is essential to select optimum drilling process parameters to minimize burr size at the production stage. This paper presents the application of the Taguchi optimization method for simultaneous minimization of burr height and burr thickness influenced by cutting conditions and drill geometry. The Taguchi design approach to the multi-objective optimization problem is based on the introduction of a new concept of fitness function for each trial of orthogonal array. The fitness function is derived through mapping the objective functions of the drill optimization problem. In the present work, optimal values of cutting speed, feed, point angle and lip clearance angle are determined for selected drill diameter values to minimize burr height and burr thickness during drilling of AISI 316L stainless steel workpieces. The details of experimentation, analysis of means and analysis of variance are presented in the paper. [ABSTRACT FROM AUTHOR]

The ability to produce precision ring-shaped parts using the turning process depends significantly on the workholding characteristics. Workholding parameters such as the number of jaws and chucking force are known to influence the roundness tolerance of ring-shaped parts commonly used in ball/roller bearing applications. Experimental trial-and-error methods are often used in practice to optimize the workholding parameters to achieve the desired part quality. This paper presents a systematic mathematical approach for optimizing these parameters using a previously developed analytical model of ring deformation and a model for determining the minimum chucking force. The optimization approach takes as input the required roundness tolerance, geometry, and mechanical properties of the ring, cutting forces, and the coefficient of friction between the jaws and the ring. The output consists of the minimum number of jaws and the range of acceptable chucking forces that ensures the required tolerance while preventing slip of the ring. Simulation examples illustrate the use of the proposed workholding optimization approach for a turning application. In addition, the paper proposes the concept of dynamic chucking force control that promises to yield part roundness that is superior to that obtained via conventional constant force chucking. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained. [ABSTRACT FROM AUTHOR]

Examining the economics of multi-pass machining operations has significant practical importance. Traditional optimization techniques have been used, but are limited in application. Non-traditional optimization techniques like genetic algorithms, simulated Annealing and ant colony optimization are increasingly used to solve optimization problems. This paper discusses the use of non-traditional optimization techniques for optimizing the depth of cut in multi-pass turning. The resulting subdivision of the cut depth indicates the proposed methodologies are competent, efficient and accurate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Machining process parameters (MPP) directly affect the machining quality and efficiency of heavy-duty CNC machine tools (HCMT). The selection of MPP is very important to effectively improve machining performance. Machining performance has been closely related to the HCMT running state. In order to maintain HCMT sustainably manufacturing with high accuracy and low consumption after machining performance degradation for a long time running, MPP should be re-optimized according to the current state of the machine tools. Thus, this paper proposed a MPP optimization method for running HCMT to obtain optimal MPP based on current running state. A multi-objective optimization model was built, considering both the linear factors such as machining time and machining cost and nonlinear factors such as chatter in machining process. The nonlinear factors were reflected by the nonlinear dynamic model of machining process. Furthermore, a grid optimization algorithm was introduced to search the optimal MPP from the multi-objective optimization model. Finally, a case study was implemented to verify the feasibility of the nonlinear dynamic model and the superiority of the multi-objective optimization method compared with single-objective optimization method. [ABSTRACT FROM AUTHOR]

The error control and distribution of multi-operational machining processes is an active and important research topic in manufacturing. Most of the recent studies focus on statistical methods and the robustness of the evaluating process. However, these studies mainly serve to provide a change prediction tool rather than a method to identify and control root causes of machining errors. In this paper, in order to effectively control and distribute products of high-quality precision, a method of integrating a stream of variations (SoV) and multi-objective optimization is proposed. First, a machining process decomposition method is introduced to identify the sources of error. The multi-operation machining process is divided into three levels: the operation level, the station level, and the key characteristics level. Second, the sensitivities of the machining operation are evaluated level by level based on the SoV model; results of this evaluation will be used to determine the maximum error sources affecting the quality of products. Third, a distribution technique for each quality precision is achieved by solving a multi-objective optimization problem to minimize the cost and error associated with the sensitivities. Finally, a machining instance containing four operations is presented to demonstrate the effectiveness of the methodology. [ABSTRACT FROM AUTHOR]

To enhance the efficiency of pocket machining, considerable researches on strategies of tool selection and tool path optimization have been conducted. However, few studies are devoted to tool path planning with consideration of hybrid tool path patterns and multi-cutter selection. This research gap leads to little promotion to industrial application of the available research results. This paper proposes an automated CNC programming approach considering both multiple cutters and hybrid tool path patterns for pockets machining with complex islands and curvilinear boundaries. Firstly, the largest cutter capacity (LCC) of a pocket, which restricts essentially the pocket machining time, is investigated and defined. Secondly, to enlarge the LCC and to generate optimal tool path more easily, the pocket is split into many sub-regions according to the bottleneck lines of its boundary. After subdivision, tool selection and path pattern determination rules for each sub-area are introduced, respectively. Then, several heuristic principles for region recombination are developed to minimize the number of sub-polygons. Besides, based on the soft edges of each sub-area, expansion technique is employed to generate machining bounds for residuals. Finally, to demonstrate the advantages of this approach, two examples are rendered. And, the results show that the presented method performs better than the conventional ways. [ABSTRACT FROM AUTHOR]

In turn-milling machining, we found the cutter wear concentrated on a small section around the corner of the cutter and affect the cutter life. In this paper, the idea of swept area is proposed firstly based on the analyses for cutter wear, and it can indicate the cutter wear. Balance of swept area will lead to balanced wear along cutting edge, and expressions of swept areas of infinitesimal elements on the cutting edge are given. Secondly, the cutting depth and cutting angle, which are the key parameters in calculating the swept area, are analyzed based on different contact situations between the cutter and workpiece, and the detailed calculating method is given. Then, the optimization model to balance swept areas along the cutting edge is established, and the cutting parameters including cutter offset and machining length are optimized. Finally, the orthogonal turn-milling machining experiments with different cutting parameters are conducted; the result shows that turn-milling with optimized parameters can actually balance the cutter wear and increase the cutter life. [ABSTRACT FROM AUTHOR]

To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major role in the selection of efficient parameter values; however, attaining optimum ones each time by even skilled end users, is extremely difficult. This paper takes advantage of the possibilities of current computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and implements a genetic algorithm for optimising CNC machining operations mainly for sculptured surfaces. The algorithm has been developed as a hosted application to a cutting-edge CAD/CAM system. Collaboration among applications has been achieved through programming for software automation by utilising the application programme interface of the system. The approach was implemented to a group of test sculptured models with different properties whilst one of them has been actually machined using typical resources. Results obtained after the implementation indicated that the methodology is capable of providing optimum values for process parameters on its way to maintain both productivity and high quality. [ABSTRACT FROM AUTHOR]

Helical groove is one of the key structures of end mills. Its grinding process is the hardest and most time-consuming in end mill machining. According to the practical groove-grinding process, a Boolean method to simulate the groove-grinding process is proposed in this paper. The groove model is obtained with the known wheel geometry by a sequence of Boolean operations. Accordingly, the wheel used to machine a desired groove is deduced with the similar method. Based on the Boolean operation results and principles of differential geometry, contact lines of the wheel and groove are illustrated and calculated to optimize the groove-grinding process. Wheel positions relative to the blank are described by a sequence of mathematical coordinates. Wheel profile expressions are built with the corresponding transformation matrixes including groove geometry transformation matrix, cutter tip transformation matrix, and start processing position transformation matrix. Some simulated examples are included to illustrate the presented method. [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]

A good adaptive feedrate will be helpful for improving machining accuracy and efficiency, as well as avoiding the excess of the machine's physical capabilities and feed fluctuations during machining. Therefore, it is highly desirable to consider the constraints of geometric error, cutting performance, and drive constraints in the feedrate scheduling of the parametric curve interpolator for five-axis computer numerical control machining. In this paper, a novel multiconstraints feedrate scheduling method is proposed for the parametric curve interpolator in five-axis machining. In the method, the feed optimization model is first built with the constraints of geometric error, the maximum feedrate and acceleration of cutter tip, and the maximum feedrate and acceleration of five-drive axes. Then, the relations between each constraint and the cutter tip feedrate are derived by means of near arc length parameterization. After that, a linear programming algorithm is applied to obtain the optimal feed profile on the sampling positions of the given tool path. Finally, illustrated examples are given to validate the feasibility and applicability of the proposed feedrate scheduling method. The comparison results show that the proposed method has an ability of the simultaneous guarantees of geometric accuracy, cutting performance, and drive characters of machine tools. [ABSTRACT FROM AUTHOR]

Correct selection of cutting parameters is one of effective approaches to achieve optimum machining process, including reducing energy consumption. For the close relationship between cutting parameters and energy consumption in machining process, energy consumed is modeled and to be reduced based on analyzing the energy consumption in this paper. According to the different requirements in roughing process and finishing process, corresponding multi-objective optimization functions are formulated considering energy consumption. Taking the optimization of milling operations on aluminum alloy as an example, experiments are carried out to analyze the energy consumption and production rate with sets of optimized/un-optimized cutting parameters for different objectives. The experimental results show that the objectives of low consumed energy and high production rate can be simultaneously achieved by optimization of cutting parameters. [ABSTRACT FROM AUTHOR]

In this paper, an effective approach, Taguchi grey relational analysis, has been applied to experimental results of wire cut electrical discharge machining (WEDM) on Inconel 825 with consideration of multiple response measures. The approach combines the orthogonal array design of experiment with grey relational analysis. The main objective of this study is to obtain improved material removal rate, surface roughness, and spark gap. Grey relational theory is adopted to determine the best process parameters that optimize the response measures. The experiment has been done by using Taguchi's orthogonal array L36 (2 × 3). Each experiment was conducted under different conditions of input parameters. The response table and the grey relational grade for each level of the machining parameters have been established. From 36 experiments, the best combination of parameters was found. The experimental results confirm that the proposed method in this study effectively improves the machining performance of WEDM process. [ABSTRACT FROM AUTHOR]

This paper presents a method for tolerance balancing in machining process planning. The surface position tolerance (SPT) method offers several advantages in comparison with the tolerance charting technique. These advantages arise from the separate consideration of the datum and machining surface position capabilities. To solve the problem of tolerance balancing, the mathematical model used in the literature is adapted to the SPT method. When solving the problem, the objective of maximizing process tolerances was chosen. The proposal was applied to a problem that has been used by many authors in the literature and so a wide comparison can be made of the results. The solution enables the tolerances in the process to be improved and the solution can also be optimized with respect to the capability of the fixture. [ABSTRACT FROM AUTHOR]

Improvements in the machine tool and the machining process technologies increased the need for generic postprocessors in order to exploit the capabilities of the machine tools. Contrary to conventional machining approach, next-generation machining technologies such as force-based feedrate scheduling and toolpath optimization requires the implementation of the variable feedrate during toolpath which constitutes the aim of this article. Therefore, this paper introduces a postprocessor for table-tilting type five-axis machine tool based on generalized kinematics with variable feedrate implementation. Furthermore, a practical yet effective method for avoiding kinematic singularities by spherical interpolation and NC data correction is presented as well. Proposed approach is validated for various five-axis machine tools with different kinematic configurations via virtual machine simulation module. Results of the verification tests show that presented postprocessing approach can accurately convert the cutter location information into NC codes and it is demonstrated that integrated virtual simulation module can simulate toolpaths with large number of blocks. [ABSTRACT FROM AUTHOR]

Early studies on robot machining were reported in the 1990s. Even though there are continuous worldwide researches on robot machining ever since, the potential of robot applications in machining has yet to be realized. In this paper, the authors will first look into recent development of robot machining. Such development can be roughly categorized into researches on robot machining system development, robot machining path planning, vibration/chatter analysis including path tracking and compensation, dynamic, or stiffness modeling. These researches will obviously improve the accuracy and efficiency of robot machining and provide useful references for developing robot machining systems for tasks once thought to only be capable by CNC machines. In order to advance the technology of robot machining to the next level so that more practical and competitive systems could be developed, the authors suggest that future researches on robot machining should also focus on robot machining efficiency analysis, stiffness map-based path planning, robotic arm link optimization, planning, and scheduling for a line of machining robots. [ABSTRACT FROM AUTHOR]

With the development of manufacturing technology, five-axis milling has been one of the most important solution strategies in machining field. To deepen the understanding of multi-axis processing and improve the application level of the technology, the current work was carried out. This paper investigated the effects of tilt and lead angle on the scallop height, surface roughness, surface topography, and surface damages in five-axis ball-end milling process. Both geometrical analysis and experiment research are conducted to investigate the scallop height after five-axis milling, and variation of the surface roughness and surface topography with tool inclination angle obtained from the experiments was analyzed. Surface damages under the different inclination angles were also observed and analyzed with optical profiler. Several conclusions are made as follows. The inclination angles of the ball-end mill have no effect on the scallop height when only the spherical part of the cutter participates in the cutting process according to the geometrical analysis. Surface roughness with regard to tilt angles presents symmetrical characteristic around 0°. Surface texture feature, especially the texture direction, is closely related with the tool posture. The surface concave pits, convex marks, microscopic cracks, and spot corrosions are mainly the damage forms of the machined surface. More surface blemishes appeared when small inclination angles are adopted in cutting. As a result, the recommendatory inclination angle values for inclination angle are proposed. A better understanding of the five-axis machining process would be given by the detailed analysis of generation reason of the machined surface features, and the results could provide support for process parameter optimization. [ABSTRACT FROM AUTHOR]

In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout. [ABSTRACT FROM AUTHOR]

Fast tool servo (FTS) diamond turning is a promising machining process for fabricating ultra-precision micro-structured surfaces which have already gained wide application especially in the optics industry. However, most research focused on designing different types of FTSs for better performance, including bandwidth, stroke, stiffness, acceleration, accuracy, etc. There is a lack of systematical tool path generation (TPG) for FTS diamond turning, especially from the influence of TPG on surface quality point of view. This paper presents an optimized tool path generation for FTS diamond turning of micro-structured surfaces with either analytical description or non-uniform rational B-spline description. The tool path is optimized from three aspects to ensure high precision form and nanometric surface finish. Firstly, the tool path is optimized by a stable tool nose radius compensation method, which is different from the conventional one, to reduce the form error caused by the tool nose radius. Secondly, the ideal machined surface profiles are simulated to predict the ideal surface quality, with which the cutting conditions and tool geometry can be optimized to ensure nanometric surface finish and avoid tool interference. Thirdly, the influence of the control dynamics of the machine axes on machined surface quality, which is ignored in conventional machining, was considered. As the FTS dynamics dominate the influence, it is controlled to be a desired dynamics which was then used to modify the tool path to minimize the form error in machined surface. Experiments were carried out to prove the effect of TPG on surface quality and the effectiveness of the optimized TPG method. [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]

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved. [ABSTRACT FROM AUTHOR]

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L (3) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance. [ABSTRACT FROM AUTHOR]

In free-form surface machining, the prediction of five-axis ball-end milling forces is quite a challenge due to difficulties of determining the underformed chip thickness and engaged cutting edge. Part and tool deflections under high cutting forces may result in poor part quality. To solve these concerns, this paper presents process modeling and optimization method for five-axis milling based on tool motion analysis. The method selected for geometric stock modeling is the dexel approach, and the extracted cutter workpiece engagements are used as input to a force prediction. The cutter entry-exit angles and depth of cuts are found and used to calculate the instantaneous cutting forces. The process is optimized by varying the feed as the tool-workpiece engagements vary along the toolpath, and the unified model provides a powerful tool for analyzing five-axis milling. The new feedrate profiles are shown to considerably reduce the machining time while avoiding process faults. [ABSTRACT FROM AUTHOR]

This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process. [ABSTRACT FROM AUTHOR]

This paper introduces a mathematical model of tolerance chart balancing for machining process planning under complete inspection. The criteria considered in this study are based on the combined effects of manufacturing cost and quality loss, under constraints such as process capability limits, product design specifications, and product quality requirements. Manufacturing costs include the machining cost, part cost, inspection cost, reworking cost, and replacement cost. The machining cost is expressed in geometrical decreasing functions, which represent tolerances to be assigned. Process variability is expressed in quadratic loss functions, which represent the deviation between the part measurement and the target value. An example is presented to demonstrate the proposed model. A comparison made with previous methods shows that the proposed model minimizes the total cost of manufacturing activities and quality-related issues in machining process planning, particularly in the early stages. Moreover, the applications are not be limited to machining process planning but can also be used in other forms of production planning. [ABSTRACT FROM AUTHOR]

ive flow machining (AFM) is gaining widespread application finishing process on difficult to reach surfaces in aviation, automobile, and tooling industry. Al/SiC-MMC is a promising material in these industries. Here, AFM has been used to finish conventionally machined cylindrical surface of Al/15 wt% SiC-MMC workpiece. This paper presents the utilization of robust design-based Taguchi method for optimization of AFM parameters. The influences of AFM process parameters on surface finish and material removal have been analyzed. Taguchi experimental design concept, L (6 × 3) mixed orthogonal array is used to determine the S/N ratio and optimize the AFM process parameters. Analysis of variance and F test values also indicates the significant AFM parameters affecting the finishing performance. The mathematical models for R, R, Δ R, and Δ R and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. Scanning electron micrographs testifies the effectiveness of AFM process in fine finishing of Al/15 wt% SiC-MMC. [ABSTRACT FROM AUTHOR]