Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy

Electrical discharge machining (EDM) is a non-traditional machining process widely used to machine geometrically complex and hard materials. In EDM, selection of optimal EDM parameters is important to have high quality products and increase productivity. However, one of the major issues is to obtain better machining performance at optimal value of these machining parameters. Modelling and optimization of EDM parameters have been considered to identify optimal EDM parameters that would lead to better EDM performance. Due to the complexity and uncertainty of the machining process, computational approaches have been implemented to solve the EDM problem. Thus, this study conducted a comprehensive investigation concerning the influence of EDM parameters on material removal rate (MRR), surface roughness (Ra) and dimensional accuracy (DA) through an experimental design. The experiment was performed based on full factorial design of experiment (DOE) with added center points of pulse on time (Ton), pulse off time (Toff), peak current (Ip) and servo voltage (Sv). In the EDM optimization, glowworm swarm optimization (GSO) algorithm was implemented. However, previous works indicated that GSO algorithm has always been trapped in the local optima solution and is slow in convergence. Therefore, this study developed a new hybrid artificial fish and glowworm swarm optimization (AF-GSO) algorithm to overcome the weaknesses of GSO algorithm in order to have a better EDM performance. For the modeling process, four types of regression models, namely multiple linear regression (MLR), two factor interaction (2FI), multiple polynomial regression (MPR) and stepwise regression (SR) were developed. These regression models were implemented in the optimization process as an objective function equation. Analysis of the optimization proved that AF-GSO algorithm has successfully outperformed the standard GSO algorithm. 2FI model of AF-GSO optimization for MRR and DA gave optimal solutions of 0.0042g/min and 0.00129%, respectively. On the other hand, the SR model for Ra of AF-GSO optimization gave the optimal solution of 1.8216p,s. Overall, it can be concluded that AF-GSO algorithm has successfully improved the quality and productivity of the EDM problems

Machining Performance Analysis in End Milling: Predicting Using ANN and a Comparative Optimisation Study of ANN/BB-BC and ANN/PSO

End milling machining performance indicators such as surface roughness, tool wear and machining time are the principally indicators of machine tool industrial productivity, cost and competitiveness. Since accurate predictions and optimisations are necessary for control purposes, new merit-driven approaches are for good results. The aim of this work is two folds: prediction of machining performance for surface roughness, tool wear and machining time with ANN and the optimisation of these performance indicators using the combined models of ANN-BB-BC and ANN-PSO. However, the optimisation platform is hinged on the fuzzy goal programming model, which facilitates comparisons between the performance of the BB-BC and the PSO algorithms. To demonstrate the approach, optimal tool wear and surface roughness were obtained from a fuzzy goal programme, then converted to a bi-objective non-linear programming model, and solved with the BB-BC and the PSO algorithms. The outputs of the artificial neural network (ANN) were integrated with the optimisation models. The effectiveness of the method was ascertained using extensive literature data. Thus, prediction and optimisation of complex end milling parameters was attained using appropriate selection of parameters with high quality outputs, enhanced by precise prediction and optimisation tools in this proposed approach

The selected laser melting production and subsequent post-processing of Ti-6Al-4V prosthetic acetabular

&nbsp;Processing and post processing of human prosthetic acetabular cup by using 3D printing. The results showed using 3D printers leads to fabrication customized implants with higher quality.<br /

Modeling and optimization of turn-milling processes for cutting parameter selection

Turn-milling is a relatively new machining process technology offering important advantages such as increased productivity, reduced tool wear and better surface finish. Because two conventional cutting processes turning and milling are combined in turn-milling, there are many parameters that affect the process making their optimal selection challenging. Optimization studies performed on turn-milling processes are very limited and consider one objective at a time. In this work, orthogonal turn-milling is considered where spindle and work rotational speeds, cutter (tool-work axes) offset, depth of cut and feed per revolution are selected as process parameters. The effects of each parameter on tool wear, surface roughness, circularity, cusp height, material removal rate (MRR) and cutting forces were investigated through process model based simulations and experiments carried out on a multi-tasking CNC machine tool. Tool life and surface roughness are formulated including cutter offset for the first time in this present work. Also, for the first time, turn-milling process is defined as a multi-objective problem and an effective method is proposed to handle this optimization problem. Minimum surface error, minimum production cost and minimum production time are aimed at the same time, and results are generated for selection of optimal cutting process parameters. After optimal parameter sets are found, they are compared with the parameters proposed by tool suppliers in machining tests. In addition, orthogonal turn-milling process is compared with conventional turning process comprehensively in order to demonstrate the process advantages

Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization

Carbon Fiber Reinforced Polymer (CFRP) composites are characterized by their excellent mechanical properties (high specific strength and stiffness, light weight, high damping capacity etc.) as compared to conventional metals, which results in their increased utilization especially for aircraft and aerospace applications, automotive, defense as well as sporting industries. With increasing applications of CFRP composites, determining economical techniques of production is very important. However, as compared to conventional metals, machining behavior of composites is somewhat different. This is mainly because these materials behave extremely abrasive during machining operations. Machining of CFRP appears difficult due to their material discontinuity, inhomogeneity and anisotropic nature. Moreover, the machining behavior of composites largely depends on the fiber form, the fiber content, fiber orientations of composites and the variability of matrix material. Difficulties are faced during machining of composites due to occurrence of various modes of damages like fiber breakage, matrix cracking, fiber–matrix debonding and delamination. Hence, adequate knowledge and in-depth understanding of the process behavior is indeed necessary to identify the most favorable machining environment in view of various requirements of process performance yields. In this context, present work attempts to investigate aspects of machining performance optimization during machining (turning and drilling) of CFRP composites. In case of turning experiments, the following parameters viz. cutting force, Material Removal Rate (MRR), roughness average (Ra) and maximum tool-tip temperature generated during machining have been considered as process output responses. In case of drilling, the following process performance features viz. load (thrust), torque, roughness average (of the drilled hole) and delamination factor (entry and exit both) have been considered. Attempt has been made to determine the optimal machining parameters setting that can simultaneously satisfy aforesaid response features up to the desired extent. Using Fuzzy Inference System (FIS), multiple response features have been aggregated to obtain an equivalent single performance index called Multi-Performance Characteristic Index (MPCI). A nonlinear regression model has been established in which MPCI has been represented as a function of the machining parameters under consideration. The aforesaid regression model has been considered as the fitness function, and finally optimized by evolutionary algorithms like Harmony Search (HS), Teaching-Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) etc. However, the limitation of these algorithms is that they assume a continuous search within parametric domain. These algorithms can give global optima; but the predicted optimal setting may not be possible to adjust in the machine/setup. Since, in most of the machines/setups, provision is given only to adjust factors (process input parameters) at some discrete levels. On the contrary, Taguchi method is based on discrete search philosophy in which predicted optimal setting can easily be achieved in reality.However, Taguchi method fails to solve multi-response optimization problems. Another important aspect that comes into picture while dealing with multi-response optimization problems is the existence of response correlation. Existing Taguchi based integrated optimization approaches (grey-Taguchi, utility-Taguchi, desirability function based Taguchi, TOPSIS, MOORA etc.) may provide erroneous outcome unless response correlation is eliminated. To get rid of that, the present work proposes a PCA-FuzzyTaguchi integrated optimization approach for correlated multi-response optimization in the context of machining CFRP composites. Application potential of aforementioned approach has been compared over various evolutionary algorithms

New Trends in 3D Printing

A quarter century period of the 3D printing technology development affords ground for speaking about new realities or the formation of a new technological system of digital manufacture and partnership. The up-to-date 3D printing is at the top of its own overrated expectations. So the development of scalable, high-speed methods of the material 3D printing aimed to increase the productivity and operating volume of the 3D printing machines requires new original decisions. It is necessary to study the 3D printing applicability for manufacturing of the materials with multilevel hierarchical functionality on nano-, micro- and meso-scales that can find applications for medical, aerospace and/or automotive industries. Some of the above-mentioned problems and new trends are considered in this book

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

Miniaturised experimental simulation of ingot-to-billet conversion

Ingot-to-billet conversion processing, one process of which is known as “cogging”, is an important production step in high-value metallurgical manufacturing. It is necessary to homogenise and refine the microstructure of high-performance alloys before they proceed to subsequent processing stages. Despite its importance, the process is still not very well understood for many modern advanced alloys and few published studies exist. The limited knowledge of the deformation and microstructure evolution leads to difficulties in achieving the desired accuracy in microstructural control. Traditional uni-axial testing is not fully representative of the forging processes seen in industry, and does not capture different elements of open-die forging parameters. Given significant costs of large multi-tonne workpiece ingots and the difficulties with their non-destructive evaluation, it is crucial to develop a laboratory-scale evaluation for the cogging process so that scrapping and re-processing can be avoided. The “Micro Future Forge” has been developed as a reproducible laboratory-scale experimental method for exploring the various thermo-mechanical process mechanisms of hot open die forging. This novel methodology employs a purpose-built apparatus, that has been designed to be cost-effective and portable. The test set-up uses a remotely operated manipulator assembly constructed predominantly from standard off-the-shelf components in conjunction with a conventional uni-axial load frame. This combination allows for high operational scalability. Multi-directional open-die forging (cogging) of single and dual-phase alloys has been successfully accomplished using the described apparatus, demonstrating an ability to attain the desired beneficial refinement of the microstructure. Application of this experimental approach provides precisely controlled conditions and allows high research specimen throughput to discover new insights into the structural transformations that occur in industry-scale forgings, while offering savings in energy, material, time and capital investment. The obtained experimental data can be used for thermo-mechanical process optimisation of high-performance alloys, guiding larger scale testing and manufacturing trials (e.g., AFRC Catapult Future Forge), as well as informing the development of digital-twins for various high-value metallurgical manufacturing processes.Ingot-to-billet conversion processing, one process of which is known as “cogging”, is an important production step in high-value metallurgical manufacturing. It is necessary to homogenise and refine the microstructure of high-performance alloys before they proceed to subsequent processing stages. Despite its importance, the process is still not very well understood for many modern advanced alloys and few published studies exist. The limited knowledge of the deformation and microstructure evolution leads to difficulties in achieving the desired accuracy in microstructural control. Traditional uni-axial testing is not fully representative of the forging processes seen in industry, and does not capture different elements of open-die forging parameters. Given significant costs of large multi-tonne workpiece ingots and the difficulties with their non-destructive evaluation, it is crucial to develop a laboratory-scale evaluation for the cogging process so that scrapping and re-processing can be avoided. The “Micro Future Forge” has been developed as a reproducible laboratory-scale experimental method for exploring the various thermo-mechanical process mechanisms of hot open die forging. This novel methodology employs a purpose-built apparatus, that has been designed to be cost-effective and portable. The test set-up uses a remotely operated manipulator assembly constructed predominantly from standard off-the-shelf components in conjunction with a conventional uni-axial load frame. This combination allows for high operational scalability. Multi-directional open-die forging (cogging) of single and dual-phase alloys has been successfully accomplished using the described apparatus, demonstrating an ability to attain the desired beneficial refinement of the microstructure. Application of this experimental approach provides precisely controlled conditions and allows high research specimen throughput to discover new insights into the structural transformations that occur in industry-scale forgings, while offering savings in energy, material, time and capital investment. The obtained experimental data can be used for thermo-mechanical process optimisation of high-performance alloys, guiding larger scale testing and manufacturing trials (e.g., AFRC Catapult Future Forge), as well as informing the development of digital-twins for various high-value metallurgical manufacturing processes

Micromechanical Modelling of Damage Healing in Free Cutting Steel

Continuous casting is used to solidify most of the steel produced in the world every year. The process reduces the number of required milling stages and results in qualitative semi-finished products such as billets, blooms and slabs which will later be rolled into more specific shapes. Extending the range of finished product sizes produced from a given concast bloom or billet section is often limited by the minimum area reduction required to ensure effective consolidation and final mechanical properties. Predicting effective consolidation or level of remnant porosity has always been an important issue for steel producers as it will affect the mechanical properties of final products (strength, ductility, etc.). It is known that partial or complete recovery of strength in such porous materials can be obtained by pore closure and diffusive healing processes at elevated temperature. Devising an appropriate healing process which does not cause discontinuity in the microstructure and mechanical properties at the healing sites and prevents distortion of the component during bonding requires an accurate choice of thermo-mechanical processing parameters. Although there has been considerable work on materials such as titanium alloys, aluminium alloys and copper, damage healing in free cutting steel has not received much attention. The main aim of this research is to develop a realistic damage healing computational approach that can predict damage healing or recovery during soaking under different compressive stress levels, and be used for hot rolling applications. This study investigates the void elimination process through two stages of void closure and healing. An Abaqus/UMAT subroutine has been developed for the analysis of the material porosity elimination process including two stages of void closure and healing. This study uses the Gurson-Tvergaard model under hydrostatic compression to predict the void closure. A novel approach has been developed in the present work to identify the Gurson-Tvergaard model parameters using a non-gradient based optimisation search method (Pattern Search Method). The healing process is modelled based on a combination of diffusion bonding, creep and plasticity following the Pilling model and can be adapted to any other healing/diffusion bonding model. The material model has been calibrated for free cutting steel and a stress state representative of the rolling process, and used to predict the closure and healing processes under rolling. The effect of parameters such as Roll Gap shape Factor (RGF), initial amount and distribution of void volume fraction on porosity elimination during rolling has also been investigated. An experimental technique has been developed to identify the conditions (temperature, pressure, time) required for void elimination in Free Cutting Steel (FCS). Different combinations of load and time were tested and optimum conditions have been obtained. Tensile tests on the bonded specimens have been carried out to measure the strength of the bonded region. The position of fracture on the specimen and also the cross section of the fracture surface have been inspected. The experimental results have been used to calibrate the developed void elimination model. Using the developed model, predictions of densification and healing can be made for optimisation of the rolling schedule.Open Acces