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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]

The laser-based powder bed fusion (LPBF) process is able to produce complex part geometries. The fast development of the LPBF process offers new opportunities for the industries. Most research done to date has focused on the modeling of the process, which shows that both part geometries and process parameters play an essential role in the result of end-product quality. The definition of the manufacturability of the LPBF is vague. In this review, the focus is set on the manufacturability of the metal-LPBF process. What manufacturability is in the LPBF process and how it is investigated so far are discussed. All process parameters and design constraints for LPBF processes are introduced. The relationship between process parameters and design constraints and how they affect the manufacturability are discussed as well. A detailed discussion on how other researchers evaluate manufacturability analysis of LPBF is conducted. Finally, the manufacturability of LPBF is defined, and future prospects on filling the research gaps on the manufacturability analysis of the LPBF are presented. [ABSTRACT FROM AUTHOR]

Laser powder–based directed energy deposition (DED) is a specific additive manufacturing process that offers an effective way to fabricate parts via simultaneous delivery of powders and laser beam. It has been developing greatly in the recent decades and being widely used for manufacturing, prototyping, and repairing. Complex physical events take place during the manufacturing process and have great impacts on its overall performance. To build high-quality parts through the laser powder–based DED process, its physical insights and process parameters need to be understood and optimized, for which modeling provides an efficient way. This article gives a review of the modeling work for the laser powder–based DED process, in which the models developed for powder stream and its interaction with laser beam, melt-pool, and bulk heating are discussed in detail. Different modeling approaches and methods towards overall and specific physical processes of the laser powder–based DED are analyzed and compared. Suggestions towards the modeling are also given at the end. [ABSTRACT FROM AUTHOR]

Additive manufacturing has become very popular nowadays to create complex geometries that cannot be achieved by any other manufacturing processes. The purpose of this study is to investigate and characterize the electrochemical polishing and chemical etching methods as a post-process treatment to improve the surface finish of lattice structures made from Ti-6Al-4V manufactured by direct metal laser sintering (DMLS). A lattice sample is designed with external and internal struts to compare the influence of the post-processing on different features. The post-processing parameters are studied via the Taguchi method. This work will provide critical information on the surface and geometrical characterization as well as the post-process treatment of a Ti-6Al-4V lattice. The metrological data will be analyzed to determine how the post-process treatment affects the surface finish, considering the final roughness and geometrical integrity of lattice samples. It is found that the material removal rate was more important at the edges, specifically on the external features. It was also confirmed that the average roughness is linked to the material removal for both processes. The more material is removed from the parts, the better the surface finish. It is also found that the etching process helps the electrochemical polishing go deeper inside the geometry. [ABSTRACT FROM AUTHOR]

Lattice structures with different desired physical properties are promising for a broad spectrum of applications. The availability of additive manufacturing (AM) technology has relaxed the fabricating limitation of lattice structures. However, manufacturing constraints still exist for AM-fabricated lattice structures, which have a significant influence on the printing quality and mechanical properties of lattice struts. In this paper, a design and optimization strategy is proposed for lattice structures with the consideration of manufacturability to ensure desired printing quality. The concept of manufacturable element is used to link the design and manufacturing process. A meta-model is constructed by experiments and the artificial neural network to obtain the manufacturing constraints. Sizes of struts are optimized by a bidirectional evolutionary structural optimization-based algorithm with these manufacturing constraints. An arm of quadcopter is redesigned and optimized to validate the proposed method. Its result shows that optimized heterogeneous lattice structures can improve the stiffness of the model compared to the homogeneous lattice structure and the original design. Both the Von-Mises stress and the maximum displacement are reduced without increasing the weight of designed part. And by considering the manufacturability constraints, the optimized design has been successfully fabricated by the selected additive manufacturing process. Note to Practitioners—Lattice structures might fail to be fabricated by the additive manufacturing technique if the designed model exceeds the processability of the machine. Our approach has the capability of considering the manufacturing constraints in the design and optimization process. We conducted experiments to investigate the manufacturability and proposed a method that can give the domain of the design variables for a selected manufacturing process. And we also designed an algorithm that can optimize the lattice structure inside the domain of design variables. It ensures that the lattice model can be successfully fabricated by the selected process and the performance is dramatically increased compared to the original design. Engineers can use our approach to optimize the lattice structure automatically without knowing the knowledge of optimization and manufacturability. [ABSTRACT FROM AUTHOR]

Data-driven applications are penetrating every aspect of Additive Manufacturing (AM) to enable efficient resolution of key existing challenges. One such application is semantic segmentation which automatically quantifies post-process structure data from graphic or 3D computer representations. In industrial settings, these models can expedite the development of new materials or processes. However, Additively Manufactured (AMed) materials offer unique challenges, which require new models to be developed and trained. These challenges include out-of-balance classes (e.g., defects), massive labeling efforts, and significant data preparation costs. Recent applications of semantic segmentation in AM have shown the potential of ensemble-based approaches at the expense of increased parameters and computational burden while ignoring the minority classes. In this work, we propose a reproducible method and develop an associated tool to rapidly segment and subsequently quantify industrial AMed metallographic images. First, two new datasets representing AMed materials are generated from extensive experimentation. Subsequently, state-of-the-art models from convolutional and self-attention categories are evaluated in their ability to segment the datasets of interest. Finally, a modular software tool is developed for industrial applications. The developed Microstructure Segmentation, Quantification, and Fusion (+) tool or MicroSegQ+ enables a weighted pairing of predictions from different semantic segmentation models. The pairing strategy helps to exploit the complimentary performance of convolutional and transformer models on multi-class metallographic images from a Metal Matrix Composite (MMC) material system. In addition to developing models on this MMC dataset, the proposed strategy is evaluated on an open-source metal AM dataset. We also evaluate the generalizability of the best-performing model on a second MMC dataset from a different industrial setting that shows an overall accuracy of 93 % without fine-tuning. The proposed tool enables rapid quantification under 1 min, unlike the existing semi-automatic approach that takes 1–2 h of segmentation effort on single bead cross-sections. The tool is wrapped into a single executable file for industrial deployment. The work also contributes to two annotated datasets from a direct energy deposition (DED) AM process. The datasets can be used to develop and validate new semantic segmentation models of AMed microstructures, whereas the functionality of the tool can be expanded upstream to include data and modeling steps, enabling a no-code pipeline for the industry. [ABSTRACT FROM AUTHOR]