The effective estimation and consideration of process cost, time, and quality for additive manufacturing operations, when a series of suitable technologies and resources are available, is very important for making informed product design and development decisions. The main objective of this paper is to propose the design, deployment, and use of an agent-based decision support platform, which is capable of proposing alternative additive manufacturing resources and process configurations to design engineers while reducing the number of communication steps among engineering teams and organizations. Different computer-aided systems are utilised and interfaced for automating the information exchange as well as for accelerating the overall product development process.

Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training dataset. A multi-step classification system is then proposed to classify the presence and type of porosity in a voxel, which can then be utilised to find the distribution of porosity within the build volume. Titanium parts were printed using a laser powder bed fusion system. Two discretisation techniques based on voxelisation were utilised: a defect-centric and a uniform discretisation method. Different machine learning models, feature sets, and other parameters were also tested. Promising results were achieved in identifying porous voxels; however, the accuracy of the classification requires improvement before being applied industrially. The potential of the voxelisation-based framework for this application and its ability to incorporate data from different stages of the additive manufacturing workflow as well as different machine learning models was clearly demonstrated.