Modelling of manufacturing processes by a computational intelligence approach
Date of Issue2015
School of Mechanical and Aerospace Engineering
Centre for Advanced Numerical Engineering Simulations
Modelling is a term widely used in System Identification (SI), which is referred to as the art and science of building mathematical models of systems using some measured data. The systems of interest in this thesis are additive manufacturing processes such as fused deposition modelling, machining processes such as turning, and finishing processes such as vibratory finishing. These processes comprise multiple input and output variables, making their operating mechanisms complex. In addition, it can be costly to obtain the process data and therefore there is a strong need for effective and efficient ways of modelling these systems. The models developed for a system can help to reveal hidden information such as the dominant input variables and their appropriate settings for operating the system in an optimal way. The models formulated must not only predict the values of output variables accurately on the testing samples but should also be able to capture the dynamics of the systems. This is known as a generalization problem in modelling. The generalization of data obtained from manufacturing systems is a capability highly demanded by the industry. Several modelling methods and types of models were studied by classifying SI in different ways, such as (1) black box, grey box and white box, (2) parametric and non-parametric, and (3) linear SI, non-linear SI and evolutionary SI. A study of the literature also reveals that extensive focus has been paid to computational intelligence (CI) methods such as genetic programming (GP), M5ʹ, adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN), support vector regression (SVR), etc. for modelling the output variables of the systems because of their ability to formulate the models based only on data obtained from the system. It was also learned that by embedding the features of several methods from different fields of SI into a given method, it is possible to improve its generalization ability. Popular variants of GP such as multi-gene genetic programming (MGGP), which evolves the model structure and its coefficients automatically, has been applied extensively. However, the full potential of MGGP has not been achieved due to some shortcomings leading to its poor generalization ability. In the present work, four variants/methods of MGGP are proposed to counter the four shortcomings identified, namely (1) inappropriate procedure of formulation of the MGGP model, (2) inappropriate complexity measure of the MGGP model, (3) difficulty in model selection, and (4) ensuring greater trustworthiness of prediction ability of the model on unseen samples. A robust CI approach was also developed by applying these four variants of MGGP and the M5ʹ method in parallel. These methods are applied in modelling of output variables of various manufacturing systems such as turning, fused deposition modelling and vibratory finishing process. The performance is compared to those of the other methods such as MGGP, SVR, ANFIS and ANN. The statistical comparison conducted reveals that the generalization ability achieved from the four variants of MGGP and robust CI approach is better than those of the other methods. Furthermore, the sensitivity and parametric analysis conducted validates the robustness of the proposed models by unveiling the dominant input variables and hidden non-linear relationships.
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence