Itziar Cabanes

Development of optimum electrodischarge machining technology for advanced ceramics

In recent years, ceramic materials with improved properties have been developed to meet a large number of industrial applications. However, in most cases, the cost of the ceramic components is very high. On some occasions, the final machining of the component (especially if complex geometries are to be obtained) accounts for an important percentage of the final cost. The electrodischarge machining process can be a good choice if the material has at least a minimum electrical conductivity, since it can produce very complex shapes and it is not dependent on the hardness or abrasiveness of the material itself. In this paper, the development of sinking and wire electrodischarge machining technology for two ceramics with a promising future (boron carbide and silicon infiltrated silicon carbide) is described. The high removal rates, as well as the possibility of obtaining an excellent surface finish, prove the feasibility of the industrial application of this production method.

Cutting conditions and tool optimization in the high-speed milling of aluminium alloys:

Abstract The high-speed milling (HSM) of aluminium alloys was one of the first fields of application of this technology. It might be thought that this is the field where the technology has been most commonly used, with the largest number of users, and is therefore best known. However, the continuous improvements in modern machine tools on the one hand, together with the development of new tool materials and tool geometries on the other, have produced a constant increase in the cutting conditions (i.e. speed and feed rate). During the past 5 years the use of high-silicon (12–21 per cent) castings has grown considerably owing to the fact that a high silicon content results in excellent properties, mainly wear and high-temperature resistance. However, the machinability of the material becomes too poor on account of its abrasiveness. Aluminium alloys for structural elements in the aeronautical industry are nowadays high-speed milled (HSMed) at very high removal rates, which makes possible the use of monolithic structures instead of sheet metal formed structures. Complete removal of coolant fluids has not been possible so far, but their use can be effectively minimized if techniques such as micropulverization are applied. In this paper, the problems in the machining of the previously mentioned alloys are analysed, along with the possibilities of the so-called minimum quantity of lubricant (MQL) technique.

A procedure to evaluate Extended Computed Torque Control configurations in the Stewart–Gough platform

Abstract Parallel robots have become the best solution when high speed and/or accuracy are needed in industrial robotic operations. However, in order to meet the requirements of these tasks, advanced model based controllers such as the Extended CTC scheme are required. This CTC-based scheme requires the introduction of extra sensors in the passive joints of the parallel robot. This redundant information allows to increase the robustness and performance of the control law, leading to better trajectory tracking. However, in order to achieve the best performance, a proper extra sensor distribution is required. In this paper, a sensitivity analysis based approach is applied to the well known Gough Platform in order to evaluate different extra sensor distributions. The obtained results are compared with those obtained by a statistically significant set of simulations, demonstrating the effectiveness of the methodology.

Design of a Virtual-Instrumentation System for a Machining Process

This paper presents a virtual-instrumentation system (VIS) that aims at measuring the evolution of key magnitudes in a nonconventional machining process called wire electrical discharge machining (WEDM). The VIS consists of two well-different parts: the acquisition system that measures process signals (voltage and current) and the virtual measurement of relevant magnitudes (such as energy, peak-current distribution, and ignition delay time). The data-acquisition system provides flexibility and ease of storing tests under different machining conditions without extra hardware construction or adaptation. It is based on a commercial data-acquisition board that works at very high frequencies (up to 10 MSamples/s). The virtual measurement is carried out by modeling and processing the acquired signals. The VIS has been employed to monitor and detect low-quality cutting regimes in WEDM.

Computer simulation of performance of electrical discharge machining operations

The electrical discharge machining (EDM) process is optimum for accurate machining of complex geometries in hard materials, as those required in the tooling industry. It has become by far the most popular among the non-conventional machining processes. However, although a large number of EDM machines are sold every year, available knowledge of the process is still very empirical. Experimental trials are required in many cases to set up the optimum conditions for an EDM operation, resulting in increases in lead-time and cost for the final part. The reason for this is the complex nature of the process, highly stochastic, that involves simultaneous interaction of thermal, mechanical, chemical and electrical phenomena. Therefore, research efforts must be directed towards process modelling in order to reduce the experimental cost associated to the technology. In this work, an original computer simulation model of the EDM process is presented. The model is based on the numerical calculation of temperature fields within the workpiece, from which the amount of part material removed per discharge can be estimated. The objective is to theoretically predict material removal rate (MRR) and the final surface finish of the machined part using as input variables the EDM process parameters and the properties of the work material. The model has been validated by carrying out tests on an industrial EDM machine, showing that it can adequately predict MRR and surface roughness with errors below 9%.

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 100mm). 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%.

Control of parallel robots using passive sensor data

A novel control architecture for parallel robots is introduced to fully exploit the advantages of these robots on high-speed and precision operation. A closed form of the dynamic model of parallel robots is difficult to obtain, due to the complex kinematic relations of these kind of mechanism. However, with the use of the extra data provided by sensors placed in strategic passive joints, kinematic and dynamic modelling can be simplified. The dynamic model can be used to implement advanced control techniques to improve the efficiency of parallel robots. In this paper, monoarticular and multiarticular control techniques are implemented on a 5R parallel robot, showing that the use of extra sensor data leads to a better and accurate control.

Virtual sensors for on-line wheel wear and part roughness measurement in the grinding process.

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensors results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.

A redundant dynamic model of parallel robots for model-based control

The use of extra sensors in parallel robots can provide an increase in control performance, making it possible to fully exploit the potential of these mechanisms. In this paper, a comprehensive redundant dynamic modelling procedure for the six-degree-of-freedom Gough platform is presented. The proposed methodology makes it possible to define the model in terms of all sensorized joint variables in order to implement redundant information-based control, and an example, the Extended Computed Torque Control Extended CTC approach, is developed. This, applied to parallel robots, ensures better dynamic performance than the traditional CTC approach. In order to validate dynamic modelling, a two-step procedure is used in this paper. First, the redundant dynamic model is validated by comparing its dynamic performance with the previous research in the field. Second, an exhaustive study is carried out that demonstrates the advantages of the redundant dynamic model when used in the Extended CTC approach.

A computer assistant for monitoring tool performance during the drilling process

Nowadays, making holes using the drilling process plays a fundamental role in the manufacturing industry. However, preventing tool wear and/or breakage is still a major concern. From the production process point of view, several undesirable consequences may arise, such as downtimes, increased production costs, loss of dimensional tolerances and even irreversible workpiece defects. In this sense, the present article presents a computer assistant, which provides information about the current drilling process system. In particular, the computer assistant detects both tool wear and material heterogeneities.