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Machine learning is a promising method to model production processes and predict product quality. It is challenging to accurately model complex systems due to data scarcity, as mass customisation leads to various high-variety low-volume products. This study conceptualised knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge embedded in similar entities to address data scarcity. A sequential cross-product KAET (SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing, feature selection (FS), feature learning (FL), and transfer learning (TL). The FS and FL modules conduct knowledge extraction and help address various practical challenges such as changing operating conditions and unbalanced datasets. In this paper, sequential TL is introduced to production modelling to conduct knowledge transfer among multiple entities. The first case study of auxetic material performance prediction demonstrates the effectiveness of sequential TL. Compared with conventional TL, sequential TL can achieve the same test mean square errors with 300 fewer training examples when facing data scarcity. In the second case study, balancing anomaly detection models were constructed for two gas turbines in the same series using real-world production data. With SeqTrans, the F1-score of the anomaly detection model of the data-poor engine was improved from 0.769 to 0.909. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications in AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods are discussed and suggestions on perspectives are given. [ABSTRACT FROM AUTHOR]

Combining different features inspired by biological systems is necessary to obtain uncommon and unique multifunctional biologically inspired conceptual designs. The Expandable Domain Integrated Design (xDID) model is proposed to facilitate the multifunctional concept generation process. The xDID model extends the previously defined Domain Integrated Design (DID) method. The xDID model classifies biological features by their feature characteristics taken from various case-based bio-inspired design examples into their respective geometric designations called domains. The classified biological features are mapped to the respective plant and animal tissues from which they originate. Furthermore, the paper proposes a representation of the functions exhibited by the biological features at the embodiment level as a combination of the integrated structure (multiscale) and the structural strategy associated with the integrated structure. The xDID model is validated using three multifunctional bio-inspired design case studies at the end of the paper. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing (MAM) technology is now changing the pattern of the high-end manufacturing industry, among which MAM fabricated Ti6Al4V has been far the most extensively investigated material and attracts a lot of research interests. This work established a deep neural network (DNN) to investigate the grain boundary in competitive grain growth for a bi-crystal system, the column β grains of Ti6Al4V as an example. Because of the limited number of experimental samples, the DNN is trained based on the data coming from the Geometrical Limited criterion. A series of direct energy deposition experiment using Ti6Al4V is carried out under the Taguchi experimental design. The grain boundary angles between the column grains are measured in the experiment and used to evaluate the accuracy of DNN.