Eva Portillo

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.

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%.

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.

Towards an infrastructure model for composing and reconfiguring cyber-physical systems

Cyber Physical Systems involve the use of different computing, communication and control technologies and require satisfying simultaneously several restrictive constraints. One characteristic of CPS is that there is a constant evolution of technologies and tools that help designers to design this kind of systems. Due to its complexity, middleware architectures are frequently used to implement CPS. Most of these middleware architectures tend to use models of the software entities of the system. However, quite often, they do not consider the infrastructure (hardware and network) over which these systems are executed even though this is a key issue for these systems. In addition, frequently, it is necessary to make some composition and reconfiguration decisions at run-time over the current status of the infrastructure. This work proposes a model developed within the iLAND project that represents the static and operational status of the CPS infrastructure.

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.

Real time direct kinematic problem computation of the 3PRS robot using neural networks

Abstract The reliable calculation of the Direct Kinematic Problem (DKP) is one of the main challenges for the implementation of Real-Time (RT) controllers in Parallel Robots. The DKP estimates the pose of the end effector of the robot in terms of the sensors placed on the actuators. However, this calculation requires the use of time-consuming numerical iterative procedures. Artificial Neural Networks have been proposed to implement the complex DKP equation mapping due to their universal approximator property. However, the proposals in this area do not consider the Real Time implementation of the ANN based solution, and no approximation error vs computational time analysis is carried out. In this work, a methodology that uses Artificial Neural Networks (ANNs) to approximate the DKP is proposed. Based on the 3PRS parallel robot, a comprehensive study is carried out in which several network configurations are proposed to approximate the DKP. Moreover, to demonstrate the effectiveness of the approach, the proposed networks are evaluated considering not only their approximation capabilities, but also their Real Time performance in comparison with the traditional iterative procedures used in robotics.

Downsizing training data with weighted FCM for predicting the evolution of specific grinding energy with RNNs

Abstract Grinding plays a prominent role in modern manufacturing due to its capacity for producing parts of high accuracy and precision. Among the various grinding variables, the specific grinding energy (e c ) is key because it measures the energy required to remove the unit volume of part material and therefore it gives information about the performance of the grinding process. In addition, a measure of the specific grinding energy is also useful for estimating the power requirements of the grinding machine. Thus, a Recurrent Neural Network (RNN) is used for predicting e c in a more realistic manner that involves the development of the e c over time. Moreover, since performing grinding experiments is a highly time and resource consuming task, it would be very useful to downsize the required dataset to train the RNN since it could substantially reduce the time and costs involved in carrying out the experiments to generate the dataset, as well as in training the RNNs. Therefore, in this work a methodology combining Fuzzy C-Means (FCM) and RNNs for downsizing the dataset and predicting specific grinding energy is proposed. Unlike other approaches for reducing the dataset using FCM, in the current work the inputs are weighted. To achieve this, the knowledge is extracted from the weights of satisfactorily trained RNN obtained from previous work. The results show that under reduced training datasets (weighted and non-weighted FCM inputs) and non-reduced datasets (all available experiments), superior results were yielded with the RNNs obtained with the weighted approach. In fact, in some cases, for the reduced training dataset (weighted) the error is halved. Furthermore, the results show that it is more advantageous to use a reduced training dataset obtained after FCM, since this reduces the costs associated with experimental time, as well as the training time required for RNNs.

Extended CTC control for parallel robots

Parallel robots have recently arisen as an interesting robotic architecture capable of performing task at high speed and precision. In order to exploit all the potential of these mechanism, model based control approaches are required. However, due to their complex structure, kinematic and dynamic modelling is a complex task, which usually leads to models with parameter uncertainties. In order to reduce the effect of parameter uncertainties, in this paper a redundant dynamic model based Extended CTC approach is proposed, and its stability and sensitivity analyzed for the 3RRR parallel robot. Results show that this approach provides more robustness than classical CTC approach to model parameter uncertainties.

Virtual Sensors for Advanced Controllers in Rehabilitation Robotics

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.

A stable model-based control scheme for parallel robots using additional sensors

The use of parallel robots has been demonstrated to be an interesting alternative when high accuracy and/or high speed is required. However, in order to achieve these goals, model based controllers are required. This work presents a new model based control approach, the stable Extended CTC, that uses extra data from additional sensors introduced in the passive joints of parallel robot in the controller. The proposed controller guarantees asymptotic stability, which is an important contribution over the previously presented approaches. The use of redundant information increases controller robustness and performance, allowing to reduce tracking error with respect to traditional CTC approaches. The effectiveness of the proposed control law is demonstrated by implementing it in a Delta robot which has been modeled in ADAMS multibody software.