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.     

Micro wire EDM for high aspect ratio 3D microstructuring of ceramics and metals

Components for microsystems are basically produced using processes from the semiconductor technology or by LIGA. Silicon serves as basic material for this components. The material properties of silicon often dont achieve the demands of for example: micro-surgery, biotechnology, fluidics or high temperature environments. High volume production by replication techniques also needs tool materials with more adapted properties and high lifetime. Therefore materials such as polymers, metals, composits and ceramics becoming more and more important in MST and there is an increasing requirement of suitable manufacturing techniques and processes for this materials. For complex-shapes electro-discharge-machining (EDM) can be applied. Wire EDM (WEDM) is usefull for cutting shapes in materials with an minimum conductivity of about 0,01 s/cm. In our department we use wire diameters down to 20 m. We will show the newest application from our department made by very fine wires on an universal WEDM machine.We thank Mrs. Ulrike Mock, Chemistry&Physics of Interfaces, IMTEK, University Freiburg     

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。     

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.     

Multi-attribute decision making for green electrical discharge machining

This paper aims to develop a combination of Taguchi and fuzzy TOPSIS methods to solve multi-response parameter optimization problems in green manufacturing. Electrical Discharge Machining (EDM), a commonly used non-traditional manufacturing process was considered in this study. A decision making model for the selection of process parameters in order to achieve green EDM was developed. An experimental investigation was carried out based on Taguchi L9 orthogonal array to analyze the sensitivity of green manufacturing attributes to the variations in process parameters such as peak current, pulse duration, dielectric level and flushing pressure. Weighing factors for the output responses were determined using triangular fuzzy numbers and the most desirable factor level combinations were selected based on TOPSIS technique. The model developed in this study can be used as a systematic framework for parameter optimization in environmentally conscious manufacturing processes.     

On-line prevention of wire breakage in wire electro-discharge machining

Wire electro-discharge machining (WEDM) is a fully extended and competitive machining process widely used to produce dies and moulds. However, the risk of wire breakage affects adversely the full potential of WEDM since the overall process efficiency is considerably reduced. The present paper discusses the results of the analyses of an exhaustive experimental database that reproduces unexpected disturbances that may appear during normal operation. The results of the analyses reveal new symptoms that allow one to predict wire breakage. These symptoms are especially related to the occurrence of an increase in discharge energy, peak current, as well as increases and/or decreases in ignition delay time. The differences observed in the symptoms related to workpiece thickness are also studied. Another contribution of this paper is the analyses of the distribution of the anticipation time for different validation tests.Based on the results of the analyses, this paper contributes to improve the process performance through a novel wire breakage monitoring and diagnosing system. It consists of two well differentiated parts: the virtual instrumentation system (VIS) that measures relevant magnitudes, and the diagnostic system (DS) that detects low quality cutting regimes and predicts wire breakage. It has been successfully validated through a considerable number of experimental tests performed on an industrial WEDM machine for different workpiece thickness. The efficiency of the supervision system has been quantified through an efficiency rate defined in this paper.     

Automation in the design of EDM electrodes

Plastic parts, especially those designed for consumer electronics products, are becoming more compact and freeform in shape. This requires the construction of compact and delicate geometric features in the injection moulds that produce these parts. It is now common practice to employ electric discharge machining (EDM) whenever the conventional machining process fails to machine such delicate features. Although CAD/CAM systems are widely used in mould design and manufacturing, and specific commercial CAD/CAM systems that are customized for injection mould applications are also available, there are still no intelligent CAD tools that address the specific requirements of EDM electrode design. The aim of this research is to develop an intelligent CAD tool that supports EDM electrode design. This CAD tool uses a new technique that recursively splits the EDM region into sub-regions until machinable electrodes can be constructed. The CAD tool has been implemented and integrated into a commercial CAD/CAM system, and 40 different real designs has been used to test its capability in handling a wide variety of geometric shapes. Performance data show that the efficiency of the design process can be improved by at least 50%, with a potential to achieve 85% through improved implementation and further research.     

Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization

Manufacturing processes could be well characterized by both the quantitative and the qualitative measurements of their performances. In case of conflicting type performance measures, it is necessary to get possible optimum values of all performances simultaneously, like higher material removal rate (MRR) with lower average surface roughness (ASR) in electric discharge machining (EDM) process. EDM itself is a stochastic process and predictions of responses – MRR and ASR are still difficult. Advanced structural risk minimization based learning system – support vector machine (SVM) is, therefore, applied to capture the random variations in EDM responses in a robust way. Internal parameters of SVM – C, ɛ and σ are tuned by modified teaching learning based optimization (TLBO) procedure. Subsequently, using the developed SVM model as a virtual data generator of EDM process, responses are generated at the different points in the experimental space and power law models are fitted to the estimated data. Varying the weight factors, different weighted combinations of the inverse of MRR and the ASR are minimized by modified TLBO. Pseudo Pareto front passing through the optimum results, thus obtained, gives a guideline for selection of optimum achievable value of ASR for a specific demand of MRR. Further, inverse solution procedure is elaborated to find the near-optimum setting of process parameters in EDM machine to obtain the specific need based MRR-ASR combination.     

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.     

Tool electrode geometry and process parameters influence on different feature geometry and surface quality in electrical discharge machining of AISI H13 steel

Electro discharge machining process (EDM) is frequently used when machining of high complex and accurate features is required. Indeed, it is specially recommended for hard materials and micro-machined features. However, due to the process nature, there is still incomprehension on process parameters influence at the final quality features, ending up by lower productivity and quality ratios. On the other hand, fashioning and re-shaping of required electrodes for each feature are time consuming phases and the number of stored electrodes is very high. Therefore, in order to increase the global EDM process productivity, quality and flexibility, standardized simple electrode shapes, capable to machine different features, must be found. This study presents the influence of the main EDM process parameters and different tool geometries on basic process performance measures. A set of designed experiments with varying parameters such as pulsed current, open voltage, pulse time and pulse pause time are carried out in H13 steel using different geometries of copper electrodes. In addition, material removal rate , surface roughness and different dimensional and geometrical micro-accuracies are analyzed through statistical methods. Results help to select appropriate EDM process parameters to machine parts depending on product requirements.     

The multi-scale physical and numerical modeling of fracture phenomena in the MgCa0.8 alloy

The MgCa0.8 alloy can be used to produce surgical threads. However, the low plasticity of this alloy causes problems during wire drawing. A numerical model of the drawing process can be used to optimize the deformation parameters. Research has shown that fractures in MgCa0.8 start within grain boundaries; therefore, a model of the drawing process should consider this mechanism. The main purpose of this work is to develop a micro-scale numerical model of fracture in MgCa0.8 using the boundary element method (BEM). Additionally, the potential for using the developed BEM model as a part of the multi-scale model of the drawing process is investigated.     

Process parameter analysis in ablating micro-mold manufacturing

The accuracy and process reliability of both laser ablation and EDM depend on various influences like machine tool performance, temperature, material properties and process parameters. This paper focuses on the variation of the most important process parameters to reduce surface roughness and machining time. For the experiments standard tool steel (X38CrMoV5-1) was used. In the following the variation of process parameters is described and validated by selected machining examples for EDM and laser ablation, respectively.     

材料中应变率力学性能测试数据处理与表征方法

针对材料的中应变率力学性能测试数据,为数据处理与本构表征设计专用的分析软件。给出了载荷测量和应变测量方法,梳理了从测试数据到本构参数所历经的数据处理流程和方法,按照逻辑程序和功能模块化设计,基于MATLAB GUI平台编制了数据处理软件。利用该软件处理了S580B合金钢的测试数据,得到3种动态本构模型对应的应变强化参数和应变率敏感性参数,结果表明,Johnson-Cook模型相对于Cowper-Symonds模型和塑性随动模型更适合反映S580B合金钢的动态力学性能,拟合曲线和试验结果较为吻合。利用该专用软件,处理手段和过程完整可追溯,本构参数更容易获得,很大程度地提高了中应变率动态力学性能测试工作的效率和规范性,为冲击动力学数值仿真提供了重要的数据支持。     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

Continuous-time simulation of semi-orthogonal metal cutting on a lathe

There has been considerable attention to applications of feedback control to machine tools. Certainly all computer numerically controlled (CNC) machines use servomechanisms for the spindle and feed drives. When feedback control is extended to the metal cutting process itself, there has been an unfortunate tendency to call such systems adaptive controls, for example, adaptive control constraint (ACC) and adaptive control optimization (ACO), even though they have not been adaptive in the sense of modern control theory. A special name, parameter adaptive control, has been proposed to describe the application of truly adaptive control to metal cutting. Even though there is substantial work on adaptive control within the body of modern control theory, there have been few reductions of parameter adaptive control to actual machine tools. It has been suggested that the primary impediments to such application are the lack of mathematical models of manufacturing processes in forms appropriate to the control problem, and the lack of in-process sensing techniques that would be complementary to such models. This paper describes one step in a sequence of research efforts that is intended to lead to the adaptive control of unattended machine tools. Numerical parameters are used to evaluate coefficients of a previously developed state space model of semi-orthogonal metal cutting on a lathe. An Advanced Continuous Simulation Language (ACSL) program is presented.     

Development of copper based miniature electrostatic actuator using WEDM with low actuation voltage

Fabricating electrostatic micro actuator, such as comb-drive actuator, is one of the demanding areas of the MEMS technology because of the promising applications in modern engineering, such as, micro-switches, attenuators, filters, micro-lenses, optical waveguide couplers, modulation, interferometer, dynamic focus mirror, and chopper. For the fabrication, most of the cases silicon monocrystalline wafers are used through complex process. To etch the silicon substrates, researchers often use deep reactive-ion etching or anisotropic wet etching procedure which are time consuming and unsuitable for batch fabrication process. Again, resent research shows that comb-drive actuators need comparatively high voltage for actuation. In solving these problems, the study presents a copper based electrostatic micro actuator with low actuation voltage. Using wire electrical discharge machine (WEDM), the actuator is fabricated where a light weight flexible spring model is introduced. Capacitor design model is applied to present a voltage controlling electronic circuit using Arduino micro controller unit. The experimental result shows that the actuator is able to produce 1.38 mN force for 15 V DC. The experiment also proves that coper based actuator design using WEDM technology is much easier for batch processing and could provide the advantages in rapid prototyping.     

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.

     

MACHINE LEARNING IN HYBRID HIERARCHICAL AND PARTIAL-ORDER PLANNERS FOR MANUFACTURING DOMAINS

ABSTRACT

The application of AI planning techniques to manufacturing systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly used as the sequential control programs which drive the operation of manufacturing systems. HYBIS is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be used as sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modeling tools. This document describes a work aimed to improve the efficiency of solving problems with HYBIS by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem-solving episodes. The learning approach builds on HAMLET, a system that learns control knowledge in the form of control rules.     

High aspect ratio micro tool manufacturing for polymer replication using μEDM of silicon, selective etching and electroforming

Mass fabrication of polymer micro components with high aspect ratio micro-structures requires high performance micro tools allowing the use of low cost replication processes such as micro injection moulding. In this regard an innovative process chain, based on a combination of micro electrical discharge machining (μEDM) of a silicon substrate, electroforming and selective etching was used for the manufacturing of a micro tool. The micro tool was employed for polymer replication by means of the injection moulding process.     

A novel spark erosion technique for the fabrication of high aspect ratio micro-grooves

Micro groove is an important geometrical feature of components used in microsystem technology (MST). Straight micro grooves are the predominant features in microsystem components such as micro heat exchangers and diffraction gratings. Micro Electrical Discharge Machining (micro EDM) is a complementary microfabrication technique adopted from the conventional EDM machining process for the purpose of micro machining. Using micro EDM it is possible to machine all electrically conductive materials irrespective of their hardness. High aspect ratio microgroove machining for length as high as 20 mm is a formidable task for the conventional micro EDM. In the present work, a novel spark erosion technique has been described wherein a graphite foil has been used instead of the traditional pin shaped tool electrode, for the purpose of making straight grooves. In a single setup microgroove of 20 mm length and an aspect ratio of about 2.3 has been achieved on hardened tool steel by this technique. This process is further refined by using the gravitational effect for the effective debris removal, which has improved the aspect ratio to about 8.Accepted: September 2003