Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075

Received 06 October 2021; revised 23 August 2022; accepted 24 August 2022 Mould preparation is an important phase in the injection moulding process. The surface roughness of the mould affects the surface finish of the final plastic product. Quality product with a better production rate is required to meet the competition in the present market. To achieve this objective, manufacturers try to select the best combination of parameters. Multi-objective optimization is one such technique to obtain the optimal process parameters that give better quality with a good production rate. The current paper describes the application of Multi-Objective Genetic Algorithms (MOGA) on the Artificial Neural Network (ANN) model for pocket milling on AA7075. Through the application of ANN with MOGA minimum Surface Roughness (SR) is achieved with a better Material Removal Rate (MRR). From the confirmation experiments, it is evident that follow-periphery tool path gives a better surface finish with higher MRR and the percentage error observed is 1.9553 and 1.8282 respectively

Prilagodljivi neuro-fazi model za predviđanje tehnoloških parametara

The main goal of each technologist is the prediction of technological parameters by fulfilling the set design and technological demands. The work of the technologist is made easier by acquired knowledge and previous experience. A plan of input-output data was made by using the hybrid system of modelling ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the results of seam tube production. This plan is the prerequisite for generating the system of fuzzy logic. The generated system can be used to estimate the output (speed of polishing) based on the given input (external tube diameter, oval shaping of the tube after the first phase of production, gradation of belts for grinding or polishing, condition of belts - time of usage, pressure of belts).The more precise predictions of technological time provided by the model supplement the previously defined manufacturing operations, replace the predictions based on the technologists\u27 experience and form the basis on which to plan production and control delivery times. The work of technologists is thus made easier and the production preparation technological time shorter.Procijeniti tehnološke parametre na način da se ispune postavljeni konstrukcijski i tehnološki zahtjevi cilj je i želja svakog tehnologa. Procjenu tehnologu mogu olakšati prikupljena znanja i ranije stečena iskustva. Na temelju sustavno prikupljenih podataka iz proizvodnje šavnih cijevi u radu je primjenom hibridnog sustava za modeliranje ANFIS (Adaptive Neuro-Fuzzy Inference System) oblikovan plan ulazno/izlaznih podataka. Taj je plan pretpostavka za generiranje sustava neizrazitog zaključivanja. Generirani sustav ima mogućnost procjene izlaza (brzine poliranja) na temelju danih ulaza (vanjski promjer cijevi, ovalnost cijevi nakon prve faze proizvodnje, gradacija remenja za brušenje ili poliranje, stanje remenja - vrijeme uporabe remenja, pritisak remenja). Točnije procjene tehnološkog vremena koje daje model upotpunjavaju prethodno definirane tehnološke operacije, zamjenjuje iskustvene procjene tehnologa i predstavljaju osnovu za planiranje proizvodnje i kontrolu rokova isporuke. Na ovaj se način olakšava rad tehnologa i skraćuje vrijeme tehnološke pripreme proizvodnje

Strategies in 3 and 5-axis abrasive water jet machining of titanium alloys

L'alliage de titane est généralement utilisé pour les pièces structurelles aéronautiques ayant une taille importante et ainsi que des parois minces tout en devant résister à des efforts considérables. L'usinage de ces pièces est difficile avec les méthodes classiques telles que le fraisage, car les forces de coupe sont élevées et les parois minces peuvent être facilement déformées. L'usinage de l'alliage de titane (Ti6Al4V) par un procédé utilisant un jet d'eau abrasif (AWJ) peut potentiellement être utilisé pour remplacer les méthodes d'usinage conventionnelles. Cependant, la compréhension des différents aspects de ce procédé est insuffisante pour autoriser son industrialisation. Cette thèse présente un modèle de prévision de la profondeur usinée dans deux cas de direction du jet : un jet perpendiculaire à la surface de la pièce et un jet incliné. Dans un premier temps, la compréhension du processus d'enlèvement de matière et de la qualité de surface obtenue est étudiée à travers l'observation de l'influence des paramètres du processus. Dans un second temps, un modèle basé sur la distribution gaussienne des particules abrasives dans le jet d'eau est proposé pour caractériser un passage élémentaire et pour prédire le profil du fond de poche obtenu par une succession de passages élémentaires. Ensuite, une méthodologie d'usinage des coins de poche utilisant un contrôle adaptatif de la vitesse d'avance est présentée. Enfin un nouveau modèle du profil du fond de poche prenant en compte l'angle d'inclinaison du jet est présenté. Tout au long de ce travail de thèse, la validation expérimentale a montré un bon accord entre les valeurs mesurées et modélisées et a ainsi démontré la capacité du jet d'eau abrasif à usiner à une profondeur contrôlée.Titanium alloy is generally used for aeronautical structural parts having a large size and as thin walls while having to withstand considerable effort. Machining these parts is difficult with conventional methods such as milling, because the high cutting forces can easily deform the part. Machining of titanium alloy (Ti6Al4V) by an abrasive water jet (AWJ) process can potentially be used to replace conventional machining methods. However, the understanding of the different aspects of this process is insufficient to allow its industrialization. This thesis presents a model of prediction of the machined depth in two cases of direction of the jet: a jet perpendicular to the surface of the part and an inclined jet. At first, the understanding of the removal material process and the obtained surface quality is studied through the observation of the influence of the process parameters. In a second step, a model based on the Gaussian distribution of abrasive particles in the water jet is proposed to characterize an elementary pass and to predict the pocket bottom profile obtained by a succession of elementary passes. Then, a method to machine pocket corners using an adaptive control of the feed rate is presented. Finally, a new model of the pocket bottom profile taking into account the angle of inclination of the jet is presented. Throughout this thesis work, the experimental validation showed a good agreement between the measured and modeled values and thus demonstrated the ability of the abrasive water jet milling to machine to a controlled depth

Prediction of Surface Roughness When End Milling Ti6Al4V

Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end milling Ti6Al4V alloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing of ANFIS models, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability of ANFIS in modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results

Selection of micromilling conditions for improved productivity and part quality

Micromilling process has a rising demand in recent years where the production industry is becoming more competitive with the advancing technology. In hi-tech industries such as aerospace, biomedical, electronics, the increasing demand for high precise micro products with complex geometries create necessity to improve micro machining processes for more accurate, repeatable, and efficient production. Micro end milling process is relatively a new developing research area. It varies from conventional milling with its unique cutting dynamics due to the geometrical size reduction. In terms of the both micro tool and the workpiece, size reduction brings new difficulties to the process. The ratio of hone radius to uncut chip thickness creates a great difference between micro and conventional milling processes and precludes the application of conventional milling models to micromilling process. By the nature of the micromilling and its industries of usage, high accuracy and surface quality is expected in micro products. This makes the surface roughness and burr formation critical for micromilling process. On the other hand, since micro tools with very small radii are more fragile than conventional milling tools, workpiece-tool contact and cutting forces have also a remarkable importance. Due to the same reason, tool costs and tool life are another significant point. All of these conditions make pre analysis and the parameter selection of micro end milling process essential for micromilling. The main aim of this research is to determine micromilling parameters and conditions for improved productivity and part quality by considering multiple constraints and objectives at a time, unlike the previous studies on micro end milling process optimization which are limited and focus to optimize one objective at a time. The objectives in this study are the production time and production cost. Production time involves the actual cutting time, which involves the calculation of material removal rate (MRR), tool idling and changing time and time spent without any cutting. In production cost, tool costs, which is related with the tool life, machine idling costs, labour and overhead costs are included. Production time and cost are minimized respecting certain values of cutting forces, burr size and surface quality constraints. The effects of parameters; cutting speed, feed rate, and depth of cut on these objectives and constraints were investigated. For the first time, parameter selection in micro end milling process is done through multi-objective optimization using Particle Swarm Optimization method. Optimal process parameters are proposed for minimum process cost and minimum process time

Integrated process planning and scheduling for common prismatic parts in a 5-axis CNC environment

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A Study On Parametric Appraisal of Fused Deposition Modelling (FDM) Process

The manufacturing industries are contemplating to develop new technologies for production of complex end use parts possessing high strength and low product development cycle in order to meet the global competition. Rapid prototyping (RP) is one of the proficient processes having the ability to build complex geometry parts in reasonably less time and material waste. Fused deposition modelling (FDM) is one of the RP processes that can manufacture 3D complex geometry accurately with good mechanical strength and durability. Normally, the FDM process is a parametric dependant process due to its layer-by-layer build mechanism. As FDM build parts are used as end use parts, it is prudent to study the effect of process parameters on the mechanical strength under both static and dynamic loading conditions and wear (sliding) behaviour. In order to investigate the behaviour of build parts in a systematic manner with less number of experimental runs, design of experiment (DOE) approach has been used to save cost and time of experimentation. As the selection of input process parameters influence on build mechanism, the mechanical properties and wear behaviour of FDM build parts change with process parameters. Notably, the raster fill pattern during part building causes FDM build parts to exhibit anisotropic behaviour when subject to loading (static or dynamic). In this research work, an attempt has been made to minimise the anisotropic behaviour through controlling the raster fill pattern during part building by adequate selection of process parameters. Statistical significance of the process parameters is analysed using analysis of variance (ANOVA). Influence of process parameters on performance characteristics like mechanical strength, fatigue life and wear of build part is analysed with the help of surface plots. Internal structure of rasters, failure of rasters, formation of pits and crack are evaluated using scanning electron machine (SEM) micro-graphs. Empirical models have been proposed to relate the performance characteristics with process parameters. Optimal parameter setting has been suggested using a nature inspired metaheuristic firefly algorithm to improve the mechanical strength. Finally, genetic programming (GP) and least square support vector machine (LS-SVM) are adopted to develop predictive models for various performance characteristic

Special Issue of the Manufacturing Engineering Society (MES)

This book derives from the Special Issue of the Manufacturing Engineering Society (MES) that was launched as a Special Issue of the journal Materials. The 48 contributions, published in this book, explore the evolution of traditional manufacturing models toward the new requirements of the Manufacturing Industry 4.0 and present cutting-edge advances in the field of Manufacturing Engineering focusing on additive manufacturing and 3D printing, advances and innovations in manufacturing processes, sustainable and green manufacturing, manufacturing systems (machines, equipment and tooling), metrology and quality in manufacturing, Industry 4.0, product lifecycle management (PLM) technologies, and production planning and risks