Additive manufacturing (AM) is a layer-by-layer material deposition process that allows for more manufacturing flexibility and design complexity than traditional manufacturing processes. However, the print quality in metal AM is hard to be predicted and controlled due to its high process variability. Numerous process parameters are correlated/intertwined and have a direct/indirect relationship with the final build quality. However, these interconnections among the process parameters and print qualities are not yet fully understood and identified. To achieve a functional part without additional post-processing to improve the surface finish/mechanical properties, the comprehensive process model, which includes the semantic and qualitative connections among the process inputs-signatures-qualities, is urgently needed. Currently, surrogate models (regression models/ physical-based models) are widely used for predicting part qualities, specifically thermal distortion. These surrogate models can provide a simulated result based on the user-defined parameter settings. However, the reverse determination of the highly correlated parameters that can manage the print qualities in the desired standard cannot be achieved through these surrogate models. Understanding this interconnectivity is the top priority in building process models that will help to make the process more predictable and controllable. An ontology-based process map that captures the complicated metal AM processes, including all input parameters, physical, thermal model, and resultant build qualities (microstructure and mechanical properties), is provided in this manuscript. The connections among these factors are used to diagnose the relevant process variables regarding the specific build qualities, which can further combine with experimental data to contribute to a “printable” zone. The ontology-based process map links variable selection to quality requirements, creating a printable zone. A printable zone indicates the allowable process parameter selections, contributing to the desired part quality. Ultimately, the selected variable process data from the ontological process map will scope the parametrical analysis. The implementations of the ontology-based process map and printable zone are presented to demonstrate this proposed method's effectiveness by demonstrating that the proposed printable zone's validity and desired build quality can be achieved.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Mechanics of Materials
- Mechanical Engineering