1 research outputs found
Task-driven data fusion for additive manufacturing
Additive manufacturing (AM) is a critical technology for the next industrial revolution,
offering the prospect of mass customization, flexible production, and on-demand
manufacturing. However, difficulties in understanding underlying mechanisms and
identifying latent factors that influence AM processes build up barriers to in-depth
research and hinder its widespread adoption in industries. Recent advancements in data
sensing and collection technologies have enabled capturing extensive data from AM
production for analytics to improve process reliability and part quality. However,
modelling the complex relationships between the manufacturing process and its
outcomes is challenging due to the multi-physics nature of AM processes. The critical
information of AM production is embedded within multi-source, multi-dimensional,
and multi-modal heterogeneous data, leading to difficulties when jointly analysing.
Therefore, how to bridge the gap between the multi-physics interactions and their
outcomes through heterogeneous data analytics becomes a crucial research challenge.
Data fusion strategies and techniques can effectively leverage multi-faceted
information. Since AM tasks can have various requirements, the corresponding fusion
techniques should be task-specific. Hence, this thesis will focus on how to deal with
task-driven data fusion for AM.
To address the challenges stated above, a comprehensive task-driven data fusion
framework and methodology are proposed to provide systematic guidelines to identify,
collect, characterise, and fuse AM data for supporting decision-making activities. In
this framework, AM data is classified into three major categories, process-input data,
process-generated data, and validation data. The proposed methodology consists of
three steps, including the identification of data analytics types, data required for tasks,
acquisition, and characterization, and task-driven data fusion techniques. To
implement the framework and methodology, critical strategies for multi-source and
multi-hierarchy data fusion, and Cloud-edge fusion, are introduced and the detailed
approaches are described in the following chapters.
One of the major challenges in AM data fusion is that the multi-source data normally
has various dimensions, involving nested hierarchies. To fuse this data for analytics, a hybrid deep learning (DL) model called M-CNN-LSTM is developed. In general, two
levels of data and information are focused on, layer level and build level. In the
proposed hybrid model, the CNN part is used to extract features from layer-wise
images of sliced 3D models, and the LSTM is used to process the layer-level data
concatenated with convolutional features for time-series modelling. The build-level
information is used as input into a separate neural network and merged with the CNN-LSTM for final predictions. An experimental study on an energy consumption
prediction task was conducted where the results demonstrated the merits of the
proposed approach.
In many AM tasks at the initial stage, it is usually time-consuming and costly to acquire
sufficient data for training DL-based models. Additionally, these models are hard to
make fast inferences during production. Hence, a Cloud-edge fusion paradigm based
on transfer learning and knowledge distillation (KD)-enabled incremental learning is
proposed to tackle the challenges. The proposed methodology consists of three main
steps, including (1) transfer learning for feature extraction, (2) base model building via
deep mutual learning (DML) and model ensemble, and (3) multi-stage KD-enabled
incremental learning. The 3-step method is developed to transfer knowledge from the
ensemble model to the compressed model and learn new knowledge incrementally
from newly collected data. After each incremental learning in the Cloud platform, the
compressed model will be updated to the edge devices for making inferences on the
incoming new data. An experimental study on the AM energy consumption prediction
task was carried out for demonstration.
Under the proposed task-driven data fusion framework and methodology, case studies
focusing on three different AM tasks, (1) mechanical property prediction of additively
manufactured lattice structures (LS), (2) porosity defects classification of parts, and (3)
investigating the effect of the remelting process on part density, were carried out for
demonstration. Experimental results were presented and discussed, revealing the
feasibility and effectiveness of the proposed framework and approaches. This research
aims to pave the way for leveraging AM data with various sources and modalities to
support decision-making for AM tasks using data fusion and advanced data analytics
techniques. The feasibility and effectiveness of the developed fusion strategies and
methods demonstrate their potential to facilitate the AM industry, making it more
adaptable and responsive to the dynamic demands of modern manufacturing