MATHEMATICAL MODELLING OF THE CO2 LASER CUTTING PROCESS USING GENETIC PROGRAMMING

The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots

Prediction of laser drilled hole geometries from linear cutting operation by way of artificial neural networks

AbstractThis paper deals on artificial intelligence (AI) application for the estimation of kerf geometry and hole diameters for laser micro-cutting and laser micro-drilling operations. To this aim laser cutting and laser drilling operation were performed on NIMONIC 263 superalloy sheet, 0.38 mm in nominal thickness, by way of a 100 W fibre laser in modulated wave regime. Linear cuts and holes (by trepanning) were performed fixing the average power at 80 W and changing the pulse duration, the cutting speed, the focus depth and the laser path (the latter only for the drilling operations). Kerf width and the holed diameter, at the upper and downsides, were measured by digital microscopy. Different artificial neural networks (ANNs) were developed and tested to predict the kerf widths and the diameters (at the upper and downside). Two ANNs were addressed to the linear cutting process modelling; also, two further ANNs were developed for micro-drilling on the base of the linear cutting process features. The networks were trained with a subset of data containing the process conditions and the kerf/hole geometry. The ANN test was performed with the remaining data. The results show that ANNs can model the cut and hole geometry as a function of the process parameters. Moreover, the ANN trained with kerf geometry is more efficient. Therefore, a functional correlation between the kerf geometries achievable in the linear cutting process and micro-drilling was assessed

Modelling laser milling of microcavities for the manufacturing of DES with ensembles

A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.This work was partially funded through Grants fromthe IREBID Project (FP7-PEOPLE-2009-IRSES- 247476) of the European Commission and Projects TIN2011- 24046 and TECNIPLAD (DPI2009-09852) of the Spanish Ministry of Economy and Competitivenes

Analysis of correlations of multiple-performance characteristics for optimization of CO2 laser nitrogen cutting of AISI 304 stainless steel

The identification of laser cutting conditions for satisfying different requirements such as improving cut quality characteristics and material removal rate is of great importance. In this paper, an attempt has been made to develop mathematical models in order to relate laser cutting parameters such as the laser power, cutting speed, assist gas pressure and focus position, and cut quality characteristics such as the surface roughness, kerf width and width of heat affected zone (HAZ). A laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of laser cutting parameters considered. 3 mm thick AISI 304 stainless steel was used as workpiece material. Mathematical models were developed using a single hidden layer artificial neural network (ANN) trained with the Levenberg– Marquardt algorithm. On the basis of the developed ANN models the effects of the laser cutting parameters on the cut quality characteristics were presented. It was observed that laser cutting parameters variously affect cut quality characteristics. Also, for the range of operating conditions considered in the experiment, laser cut quality operating diagrams were shown. From these operating diagrams one can see the values of cut quality characteristics that can be achieved and subsequently select laser cutting parameter values. Furthermore, the analysis includes correlations between cut quality characteristics and material removal rate. To this aim, six trade-off operating diagrams for improving multiple responses at the same time were given

Feature Extraction from Indirect Monitoring in Marine Oil Separation Systems

In this article, a study of characteristic vibrations of marine oils separation system is presented. Vibrations analysis allows for the extraction of representative features that could be related to the lifetime of their pieces. Actual measurements were carried out on these systems on Ro-Pax vessels to transport passengers and freight. The vibrations obtained were processed in the frequency domain and following this, they were used in a Genetic Neuro-Fuzzy System in order to design new predictive maintenance strategies. The obtained results show that these techniques as a promising strategy can be utilized to determine incipient faults.This work has been supported by the Spanish Government [MAQ-STATUS DPI2015-69325-C2] and [DPI2015-69 1808271602] of Ministerio de Economía y Competitividad and with European Funds of Regional Development (FEDER)

Arquitectura e inteligencia computacional embebida para la supervisión de procesos: Aplicación a un proceso de microfabricación

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 25 de mayo de 201

Future research directions in the machining of Inconel 718

Inconel 718 is the most popular nickel-based superalloy, extensively used in aerospace, automotive and energy industries owing to its extraordinary thermomechanical properties. It is also notoriously a difficult-to-cut material, due to its short tool life and low productivity in machining operations. Despite significant progress in cutting tool technologies, the machining of Inconel 718 is still considered a grand challenge.This paper provides a comprehensive review of recent advances in machining Inconel 718. The progress in cutting tools’ materials, coatings, geometries and surface texturing for machining Inconel 718 is reviewed. The investigation is focused on the most adopted tool materials for machining of Inconel 718, namely Cubic Boron Nitrides (CBNs), ceramics and coated carbides. The thermal conductivity of cutting tool materials has been identified as a major parameter of interest. Process control, based on sensor data for monitoring the machining of Inconel 718 alloy and detecting surface anomalies and tool wear are reviewed and discussed. This has been identified as the major step towards realising real-time control for machining safety critical Inconel 718 components. Recent advances in various processes, e.g. turning, milling and drilling for machining Inconel 718 are investigated and discussed. Recent studies related to machining additively manufactured Inconel 718 are also discussed and compared with the wrought alloy. Finally, the state of current research is established, and future research directions proposed.<br/

Design a CPW antenna on rubber substrate for multiband applications

This paper presents a compact CPW monopole antenna on rubber substrate for multiband applications. The multi band applications (2.45 and 3.65 GHz) is achieved on this antenna design with better antenna performances. Specially this antenna focused on ISM band application meanwhile some of slots (S1, S2, S3) have been used and attained another frequency band at 3.65 GHz for WiMAX application. The achievement of the antenna outcomes from this design that the bandwidth of 520 MHz for first band, the second band was 76 MHz for WiMAX application and the radiation efficiency attained around 90%. Moreover, the realized gain was at 4.27 dBi which overcome the most of existing design on that field. CST microwave studio has been used for antenna simulation

An intelligent approach for multi-response optimization: a case study of non-traditional machining process

The present work proposes an intelligent approach to solve multi-response optimization problem in electrical discharge machining of AISI D2 using response surface methodology (RSM) combined with optimization techniques. Four process parameters (factors) such as discharge current (Ip), pulse-on-time (Ton), duty factor (τ) and flushing pressure (Fp) and four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component are considered in this study. A Box-Behnken RSM design is used to collect experimental data and develop empirical models relating input parameters and responses. Genetic algorithm (GA), an efficient search technique, is used to obtain the optimal setting for desired responses. It is to be noted that there is no single optimal setting which will produce best performance satisfying all the responses. In industries, to solve such problems, managers frequently depend on their past experience and judgement. Human intervention causes uncertainties present in the decision making process gleaned into solution methodology resulting in inferior solutions. Fuzzy inference system has been a viable option to address multiple response problems considering uncertainties and impreciseness caused during judgement process and experimental data collection. However, choosing right kind of membership functions and development of fuzzy rule base happen to be cumbersome job for the managers. To address this issue, a methodology based on combined neuro-fuzzy system and particle swarm optimization (PSO) is adopted to optimize multiple responses simultaneously. To avoid the conflicting nature of responses, they are first converted to signal-to-noise (S/N) ratio and then normalized. The proposed neuro-fuzzy approach is used to convert the responses into a single equivalent response known as Multi-response Performance Characteristic Index (MPCI). The effect of parameters on MPCI values has been studied in detail and a process model has been developed. Finally, optimal parameter setting is obtained by particle swarm optimization technique. The optimal setting so generated that satisfy all the responses may not be the best one due to aggregation of responses into a single response during neuro-fuzzy stage. In this direction, a multi-objective optimization based on non-dominated sorting genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters. The proposed optimal settings are validated using thermal-modeling of finite element analysis