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The purpose of this paper is to clarify the relationship between the use of different polymer matrices for the preparation of composite materials, namely, polyethylene terephthalate-glycol (PET-G) and polyamide (PA), using Composite Fiber Co-Extrusion technology with the application of two types of carbon fibers, short and continuous. The aim of the study is also to extend the knowledge of the production of composite materials with a defined structure from the point of view of their influence on the microstructure and their physical-mechanical properties.

As part of the experiment, four types of samples were prepared, namely, two types of samples with PA polymer matrix and two types with PET-G polymer matrix. All types contained short carbon fibers and always one set from each polymer matrix in addition to continuous carbon fibers. All types were prepared using the same 3D printing parameters to avoid any further influence. The samples were then tested for microstructure using microCT, mechanical properties using a tensile test and dilatation characteristics from the point of view of aerospace applications. Finally, the raw materials themselves were tested.

The paper provides insight into the influence of polymer matrix types on the physico-mechanical properties of 3D printed composites. The analysis confirmed that the physico-mechanical results varied with respect to the interface between the polymer matrix and the carbon fiber. The implications of the conclusions can be extended to the development of products in the aerospace and automotive sectors.

This study provides information for composite applications in the aerospace industry, focusing on evaluating dilatation characteristics within very low temperatures (−60 °C) when using carbon fibers (continuous carbon fibers, short carbon fibers and a combination of both) in two types of thermoplastic matrices. This perspective on materials characterisation for aerospace applications is a very important and unpublished approach within the 3D printing of composites. These characteristics are important parameters in the design of prototypes and functional samples with regard to the resulting behaviour in real conditions.

This study aims to propose the reuse of PA12 (powder) in another AM process, binder jettiinng, which is less sensitive to the chemical and mechanical degradation of the powder after multiple cycles in the laser system.

The experimental process for evaluating the reuse of SLS powders in a subsequent binder jetting process consists of four phases: powder characterization, bonding analysis, mixture testing and mixture characteristics. Analyses were carried out using techniques such as Fourier Transform Infrared Spectroscopy, scanning electron microscopy, thermogravimetric analysis and stress–strain tests for tension and compression. The surface roughness, color, hardness and density of the new mixture were also determined to find physical characteristics. A Taguchi design L8 was used to search for a mixture with the best mechanical strength.

The results indicated that the integration of waste powder PA12 with calcium sulfate hemihydrate (CSH) generates appropriate particle distribution with rounded particles of PA12 that improve powder flowability. The micropores observed with less than 60 µm, facilitated binder and infiltrant penetration on 3D parts. The 60/40 (CSH-PA12) mixture with epoxy resin postprocessing was found to be the best-bonded mixture in mechanical testing, rugosity and hardness results. The new CSH-PA12 mixture resulted lighter and stronger than the CSH powder commonly used in binder jetting technology.

This study adds value to the polymer powder bed fusion process by using its waste in a circular process. The novel reuse of PA12 waste in an established process was achieved in an accessible and economical manner.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.