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A wire-based additive manufacturing system works with high manufacturing efficiency and low dimensional precision. The purpose of this paper is to study the dimensional characteristics of Ti-6Al-4V thin-walled parts with wire-based multi-laser additive manufacturing in vacuum.

Wire-based multi-laser additive manufacturing was carried out to understand the effect brought from different parameters. The Ti-6Al-4V thin-walled parts were formed by different height increments, power inputs and inter-layer cooling times in vacuum.

The result shows that, with the number of layers increment, the layer width of thin-walled part increases gradually in the beginning and stabilizes soon afterward. Height increment, laser power and inter-layer cooling time could affect the energy input to the deposited bead and heat accumulation of thin-walled part. The layer width decreases, while the height increment increases. The increment of laser power could increase the layer width. And, the increment of inter-layer cooling time (more than 5 s) has little effect on the layer width.

The heat dissipation mode of thin-walled parts in vacuum and the influence of different parameters on layer width are explained in this paper. It provides a reference for further understanding and controlling dimension precision of Ti-6Al-4V thin-walled part with wire-based multi-laser additive manufacturing in vacuum. At the same time, it provides a reference for researches of dimensional characteristics in the additive manufacturing industry.

The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

Additive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system.

This study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations.

Density, Young’s modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects.

To the best of the authors’ knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Metal additive manufacturing is an inherently thermal process, with intense localised heating and for sparse lattice structures, often rapid uneven cooling. Thermal effects influence manufactured geometry through residual stresses and may also result in non-isotropic material properties. This paper aims to increase understanding of the evolution of the temperature field during fabrication of lattice structures through numerical simulation.

This paper uses a reduced order numerical analysis based on “best-practice” compromise found in literature to explore design permutations for lattice structures and provide first-order insight into the effect of these design variables on the temperature field.

Instantaneous and peak temperatures are examined to discover trends at select lattice locations. Insights include the presence of vertical struts reduces overall lattice temperatures by providing additional heat transfer paths; at a given layer, the lower surface of an inclined strut experiences higher temperatures than the upper surface throughout the fabrication of the lattice; during fabrication of the lower layers of the lattice, isolated regions of material can experience significantly higher temperatures than adjacent regions.

Due to the simplifying assumptions and multi-layer material additions, the findings are qualitative in nature. Future research should incorporate additional heat transfer mechanisms.

These findings point towards thermal differences within the lattice which may manifest as dimensional differences and microstructural changes in the built part.

The paper provides qualitative insights into the effect of local geometry and topology upon the evolution of temperature within lattice structures fabricated in metal additive manufacturing.

Additive manufacturing has achieved rapid development in recent years. The purpose of this paper is to visualize the intellectual landscapes of additive manufacturing and identify the hotspots and emerging trends of additive manufacturing, which can provide references for scholars, enterprises and governments to promote the development of theory and practice in the additive manufacturing field.

Science mapping is a fast-growing interdisciplinary field originated in information science and technology. Based on this methodology, guided by a computational approach, the paper visualizes the co-occurring keywords network and co-citation references network by CiteSpaceIII software to explore the hotspots and emerging trends of additive manufacturing by the following five indicators: highly cited keywords, burst keywords, clusters, landmark references and burst references.

“Additive manufacturing,” “3D printing,” “3D powder printing,” “consolidation phenomena,” “microstructure,” “rapid prototyping,” etc., are the main hotspots of additive manufacturing. The trends of additive manufacturing generally consist of three stages: the fundamental concepts stage from 1995 to 2000 (“rapid prototyping,” “additive manufacturing,” etc.), the approaches and techniques applications stage from 2001 to 2010 (“stereolithography,” “scaffold,” etc.), and the emerging trends stage from 2011 to the present (“stem cell”, “selective laser,” “ti-6al-4v,” etc.). The research is most abundant in 2010 and 2012. The medical field is an important hotspot of additive manufacturing. Additive manufacturing has been researched in interdiscipline.

The paper maps the perspective of additive manufacturing and explore the hotspots and emerging trends of additive manufacturing.

This paper aims to provide a multi-objective optimization problem in design for manufacturing (DFM) approach for fused deposition modeling (FDM). This method considers the manufacturing criteria and constraints during the design by selecting the best manufacturing parameters to guide the designer and manufacturer in fabrication with FDM.

Topological optimization and bi-objective optimization problems are suggested to complete the DFM approach for design for additive manufacturing (DFAM) to define a product. Topological optimization allows the shape improvement of the product through a material distribution for weight gain based on the desired mechanical behavior. The bi-objective optimization problem plays an important role to evaluate the manufacturability by quantification and optimization of the manufacturing criteria and constraint simultaneously. Actually, it optimizes the production time, required material regarding surface quality and mechanical properties of the product because of two significant parameters as layer thickness and part orientation.

A comprehensive analysis of the existing DFAM approaches illustrates that these approaches are not developed sufficiently in terms of manufacturability evaluation in quantification and optimization levels. There is no approach that investigates the AM criteria and constraints simultaneously. It is necessary to provide a decision-making tool for the designers and manufacturers to lead to better design and manufacturing regarding the different AM characteristics.

To assess the efficiency of this approach, a wheel spindle is considered as a case study which shows how this method is capable to find the best design and manufacturing solutions.

A multi-criteria decision-making approach as the main contribution is developed to analyze FDM technology and its attributes, criteria and drawbacks. It completes the DFAM approach for FDM through a bi-objective optimization problem which deals with finding the best manufacturing parameters by optimizing production time and material mass because of the product mechanical properties and surface roughness.

Laser additive manufacturing is widely utilized to fabricate the Ti6Al4V alloy, but it requires post-processing to improve its performance. This paper aims to propose laser peening (LP) as an effective way to improve the surface characteristics of the Ti6Al4V alloy fabricated by direct laser deposition (DLD).

Surface integrity including surface roughness, porosity, residual stress and microhardness are investigated in detail before and after LP treatment. Microstructure evolution is characterized by the electron backscatter diffraction (EBSD) to analyze crystal phase, grain boundary misorientation and texture.

Multiple overlapping layers of LP treatment result in slight influence on the polished surface of DLD-built samples. Porosity measured by the Archimedes test is found to be greatly decreased after LP treatment. Compressive residual stresses are significantly induced, the magnitude of which is greatly increased by increasing layers of LP treatment. And, local weakening or enhancement of residual stress in depth is observed because of pore and inclusion defects in the DLD-built Ti6Al4V alloy. Favorable hardness property can be obtained after multiple overlapping layers of LP treatment. EBSD analysis shows that LP treatment with multiple layers can introduce a large amount of lower-angle boundaries, indicating that dislocations beneath the top surface could induce a strain-hardened layer. The microtexture of the DLD-built Ti6Al4V alloy cannot be eliminated to decrease the anisotropy of the mechanical property.

The variation of porosity observed after LP inside the DLD-built Ti-Al-4V is attractive but requires more detailed work to analyze the evolution of pore geometry.

Surface treatment of an additive manufactured titanium alloy was carried out to improve its fatigue resistance.

This work is original in proposing LP as an effective post process for the surface treatment of an additive manufactured titanium alloy through analyzing the surface integrity and microstructure evolution.

Binder jetting is one of the essential additive manufacturing methods because it is cost-effective, has no thermal stress problems and has a wide range of different materials. Using binder jetting technology in the industry is becoming more common recently. However, it has disadvantages compared to traditional manufacturing methods regarding speed. This study aims to increase the manufacturing speed of binder jetting.

This study used adaptive slicing to increase the manufacturing speed of binder jetting. In addition, a variable binder amount algorithm has been developed to use adaptive slicing efficiently. Quarter-spherical shaped samples were manufactured using a variable binder amount algorithm and adaptive slicing method.

Samples were sintered at 1250°C for 2 h with 10°C/min heating and cooling ramp. Scanning electron microscope analysis, surface roughness tests, and density calculations were done. According to the results obtained from the analyzes, similar surface quality is achieved by using 38% fewer layers than uniform slicing.

More work is needed to implement adaptive slicing to binder jetting. Because the software of commercial printers is very difficult to modify, an open-source printer was used. For this reason, it can be challenging to produce perfect samples. However, a good start has been made in this area.

To the best of the authors’ knowledge, the actual use of adaptive slicing in binder jetting was applied for the first time in this study. A variable binder amount algorithm has been developed to implement adaptive slicing in binder jetting.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.