OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives

Multi-objective Optimization of Hard Milling Using Taguchi Based Grey Relational Analysis

The influence of hard coatings and machining parameters, in particular cutting speed, feed per tooth and depth of cut on specific cutting energy, productivity and surface quality in milling of hardened cold work tool steel, were investigated in this paper. Taguchi\u27s design of experiments was employed for planning of experiments using L27 orthogonal array. Optimal setting of machining parameters for multi-objective characteristics was determined using grey relational analysis. The principal component analysis was used to define the corresponding weight factors of each quality characteristics under optimization. Analysis of variance was conducted and it was revealed that feed per tooth is the most significant parameter affecting quality characteristics. Finally, results of confirmation test with the optimal machining parameters settings have shown that the proposed model improves overall performance of hard milling process

Evolutionary approaches to optimisation in rough machining

This thesis concerns the use of Evolutionary Computation to optimise the sequence and selection of tools and machining parameters in rough milling applications. These processes are not automated in current Computer-Aided Manufacturing (CAM) software and this work, undertaken in collaboration with an industrial partner, aims to address this. Related research has mainly approached tool sequence optimisation using only a single tool type, and machining parameter optimisation of a single-tool sequence. In a real world industrial setting, tools with different geometrical profiles are commonly used in combination on rough machining tasks in order to produce components with complex sculptured surfaces. This work introduces a new representation scheme and search operators to support the use of the three most commonly used tool types: end mill, ball nose and toroidal. Using these operators, single-objective metaheuristic algorithms are shown to find near-optimal solutions, while surveying only a small number of tool sequences. For the first time, a multi-objective approach is taken to tool sequence optimisation. The process of ‘multi objectivisation’ is shown to offer two benefits: escaping local optima on deceptive multimodal search spaces and providing a selection of tool sequence alternatives to a machinist. The multi-objective approach is also used to produce a varied set of near-Pareto optimal solutions, offering different trade-offs between total machining time and total tooling costs, simultaneously optimising tool sequences and the cutting speeds of individual tools. A challenge for using computationally expensive CAM software, important for real world machining, is the time cost of evaluations. An asynchronous parallel evolutionary optimisation system is presented that can provide a significant speed up, even in the presence of heterogeneous evaluation times produced by variable length tool sequences. This system uses a distributed network of processors that could be easily and inexpensively implemented on existing commercial hardware, and accessible to even small workshops

Multi-objective Tool Sequence Optimization in 2.5D Pocket CNC Milling for Minimizing Energy Consumption and Machining Cost

Tool sequence selection is an important task for 2.5D pocket milling and has a significant influence on both the energy consumption and machining cost of the final product. In this paper, the influence of tool sequence on energy consumption is firstly analyzed. Then a multi-objective tool sequence optimization model is proposed with the objective of minimizing energy consumption and machining cost and solved by the graph algorithm. Finally, a case study is carried out to validate the proposed model and search for the trade-off solutions between energy consumption and machining cost

Computer aided process planning for multi-axis CNC machining using feature free polygonal CAD models

This dissertation provides new methods for the general area of Computer Aided Process Planning, often referred to as CAPP. It specifically focuses on 3 challenging problems in the area of multi-axis CNC machining process using feature free polygonal CAD models. The first research problem involves a new method for the rapid machining of Multi-Surface Parts. These types of parts typically have different requirements for each surface, for example, surface finish, accuracy, or functionality. The CAPP algorithms developed for this problem ensure the complete rapid machining of multi surface parts by providing better setup orientations to machine each surface. The second research problem is related to a new method for discrete multi-axis CNC machining of part models using feature free polygonal CAD models. This problem specifically considers a generic 3-axis CNC machining process for which CAPP algorithms are developed. These algorithms allow the rapid machining of a wide variety of parts with higher geometric accuracy by enabling access to visible surfaces through the choice of appropriate machine tool configurations (i.e. number of axes). The third research problem addresses challenges with geometric singularities that can occur when 2D slice models are used in process planning. The conversion from CAD to slice model results in the loss of model surface information, the consequence of which could be suboptimal or incorrect process planning. The algorithms developed here facilitate transfer of complete surface geometry information from CAD to slice models. The work of this dissertation will aid in developing the next generation of CAPP tools and result in lower cost and more accurately machined components

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimiza- tion problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumula- tive distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘ closed ’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technolog- ical limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing op- eration; therefore, the economic objectives are minimized un- der imposed maximum probabilities of failure. In both situa- tions, the efficiency and robustness of optimal solution

Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

Thin-wall parts are common in the aeronautical sector. However, their machining presents serious challenges such as vibrations and part deflections. To deal with these challenges, di erent approaches have been followed in recent years. This work presents the state of the art of thin-wall light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the causes of both instability and deformation through analytical models, summarizing the computational techniques used, and presenting the solutions proposed by di erent authors from an industrial point of view. Finally, some further research lines are proposed

Agent collaboration in a multi-agent-system for analysis and optimization of mechanical engineering parts

In mechanical engineering, designers have to review a designed artefact iteratively with different domain experts, e.g. from manufacturing, to avoid later changes and find a robust, optimized design. To support the designer, knowledge-based engineering offers a set of approaches and techniques that formalize and implement engineering knowledge into generic product models or decision support systems. An implementation which satisfies especially the concurrent nature of today's design processes and allow for multi-objective decision-making is multi-agent systems. Such systems consist of entities that are capable of autonomous action, interact intelligently with their environment, communicate and collaborate. In this paper, such a multi-agent system is discussed as extension for a computer-aided design software where the agents take the role of domain experts, like e.g. manufacturing technologists and make suggestions for the optimization of the design of mechanical engineering parts. A focal point is set on the collaboration concept of the single agents. Therefore, the paper proposes the use of an action-item-list as central information and knowledge sharing platform. © 2020 The Authors. Published by Elsevier B.V

Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels

El fresado químico es un proceso diseñado para la reducción de peso de pieles metálicas que, a pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin embargo, el fresado químico es una tecnología contaminante y costosa que tiende a ser sustituida. Gracias a los avances realizados en el mecanizado, la tecnología de fresado convencional permite alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexión de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje empleados. Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través de una revisión bibliográfica que cubre los modelos analíticos, las técnicas computacionales y las soluciones industriales en estudio actualmente. En ella, se aprecia cómo los modelos analíticos y las soluciones computacionales y de simulación se centran principalmente en la predicción off-line de vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez y controles adaptativos apoyados en simulaciones o en la selección estadística de parámetros. Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales. En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sólido rígido en términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicación industrial en la simplificación de los sistemas de amarre o en el incremento de la eficiencia del proceso. Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar fluidos de corte. Una correcta lubricación puede reducir la temperatura del proceso y las tensiones residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados en el uso de líquidos iónicos (IL) y se han comparado con el comportamiento tribológico del par de contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los líquidos iónicos en un tribómetro tipo pin-on-disk demuestran que el IL no halogenado reduce significativamente el desgaste y la fricción entre el aluminio, material a mecanizar, y el carburo de tungsteno, material de la herramienta, eliminando casi toda la adhesión del aluminio sobre el pin, lo que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process has been used since 1950s in the aerospace industry despite its environmental concern. Among its advantages, chemical milling does not induce residual stress and parts meet the required tolerances. However, this process is a pollutant and costly technology. Thanks to the last advances in conventional milling, machining processes can achieve similar quality results meanwhile vibration and part deflection are avoided. Both problems are usually related to the cutting parameters and the workholding. This thesis analyses the causes of the cutting instability and part deformation through a literature review that covers analytical models, computational techniques and industrial solutions. Analytics and computational solutions are mainly focused on chatter and deflection prediction and industrial approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening devices, adaptive control systems based on simulations and the statistical parameters selection, and CAM solutions combined with the use of virtual twins applications. In this literature review, few research works about thin-plates and thin-floors is found so the effect of the cutting parameters is also studied experimentally. These experiments confirm that even using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining. On the one hand, roughness values meet the required tolerances under every set of the tested parameters. On the other hand, a proper parameter selection reduces the cutting forces and process tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify workholding and increase the machine efficiency. Another way to improve the process efficiency is to increase tool life by using cutting fluids. Their use can also decrease the temperature of the process and the induced stresses. For this purpose, different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of the ILs is used, which, therefore, could considerably increase tool life