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This paper aims to investigate the influence of post-curing temperature, post-curing time and gamma ray irradiation dose upon the tensile and compressive mechanical properties of the medical graded vat photopolymerization parts.

Medical graded vat photopolymerization specimens, made from photopolymer resin, were fabricated using bottom-up vat photopolymerization machine. Tensile and compressive tests were conducted to assess the mechanical properties. The specimens were categorized into uncured and post-curing groups. Temperature post-processing and/or gamma irradiation exposure were for post-curing specimens. The post-curing parameters considered included temperature levels of 50°C, 60°C and 70°C, with 1, 2, 3 and 4 h periods. For the gamma irradiation, the exposure doses were 25, 50, 75 and 100 kGy.

Post-curing improved the mechanical properties of medical graded vat photopolymerization parts for both tensile and compressive specimens. Post-curing temperature greater than 50°C or a prolonged post-curing period of more than 1 h made insignificant changes or deterioration in mechanical properties. The optimal post-curing condition was therefore a 50°C post-curing temperature with 1 h post-curing time. Exposure to gamma ray improved the compressive mechanical properties, but deteriorated tensile mechanical properties. Higher gamma irradiation doses could decrease the mechanical properties and also make the part more brittle, especially for doses more than 25 kGy.

The obtained results would be beneficial to the medical device manufacturer who fabricated the invasive temporary contact personalized surgical instruments by vat photopolymerization technique. In addition, it also raised awareness in excessive gamma sterilization in the medical graded vat photopolymerization parts.

The purpose of this paper is to present the freely available finite element simulation software Pyrit.

In a first step, the design principles and the objective of the software project are defined. Then, the software’s structure is established: The software is organized in packages for which an overview is given. The structure is based on the typical steps of a simulation workflow, i.e., problem definition, problem-solving and post-processing. State-of-the-art software engineering principles are applied to ensure a high code quality at all times. Finally, the modeling and simulation workflow of Pyrit is demonstrated by three examples.

Pyrit is a field simulation software based on the finite element method written in Python to solve coupled systems of partial differential equations. It is designed as a modular software that is easily modifiable and extendable. The framework can, therefore, be adapted to various activities, i.e., research, education and industry collaboration.

The focus of Pyrit are static and quasistatic electromagnetic problems as well as (coupled) heat conduction problems. It allows for both time domain and frequency domain simulations.

In research, problem-specific modifications and direct access to the source code of simulation tools are essential. With Pyrit, the authors present a computationally efficient and platform-independent simulation software for various electromagnetic and thermal field problems.

The purpose of this paper is to give an insight into relevant aspects of 3D printing of clay paste enhanced with scrap polymer powder which have not been investigated by previous studies. Specifically, the geometrical features of the deposited lines, dimensional accuracy of benchmarks and mechanical properties of printed parts are investigated.

Firstly, the 3D printer is used to deposit lines of the paste under various combinations of material composition and process parameters. 3D scanning is used to measure their dimensional and geometrical errors. The results are elaborated through statistics to highlight the role of material and processing conditions. Then, four benchmark parts are printed using materials with different percentages of polymer powder. The parts are scanned after each step of the post-processing to quantify the effects of printing, drying and melting on dimensional accuracy. Finally, drop weight tests are carried out to investigate the impact resistance of specimens with different powder contents.

It is found that the quality of deposition varies with the printing speed, nozzle acceleration and material composition. Also, significant differences are observed at the ends of the lines. Materials with 10 Wt.% and 40 Wt.% of powder exhibit relevant shape variations due to the separation of phases. Accuracy analyses show significant deformations of parts at the green state due to material weight. This effect is more pronounced for higher powder contents. On the other hand, the polymer reduces shrinkage during drying. Furthermore, the impact test results showed that the polymer caused a large increase in impact resistance as compared to pure clay. Nonetheless, a decrease is observed for 40 Wt.% due to the higher amount of porosities.

The results of this study advance the knowledge on the 3D printing of clay paste reinforced with a scrap polymer powder. This offers a new opportunity to reuse leftover powders from powder bed fusion processes. The findings presented here are expected to foster the adoption of this technique reducing the amount of waste powder disposed of by additive manufacturing companies.

This study offers some important insights into the relations between process conditions and the geometry of the deposited lines. This is of practical relevance to toolpath planning. The dimensional analyses allow for understanding the role of each post-processing step on the dimensional error. Also, the comparison with previous findings highlights the role of part dimensions. The present research explores, for the first time, the impact resistance of parts produced by this technology. The observed enhancement of this property with respect to pure clay may open new opportunities for the application of this manufacturing process.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

This study aims to present the optimization of parameters and effects of annealing and vapor smoothing post-processing treatments on the surface roughness and tensile mechanical properties of fused deposition modeling (FDM) printed acrylonitrile butadiene styrene (ABS).

Full-factorial test matrices were designed to determine the most effective treatment parameters for post-processing. The parameters for annealing were temperature and time, whereas the parameters for the vapor smoothing were volume of acetone and time. Analysis of surface roughness and tensile test results determined influences of the levels of parameters to find an ideal balance between mechanical properties and roughness.

Optimal parameters for vapor smoothing and annealing were determined. Vapor smoothing resulted in significantly higher improvements to surface roughness than annealing. Both treatments generally resulted in decreased mechanical properties. Of all treatments tested, annealing at 100 °C for 60 min provided the greatest benefit to tensile properties and vapor smoothing with 20 mL of acetone for 15 min provided the greatest benefit to surface roughness while balancing effects on properties.

Vapor smoothing and annealing of FDM ABS have typically been studied independently for their effects on surface roughness and material properties, respectively, with varying materials and manufacturing methods. This study objectively compares the effects of each treatment on both characteristics simultaneously to recommend ideal treatments for maximizing the balance between the final quality and performance of FDM components. The significance of the input variables for each treatment have also been analyzed. These findings should provide value to end-users of 3D printed components seeking to balance these critical aspects of manufacturing.

The purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process.

To overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated.

Inter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy.

To the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.

Breast cancer is the most diagnosed type of cancer in the world, and mastectomies to remove tumors are still common. An external breast prosthesis (EBP) can be used to minimize the asymmetry, due to the ablation. Some governments do not cover costs of that assistive technology, and women end up using socks and fabric pockets filled with seeds, to simulate the volume lost in the surgery. This study aims to offer to those women a decent solution, ergonomic, but still affordable.

The authors interviewed 20 mastectomized Brazilian women, listened to their relate and 3D scanned them, to give rise to personalized external lightweight breast prostheses. The authors used free software for computer-aided design and computer-aided manufacturing, and low-cost 3D printers. From the strategy of bespoke products, this study generalized the method, to conceive mass customized prostheses, in a compromise solution that reduces personalization, conserving the best features of design.

This study achieved a method to manufacture ergonomic, bespoke external breast prostheses, using low-cost technology. Previous literature made them using expensive scanners, software and printers.

The authors validated this method during pandemic, which restricted the number of patients the authors could have access to. This impacted authors’ possibility to work on matching the color of the final product and real skin. The authors understood, though, that precision of color, in the final product, is challenging, because of the peculiar aspects of human skin.

Using the method the authors proposed, personalized external breast prostheses can be manufactured using low-cost resources, democratizing better quality of life for more breast cancer survivors.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

The purpose of this paper is to predict the effect of bra pad specifications on breast deformation during jumping using a finite element (FE) method. Breast deformation is a key concern for women during exercise and can be effectively controlled with sports bras. In most studies, the deformation of breasts when wearing a sports bra is measured using motion capture devices to judge their effectiveness. However, the operation of such devices is highly complex and time-consuming. Computer-aided technology is an efficient way to simulate these experiments.

In this study, the breast model was obtained using three-dimensional (3D) scanning. Assembling models were obtained for FE analysis using reverse engineering and computer-aided design (CAD) software. The breast deformation results were obtained by completing pre-processing, solving and post-processing in the FE simulation software. To extend the application of these models, pads of different sizes and thicknesses within the bra were constructed to simulate the effect of pads on breast deformation.

The calculated root mean square errors were <1%, which indicated good agreement between the FE and experimental data in all the models. Nipple deformation was always the largest in most models. The smallest deformation occurred at the superior position of breasts in all models. In addition, larger pads were not effective in reducing breast deformation; however, thicker pads were.

The method developed in this study provides an effective way to predict breast deformation in multiple positions and is convenient for designing compression bras.