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The purpose of this paper is to present a Design for Additive Manufacturing (DfAM) methodology that connects several methods, from geometrical design to post-process selection, into a common optimisation framework.

A design methodology is formulated and tested in a case study. The outcome of the case study is analysed by comparing the obtained results with alternative designs achieved by using other design methods. The design process in the case study and the potential of the method to be used in different settings are also discussed. Finally, the work is concluded by stating the main contribution of the paper and highlighting where further research is needed.

The proposed method is implemented in a novel framework which is applied to a physical component in the case study. The component is a structural aircraft part that was designed to minimise weight while respecting several static and fatigue structural load cases. An addition goal is to minimise the manufacturing cost. Designs optimised for manufacturing by two different AM machines (EOS M400 and Arcam Q20+), with and without post-processing (centrifugal finishing) are considered. The designs achieved in this study show a significant reduction in both weight and cost compared to one AM manufactured geometry designed using more conventional methods and one design milled in aluminium.

The method in this paper allows for the holistic design and optimisation of components while considering manufacturability, cost and component functionality. Within the same framework, designs optimised for different setups of AM machines and post-processing can be automatically evaluated without any additional manual work.

This paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.

The method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.

Existing DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.

The review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.

A mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.

Based on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.

The DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.

The research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

This paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.

After the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.

This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The paper aims to investigate the comfort-related performances of an innovative solar shading solution based on a new composite patented material that consists of a cement-based matrix coupled with a stretchable three-dimensional textile. The paper’s aim is, through a performance-based generative design approach, to develop a high-performance static shading system able to guarantee adequate daylit spaces, a connection with the outdoors and a glare-free environment in the view of a holistic and occupant-centric daylight assessment.

The paper describes the design and simulation process of a complex static shading system for digital manufacturing purposes. Initially, the optical material properties were characterized to calibrate radiance-based simulations. The developed models were then implemented in a multi-objective genetic optimization algorithm to improve the shading geometries, and their performance was assessed and compared with traditional external louvres and overhangs.

The system developed demonstrates, for a reference office space located in Milan (Italy), the potential of increasing useful daylight illuminance by 35% with a reduced glare of up to 70%–80% while providing better uniformity and connection with the outdoors as a result of a topological optimization of the shape and position of the openings.

The paper presents the innovative nature of a new composite material that, coupled with the proposed performance-based optimization process, enables the fabrication of optimized shading/cladding surfaces with complex geometries whose formability does not require ad hoc formworks, making the process fast and economic.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

In this contribution to EDI's professional insights, the authors develop practical and evidence-based recommendations that are developed for bias mitigation, discretion elimination and process optimization in panel evaluations and decisions in research funding. An analysis is made of how the expectation of “selling science” adds layers of complexity to the evaluation and decision process. The insights are relevant for optimization of similar processes, including publication, recruitment and selection, tenure and promotion.

The recommendations are informed by experiences and evidence from commissioned projects with European research funding organizations. The authors distinguish between three aspects of the evaluation process: written applications, enacted performance and group dynamics. Vignettes are provided to set the stage for the analysis of how bias and (lack of) fit to an ideal image makes it easier for some than for others to be funded.

In research funding decisions, (over)selling science is expected but creates shifting standards for evaluation, resulting in a narrow band of acceptable behavior for applicants. In the authors' recommendations, research funding organizations, evaluators and panel chairs will find practical ideas and levers for process optimization, standardization and customization, in terms of awareness, accountability, biased language, criteria, structure and time.

Showing how “selling science” in research funding adds to the cumulative disadvantage of bias, the authors offer design specifications for interventions to mitigate the negative effects of bias on evaluations and decisions, improve selection habits, eliminate discretion and create a more inclusive process.