The additive manufacturing of metals requires optimisation to find the melting conditions that give the desired material properties. A key aspect of the optimisation is minimising the porosity that forms during the melting process. A corresponding analysis of pores of different types (e.g. lack of fusion or keyholes) is therefore desirable. Knowing that pores form under different thermal conditions allows greater insight into the optimisation process. In this work, two pore classification methods were trialled: unsupervised machine learning and defined limits. These methods were applied to 3D pore data from X-ray computed tomography and 2D pore data from micrographs. Data were collected from multiple alloys (Ti-6Al-4V, Inconel 718, Ti-5553 and Haynes 282). Machine learning was found to be the most useful for 3D pore data and defined limits for the 2D pore data; the latter worked by optimising the limits using energy densities.

Tool condition monitoring is critical in ultra-precision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, Deep Learning has been successfully applied in numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel Deep Learning data-driven modeling framework is presented, which includes fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultra-precision machining. The proposed computational framework consists of two main structures. A training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features; and a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultra-precision machining case study with over 96% accuracy, while also outperforms comparable methodologies.

In this paper, an interval type-2 radial basis function neural network (IT2-RBF-NN) is proposed as a new modeling framework. We take advantage of the functional equivalence of radial basis function neural networks (RBF-NNs) to a class of type-1 fuzzy logic systems (T1-FLS) to propose a new interval type-2 equivalent system; it is systematically shown that the type equivalence (between RBF and FLS) of the new modeling structure is maintained in the case of the IT2 system. The new IT2-RBF-NN incorporates interval type-2 fuzzy sets within the radial basis function layer of the neural network in order to account for linguistic uncertainty in the system's variables. The antecedent part in each rule in the IT2-RBF-NN is an interval type-2 fuzzy set, and the consequent part is of Mamdani type with interval weights, which are used for the Karnik and Mendel type-reduction process in the output layer of the network. The structural and parametric optimization of the IT2-RBF-NN parameters is carried out by a hybrid approach that is based on estimating the initial rule base and footprint of uncertainty (FOU) directly via a granular computing approach and an adaptive back propagation approach. The effectiveness of the new modeling framework is assessed in two parts. First, the IT2-RBF-NN is tested against a number of popular benchmark datasets, and second, it is demonstrated in a real-world industrial application that has particular challenges that are related to the uncertainty of the raw information. Via simulation results, it is shown that the proposed modeling framework performs well as compared with its T1 equivalent system. In addition, a very good computational efficiency is demonstrated as a result of the systematic and automatic creation of IT2 linguistic information and the FOU. Crucially, the proposed modeling framework opens up a host of opportunities for the academic community that already uses the popular T1-RBF-NN-based structure to try the new IT2-RBF-NN and take advantage of the numerous existing RBF-based adaptive learning algorithms, RBF-based multiobjective optimization techniques, granular computing-based information capture techniques, and real-world FLS implementations, and, in general, take advantage of the computational efficiency of the fusion of IT2-FLS and RBF-NN.

This paper proposes a new framework for the online monitoring and adaptive control of automation in complex and safety-critical human-machine systems using psychophysiological markers relating to humans under mental stress. The starting point of this framework relates to the assessment of the so-called operator functional state using psychophysiological measures. An adaptive fuzzy model linking heart-rate variability and task load index with the subjects' optimal performance has been elicited and validated offline via a series of experiments involving process control tasks simulated on an automation-enhanced Cabin Air Management System. The elicited model has been used as the basis for an online control system via the predictions of the system performance indicators corresponding to the operator stressful state. These indicators have been used by a fuzzy decision maker to modify the level of automation under which the system may operate. A real-time architecture has been developed as a platform for this approach. It has been validated in a series of human volunteer studies with promising improvement in performance.