Activitybased Cost Model for Material Extrusion Processes Along the Additive Manufacturing Process Chain The utilization of additive manufacturing (AM) has entailed numerous benefits such as shortened timetomarket, high product innovation and individualization. The application of AM has gained in significance within the automotive industry as it is transferring from a prototype technology to series applications. Besides technological barriers, an approach to estimate cost to set up a business case of an application is crucial. Material extrusion (ME) as an additive process category is theoretically capable of meeting high requirements in industry. Despite various processes belonging to ME, Fused Layer Modeling (FLM) is the only one entrenched. The establishment of pelletbased processes could overcome restrictions for ME. Although there are various cost estimations for AM, a transparent methodology to carry out a profitability analysis between ME processes is missing. In this study, a cost model for ME is developed to estimate occurring cost and cost drivers. Existing cost models are compared regarding their suitability for ME. The final cost model for ME is activitybased and measuring occurring unit cost during the process steps pre, in and postprocessing. The duration of costgenerating activities is considered.

A RealTime Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes Additive Manufacturing (AM) is a manufacturing paradigm that builds threedimensional objects from a computeraided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and timeconsuming for predicting multiscale multiphysics phenomena in AM, physicsinformed datadriven machinelearning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower realtime control systems using insitu data. In this paper, we design and develop essential components of a scientific framework for developing a datadriven modelbased realtime control system. Finite element methods are employed for solving timedependent heat equations and developing the database. The proposed framework uses extremely randomized trees  an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https:github.compaularindammliteradditive.