A crucial issue in the design of aircraft components is the evaluation of a larger number of potential design alternatives. This evaluation involves too expensive procedures, consequently, it slows down the search for optimal design samples. As a result, scarce or small number of design samples with high dimensional parameter space and high non-linearity pose issues in learning of surrogate models. Furthermore, surrogate models have more issues in handling qualitative data (discrete) than in handling quantitative data (continuous). These issues bring the need for investigations of methods of surrogate modelling for the most effective use of available data. 

 The thesis goal is to support engineers in the early design phase of development of new aircraft engines, specifically, a component of the engine known as Turbine Rear Structure (TRS). For this, tree-based approaches are explored for surrogate modelling for the purpose of exploration of larger search spaces and for speeding up the evaluations of design alternatives. First, we have investigated the performance of tree models on the design concepts of TRS. Second, we have presented an approach to explore design space using tree models, Random Forests. This approach includes hyperparameter tuning, extraction of parameters importance and if-then rules from surrogate models for a better understanding of the design problem. With this presented approach, we have shown that the performance of tree models improved by hyperparameter tuning when using design concepts data of TRS. Third, we performed sensitivity analysis to study the thermal variations on TRS and hence support robust design using tree models. Furthermore, the performance of tree models has been evaluated on mathematical linear and non-linear functions. The results of this study have shown that tree models fit well on non-linear functions. Last, we have shown how tree models support integration of value and sustainability parameters data (quantitative and qualitative data) together with TRS design concepts data in order to assess these parameters impact on the product life cycle in the early design phase.

A crucial issue in the design of aircraft components is the evaluation of a large number of potential design alternatives.  This evaluation involves too expensive procedures, consequently, it slows down the search for optimal design samples.  As a result, a scarce or small number of design samples with high dimensional parameter space will pose issues in the learning of surrogate models. These issues bring the need to investigate methods for surrogate modelling for the most effective use of available data. Furthermore, during the manufacturing of components, it is crucial to monitor (in-situ process monitoring) the welding process for quality assurance. A large amount of process data is generated from these in-situ monitoring methods, which can be used to build prediction models for defects classification. However, the process data are unstructured, and defects are unknown, which brings the need for investigations to address these issues to build defect classification models. 

 The thesis goal is to support engineers in the early design and manufacturing phases of aircraft engine components via (1) surrogate modelling for the purpose of exploration of larger search spaces and for speeding up the evaluation of design configurations, and (2) defects classification to support in-situ process monitoring to speed up defects' analysis. 

The first part of the thesis focuses on addressing challenges in design data when building surrogate models. For this, the thesis explores, evaluates, and improves tree models for design space exploration. The second part of the thesis focuses on addressing challenges in process data when building defect classification models. For this, the thesis (1) investigates the performance of selected handcrafted feature extraction techniques, (2) proposes an oversampling technique to balance process datasets, and (3) proposes an active learning approach for labelling data.