Polylactide acid (PLA) is one of the most used plastics in extrusion-based additive manufacturing (AM). Although it is bio-based and in theory biodegradable, its recyclability for fused filament fabrication (FFF) is limited due to material degradation. To better understand the material’s recyclability, blends with different contents of recycled PLA (rPLA) are investigated alongside a coextruded filament comprised of a core layer with high rPLA content and a skin layer from virgin PLA. The goal was to determine whether this coextrusion approach is more efficient than blending rPLA with virgin PLA. Different filaments were extruded and subsequently used to manufacture samples using FFF. While the strength of the individual strands did not decrease significantly, layer adhesion decreased by up to 67%. The coextruded filament was found to be more brittle than its monoextruded counterparts. Additionally, no continuous weld line could be formed between the layers of coextruded material, leading to a decreased tensile strength. However, the coextruded filament proved to be able to save on master batch and colorants, as the outer layer of the filament has the most impact on the part’s coloring. Therefore, switching to a coextruded filament could provide economical savings on master batch material.

Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model’s feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established.

Additive manufacturing has revolutionized prototyping and small-scale production in the past years. By creating parts layer by layer, a tool-less production technology is established, which allows for rapid adaption of the manufacturing process and customization of the product. However, the geometric freedom of the technologies comes with a large number of process parameters, especially in Fused Deposition Modeling (FDM), all of which influence the resulting part’s properties. Since those parameters show interdependencies and non-linearities, choosing a suitable set to create the desired part properties is not trivial. This study demonstrates the use of Invertible Neural Networks (INN) for generating process parameters objectively. By specifying the desired part in the categories of mechanical properties, optical properties and manufacturing time, the demonstrated INN generates process parameters capable of closely replicating the desired part. Validation trials prove the precision of the solution with measured properties achieving the desired properties to up to 99.96% and a mean accuracy of 85.34%.

The present paper addresses experimental and numerical investigations of a Large Scale Additive Manufacturing (LSAM) process using polymers. By producing large components without geometrical constraints quickly and economically, LSAM processes have the capability to revolutionize many industries. Accurate prediction and control of the thermal history is key for a successful manufacturing process and for achieving high quality and good mechanical properties of the manufactured part. During the LSAM process, the heat emitted by the nozzle leads to an increase in the temperature of the previously deposited layer, which prepares the surface for better adhesion of the new layer. It is therefore necessary to take into account this part of heat source in the transient heat transfer equation to correctly and completely describe the process and predict the temperature field of the manufactured part. The present study contributes to experimental investigations and numerical analysis during the LSAM process. During the process, two types of measurements are performed: firstly, the heat emitted by the nozzle is measured via a radiative heat sensor; secondly, the temperature field is measured using an infrared camera while varying the process speed. At the same time, a numerical simulation model is developed in order to validate the experimental results. The temperature fields of the manufactured parts computed by numerical simulations are in very good agreement with the temperature fields measured by infrared thermograph with the contribution of the nozzle’s heat exchange.

In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis. The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed toidentify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.

In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis. The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed to identify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.