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The volume and complexity of Biomedical Imaging (BMI) data can be handled by well-known Product Lifecycle Management (PLM) solutions if a research study in this field is modeled as a cyclic process of four phases: study specifications; raw data acquisition; data processing and results publication. However, current PLM systems do not provide easy, flexible and user-adapted data access, especially in the context of heterogeneity expertise environments such as BMI. This paper presents VAQUERO (VisuAlization and QUERy based Ontology), a visual ontology-based data query approach, that aims at providing different kinds of users in the BMI field (common/ external, domain expert and technical users) with easy self-access to their data stored in a PLM Teamcenter system.

For effective bead deposition based additive manufacturing (AM) processes such as directed energy deposition, the final mechanical and physical properties should be predicted in tandem with the bead geometry characteristics. Experimental approaches to investigate the final geometry and the mechanical properties are costly, and simulation solutions are time-consuming. Alternative artificial intelligent (AI) systems are explored as they are a powerful approach to predict such properties. In the present study, the geometrical properties, as well as the mechanical properties (residual stress and hardness) for single bead clads are investigated. Experimental data is used to calibrate multi-physics finite element models, and both data sets are used to seed the AI models. The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (cross sections), and 3D (complete bead) domains. The prediction results are evaluated using the mean relative error measure. The ANFIS predictions are more precise than those from the ANN for the 1D and 2D domains, but the ANN had less error for the 3D scenario. These models are capable of predicting the geometrical and the mechanical properties values very well, including capturing the mechanical properties in transient regions; however, this research should be extended for multi- bead scenarios before a conclusive ‘best approach’ strategy can be determined.

For effective bead deposition based additive manufacturing (AM) processes such as directed energy deposition, the final mechanical and physical properties should be predicted in tandem with the bead geometry characteristics. Experimental approaches to investigate the final geometry and the mechanical properties are costly, and simulation solutions are timeconsuming. Alternative artificial intelligent (AI) systems are explored as they are a powerful approach to predict such properties. In the present study, the geometrical properties, as well as the mechanical properties (residual stress and hardness) for single bead clads are investigated.Experimental data is used to calibrate multi-physics finite element models, and both data sets are used to seed the AI models. The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (cross sections), and 3D (complete bead) domains. The prediction results are evaluated using the mean relative error measure. The ANFIS predictions are more precise than those from the ANN for the 1D and 2D domains, but the ANN had less error for the 3D scenario. These models are capable of predicting the geometrical and the mechanical properties values very well, including capturing the mechanical properties in transient regions; however, this research should be extended for multibead scenarios before a conclusive 'best approach' strategy can be determined.

Laser cladding is a directed energy deposition process, and can lead to high residual stresses, which can compromise the quality of the specimen. As a result, it is crucial to accurately predict and investigate the residual stress distribution in cladded parts and understand the mechanism of formation. In this study a thermo-mechanical metallurgical simulation model of the laser cladding process was developed for three different path strategies with respect to the deposition sequence and direction for a thin wall hexagon with inner junctions to investigate the formation of residual stress. The study was performed for single and multilayer scenarios. Two types of computational techniques, the detailed transient approach and the imposed thermal cycle approach, was performed and comparisons conducted. Consistent results were observed when comparing the resultant stress patterns for the single layer; subsequently, the imposed thermal cycle method was applied for the five layer models. A preheat scenario is explored. This reduced the computational cost significantly, but the stress patterns were not similar. This indicates that building up worn regions at the top of a thin walled component, such as a roll die, needs to be investigated further as unique issues have been highlighted. The differences between the implemented computational techniques are described as well as the advantages and disadvantages of each. Knowledge obtained from these case studies provides a foundation for efficient and rapid optimization of laser cladding processes, with the aim of minimizing residual stress in both simple and complex laser cladding structures.