One of the main challenges encountered in the Laser-based Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) process is the fabrication of defect-free parts. The presence of defects severely degrades the mechanical performance of AM parts and especially their fatigue strength. The most popular and reliable method to assess the ability of the employed process parameters for the fabrication of full-density parts is the process windows map, also known as printability map. However, the experimental procedure for the design of the printability maps and the identification of the optimum-density process parameters is usually time-consuming and expensive. In the present work, a modelling framework is presented for the determination of a printability map and the optimization of the L-PBF process based on the prediction and characterization of melt-pool geometric features and the prediction of porosity of small samples of 316L SS and Ti-6Al-4V metal alloys. The results are compared with available experimental data and present a good correlation, verifying the modelling methodology. The suitability of the employed defect criteria for each material and the effect of the hatch-spacing process parameter on the optimum-density parameters are also presented.

Laser Powder Bed Fusion (LPBF) process is one of the advanced Additive Manufacturing (AM) processes, which is employed for the fabrication of complex metallic components. One of the major drawbacks of the LPBF is the development of residual stresses due to the high temperature gradients developed during the process thermal cycles. Reliable models for the prediction of residual strain and stress at part scale are required to support the LPBF process optimization. Due to the computational cost of the LPBF simulation, the current modelling methodology utilizes assumptions to make feasible the prediction of residual stresses at parts or component level. To this scope, a thermomechanical modelling approach for the simulation of LPBF process is presented with focus to residual stress and strain prediction. The modelling efficiency of the proposed approach was tested on a series on cases for which experimental data were available. The good comparison between the predicted and experimental data validated the modelling method. The efficiency of the thermomechanical modelling method is demonstrated by the reduced computational time required.

Additive Manufacturing (AM), also popularly known as 3D Printing, refers to a group of manufacturing methods which are guided by a digital model and use incremental addition of material, usually in a layer-by-layer manner, for the fabrication of objects. Lately, AM is widely used in both industry and academia, exhibiting, among others, the advantages of flexibility and ability to produce on demand, with reduced cost, time and waste. This paper seeks to shed light on the contribution of 3D printing in the transition towards the objective of circular economy. Towards this end, we focus on three AM processes (Cold Spray, Direct Energy Deposition, and Powder Bed Fusion), and we review 27 distinct case studies in the laboratory as well as industrial settings, exploring their circularity based on the comprehensive 9R framework (recover, recycle, repurpose, remanufacture, refurbish, repair, reuse, reduce, rethink, refuse). Overall, the results provide a structured assessment of the degree of circularity, and identify the dominant “R” strategies employed by the specific methods of AM. They also reveal the positive impact, and the corresponding advantages, of 3D printing in the extension of product(s) life cycle, with respect to specific material and industry sectors.

Strut-based or open lattice materials are a category of advanced materials used in medical and aerospace applications due to their properties, such as high strength-to-weight ratio and energy absorption capability. The most prominent method for the fabrication of lattice materials is the Laser-based Powder Bed Fusion (L-PBF) additive manufacturing (AM) process, due to its ability to produce parts of complex geometries. The current work presents an efficient meso-scale finite element (FE) modeling methodology of the L-PBF process demonstrated in the fabrication of body-centered cubic (BCC) lattice materials. The modeling efficiency is gained through an adaptive mesh refinement technique, which results in accurate and efficient prediction of the temperature field during the process evolution. To examine the efficiency of the modeling method, the computational time is compared with that of a conventional FE simulation, based on the element and birth technique. The temperature history difference between the two approaches is minor but the adaptive mesh modeling requires only a small portion of the simulation time of the conventional model. In addition, the computational results present a good correlation with the available experimental measurements for various process parameters validating the presented efficient method.