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The following technique describes the virtual planning of a single implant by combining an intraoral digital scan, an open-source computer-aided design software program, bone sounding, and 2D radiographic imaging. The surgical implant guide is fabricated using additive manufacturing technologies. Further, the surgical implant guide positioned in the patient's mouth is used to radiographically verify the estimated mesiodistal implant angulation before proceeding with the surgical intervention and can be modified if necessary. When a CBCT scan is not available, this technique eases implant planning procedures and minimizes possible surgical complications. [ABSTRACT FROM AUTHOR]

The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring. [ABSTRACT FROM AUTHOR]

In additive manufacturing processes such as laser powder bed fusion, the build orientation and packing of components affect the required support structures, the number of parts in each build, and the surface roughness of the printed parts, among other factors. Maximising the packing density while minimising the build height can increase effective machine utilisation and decrease per-part cost. Yet, the build layout optimisation problem is highly nonlinear and difficult to solve using human intuition, so a systematic algorithm approach is required. Here, we present and demonstrate a voxel-based analysis method with Bayesian optimisation for determining component build orientation in additive manufacturing. We introduce selected case studies incorporating exemplary process attributes of laser powder bed fusion, including the determination of orientation and packing configurations based on support removal and tool-accessibility constraints. [ABSTRACT FROM AUTHOR]

The use of Additive Manufacturing (AM) technology has largely increased in the last years. Because of its large differences from conventional technologies, the use of AM in production systems might call for new strategies in production planning and control. To this aim, this paper proposes analytical models to predict aggregate performance measures such as flow time, work in process, and production throughput, for production systems characterised by Laser Powder Bed Fusion AM technology. These indicators could be used both in operations strategy development and in technology comparison. The proposed models differentiate for their detail of the analysis and the number of input parameters that need to be estimated. The results show that the level of detail of the model affects the analysis leading to quite different values of the performance measures, especially in the case of highly saturated systems. Also, a discussion about the applicability of the proposed model to other AM technologies show whether and to what extent the proposed models can be applied for modelling other AM technologies. [ABSTRACT FROM AUTHOR]

Cloud Manufacturing (CMfg) is a promising approach that leverages the sharing economy to reduce costs and enhance supply chain flexibility. Particularly, when utilised alongside Additive Manufacturing (AM), CMfg is considered a key enabler for collaborative production (CP) systems. However, there is still a lack of planning models that reduce entry barriers for CP. Therefore, we propose a decentralised CP planning framework for AM. In our approach, machines autonomously select jobs from an existing production plan to forward them to other suppliers that can produce these parts more efficiently. A CMfg platform facilitates job forwarding and creates promising part bundles and manufacturing machines autonomously places bids on the packages via a combinatorial $ 2^{nd} $ 2 nd price reverse auction. Costs of the reallocated bundles are shared throughout a Shapley value-based approach without the need to disclose critical information. We benchmark our proposed framework against a centralised planning approach and find that it achieves comparable effectiveness as the benchmark solution. We also show that this mechanism promotes individual rationality and that agents particularly benefit when participating in both offering and acquiring production jobs through the auction. [ABSTRACT FROM AUTHOR]

Laser-based powder bed fusion (L-PBF) has become the de facto choice for metal additive manufacturing (AM) processes. Even after considerable research investments, components manufactured using L-PBF lack consistency in their quality. Realizing the crucial role of the melt pool in controlling the final build quality, we predict the morphology of the melt pool directly from the build commands in an L-PBF process. We leverage machine learning techniques to predict quantitative attributes like the size as well as qualitative attributes like the shape of the melt pool. The area of the melt pool is predicted using an LSTM network. The outlined LSTM-based approach estimates the area with $ 90.7\% $ 90.7 % accuracy. The shape is inferred by synthesising the images of the melt pool by using a Melt Pool Generative Adversarial Network (MP-GAN). The synthetic images attain a structural similarity score of 0.91. The precision and accuracy of the results showcase the efficacy of the outlined approach and pave the way for real-time monitoring and control of the melt pool to build products with consistently better quality. [ABSTRACT FROM AUTHOR]

The properties and microstructure of Type 304L stainless steel produced by two additive manufacturing (AM) methods—directed energy deposition (DED) and powder bed fusion (PBF)—are evaluated and compared. Localized heating and steep temperature gradients of AM processes lead to significant residual stress and distinctive microstructures, which may be process-specific and influence mechanical behavior. Test data show that materials produced by DED and PDF have small differences in tensile strengths but clear differences in residual stress and microstructural features. Measured fatigue crack growth rates (FCGRs) for cracks propagating parallel to and perpendicular to the build directions differ between the two AM materials. To separate the influences of residual stress and microstructure, K-control test procedures with decreasing and constant stress intensity factor ranges are used to measure FCGRs in the near-threshold regime (crack growth rates ≤ 1 × 10−8 m/cycle). Residual stress is quantified by the residual stress intensity factor, Kres, measured by the online crack compliance method. Correcting the FCGR data for differences in Kres brings results for specimens of the two AM materials into agreement with each other and with results for wrought specimens, when the latter are corrected for crack closure. Differences in microstructure and tensile strength have an insignificant influence on FCGRs in these tests.

This paper studies the customer order scheduling problem in the context of additive manufacturing. The study discusses an integrated problem involving the nesting of parts as well as the scheduling of batches of nested parts onto unrelated parallel machines. A mixed-integer programming model is presented, based on existing formulations from the literature, that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed for the problem configuration under consideration. Focusing on minimizing the total weighted tardiness of orders, the efficiency of the heuristic approach is evaluated using comprehensive test data. Further, we show the importance of the considered order-related objective by using qualitative analysis. [ABSTRACT FROM AUTHOR]

Purpose: This investigation aimed to assess the impact of additive manufacturing-generated surface textures on zirconia bond strength. Materials and Methods: Zirconia samples (n = 144) fabricated using digital light-processing (DLP) technology were categorized into 6 groups according to the type of surface conditioning (group NN: no designs, no air abrasion; group NY: no designs, with air abrasion; group GN: groove designs, no air abrasion; group GY: groove designs with air abrasion; group HN: hexagon grid, no air abrasion; group HY: hexagon grid, with air abrasion). Composite resin cylinders were cemented to the treated zirconia surfaces with dual-curing, self-adhesive resin cement (Clearfil SA Luting). The shear bond strength (SBS) was tested after water storage for 3 days or 3 days with an additional 10,000 thermocycles. Results: The zirconia samples fabricated using DLP technology have high accuracy. The SBS of the NY, GY, and HY groups did not significantly differ after 3 days, and neither did the SBS of the NN, GN, and HN groups. The NN, NY, and HY groups exhibited reduced SBS compared to their initial values following artificial aging, while the SBS of the remaining three groups were not diminished. The GY group obtained the highest SBS value after aging. Conclusion: Printing grooves with air abrasion can improve the bond strength. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) introduces a set of technology-specific problems, such as the proper orientation of parts or the placement of several heterogeneous parts in the same build cycle, which are not addressed by traditional approaches to production planning and scheduling. Although these new production subproblems have been implicitly addressed by several works according to generic nesting and scheduling concepts, a literature review revealed that there is no uniformity in identifying and, thus, solving all these subproblems. For this reason, and as a result of an in-depth analysis of the existent literature on AM production planning and an analogy with classic cutting and packing typologies, the present paper offers a framework to formalise the production planning problem in AM at the operational level. This framework can be used as a reference to focus on and address these AM-related problems for efficient production planning. It is designed at the subproblem level and centres on production order processing in AM. A coding strategy is specifically developed for the framework, which is applied to a review of relevant works that propose models for the production planning of AM systems. Finally, the review results are discussed and possible extensions of the framework are proposed. [ABSTRACT FROM AUTHOR]

The expeditiously growing interest in Additive Manufacturing (AM) in various domains and applications leads to different challenges in terms of management and optimisation. These issues are due to the vast amount and type variety of data and knowledge generated across the AM value chain. Therefore, it becomes essential to deliver useful help to support information management and critical decisions along the AM product realisation. This paper proposes a knowledge-based Framework for traceability and decision aid in AM processes. The proposed solution is applied in a critical phase concerning process generation and cost estimation at the beginning of AM project. A software demonstrator is deployed in a real industrial case study: Heat Exchanger for Aircraft. [ABSTRACT FROM AUTHOR]

Industrial firms increasingly use advanced technologies to navigate market fluctuations, bolster demand, and enhance competitiveness. Six Sigma methodology presents a pathway for these firms to overcome challenges and pioneer new business strategies. This study utilizes the Define-Measure-Analyze-Improve-Control (DMAIC) method to minimize process variability in additive manufacturing (AM) for spur gears. The process commenced by defining customer requirements and collecting data on the gear 3D printing process. Subsequent steps involved analyzing data to identify root causes of defects, implementing improvement solutions, and establishing process controls for sustainability. The standard deviation reduced significantly by 90.55%, with the process capability index (Cp) improving by 87.15% and the process performance capability index (Cpk) by 92.98%. The application of DMAIC proves invaluable in defect reduction and ongoing performance enhancement within the 3D printing gear sector. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM), an economical additive manufacturing (AM) technique, is widely used for extruding thermoplastic filaments. Acrylonitrile butadiene styrene (ABS) is a widely used polymer for FDM technique due to its inexpensive cost, strong impact strength, great durability, and intriguing uses. ABS materials are used for interior parts of automotive applications, drug-delivery systems, tracheal tubes, valves for ventilators, and medical masks. Nonetheless, shrinkage and warping are the primary weaknesses of ABS during the FDM process, affecting the dimensional stability of printed parts. In this context, a patent-pending radiant heating system has been developed to improve the overall performance of printed parts. This study aimed to evaluate the effect of in situ thermal treatment on the interlayer adhesion and mechanical properties of printed ABS parts. The thermal treatment was carried out on a radiant heating system at 240 °C and a printing speed of 35 mm·s−1. The physical and mechanical properties of ABS parts printed with and without radiant heating were then characterized. Various techniques, including tensile tests, X-ray microtomography (µ-CT), optical profilometry (OP), atomic force microscopy (AFM), and dynamic mechanical analysis (DMA), were conducted to investigate mechanical, microstructural, and topological properties of printed ABS parts. The results show that treated samples exhibit better interlayer adhesion than untreated ones. In addition, the treated samples had a lower porosity (1.6%) than the untreated samples (3%). Furthermore, the tensile strength, elastic modulus, and elongation at break of treated samples increased by 62%, 6%, and 110%, respectively, compared to untreated ones. [ABSTRACT FROM AUTHOR]

4D printing is the next step in additive manufacturing. Magnetoresponsive materials facilitate the creation of gripping tools through 4D printing, allowing for structural changes in response to external stimuli. In this study, the structural change is manifested as motion, triggered by an external magnetic field. This technology offers significant advantages in medical and industrial applications, including the printing of life-like moving organ models for medical training and the development of actuators for use in explosive environments. Magnetoresponsive materials are programmed with a magnetic profile and actuated by an external magnetic field. A compound of strontium ferrite microparticles S r F e 12 O 19 ( ≤ 20 μ m ) and an elastic polymer (thermoplastic copolyester) with a Hardness of Shore D 40 was produced. A star-shaped body was programmed and actuated by two permanent magnets, each of B r = 1.29 - 1.32 T . As there is no analytical approach for calculating the required actuation flux density, one has been developed. The approach is verified experimentally by using a Hall probe. It is appropriate to set the field with a Helmholtz coil, despite the utilization of two permanent magnets. The use of a commercial fused filament fabrication printer for the processing of magnetoresponsive materials has been realized here for the first time. The main contributions are the short time constant (around t a = 0.1 s ) for actuation and the repeatability (around n = 200 actuation cycles) of the motion. The feasibility of multiple diverse reprogramming is a step forward in 4D printing. Hence, the post-print programming and the inhomogeneity of the field limit the ease of the presented method. [ABSTRACT FROM AUTHOR]

To alleviate residual stress of ZrO 2 /GH536 brazed joints, the micro-scale structures were fabricated on ZrO 2 via selective laser melting technology before its vacuum brazing with GH536 superalloy. The effects of micro-scale structures on the interfacial microstructure and residual stress of brazed joints were studied. The characterization results indicated that due to its reaction with excessive Ti elements from filler, the micro-scale structures prevented the detrimental effect of over-thick brittle reaction layer on the mechanical properties of joints. Furthermore, the Invar effect of the micro-scale structures facilitated the transfer of residual stress from the ceramic side to the micro-scale structures. According to the numerical simulation results, the maximum residual stress in the brazed joint with micro-scale structures decreased from 622 MPa to 310 MPa. As a result, the fracture path was changed from the flat shape to zigzag. This work reveals the stress-relaxed mechanism of micro-scale structures and provides a new method for fabricating the ceramic/metal joints with low residual stress. [ABSTRACT FROM AUTHOR]

This study explored the feasibility of fabricating a ZrO 2 -Zr 2 Cu ceramic-metal (CERMET) fuel through additive manufacturing (AM) followed by melt infiltration. ZrO 2 was selected as a surrogate for UO 2 owing to its similar properties, and Zr 2 Cu was selected owing to its low melting point and neutron absorption cross-section to explore the feasibility of forming a uniform distribution of ceramic particles within a metal matrix. The binder jetting (BJ) AM technique was employed to fabricate preforms with precise control over the particle distribution, addressing the challenges associated with particle agglomeration and ensuring preform shape integrity. Subsequently, spontaneous melt infiltration was employed to achieve a dense and homogeneous CERMET structure. This study emphasized the significance of controlling wettability and surface energies for successful melt infiltration, focusing on the interfacial reactions between Zr 2 Cu and ZrO 2. These results indicate that this novel approach can significantly enhance the thermal and irradiation performance of CERMET fuels. [ABSTRACT FROM AUTHOR]

In this work, we utilized an in-situ chemical synthesis method to fabricate a Fe 3 O 4 /K-geopolymer (KGP) composite to be applied in a microwave absorbent field. This material, as a type of ceramic precursor, is highly expected to be a 3D printing material to prepare microwave absorbing components with complex shapes. The investigation results demonstrate that the material possesses a large effective microwave absorbing bandwidth of 3.7 GHz with the minimum reflection loss reducing to −12.2 dB when the generated Fe 3 O 4 percent reaches 12 wt% in the KGP composite. This microwave absorbent performance can be attributed to a synergistic effect between the eddy current loss caused by the improved electrical conductivity of magnetic nanoparticles and the interfacial polarization effect from the geopolymer matrix and magnetic medium interface. Also, we used direct ink writing technology to print two-dimensional components with desirable patterns. Thickness-dependent wave absorption evaluation results confirmed a maximum reflection loss of −25.9 dB at 15.2 Hz was obtained from the printed sample containing 9.0 wt% Fe 3 O 4 for a layer thickness of 10 mm, indicating that wave consumption effectiveness was determined by porous microstructures, magnetic loss of Fe 3 O 4 nanoparticles as well as suitable impedance matching. The preparation method and synthesized material pave the way for integrated manufacturing of 3D printing components with microwave absorbing performance. • Fe 3 O 4 /KGP composite was fabricated using an in-situ chemical synthesis method. • The Fe 3 O 4 /KGP composite slurry could be printed into components with wave-absorbing properties. • The microwave dissipation capacity and intrinsic mechanism of the composite were investigated. [ABSTRACT FROM AUTHOR]

Dispersant is critical in the ceramic vat photopolymerization (VP) due to its decisive impact on ceramic particle dispersion and suspension stability. However, the mechanisms by which dispersants influence photopolymerization of suspension remain underexplored. In this paper, the effects of the dispersants' refractive index and absorption coefficient on the light intensity distribution within suspension was investigated using a finite element method (FEM) simulation. The results indicate that the absorption coefficient is the primary influencing factor; as the absorption coefficient increases, the light transmittance of the suspension significantly decreases. Additionally, the photopolymerization performances of Al 2 O 3 ceramic suspensions formulated with different dispersants were investigated. We found the mechanisms by which dispersants influence photopolymerization behavior: by affecting light absorption through absorption coefficient and by directly impacting the polymerization reaction through functional groups. The study successfully achieved VP 3D printing of high-precision ceramic architectures with micron-sized features via dispersant optimization. This paper provided a new insight into optimizing fabricating resolution in ceramics VP 3D printing. [ABSTRACT FROM AUTHOR]

Highlights Ice accretion is a huge concern for many wind blades installed in colder regions. The approach of active de‐icing systems based on coatings has been recently quite explored. Nevertheless, the concern about an external electrically conductive layer leads to the need to develop multilayer insulating/conductive coatings. For this purpose, a novel multilayer coating consisting of a 3D printed carbon nanotube (CNT) and graphene nanoplatelet (GNP) reinforced nanocomposite is proposed, covered by a top coating consisting of a PCL‐based epoxy blend. Here, the CNT/GNP layer presents outstanding Joule heating capabilities due to the enhanced electrical conductivity, which increases significantly when increasing the filler content without drastically affecting the quality of the printed ribbons. More specifically, the temperature reached was about 140°C when applying 30 V for the 4 wt.%CNT and 6 wt.%GNP reinforced specimens. In this regard, this resistive heating can promote the thermal stimulus needed to activate de‐icing and self‐healing mechanisms on the epoxy/PCL top layer. Healing efficiencies obtained by the Joule effect reached up to 90% efficiency in comparison to convective heating, which showed a 68%, explained by the improved heating transfer from the CNT/GNP layer when applying the resistive heating. Finally, a proof‐of‐concept test as a de‐icing system showed a complete ice removal within 2.9 min when applying the resistive heating in the intermediate CNT/GNP layer, proving the multifunctionality of the proposed multilayer coating for self‐heating and self‐healing applications. A multilayer coating with self‐healing and de‐icing capabilities was successfully developed. Great combination of 3 min de‐icing time and 90% self‐healing efficiency. Novel inner 3D printed layer allows higher CNT/GNP contents than traditional ones. A multilayer approach with optimized nanoreinforced area required. [ABSTRACT FROM AUTHOR]

Advances in Additive Manufacturing (AM) technologies have made it possible to reduce the design and prototyping costs to a minimum especially for a low-productivity material like titanium. Titanium alloys are commonly and widely used alloys in the aerospace and biomedical sector due to their advantageous material properties. This paper is an evaluation study of factors affecting the productivity characteristics of Laser Engineered Net Shaping (LENS) titanium alloy (Ti-6Al-4 V) using face milling. Some of the productivity challenges associated with titanium such as rapid tool wear, poor surface finish, and high-power consumption are explored in this paper. All materials processed using AM face the same critical problem that the manufactured part requires a post machining since AM produces relatively poor surface finish. Machining trials are conducted using the combinations of machining parameters such as spindle speed of 800 and 1600 rev/min; feed rate of 50 and 100 mm/min; and a constant depth of cut of 1 mm, respectively. Titanium being a poor thermal conductivity material, the effect of coolant was investigated using wet/dry machining. Data related to the productivity factors and material behavior under a milling trial was recorded and analyzed. The obtained data from the trials include productivity factors such as Metal Removal Rate (MRR), power consumed, and the surface finish for each plate/trial. The power consumed in dry milling was observed to be lower than that in wet milling which is contrary to the observations from conventional wet milling. The paper concludes the trends observed for LENS titanium are opposed to the trends in conventional machining such as increasing cutting speed will result in lower cutting force and power consumed. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a wide set of technologies that can be used for many different scopes. AM is now a well-integrated prototyping and development tool in most industrial endeavours, while proper end-product manufacturing has only seen very specific niche successes. Given the complexity of AM's matrix, which is full of discontinuities and non-trivial intercorrelations that play a relevant role in product design and development, it should not come as a surprise that not many end-product applications have succeeded in making use of it. This paper argues that understanding AM's complex matrix and correctly identifying suitable production niches for it are key elements of this issue, a topic for which existing literature and tools are scarce. The analysis of AM's status quo for end-product manufacturing and the review of existing approaches to integrate these technologies with product development are the basis used in this paper to propose a suitability assessment tool to fill this knowledge gap. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), owing to its unique manufacturing approach, can drive manufacturing towards higher levels of sophistication, flexibility, and intelligence within the context of Industry 4.0. However, the complexity of AM systems and their nature as data-intensive manufacturing domains present challenges in producing high-quality products. With the advancement of digital and computer technologies, data-driven machine learning (ML) has been widely applied in AM, as it provides effective methods for quality control, process optimization, and complex system modeling. This paper succinctly summarizes the various phases of utilizing ML to assist in AM processes. It elucidates the advantages of using ML over traditional methods in each phase, starting from the pre-processing phase of design for additive manufacturing (DfAM) and parameter optimization, through the processing phase of defect detection, to the post-processing phase of part quality assessment. The objective of DfAM is to optimize product design, taking into account the interactions between multiple variables. The nonlinear capability of ML can be effectively utilized in DfAM. Defect detection aims to ensure the repeatability, durability, and reliability of parts. Combining sensors with ML can guarantee part quality. Lastly, assessing part quality and inspecting various subsystems of AM are conducted from the perspective of the part's microstructure. This review explores the application of ML in addressing numerous issues related to the AM process workflow. It provides a comprehensive and systematic summary of the application of various ML models at different phases, investigates the potential of newer ML technologies to assist AM in the future, and concludes with the limitations of current ML applications in AM as well as future development directions. By intersecting these two dynamic fields, it aims to enrich the AM research domain. [ABSTRACT FROM AUTHOR]

Carbon fiber (CF) is highly valued for its exceptional strength-to-weight relation, making it best for energy-efficient applications in aerospace, automotive, and construction sectors. Recent advancements in CF technology have reduced production costs, enhancing market growth. Carbon fiber-reinforced polymer (CFRP) composites offer benefits such as fatigue resistance, weight reduction, stiffness, and corrosion resistance. The choice of matrix material, particularly thermoplastics, impacts CFRP properties, with thermoplastics favored for recyclability and mass production. Fabrication techniques like filament winding and resin transfer molding ensure precise CF alignment but have complexities. Additive manufacturing (AM) methods, such as FDM, DLP, and SLA, provide design flexibility and efficiency. Recycling approaches, including thermal, mechanical, and chemical methods, address CFRP waste. This review offers an overview of AM technologies in CFRP manufacturing, focusing on advancements in continuous, short, and nano carbon fiber composites, their properties, manufacturing methods, and recycling techniques, as well as their applications and topological optimization. [ABSTRACT FROM AUTHOR]