Nikolaos E. Karkalos

Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing

This book chapter pertains to the use of statistical methods and soft computing techniques that can be used in the modelling and optimization of machining processes. More specifically, the factorial design method, Taguchi method, response surface methodology (RSM), analysis of variance, grey relational analysis (GRA), statistical regression methods, artificial neural networks (ANN), fuzzy logic and genetic algorithms are thoroughly examined. As part of the design of experiments (DOE) the aforementioned methods and techniques have proven to be very powerful and reliable tools. Especially in machining, a plethora of works have already been published indicating the importance of these methods.

Molecular Dynamics Study of Abrasive Grain Morphology and Orientation in Nanometric Grinding

A simulation of the material removal by a single abrasive grain in nanometric grinding is presented in this paper. Molecular Dynamics method is used for modeling the diamond grain and the copper workpiece. The Morse potential function is used to simulate the interactions between the atoms involved in the procedure. The abrasive grain follows a trajectory with decreasing depth of cut within the workpiece to simulate the interaction of the grain with the workpiece. The influence of the grain shape, being either square or rectangular, and of the orientation of the grain, where the grain has rake angle 10o, -10o and-20o, are studied. From the analysis it is apparent that both grain morphology and orientation play a significant role on chip formation, grinding forces and temperatures. With the appropriate modifications, the proposed model can be used for the simulation of various nanomachining processes and operations, in which continuum mechanics cannot be applied or experimental techniques are subjected to limitations.

Machining and Machining Modeling of Metal Matrix Composites—A Review

This chapter reviews the most common machining processes used in metal matrix composites (MMCs), such as turning, milling, and drilling. Apart from the difficulties faced in each of these processes in the case of MMCs and some possible solutions, certain other important factors, such as tool wear mechanisms and the final surface quality, are discussed. Furthermore, the machinability of MMCs, in a number of different machining processes, is examined. Tapping, grinding, honing, sawing, and micro-machining are also considered. Additionally, the manufacturing of MMC products through nonconventional machining processes is discussed, as alternatives to the aforementioned processes. Finally, modeling of MMCs and their machining will be examined. The analysis will concentrate on the most popular methods used, namely finite elements method, molecular dynamics, and soft computing techniques.

Correlation Between Process Parameters and Cutting Forces in the Face Milling of Steel

In industrial practice, the production of parts must be conducted with acceptable surface quality, as well as increased dimensional accuracy and short machining time. For the creation of flat surfaces, face milling is a widely accepted method due to the possibility of achieving both high productivity and accuracy. In the present work, the correlation between process parameters and cutting forces is attempted for face milling, through a series of experiments. Experimental work is carried out based on Design of Experiments (DoE) methodology. The results are analyzed by statistical analysis tools and regression formulas correlating forces with process parameters are derived. Determination of optimum process parameters is also conducted with a view to increase process efficiency.

Molecular Dynamics Simulation of Nano-Grinding with Multiple Abrasive Grains

During grinding a large number of micrometer or sub-micrometer grains remove material from the surface of a workpiece, acting as cutting tools. As the grains perform the material removal process, the alterations on the workpiece surface are correlated to the grain characteristics, as well as process parameters. In the case of nano-grinding, only several atomic layers are removed and surface quality of nanometer level is attained. For this process, simulations can be carried out with Molecular Dynamics method, with a view to determine its characteristics. In the present study, the case of peripheral nano-grinding of a copper substrate with multiple abrasive grains is investigated for various depths of cut (namely 0.35, 0.54 and 0.72 nm) and results concerning grinding forces, temperature and workpiece deformation are presented and discussed. Cutting forces, temperature and workpiece deformation was observed to increase between the cases with 0.54 and 0.72 nm depth of cut to a greater extent than between the cases with 0.35 and 0.54 nm depth of cut.

Swarm Intelligence-Based Methods

The term “Swarm Intelligence” refers directly to the collective behavior of a group of animals, which are following very basic rules, or to an Artificial Intelligence approach, which aims at the solution of a problem using algorithms based on collective behavior of social animals. For over three decades, several algorithms based on the observation of the behavior of groups of animals were developed, such as Particle Swarm Optimization, from the observation of flocks of birds. Some of the most established Swarm Intelligence (SI) methods include the Ant Colony Optimization method, the Harmony Search method and the Artificial Bee Colony algorithm.

Other Computational Methods for Optimization

The last chapter of the present work is dedicated to methods that contain a few or no similarities to the methods presented in the two previous chapters but however, is worth mentioning due to their popularity or promising capabilities in the field of industrial engineering. These methods include Simulated Annealing, Tabu Search, Electromagnetism-like Mechanism, and Response Surface Methodology methods. More specifically, Simulated Annealing method is related to the metallurgical process of annealing and its objective function is related to the reduction of the internal energy of the system, by appropriate variation of its temperature. Tabu Search method exhibits essentially no nature-inspired characteristics, as its basic feature is a list of unacceptable moves, which is used to prevent the solution process to get trapped in a local optimum point. Electromagnetism-like Mechanism is using the natural mechanism of attraction-repulsion in electromagnetism, in order to lead the solution process to the global optimum point.

General Aspects of the Application of Computational Methods in Industry 4.0

Since the beginning of the first industrial revolution, engineers were always attempting to resolve problems related to the operation of machinery and their maintenance. They also aimed at the improvement of the efficiency of manufacturing processes and generally at the organization of the production and other relative subjects. As it was anticipated, systematic approaches for the scientific study of industry-related problems were established and the solutions were proposed. However, after the introduction of computers and development of computational methods, a new promising era for solving industry-related problems emerged, as advanced computational techniques were capable of providing approximate but significantly accurate solutions. Especially, when it is desired to increase the efficiency of manufacturing processes by determining the optimum process parameters or when the solution of hard production-based problems, such as scheduling, is required, optimization methods can be employed.

Evolutionary-Based Methods

In the current section, several metaheuristics involving the evolutionary of a population in order to create new generations of genetically superior individuals are presented. These algorithms are usually significantly influenced by the most prominent (and earliest) among them, the Genetic Algorithm (GA). Details about their basic characteristics and function, as well as some important variants, are described and applications in the field of industrial engineering are highlighted. A detailed description of the basic features of the genetic algorithm is presented at the beginning of this chapter and afterwards, other Evolutionary Algorithms (EA) are summarized. In specific, both relatively older and well established, as well as newer but promising methods are included, namely Differential Evolutionary, Memetic Algorithm, Imperialist Competitive Algorithm, Biogeography-Based Optimization algorithm, Teaching-Learning-Based optimization, Sheep Flock Heredity algorithm, Shuffled Frog-Leaping algorithm, and Bacteria Foraging Optimization algorithm.

Accelerated Method of Cutting Tool Quality Estimation During Milling Process of Inconel 718 Alloy

Cutting tool wear is a natural consequence of the engagement of cutting tool and workpiece. This phenomenon can progress in a slower or quicker rate and can be attributed to various reasons, such as abrasion, adhesion, chemical reaction, thermal or mechanical phenomena, depending on machining conditions and material properties of cutting tool and workpiece. As the replacement of worn tools is directly related to the cost of machining processes, it is important to select favorable process parameters in order to avoid high wear rates, especially when machining hard-to-cut materials. The experimental determination of tool wear during machining of various materials is a costly and time-consuming process, as it requires carrying out experiments at several cutting speeds until a tool failure criterion is reached each time. In the present work, a novel method for conducting tool wear experiments at various cutting speeds at a single experiment is proposed and applied to a case of milling an Inconel 718 workpiece. Experiments were performed for three different cutting tools and for cutting speeds in the range of 90–170 m/min, at constant feed rate, axial and radial depth of cut values. The results indicate that the proposed methodology can successfully provide an indication of the performance of various cutting tool types during machining of hard-to-cut materials.