Additive Manufacturing Products Research Articles

Differences Before and After Application of Additive Manufacturing in Production

With the popularity and development of additive manufacturing, more and more industries are using different types of additive manufacturing in the production process to improve their production efficiency. This article will analyze the impact of additive manufacturing on the production process before and after the application of additive manufacturing based on actual cases. Influence and analyze the technological status and development direction of additive manufacturing based on the conclusions.

TECHNOLOGY OF QUALITY CONTROL OF ADDITIVE MANUFACTURING PRODUCTS DURING PRINTING OF ELEMENTS OF ENERGY COMPLEXES

Additive manufacturing significantly impacts the energy sector through its ability to form complex geometries and optimize designs, enhancing the performance and reliability of energy systems and complexes. This type of manufacturing allows the creation of optimized parts considering energy efficiency and durability requirements, for example, heat exchangers can be designed and manufactured with a unique structure to improve their efficiency, and new energy devices and systems can be quickly prototyped for testing and optimization before mass production. The use of additive manufacturing can make parts production for energy complexes more localized and flexible, which is particularly beneficial when creating small and medium-sized energy systems. The paper discusses technologies for quality control of additive manufacturing products in the process of printing elements of energy complexes. Describes the state-of-the-art methods and controls used to ensure the high quality of 3D printed parts required for efficient operation of energy systems. Various aspects of inspection are covered, including printing process monitoring, flaw detection, material structure analysis, and geometric inspection. Examples are given of the use of various quality control methods in the context of the production of elements for energy complexes, making the article relevant for specialists in the field of additive manufacturing and energy. An analysis of existing printing technologies was carried out, and the advantages and disadvantages of each technology that is used in the process of serial production of additive manufacturing products for elements of power plants and complexes were highlighted. Keywords: technologies ofadditive manufacturing, additive manufacturing quality control software,artificial intelligence, energy complexes.

Optimizing the user experience of additive manufacturing products through material driven design

Optimizing the user experience of additive manufacturing products through material driven design

Evaluating the suitability of niches for additive manufacturing production: proposal for a numeric evaluation tool

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.

Foreseeing Potential Trade Effects of Additive Manufacturing: Evidence from Trade in Sound Recordings

Additive manufacturing (AM), known as three-dimensional printing, has the potential to drastically change the mode of production and trade in goods. However, it is challenging to investigate the effects of AM on trade because existing AM production patterns are still immature. As an analogy for the impacts of AM, this study investigates the effects of internet distribution on trade in sound recordings, which has changed after the emergence of online shops or streaming services. Specifically, we estimate the gravity equation in the bilateral trade in sound recordings among 197 countries in 2003–2017 and demonstrate that internet penetration significantly decreases trade in sound recordings. Furthermore, the strong protection of intellectual property rights in importing countries weakens the trade-reducing effect, whereas that in exporting countries magnifies such an effect.

Effect of Aqueous Electrolyte Composition on Efficiency of Electrochemical Post-Processing of Additive Manufacturing Products from Ti-6Al-4V Alloy Obtained by Selective Electron Beam Melting

The influence of the anionic composition of an aqueous electrolyte on the efficiency of the electrochemical leveling of the initial microgeometry of samples obtained by the selective electron beam melting (SEBM) at the following mode parameters was studied: current density – 20 A/cm2, initial interelectrode gap – 0.3 mm, average speed of electrolyte pumping – 12 m/c. Aqueous solutions of sodium chloride, nitrate, perchlorate and bisalt electrolytes based on them were studied. It was that aqueous solutions of sodium perchlorate have the best microleveling properties among the studied working media, which provides the ability to reduce the parameters Ra and Rz from values of 35 and 180 μm, respectively, to values of 3.2 and 20 μm during electrolysis of 30 s in a direct-flow electrolyzer. It was established that the microgeometry leveling corresponds to the model of the secondary distribution of dissolution rates; two main factors were identified that significantly influenced on the result: polarization of the electrodes and a decrease in the oxidation state of metal ions passing into the solution during electrochemical machining in the vicinity of the depression in relation to the protrusion due to changes in the local conditions of electrolysis. Microetching along grain boundaries in the studied electrolytes was not detected at the accepted parameters of the electrolysis mode.

A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

Abstract This article provides an insightful review of the recent applications of machine learning (ML) techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical properties, as well as the analysis and prediction of microstructures. AM is the modern digital manufacturing technique adopted in various industrial sectors because of its salient features, such as the fabrication of geometrically complex and customized parts, the fabrication of parts with unique properties and microstructures, and the fabrication of hard-to-manufacture materials. The functioning of the AM processes is complicated. Several factors, such as process parameters, defects, cooling rates, thermal histories, and machine stability, have a prominent impact on AM products' properties and microstructure. To establish the relationship between these AM factors and the end product properties and microstructure is difficult. Several studies have utilized different ML techniques to optimize AM processes and predict mechanical properties and microstructure. This paper discusses the applications of various ML techniques in AM to predict mechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis of AM fabricated material's microstructures and acceleration of microstructure prediction procedures are discussed in this paper.

A Review of Wear in Additive Manufacturing: Wear Mechanism, Materials, and Process

In fields such as industrial engineering and healthcare, additive manufacturing technology is a focal point for researchers. Wear represents a significant challenge for additive manufacturing technology, increasingly emerging as a research hotspot in recent years. This review categorizes and summarizes wear issues in additive manufacturing technology, providing a comprehensive overview of wear mechanisms, materials, and the effects of additive manufacturing processes on wear. Research indicates that different wear mechanisms result in varying wear characteristics. The inherent properties of the materials significantly influence wear during the manufacturing process. Modifying material compositions and optimizing microstructures can enhance the wear properties of additive manufacturing products. Additionally, the study of additive manufacturing technology in repair and maintenance is a current and anticipated research hotspot for the coming decades. In the research of additive manufacturing processes, the effective regulation of process parameters and their post-processing play a positive role in enhancing the wear characteristics of products produced via additive manufacturing. Lastly, the challenges and recent advancements concerning wear issues in the field of additive manufacturing technology research are summarized.

Heat treatment of FDM and SLS delicate additive manufacturing products: mechanical properties enhancement and dimensional accuracy

Heat treatment of FDM and SLS delicate additive manufacturing products: mechanical properties enhancement and dimensional accuracy

Critical condition for initiation of dynamic recrystallisation in electron beam powder bed fused Ti-6Al-4 V Alloy

Despite the unique capabilities of Additive Manufacturing (AM) processes for producing Ti components with complex geometries, the desired properties are only achievable if a holistic scheme is devised, considering the synergistic role of post-processing steps. This work aimed to enhance the applicability of metal AM products by improving the process chain in the manufacturing industry. For this purpose, the current work demonstrates a comprehensive investigation into the hot deformation of Ti-6Al-4 V (Ti64) pre-forms produced via the Electron Beam Powder Bed Fusion (EB-PBF) process focusing on the determination of critical conditions for initiating Dynamic Recrystallization (DRX) in these components. Following a sequential evaluation procedure, hot deformation experiments were carried out at temperatures ranging from 1000 to 1200 °C and strain rates of 0.001–1 s−1 to determine the critical stress and strain required for initiating DRX in Ti64 pre-forms and compare them to their wrought counterparts. In addition, a specific Finite Element Model (FEM) was coupled with DRX kinetics equations to predict the volume percentage of DRX grains during the hot deformation of the pre-forms. The analysis of flow stress curves showed a significant peak stress at low strains, which is then followed by a period of flow softening and eventually transitions into a nearly steady-state flow at higher strains. In addition, compared to their wrought counterparts, the EB-PBF samples exhibited a significantly superior flow softening behavior, as evidenced by a more significant volume fraction of DRX and faster recrystallisation rates. It was also revealed that the normalised strain for DRX initiation in the EB-PBF Ti64 and wrought one was 0.55 and 0.67, respectively. FEM results closely matched the experimental finding, confirming its reliability in providing valuable insights into microstructural evolution and offering a time-efficient alternative for process design and property optimisation, lowering dependence on trial-and-error approaches. Through a combination of experiments, numerical analysis, and finite element simulations, this study sheds light on the macroscopic deformation and microstructural transformations occurring during hot working processes.

Improving fatigue behavior of selective laser melted 316 L stainless steel by ultrasonic-assisted burnishing

PurposeIn this study, two postprocessing techniques, namely, conventional burnishing (CB) and ultrasonic-assisted burnishing (UAB), were applied to improve the fatigue behavior of 316 L stainless steel fabricated through selective laser melting (SLM). The effects of these processes on surface roughness, porosity, microhardness and fatigue performance were experimentally investigated. The purpose of this study is to evaluate the feasibility and effectiveness of ultrasonic-assisted burnishing as a preferred post-processing technique for enhancing the fatigue performance of additively manufactured components.Design/methodology/approachAll samples were subjected to a sandblasting process. Next, the samples were divided into three distinct groups. The first group (as-Built) did not undergo any additional postprocessing, apart from sandblasting. The second group was treated with CB, while the third group was treated with ultrasonic-assisted burnishing. Finally, all samples were evaluated based on their surface roughness, porosity, microhardness and fatigue performance.FindingsThe results revealed that the initial mean surface roughness (Ra) of the as-built sample was 11.438 µm. However, after undergoing CB and UAB treatments, the surface roughness decreased to 1.629 and 0.278 µm, respectively. Notably, the UAB process proved more effective in eliminating near-surface pores and improving the microhardness of the samples compared to the CB process. Furthermore, the fatigue life of the as-built sample, initially at 66,000 cycles, experienced a slight improvement after CB treatment, reaching 347,000 cycles. However, the UAB process significantly enhanced the fatigue life of the samples, extending it to 620,000 cycles.Originality/valueAfter reviewing the literature, it can be concluded that UAB will exceed the capabilities of CB in terms of enhancing the surface roughness and, subsequently, the fatigue performance of additive manufactured (AM) metals. However, the actual impact of the UAB process on the fatigue life of AM products has not yet been thoroughly researched. Therefore, in this study, this paper used the burnishing process to enhance the fatigue life of 316 L stainless steel produced through the SLM process.

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

Hybrid Physical–Virtual Digital Twin System for Additive Manufacturing

This study presents a hybrid physical–virtual digital twin system to enhance additive manufacturing (AM). The system aims to achieve real-time monitoring, predictive modeling, and improved 3D printing control. It integrates sensing-driven and simulation-driven digital twins into a comprehensive hybrid digital twin environment (HDTE) tailored for the Creality Ender-5 Plus 3D printer within the Unity 3D virtual environment. The AM process simulation employs a three-dimensional transient finite element model, enabling precise predictions of thermodynamics, residual stress, final distortion, and solidification parameters. To enhance real-time insights, external sensors like thermocouples, thermal cameras, and LIDAR ranging sensors continuously collect temperature and location data during 3D printing. Experimental results confirm the HDTE’s accuracy. Position assessment experiments show their ability to replicate component positions under varying conditions, while temperature assessment experiments demonstrate their capacity to mimic temperature variations promptly and accurately. The study also provides a detailed analysis of nodal temperature distribution within a 3D printed plate, indicating potential for predictive defect detection and print parameter optimization. Additionally, the study integrates a closed-loop control mechanism, ensuring rigorous quality control, although a detailed exposition of this system is reserved for future publications. This paper bridges the gap between virtual design and physical AM production, offering real-time monitoring, predictive capabilities, and automated control. These advancements enhance efficiency, reduce material waste, and promote sustainability. As this system evolves, it promises to revolutionize additive manufacturing, ushering in a data-driven era of intelligent 3D printing across industries.

Residual Stresses in Wire Arc Additive Manufacturing Products and Their Measurement Techniques: A Systematic Review

Residual Stresses in Wire Arc Additive Manufacturing Products and Their Measurement Techniques: A Systematic Review

Microstructural Characteristics of Modified ODS Ni-Based Superalloys Fabricated using Laser-Powder Bed Fusion

Oxide dispersion-strengthened (ODS) superalloys are usually manufactured by a hot isostatic pressing after mechanical alloying (MA-HIP). However, this process cannot produce complex-shaped parts. Here, we used an additive manufacturing product (powders produced by laser powder bed fusion, L-PBF) to address this issue. Modified ODS Ni-based superalloy powders with a similar composition to the mechanical alloy 6000 (MA6000) were manufactured using a gas atomization process. The obtained powders and L-PBF synthesized samples were analyzed by scanning electron microscopy (SEM), X-ray diffraction (XRD), LPSA, and EBSD analyses.

Probabilistic analysis of homogenized elastic property for resin products fabricated by additive manufacturing based on three-dimensional random field modeling of microstructure

Additive manufacturing (AM) techniques have been used in several fields of science and industry, and fabrication techniques are being updated. For this fact, especially, for industrial use, mechanical property evaluation methodologies for AM products and standards for product quality assessment should also be well established. In this paper, a probabilistic evaluation of the homogenized elastic properties of a resin product fabricated by a material extrusion-based AM technique is attempted by considering the randomness of both material and microscopic geometrical quantities. This AM method fabricates a resin structure by piling up melted resin, and to decrease consumed material and influence of thermal deformation, the inner structure of the fabricated products will include many pores and its geometry is difficult to be well controlled. From this fact, the products will be regarded as a heterogeneous material with complex random microstructure. This will cause difficulty in the evaluation of its apparent material properties and therefore a probabilistic homogenization analysis is attempted for their quantitative estimation in this study. In particular, to investigate probabilistic properties of microscopic geometry, a random field modeling technique is employed for the evaluation of autocorrelation of the microscopic geometrical parameter, and the results of the autocorrelation identified by experimental observation are introduced to the probabilistic homogenization analysis. The two-dimensional or three-dimensional random field modeling is attempted, and the effectiveness of this approach is investigated by comparing it with the experimental result.

Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm

AbstractPowder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Non-contact targeted removal of remnant powder particles from additive manufacturing builds with nano-second laser pulsing

Post-production precision cleaning is one critical challenge in Additive Manufacturing (AM) production of high-value, critical parts/builds. A part shedding particle during its operational use could cause severe complications in critical applications such as medical devices and aerospace components. This study presents a non-contact precision particle removal procedure based on a novel Dry Laser Cleaning (DLC) framework. DLC utilizes the thermo-elastic wave generation/propagation and high-frequency surface acceleration fields to dislodge and remove remnant particles due to inertial forces from a well-defined (user-specified) cleaning zone in a non-contact manner, as opposed to cleaning the entire build surface, which could lead to undesirable re-deposition and re-location of particles as well as high cost. The removal of spherical Stainless Steel 316 L (SS316L) micro-particles commonly used in AM from polished, atomically flat Silicon (Si) wafer substrates with uniform high free surface energy using a nano-second laser pulse is experimentally and computationally investigated. In general, a rough surface has a lower average free surface energy; thus, its cleaning, compared to Si wafer, is less challenging. The data with the SS316-Si material pair offers a consistent baseline. In parallel to experiments, a fully-coupled thermo-mechanical finite element study is also conducted for transient surface acceleration simulations to predict the effectiveness of SS316L micro-particle removal from a Si wafer substrate with laser pulsation. It is observed that SS316L micro-particles with a diameter larger than 5.61 μm, 8.43 μm, and 12.24 μm can be removed entirely at 3 mm, 6 mm, and 9 mm distances from its epicenter, respectively. Accordingly, a work-of-adhesion of 990.8 mJ/m2 is selected between the SS316-Si material pair, consistent with the literature. The reported experiments and simulations demonstrate the potential of the proposed rapid precision particle removal procedure for cleaning critical powder-based AM builds, where the targeted removal of particles is essential for the safe and reliable operation of high-value builds and parts.

Surface Finish Optimization of Vapor Smoothened PLA Fabricated Parts with Microstructural Analysis

Additive Manufacturing (AM) is a product creation method done layer-by-layer. This process tends to create an unwanted feature known as staircase effect. Vapor smoothing is considered a viable solution for polymer-based AM products to minimized surface roughness. Research literature concerning vapor smoothing of polylactic acid (PLA) parts generally limited unlike its ABS counterpart. This research aims to identify optimum level for both chamber temperature and exposure time of the AM product. Two methods were used to compare their outputs with one another. The two methods are surface roughness tester and optical microscopy. The results provided an impressive 50.88 and 62.72% improvement based on the two test methods. Lastly, a contour-plot was generated to provide future users a guideline if they want to conduct research similar study.