Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses

This paper presents the use of artificial neural networks (ANN) to diagnose degraded behaviours in wire electrical discharge machining (WEDM). The detection in advance of the degradation of the cutting process is crucial since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy. Concerning this, previous investigations have identified different types of degraded behaviours in two commonly used workpiece thicknesses (50 and 100 mm). This goal was achieved by monitoring different functions of characteristic discharge variables. However, the thresholds achieved by these functions depended on the thickness of the workpiece. Consequently, the main objective of this work is to detect the degradation of the process when machining workpiece of different thicknesses using one unique empirical model. Since artificial neural network techniques are appropriate for stochastic and non-linear nature processes, its use is investigated here to cope with workpieces of different thicknesses. The results of this work show a satisfactory performance of the presented approach. The satisfactory performance is shown by two ratios: the validation ratio, which ranges between 85% and 100%, and the test ratio, which results between 75% and 100%.     

Helical surface creation by wire electrical discharge machining for micro tools

In mechanical micromachining, micro tooling is one of the key factors affecting the finished geometrical accuracy and surface quality. To overcome the serious tool wear caused by relatively longer micromachining time, micro tools are usually made of ultra-hard materials such as polycrystalline diamond (PCD) or cubic boron nitride (CBN). Wire Electrical discharge machining (WEDM) is a good choice for efficient fabrications of micro tools made of ultra-hard materials. Considering the traces of wire motions form ruled surfaces, in this paper, typical custom micro milling tools with helical surfaces are generated by ruled surfaces. The simulation shows that the selection of guide lines and generating lines for ruled surface is the key point relating to the final geometrical accuracy and machining efficiency in custom micro tool fabrications by WEDM. Based on the mathematical models built in this paper, overcut can be avoided in the process planning stage for complicated helical surfaces. Furthermore, wire locations can be created conveniently by the introduced mathematical models for post processing in dedicated CAM systems.     

A thermal model to investigate the wire rupture phenomenon for improving performance in EDM wire cutting

A continuous trend towards automation can be noticed in wire cutting electro discharge machining due to the relatively low machining speed and large investments required. In this respect, the wire rupture phenomenon may be considered a major problem, limiting the overall efficiency of the operation. The thermal load on the wire seems to be a governing factor determining wire rupture. This has been proven by a number of tests using an EDM pulse analyzing system. In order to examine the relative influence of various process parameters on the thermal load of the wire, a mathematical thermal model was developed. The model enables the calculation of the temperature rise in the wire due to heat input by electrical discharges and from Joule's heat. The result of this analysis can be used in an adaptive control optimization system in order to maximize the cutting speed, without enhancing the risk of wire rupture.     

An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM

A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness achieved as a function of the process parameters. Pulse duration, open circuit voltage, dielectric flushing pressure and wire feed rate were taken as model’s input features. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from the experimental data. The model’s predictions were compared with experimental results for verifying the approach.     

Incorporating prior model into Gaussian processes regression for WEDM process modeling

Sufficient sampling is usually time-consuming and expensive but also is indispensable for supporting high precise data-driven modeling of wire-cut electrical discharge machining (WEDM) process. Considering the natural way to describe the behavior of a WEDM process by IF-THEN rules drawn from the field experts, engineering knowledge and experimental work, in this paper, the fuzzy logic model is chosen as prior knowledge to leverage the predictive performance. Focusing on the fusion between rough fuzzy system and very scarce noisy samples, a simple but effective re-sampling algorithm based on piecewise relational transfer interpolation is presented and it is integrated with Gaussian processes regression (GPR) for WEDM process modeling. First, by using re-sampling algorithm encoded derivative regularization, the prior model is translated into a pseudo training dataset, and then the dataset is trained by the Gaussian processes. An empirical study on two benchmark datasets intuitively demonstrates the feasibility and effectiveness of this approach. Experiments on high-speed WEDM (DK7725B) are conducted for validation of nonlinear relationship between the design variables (i.e., workpiece thickness, peak current, on-time and off-time) and the responses (i.e., material removal rate and surface roughness). The experimental result shows that combining very rough fuzzy prior model with training examples still significantly improves the predictive performance of WEDM process modeling, even with very limited training dataset. That is, given the generalized prior model, the samples needed by GPR model could be reduced greatly meanwhile keeping precise.     

电火花线切割加工自动编程系统

利用Visual C++6.0编程语言,以电火花线切割机床改造为目标,开发电火花线切割加工自动编程系统。该系统采用分布式控制技术,以PC机为主机,同时控制多台线切割机床,实现了图形化自动编程。经实验证明,操作简单方便、运行可靠。     

Multi-objective optimization in wire-electro-discharge machining of TiC reinforced composite through Neuro-Genetic technique

This paper proposed a Neuro-Genetic technique to optimize the multi-response of wire electro-discharge machining (WEDM) process. The technique was developed through hybridization of a radial basis function network (RBFN) and non-dominated sorting genetic algorithm (NSGA-II). The machining was done on 5 vol% titanium carbide (TiC) reinforced austenitic manganese steel metal matrix composite (MMC). The proposed Neuro-Genetic technique was found to be potential in finding several optimal input machining conditions which can satisfy wide requirements of a process engineer and help in efficient utilization of WEDM in industry.     

A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (T _on), pulse-off time (T _off) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.

     

Computer aided contouring operation for traveling wire electric discharge machining (EDM)

This paper develops a computational method for numerical control (NC) of traveling wire electric discharge machining (EDM) operation from geometric representation of a desired cut profile in terms of its contours. Normalized arc length parameterization of the contour curves is used to represent the cut profile and a subdivision algorithm is developed together with kinematic analysis to generate the required motions of the machine tool axes. In generating the tool motions for cutting sections with high curvatures such as corners with small radii, a geometric path lifting method is presented that increases the machining gap and prevents gauging or wire breakage.     

High aspect ratio meso-scale parts enabled by wire micro-EDM

Micro-electro discharge machining (EDM) is a subtractive meso-scale machining process. The Agie Excellence 2F wire micro EDM is capable of machining with a 25 micron diameter wire electrode and positioning the work piece to within ±1.5 microns. The over-burn gap can be controlled to within 3 microns to obtain a minimum feature radius of about 16 microns while achieving submicron surface finish and an imperceptible recast layer. For example, meso-scale gears that require vertical sidewalls and contour tolerances to within 3 microns can be wire EDMed into a variety of conductive materials. Material instabilities can affect the dimensional precision of machined meso-scale parts by material relaxation during the machining process. A study is done to investigate the machining performance of the wire micro EDM process by machining a high aspect ratio meso-scale part into a variety of metals (e.g. 304L stainless steel, Nitronic 60 Austentic Stainless, Beryllium Copper, and Titanium). Machining performance parameters such as, profile tolerance, perpendicularity, and repeatability are compared for the different materials. Pertinent inspection methods desirable for meso-scale quality assurance tasks are also evaluated. Sandia National Laboratories is developing meso-scale electro-mechanical components and has an interest in the assembly implications of piece parts fabricated by various meso-scale manufacturing processes. Although the wire EDM process is typically used to fabricate 2½ dimensional features, these features can be machined into a 3 dimensional part having other features such as hubs and chamfers to facilitate assembly.     

线接触加工数控系统的研究

分析了线接触加工的加工方法。此数控系统是基于Windows的全软件数控系统,包括多个软件控制模块,通过控制铣刀的有效走刀姿态,利用铣刀侧刃进行一次走刀完成对整个可展直纹面的加工。此外该系统具有图形自动编程功能,保证了机床加工过程的自动化。     

Manufacture of a spur tooth gear in Ti-6Al-4V alloy by electrical discharge

This paper proposes a method of manufacturing a spur tooth gear in Ti-6Al-4V alloy (grade 5) using a wire electrical discharge machine (Wire EDM). A geometrical model for the gear is drawn up and implemented using the program MATLAB.The electro-erosion parameters tested for this alloy are applied to an ONA PRIMA S-250. The parameters used (power, pause, voltage, …) are based on the ONA EDM charts. The Taguchi orthogonal array method was chosen to obtain the optimum values for cutting Titanium.The work presented follows established lines for manufacturing mechanical parts using general purpose machines and tools. In this case, the WEDM process was used. The MATLAB program was employed to obtain the interpolation points. This program simplifies the task of solving the equations originated by the mathematical model which allows the wire path to be calculated.The WEDM method used here is a commendable alternative for machining electrically conductible materials which are difficult to work with using conventional machine tools (milling, turning or boring). Furthermore, the WEDM process reduces or even eliminates the need for subsequent polishing processes due to the high-quality finish achieved.     

Integrated profile and thickness error compensation for curved part based on on-machine measurement

Curved parts are widely used in aerospace, automotive and energy industries. The profile and thickness accuracy are both critical to some curved parts such as hollow blades. Improving the profile accuracy while ignoring the wall thickness error will reduce the qualified rate of these curved parts, and vice versa. This paper proposes a comprehensive compensation method with constraints of both profile and thickness tolerances for the machining of curved parts. The actual geometry and wall thickness of the parts are obtained by on-machine measurement system after rough machining. Both touch-trigger probe and thickness gage are used to reconstruct the actual outer and inner surface of the workpiece. Then, the comprehensive constraint considering both profile and thickness constraints is established based on the reconstructed workpiece geometry. With the comprehensive constraint, a new target outer surface is constructed. The machining error is calculated from the target surface and compensated via toolpath adjustment. At last, machining experiments are conducted to verify the feasibility of the proposed method.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

Integrated approach to robotic machining with macro/micro-actuation

A novel integrated approach to high-accuracy machining with industrial robots is presented in this paper. By combining a conventional industrial robot with an external compensation mechanism, a significantly higher bandwidth of the control of the relative position between the tool and the workpiece can be achieved. A model-based feedback controller for the compensation mechanism, as well as a mid-ranging control architecture for the combined system with the robot and the compensation mechanism are developed. The system performance is evaluated in extensive machining experiments, and the workpiece accuracies achieved are quantified and compared to the corresponding results obtained with state-of-the-art approaches to robotic machining. It is shown that the proposed approach to machining offers significantly higher accuracy, up to eight times improvement for milling in steel, where the required process forces, and thus the exhibited position deviations of the robot, are significant.     

Recognition of machining features for cast then machined parts

Mechanical parts are typically manufactured using multiple manufacturing processes. Primary processes such as casting realize the primary shape of the part, while secondary processes such as machining generate more detailed shape of the part. This paper presents a feature recognition method to support machining process planning for cast-then-machined parts. From the part model including the specification of machined faces, we generate the starting workpiece for machining, which represents the casting output in sufficient detail to support machining process planning. The starting workpiece is generated by identifying faces to be made by casting followed by machining, then offsetting the part through these faces by a uniform machining thickness to obtain cast faces, and combining the halfspaces induced by machined faces and the halfspaces induced by their bounding cast faces to enclose removal volumes. Machining features are then recognized from the removal volumes using a volume decomposition method called Alternating Sum of Volumes with Partitioning.     

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

Modern machining processes are now-a-days widely used by manufacturing industries in order to produce high quality precise and very complex products. These modern machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such processes is very important to satisfy all the conflicting objectives of the process. In this research work, a newly developed advanced algorithm named ‘teaching–learning-based optimization (TLBO) algorithm’ is applied for the process parameter optimization of selected modern machining processes. This algorithm is inspired by the teaching–learning process and it works on the effect of influence of a teacher on the output of learners in a class. The important modern machining processes identified for the process parameters optimization in this work are ultrasonic machining (USM), abrasive jet machining (AJM), and wire electrical discharge machining (WEDM) process. The examples considered for these processes were attempted previously by various researchers using different optimization techniques such as genetic algorithm (GA), simulated annealing (SA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), harmony search (HS), shuffled frog leaping (SFL) etc. However, comparison between the results obtained by the proposed algorithm and those obtained by different optimization algorithms shows the better performance of the proposed algorithm.     

FPGA-based hardware CNC interpolator of Bezier,splines, B-splines and NURBS curves for industrial applications

Tool path interpolation is an important part of Computerized Numerical Control (CNC) systems because it is related to the machining accuracy, tool-motion smoothness and overall efficiency. The use of parametric curves to generate tool-motion trajectories on a workpiece for high accuracy machining has become a standard data format that is used for CAD/CAM (Computer Aided Design/Computer Aided Manufacturing) and CNC systems. Splines, Bezier, B-splines, and NURBS (Non-Uniform Rational B-splines) curves are the common parametric technique used for tool path design. However, the reported works bring out the high computational load required for this type of interpolation, and then at best only one interpolation algorithm is implemented. The contribution of this paper is the development of a hardware processing unit based on Field Programmable Gate Arrays (FPGA) for industrial CNC machines, which is capable of implementing the four main interpolation techniques. It allows the selection of the required interpolation technique according the application. Two CAD models are designed for test the CNC interpolations; experimental results show the efficiency of the proposed methodology.     

Position-oriented process monitoring in milling of thin-walled parts

Improving machining performance of thin-walled parts is of great significance in aviation industry, since most aviation parts are characterized by large size, complex shape, and thin-walled structure. Machining process monitoring is the essential premise to improve the machining performance. In order to improve the machining quality and efficiency, this paper presents a position-oriented process monitoring model based on multiple data during milling process, and corresponding solution is provided. Through obtaining the internal data set of the numerical control (NC) system during machining, it is possible to correlate the cutting position with monitoring signals including cutting force, acceleration, and spindle power. Then, process optimization is realized to improve the machining quality and efficiency based on the monitoring results. Machining tests are conducted on aircraft structural part as well as blade part, and the experimental results show this method provides a significant insight into the machining process of thin-walled part and contributes to the process optimization. By using feedrate optimization, time consumption for the rough milling process of one titanium alloy part reduced from 19.1 h to 14.4 h and the number of cutter consumption dropped from 5 to 3. And according to the result of position-oriented process monitoring, the machining strategies were optimized to reduce vibration and avoid chatter, thereby improving the machining quality.     

Generation of workpiece orientations for machining using a rule-based system

This paper deals with the automatic generation of workpiece orientations on a machine for machining operations. A system capable of this has a wide spectrum of applications in many areas of manufacturing planning and is essential to the development of dynamic process planning systems. However, such a system requires the ability to reason about geometrical and spatial relationships, a topic of great difficulty for automatic reasoning. A study of the problem characteristics is made and its results are implemented in a rule-based system. This system, when given a workpiece along with operation instructions and machine specifications is capable of generating a good orientation for the workpiece. The functioning of the system is demonstrated by an example.