Additive manufacturing (AM) technology has revolutionized the way goods are developed and produced, with numerous uses in aerospace, automotive, medical, and consumer goods industries. Its significance within the context of the fourth industrial revolution, characterized by digitalization, automation, and interconnected systems, is imperative. This paper provides a comprehensive overview of the current state of AM, including its background, underlying concepts, processes, materials, implicit benefits, and inherent limitations. It also examines the various applications of AM for the manufacture and remanufacture of parts, components, and products. This study assesses current advances in AM, such as progress in multi-material and large-scale 3D printing, improvements in speed and efficacy, precision and accuracy, automation, and sustainability. Likewise, the study investigates some of the present issues confronting AM, such as material limits, cost, intellectual property, and sustainability. Furthermore, by analyzing the review articles on various aspects of AM, this paper provides a more comprehensive overview of the subject. Finally, the study concludes with highlights of future prospects and emerging AM trends. This comprehensive review is intended to offer readers a clear grasp of AM, its inherent processes, and recent advances, as well as the challenges that must be addressed in order to fully realize the possibilities it holds. 

Revolutionizing manufacturing: A comprehensive overview of additive manufacturing processes, materials, developments, and challenges

Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The main steps of emotion recognition on masked faces include face detection by using Haar–Cascade, landmark implementation through a media-pipe face mesh model, and model training on seven emotional classes. The FER-2013 dataset was used for model training. An emotion detection model was developed for non-masked faces. Thereafter, landmarks were applied to the upper part of the face. After the detection of faces and landmark locations were extracted, we captured coordinates of emotional class landmarks and exported to a comma-separated values (csv) file. After that, model weights were transferred to the emotional classes. Finally, a landmark-based emotion recognition model for the upper facial parts was tested both on images and in real time using a web camera application. The results showed that the proposed model achieved an overall accuracy of 91.2% for seven emotional classes in the case of an image application. Image based emotion detection of the proposed model accuracy showed relatively higher results than the real-time emotion detection.

/pdf/development-of-real-time-landmark-based-emotion-recognition-1pctc9af.pdf

Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces

Uncertainties in fatigue life of laser powder bed fusion (L-PBF) additively manufactured parts arise from microstructural heterogeneities and randomly dispersed defects generated during the L-PBF. Stress-controlled fatigue performances of L-PBF-built Hastelloy X were examined in 400°C and 600°C high-temperature atmospheres. Given the inherent anisotropy of the L-PBF-built Hastelloy X, the fatigue tests were performed on the samples with distinct building orientations. Fatigue-induced damage evolution characteristics were meticulously analyzed after drawing the S-N curves with a 0.1 stress ratio, a 20 Hz loading frequency and varied stress amplitudes. We established a database of the fatigue test results with material properties of the L-PBF-built Hastelloy X to build a machine learning (ML) framework for fatigue life prediction, skipping the traditional fatigue-life mathematical models that are less applicable to L-PBF-built metals. For capturing the intricate influence of diverse factors on the fatigue life accurately, deep neural network (DNN) and support vector machine (SVM) were utilized and optimized within the ML-based framework. By comparing our ML-based approach with traditional methods for life prediction, it is indicated that ML techniques can transcend the physical limitations associated with the fatigue processes while simultaneously offering a unified depiction thereof. 

High-temperature high-cycle fatigue performance and machine learning-based fatigue life prediction of additively manufactured Hastelloy X

Ceramics possess notable attributes including excellent wear resistance, exceptional high-temperature strength, superior hardness, and outstanding biocompatibility, rendering them indispensable for the fabrication of key industrial components. Additive manufacturing presents significant benefits such as precise fabrication, design flexibility, and the ability for mass customization. This technology facilitates the creation of intricate structural or functional ceramic components with complex geometries, effectively addressing issues that are inadequately managed by conventional methods, such as the inherent brittleness of ceramics and challenges associated with ceramic machining. As advancements in aerospace, special industry, and biomedical sectors progress, the performance limitations of uniform ceramic materials and structures have emerged as a bottleneck constraining the broadening of their application range. There is a pressing need to transition from single-material ceramic additive manufacturing to multi-material printing. This paper highlights and compares various notable advancements in typical single-material ceramic additive manufacturing. In addition, it delves into the present status of multi-material ceramic additive continuous forming and the development of associated equipment, focusing on different forming mechanisms. Finally, the paper concludes by identifying the primary challenges and research priorities in multi-material ceramic additive manufacturing, and suggests potential directions for future development. 

Advancements in multi-material additive manufacturing of advanced ceramics: A review of strategies, techniques and equipment

The application of Additive Manufacturing (AM) within industrial settings brings new challenges to operational research. This paper provides in-depth analyses of nesting and scheduling problems in the context of AM. Using bibliometric and systematic review methods, we map the progression of knowledge in this territory and provide a detailed characterization of the existing mathematical models and approaches. Our findings indicate the fundamental decisions that predominate in these models and reveal the relationships between the critical studies, which form the backbone of the research. While the nesting and scheduling problem can be categorized into six types of decision, we have also found other emerging and interrelated optimization problems associated with nesting and scheduling decisions. To provide further details, an ample number of models are systematized in a reference framework for modeling nesting and scheduling in AM. Finally, pressing future research avenues that have emerged from our analysis and that need to be addressed to further advance the field are presented. 

Nesting and scheduling optimization of additive manufacturing systems: Mapping the territory

Additive manufacturing of Al2O3 ceramic core with applicable microstructure and mechanical properties via digital light processing of high solid loading slurry

Additive manufacturing and characterization of microstructure evolution of Inconel 718 superalloy produced by vat photopolymerization

A nesting optimization method based on digital contour similarity matching for additive manufacturing

Automatic selection system of the building orientation based on double-layer priority aggregation multi-attribute decision-making

Triangular meshes play critical roles in many applications, such as numerical simulation and additive manufacturing. However, the triangular meshes transformed from computer-aided design models using common algorithms may have many undesirable narrow triangles, which tends to affect the downstream applications. In this paper, we proposed two algorithms for Delaunay mesh construction and simplification to improve the quality of the triangular meshes. Two improved mesh operations of inserting vertices and collapsing vertices based on the principle of minimum volume destruction were designed. The improved vertex inserting operation is able to modify the local mesh so that it will conform to the local Delaunay property. The improved vertex collapsing operation can realize the simplification of the original mesh while maintaining the local Delaunay property. The results of visualized rendering and thermal diffusion simulations verified the improvement of the proposed algorithms in the aspects of the quantity and quality of the meshes.

/pdf/delaunay-mesh-construction-and-simplification-with-feature-201to01e.pdf

Delaunay Mesh Construction and Simplification with Feature Preserving Based on Minimal Volume Destruction

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