Casting Process Improvement by the Application of Artificial Intelligence

On the way to building smart factories as the vision of Industry 4.0, the casting process stands out as a specific manufacturing process due to its diversity and complexity. One of the segments of smart foundry design is the application of artificial intelligence in the improvement of the casting process. This paper presents an overview of the conducted research studies, which deal with the application of artificial intelligence in the improvement of the casting process. In the review, 37 studies were analyzed over the last 15 years, with a clear indication of the type of casting process, the field of application of artificial intelligence techniques, and the benefits that artificial intelligence brought. The goals of this paper are to bring to attention the great possibilities of the application of artificial intelligence for the improvement of manufacturing processes in foundries, and to encourage new ideas among researchers and engineers

Optimisation of the squeeze forming process.

This thesis presents the optimisation of the squeeze forming process, considering both the thermal and mechanical aspects. The Finite Element Method has been used to simulate the process and a Genetic Algorithm was used as an optimisation tool. The thermal optimisation has been applied to the squeeze forming process to achieve near simultaneous solidification in the cast part. The positions of the coolant channels were considered as design variables in order to achieve such an objective. The formulation of the objective functions involved two points and also considered the whole domain. The validation aspects of the optimisation of the casting processes for 2D and axi-symmetric problems were presented. The influence of the interfacial heat transfer coefficient related to optimisation of the process was explored. For the multi-objective optimisation problem, the objective was to achieve near simultaneous solidification in the cast part and also near uniform von Mises stress distribution in the die for the first and also tenth cycles. This is because it has been found that the process starts to reach cyclic stabilisation after the tenth cycle. The comparison between the design obtained from the practical solution derived from the optimisation process and also the design which has been applied in industry was also discussed. The Design Sensitivity Analysis and Design Element Concept have been applied to the squeeze forming process. For parameter sensitivity analysis, the Youngs Modulus was considered as a design variable. A few design element subdivisions have been employed to explore its application to the process. For shape sensitivities involving the coolant channels, the parameterisation was required in order to consider the coolant channel as an entity. The extent to which the tendency to move the coolant channel either in the X or Y-direction with respect to the particular von Mises stress constraint in the die was also discussed

Approximation and Visualization of Pareto Frontier in the Framework of Classical Approach to Multi-Objective Optimization

This paper is devoted to a Pareto frontier generation technique, which is aimed at subsequent visualization of the Pareto frontier in an interaction with the user. This technique known as the Interactive Decision Maps technique was initiated about 30 years ago. Now it is applied for decision support in both convex and non-convex decision problems in various fields, from machinery design to environmental planning. The number of conflicting criteria explored with the help of the Interactive Decision Maps technique is usually between three and seven, but some users manage to apply the technique in the case of a larger number of criteria. Here we outline the main ideas of the technique, concentrating at nonlinear problems

Near net shape manufacturing of metal : a review of approaches and their evolutions

In the last thirty years the concept of manufacturability has been applied to many different processes in numerous industries. This has resulted in the emergence of several different "Design for Manufacturing" methodologies which have in common the aim of reducing productions costs through the application of general manufacturing rules. Near net shape technologies have expanded these concepts, targeting mainly primary shaping process, such as casting or forging. The desired outcomes of manufacturability analysis for near-net-shape (NNS) processes are cost and lead/time reduction through minimization of process steps (in particular cutting and finishing operations) and raw material saving. Product quality improvement, variability reduction and component design functionality enhancement are also achievable through NNS optimization. Process parameters, product design and material selection are the changing variables in a manufacturing chain that interact in complex, non-linear ways. Consequently modeling and simulation play important roles in the investigation of alternative approaches. However defining the manufacturing capability of different processes is also a “moving target” because the various NNS technologies are constantly improving and evolving so there is challenge in accurately reflecting their requirements and capabilities. In the last decade, for example, CAD, CNC technologies and innovation in materials have impacted enormously on the development of NNS technologies. This paper reviews the different methods reported for NNS manufacturability assessment and examines how they can make an impact on cost, quality and process variability in the context of a specific production volume. The discussion identifies a lack of structured approaches, poor connection with process optimization methodologies and a lack of empirical models as gaps in the reported approaches

Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes

This paper reports an intelligent approach for modeling and optimisation of filling system design (FSD) in the case of sand casting process of aluminium alloy. In order to achieve this purpose, physics-based process modeling using finite element method (FEM) has been integrated with artificial neural networks (ANN) and genetic algorithm (GA) soft computing techniques. A three dimensional FE model of the studied process has been developed and validated, using experimental literature data, to predict two melt flow behaviour (MFB) indexes named ingate velocity and jet high. Two feed-forward back-propagation ANN-based process models were developed and optimised to establish the relationship between the FSD input parameters and each studied MFB index. Both ANN models were trained, tested and tuned by using database generated from FE computations. It was found that both ANN models could independently predict, with a high accuracy, the values of the ingate velocity and the jet high for training and test data. The developed ANN models were coupled with an evolutionary GA to select the optimal FSD for each one. The validity of the found solutions was tested by comparing ANN-GA prediction with FE computation for both studied MFB indexes. It was found that error between predicted and simulated values does not exceed 5.61% and 6.31% respectively for the ingate velocity and the jet high, which proves that the proposed approach is reliable and robust for FSD optimisation

Optimización de parámetros en procesos de fundición a presión y compresión mediante el algoritmo de enjambre de partículas

In this article, an algorithm, based on the particle swarm technique (PSO), is developed to optimize die casting and compression casting processes, using mathematical models to describe the behavior of both processes. In compression casting the mathematical model describes a problem with multiple objectives and constraints, and in die casting the model describes a single objective problem with constraints. The development of the PSO algorithm was carried out with the FORTRAN 90 software, and the results were compared with those reported by a teaching-learning based optimization algorithm, (TLBO), demonstrating good capabilities in the optimization of parameters in die casting and by compression. It was observed that the PSO algorithm achieves an optimal solution in all processes and the computational time were minimal.En este artículo, se desarrolló un algoritmo, basado en la técnica de enjambre de partículas (Siglas en inglés - PSO) para optimizar los procesos de fundición a presión y por compresión, utilizando modelos matemáticos para describir el comportamiento de ambos procesos. En la fundición por compresión el modelo matemático describe un problema con múltiples objetivos y restricciones, y en la fundición a presión el modelo describe un problema de un solo objetivo con restricciones. El desarrollo del algoritmo PSO se realizó con el software FORTRAN 90, y los resultados se compararon con los obtenidos usando un algoritmo de optimización basado en el proceso de enseñanza-aprendizaje (Siglas en inglés - TLBO), demostrando buenas capacidades en la optimización de parámetros en fundición a presión y por compresión. Se observó que con el algoritmo PSO se consigue una solución óptima en todos los procesos y los tiempos computacionales fueron mínimos

Effective utilization of optimization algorithms on machining operations

155-168Optimization is ruling the entire field of machining and material processing technology from the time at which the revolution took place in machining processes. Various types of machining algorithms have been developed so far for optimizing the independent control factors in order to get the improved results of desirable output responses. Each algorithm has its own special features which made them useful in deriving the optimized solutions under different conditions. Suitable algorithm is chosen for the required case depending upon the nature of the problem, the requirement of the order of precision and the availability of optimization tools. In this review article, the general flow of few important algorithms has been explained in a simpler manner to be understood by the recent researchers. Also, required numbers of case studies for each algorithm have been provided extensively. This consolidated work will surely be helpful for the new researchers those who have entered into the domain of optimization