The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

Recent trends on polycaprolactone as sustainable polymer-based drug delivery system in the treatment of cancer: Biomedical applications and nanomedicine

The unique properties-such as biocompatibility, biodegradability, bio-absorbability, low cost, easy fabrication, and high versatility-have made polycaprolactone (PCL) the center of attraction for researchers. The derived introduction in this manuscript gives a pretty detailed overview of PCL, so you can first brush up on it. Discussion on the various PCL-based derivatives involves, but is not limited to, poly(ε-caprolactone-co-lactide) (PCL-co-LA), PCL-g-PEG, PCL-g-PMMA, PCL-g-chitosan, PCL-b-PEO, and PCL-g-PU specific properties and their probable applications in biomedicine. This paper has considered examining the differences in the diverse disease subtypes and the therapeutic value of using PCL. Advanced strategies for PCL in delivery systems are also considered. In addition, this review discusses recently patented products to provide a snapshot of recent updates in this field. Furthermore, the text probes into recent advances in PCL-based DDS, for example, nanoparticles, liposomes, hydrogels, and microparticles, while giving special attention to comparing the esters in the delivery of bioactive compounds such as anticancer drugs. Finally, we review future perspectives on using PCL in biomedical applications and the hurdles of PCL-based drug delivery, including fine-tuning mechanical strength/degradation rate, biocompatibility, and long-term effects in living systems.

Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities

In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. As the Moore's Law approaches its terminus, there is an urgent need for alternative computing paradigms that satisfy this growing computing demand and break through the barrier of the von Neumann model. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. This review studies the expansion of optoelectronic devices on photonic integration platforms that has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low heat dissipation, and high parallelism. In particular, various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PCSEL lasers are examined. Lastly, it is recognized that existing neuromorphic technologies encounter obstacles in meeting the peta-level computing speed and energy efficiency threshold, and potential approaches in new devices, fabrication, materials, and integration to drive innovation are also explored. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one-by-one, photonic neuromorphic systems are bound to co-exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeable future.

Exploring the application of ICTs in decarbonizing the agriculture supply chain: A literature review and research agenda

The contemporary agricultural supply chain necessitates the integration of information and communication technologies to effectively mitigate the multifaceted challenges posed by climate change and rising global demand for food products. Furthermore, recent developments in information and communication technologies, such as blockchain, big data analytics, the internet of things, artificial intelligence, cloud computing, etc., have made this transformation possible. Each of these technologies plays a particular role in enabling the agriculture supply chain ecosystem to be intelligent enough to handle today's world's challenges. Thus, this paper reviews the crucial information and communication technologies-enabled agriculture supply chains to understand their potential uses and contemporary developments. The review is supported by 57 research papers from the Scopus database. Five research areas analyze the applications of the technology reviewed in the agriculture supply chain: food safety and traceability, security and information system management, wasting food, supervision and tracking, agricultural businesses and decision-making, and other applications not explicitly related to the agriculture supply chain. The study also emphasizes how information and communication technologies can help agriculture supply chains and promote agriculture supply chain decarbonization. An information and communication technologies application framework for a decarbonized agriculture supply chain is suggested based on the research's findings. The framework identifies the contribution of information and communication technologies to decision-making in agriculture supply chains. The review also offers guidelines to academics, policymakers, and practitioners on managing agriculture supply chains successfully for enhanced agricultural productivity and decarbonization.

Developing Eco-Friendly 3D-Printing Composite Filament: Utilizing Palm Midrib to Reinforce High-Density Polyethylene Matrix in Design Applications

Designers actively pursue the use of novel materials and concepts in furniture and interior design. By providing insights into their processing behavior and suitability for 3D-printing processes, this research helps to highlight the potential of using waste materials to create more environmentally friendly and sustainable 3D-printing filaments that can be used in furniture and interior design. Furthermore, the study evaluates the effect of incorporating palm midrib nanoparticles (DPFNPs) to reinforce a high-density polyethylene (HDPE) matrix with different loadings such as 10, 20, 30, 40, and 50 wt.%. The composites were extruded into filaments using a manual extruder, which was then utilized to fabricate 3D-printed specimens using a 3D-printing pen. The effect of adding DPFNPs on the composite's chemical, thermal, and mechanical properties was evaluated, with a particular focus on how these modifications influence the melt flow rate (MFR) and, subsequently, the material's printability. The results revealed that HDPE and filament composites presented similar FTIR spectra. On the other hand, the filament composites presented an increase in the thermal stability and a decrease in the mechanical strength with increasing DPFNP content in the HDPE matrix. The filaments were successfully printed using a 3D-printing pen. Thus, using DPFNPs in the HDPE matrix presents a low-cost alternative for filament production and may expand 3D-printing applications in interior and furniture design with more sustainable materials. Future work will delve into optimizing these composites for improved printability and assessing their recyclability, aiming to broaden their applications in 3D printing and beyond.

A systematic review of the role of 4D printing in sustainable civil engineering solutions

This systematic review, not financially supported by any funding body, aims to synthesize the current knowledge on the applications, potential benefits, and challenges of 4D printing in civil engineering, with a focus on its role in sustainable solutions. Comprehensive searches were conducted in Scopus, Web of Science, and Google Scholar using related keywords. Articles that discussed 4D printing within civil engineering and construction contexts, encompassing both conceptual and empirical studies, were included. The findings suggest that 4D printing, with its time-responsive transformation feature, can enhance design freedom, improve structural performance, and increase environmental efficiency in construction. However, challenges persist in material performance, scalability, and cost. Despite these, ongoing advancements signal potential future developments that could widen the opportunities for large-scale applications of 4D printing in civil engineering. The potential use of renewable, bio-based materials could also lead to more sustainable construction practices. This review highlights the transformative potential of 4D printing, underlining the need for further research to fully leverage its capabilities and address current limitations. 4D printing emerges as a promising avenue for sustainable civil engineering solutions, offering a transformative approach that calls for continued exploration and development.

Fault Detection in 3D Printing: A Study on Sensor Positioning and Vibrational Patterns

This work examines the use of accelerometers to identify vibrational patterns that can effectively predict the state of a 3D printer, which could be useful for predictive maintenance. Prototypes using both a simple rectangular shape and a more complex Octopus shape were fabricated and evaluated. Fast Fourier Transform, Spectrogram, and machine learning models, such as Principal Component Analysis and Support Vector Machine, were employed for data analysis. The results indicate that vibrational signals can be used to predict the state of a 3D printer. However, the position of the accelerometers is crucial for vibration-based fault detection. Specifically, the sensor closest to the nozzle could predict the state of the 3D printer faster at a 71% greater sensitivity compared to sensors mounted on the frame and print bed. Therefore, the model presented in this study is appropriate for vibrational fault detection in 3D printers.

Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning

Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model's feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established.

Recent Advances in Additive Manufacturing of Soft Magnetic Materials: A Review

Additive manufacturing (AM) is an attractive set of processes that are being employed lately to process specific materials used in the fabrication of electrical machine components. This is because AM allows for the preservation or enhancement of their magnetic properties, which may be degraded or limited when manufactured using other traditional processes. Soft magnetic materials (SMMs), such as Fe-Si, Fe-Ni, Fe-Co, and soft magnetic composites (SMCs), are suitable materials for electrical machine additive manufacturing components due to their magnetic, thermal, mechanical, and electrical properties. In addition to these, it has been observed in the literature that other alloys, such as soft ferrites, are difficult to process due to their low magnetization and brittleness. However, thanks to additive manufacturing, it is possible to leverage their high electrical resistivity to make them alternative candidates for applications in electrical machine components. It is important to highlight the significant progress in the field of materials science, which has enabled the development of novel materials such as high-entropy alloys (HEAs). These alloys, due to their complex chemical composition, can exhibit soft magnetic properties. The aim of the present work is to provide a critical review of the state-of-the-art SMMs manufactured through different AM technologies. This review covers the influence of these technologies on microstructural changes, mechanical strengths, post-processing, and magnetic parameters such as saturation magnetization (MS), coercivity (HC), remanence (Br), relative permeability (Mr), electrical resistivity (r), and thermal conductivity (k).

Shape distortion in sintering results from nonhomogeneous temperature activating a long-range mass transport

Sintering theory predicts no long-range mass transport or distortion for uniformly heated particles during particle coalescence. However, in sintering-based manufacturing processes, permanent part distortion is often observed. The driving forces and mechanisms leading to this phenomenon are not understood, and efforts to reduce distortion are largely limited to a trial-and-error approach. In this paper, we demonstrate that distortion during sintering results from mass-transport driven by nonhomogeneous temperature distribution. We then show that hitherto unknown mass transport mechanisms, working in the direction opposite to temperature gradient are the likely cause of distortion. The experimental setup, designed for this purpose, enables the quantification of distortion during sintering. Two possible mass transport mechanisms are defined, and the continuum model applicable to both is formulated. The model accurately predicts the transient and permanent distortion observed during experiments, including their size dependence. Methods to control distortion that can give rise to 4D printing are discussed.

Hierarchical data structures for flowchart

Flowcharts have broad applications in the fields of software development, engineering design, and scientific experimentation. Current flowchart data structure is mainly based on the adjacency list, cross-linked list, and adjacency matrix of the graph structure. Such design originated from the fact that any two nodes could have a connection relationship. But flowcharts have clear regularities, and their nodes have a certain inflow or outflow relationship. When graph structures such as an adjacency table or an adjacency matrix are used to store a flowchart, there is a large room for optimization in terms of traversal time and storage complexities, as well as usage convenience. In this paper we propose two hierarchical data structures for flowchart design. In the proposed structures, a flowchart is composed of levels, layers, and numbered nodes. The nodes between layers are connected according to a certain set of systematic design rules. Compared with the traditional graph data structures, the proposed schemes significantly reduce the storage space, improve the traversal efficiency, and resolve the problem of nesting between sub-charts. Experimental data based on flowchart examples used in this paper show that, compared with adjacency list, the hierarchical table data structure reduces the traversal time by 50% while their storage spaces are similar; compared with adjacency matrix, the hierarchical matrix data structure reduces the traversal time by nearly 70% and saves the storage space by about 50%. The proposed structures could have broad applications in flowchart-based software development, such as low-code engineering for smart industrial manufacturing.

P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study

To overcome the problems of long production cycle and high cost in the product manufacturing process, a P2P (platform to platform) cloud manufacturing method based on a personalized custom business model has been proposed in this paper by integrating different technologies such as deep learning and additive manufacturing (AM). This paper focuses on the manufacturing process from a photo containing an entity to the production of that entity. Essentially, this is an object-to-object fabrication. Moreover, based on the YOLOv4 algorithm and DVR technology, an object detection extractor and a 3D data generator are constructed, and a case study is carried out for a 3D printing service scenario. The case study selects online sofa photos and real car photos. The recognition rates of sofa and car were 59% and 100%, respectively. Retrograde conversion from 2D data to 3D data takes approximately 60 s. We also carry out personalized transformation design on the generated sofa digital 3D model. The results show that the proposed method has been validated, and three unindividualized models and one individualized design model have been manufactured, and the original shape is basically maintained.

Effect of porosity on mechanical and biological properties of bioprinted scaffolds

Treatment of tissue defects commonly represents a major problem in clinics due to difficulties involving a shortage of donors, inappropriate sizes, abnormal shapes, and immunological rejection. While many scaffold parameters such as pore shape, porosity percentage, and pore connectivity could be adjusted to achieve desired mechanical and biological properties. These parameters are crucial scaffold parameters that can be accurately produced by 3D bioprinting technology based on the damaged tissue. In the present research, the effect of porosity percentage (40%, 50%, and 60%) and different pore shapes (square, star, and gyroid) on the mechanical (e.g., stiffness, compressive and tensile behavior) and biological (e.g., biodegradation, and cell viability) properties of porous polycaprolactone (PCL) scaffolds coated with gelatin have been investigated. Moreover, human foreskin fibroblast cells were cultured on the scaffolds in the in-vitro procedures. MTT assay (4, 7, and 14 days) was utilized to determine the cytotoxicity of the porous scaffolds. It is revealed that the porous scaffolds produced by the bioprinter did not produce a cytotoxic effect. Among all the porous scaffolds, scaffolds with a pore size of about 500 μm and porosity of 50% showed the best cell proliferation compared to the controls after 14 days. The results demonstrated that the pore shape, porosity percentage, and pore connectivity have an important role in improving the mechanical and biological properties of porous scaffolds. These 3D bioprinted biodegradable scaffolds exhibit potential for future application as polymeric scaffolds in hard tissue engineering applications.

Deformation Behavior of 3D Printed Auxetic Structures of Thermoplastic Polymers: PLA, PBAT, and Blends

Auxetic structures have a negative Poisson's ratio and therefore expand transversely to the direction of loading instead of tapering. This unique behavior is not caused by the materials used, but by the structure, and thus offers completely new functionalities and design possibilities. As a rule, auxetic structures have a very complex geometry, which makes cost-effective production possible only by means of additive manufacturing processes. Due to the high design freedom of the strand deposition method, it makes sense to manufacture auxetic structures using this process. Therefore, in this project, polylactide acid (PLA), polybutylene adipate terephthalate (PBAT), and blends of the two polymers were produced and characterized. Filaments of the two polymers and a blend were extruded, processed into auxetic structures by strand deposition process (SDP), and investigated for their properties, primarily their Poisson's ratio. The Poisson's ratio was determined and the influence of the material on it was identified. A specific number of 5 × 5 unit cells has been found to be ideal for investigation. Dual printed specimens showed a similar auxetic behavior as the specimens made of pure PBAT. Likewise, multiple loading and unloading of the structure is possible. Furthermore, in-situ computed tomography revealed the detailed characterization of the initial state, including the warpage of the structures, damage, and traced auxetic behavior in detail.

Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China

Improving energy efficiency and lowering carbon emissions are of great importance to realize the "dual carbon" goal of carbon peak and carbon neutrality. Digital economy is a new engine of economic development, but whether or how it affects energy efficiency and carbon emissions are unclear. Utilizing panel data of China's 30 provinces from 2012 to 2019, this study empirically explores the relationships among digital economy, energy efficiency, and carbon emissions. Meanwhile, from the perspective of energy efficiency, applying mediation models and panel threshold model, it analyzes the direct, indirect, and nonlinear influencing mechanisms of digital economy on carbon emissions. The results reflect that the development of digital economy in China intensifies carbon emissions. Energy efficiency serves as a vital partial mediator between the two. The enhancement of energy efficiency can lower carbon emissions. However, the development of digital economy is not conducive to improving energy efficiency, thereby, indirectly increasing carbon emissions. The mediating effect of energy efficiency accounts for 30.58 % of the total effect of digital economy on carbon emissions. Meanwhile, taking energy efficiency into account, the impact of digital economy on carbon emissions has a significant double-threshold effect and presents an N-shaped trend. [0.824, 0.912] is the optimal range of energy efficiency, within which the growth of the digital economy can empower carbon emission abatement to some extent. In addition, the expansion of population size, the coal-based energy consumption structure, and the industrial structure significantly increase carbon emissions. The improvements in living standards and environmental regulations can help to decrease carbon emissions, but the emission abatement effects are not significant. Those conclusions reveal the importance of optimizing the level and quality of digital economy and adopting differentiated digital economy development policies based on energy efficiency to achieve carbon emission reduction.

Advanced Formulations Based on Poly(ionic liquid) Materials for Additive Manufacturing

Innovation in materials specially formulated for additive manufacturing is of great interest and can generate new opportunities for designing cost-effective smart materials for next-generation devices and engineering applications. Nevertheless, advanced molecular and nanostructured systems are frequently not possible to integrate into 3D printable materials, thus limiting their technological transferability. In some cases, this challenge can be overcome using polymeric macromolecules of ionic nature, such as polymeric ionic liquids (PILs). Due to their tuneability, wide variety in molecular composition, and macromolecular architecture, they show a remarkable ability to stabilize molecular and nanostructured materials. The technology resulting from 3D-printable PIL-based formulations represents an untapped array of potential applications, including optoelectronic, antimicrobial, catalysis, photoactive, conductive, and redox applications.

Effect of print parameters on additive manufacturing of metallic parts: performance and sustainability aspects

In this study, the effects of print parameters on the mechanical properties of additively manufactured metallic parts were investigated using a tensile test. The 17-4 PH stainless steel specimens with two print parameters, including infill density and pattern orientation, were fabricated by additive manufacturing (AM) using the bound metal deposition (BMD) technique. The mechanical properties considered in this study are the Young's modulus and ultimate tensile strength. The results demonstrate that the pattern orientations do not affect the Young's modulus of the infill specimen with the triangular pattern. In contrast, the ultimate strength significantly varies depending on the pattern orientations, where the samples with the pattern orientation of zero degrees yield the best ultimate strength. In fact, the mechanical properties of infill specimens increase with their infill density. However, when operating cost and time are considered, an index for estimating performance and sustainability is consequently established. The relationship between the normalized ultimate strength of an infill specimen and the relative density is defined as the weight efficiency. The index for assessing a sustainable product is characterized by the weight efficiency versus sustainable parameter(s). The index can help end users select an appropriate infill density for AM products by considering the operating cost and time. Different cost models, including material-only costs, direct costs, and total costs, can be included in the index model to assess a sustainable product in a particular cost context.

Additive manufacturing: expanding 3D printing horizon in industry 4.0

Additive Manufacturing (AM) technology enables the production of personalized goods at reduced development costs, shorter lead times, lower energy consumption during manufacturing, and decreased material waste. AM will be consolidated as a leading technology in numerous sectors in the near future due to the maturity of the technology, the wide range of possibilities afforded by 3D printing, and the institutional push. One of the most important aspects of Industry 4.0 is 3D printing. It may be used to fabricate complicated parts and allows companies to cut inventory, develop on-demand items, create smaller localized manufacturing conditions, and even shorten supply chains. AM is expected to increase rapidly in the future because of its above stated remarkable “performance record.“ According to a report published the AM market is predicted to produce US$2 trillion worth of components and end products by year 2030. Hence integration of smart technology and production systems or indirectly one can say that AM is promoting Industry 4.0 and it plays a pivotal role in solving some of the 4th industrial revolution’s most important needs. AM is a future paradigm for futuristic production systems, and Industry 4.0 will leverage its potential to reach essential goals. AM will be found now days in a variety of industrial applications including aerospace and health care to consumer goods. This review article discusses about brief AM technology, history, its industrial applications, challenges, and future prospective. Finally, case studies using AM has been considered.

The Influence of the Gradient Infill of PLA Samples Produced with the FDM Technique on Their Mechanical Properties

Three-dimensional printing is a dynamically developing field of industry. Its main advantage is the small amount of waste, no need to use specialized tools, and easy control of the mechanical properties of the printed model. One of the most popular techniques of 3D printing is FDM. The main factor influencing the mechanical properties of 3D-printed materials is the filling density. The aim of this study was to determine the mechanical properties of porous structures with a porosity gradient of PLA samples printed using the FDM technique. The accuracy of mapping the structures by computed tomography was assessed, and then a static compression test was performed. It has been shown that the strength properties increased with the increase in the filling density. The highest value of compression strength, amounting to 41.2 MPa, was observed for samples made of PLA with an 80% filling degree, whereas the lowest value of compression strength was found in PLA-T samples with a filling degree of 10%, reaching only 0.6 MPa. It was found that not only the core filling density, but also the outer layers, influences the mechanical properties. The assessment of spatial architecture allowed for a qualitative and quantitative assessment. The obtained images from the computed tomograph showed that the designed sample models were correctly reproduced in the entire volume.

Prioritization of Challenges for the Effectuation of Sustainable Additive Manufacturing: A Case Study Approach

Additive manufacturing (AM) is gaining significant importance, as demand for customized products is increasing nowadays. AM is one of the disruptive technologies of Industry 4.0, which can reduce waste generation, enabling sustainability. The adoption of sustainable practices in the manufacturing sector is due to the need of the current scenario to minimize harmful emissions and for human wellbeing. In this regard, AM technologies are integrated with sustainable manufacturing concepts to contribute toward sustainable AM (SAM), with various benefits from the design, manufacturing, use, and EoL perspectives. Still, many sustainability issues are associated with AM processes, namely limited speed and the uncertain performance of fabricated parts. From this viewpoint, it is essential to analyze the challenges associated with adopting SAM practices. This article presents identification and analysis of the potential challenges associated with adopting SAM practices. Fifteen SAM challenges have been identified from the literature survey and analyzed using the “Gray Technique for Order of Preference by Similarity to Ideal Solution” (G-TOPSIS) approach. The priority order of the challenges has been identified. The study identified that “training towards SAM benefits” and “limited materials recycling potential” were the significant challenges in adopting SAM practices in the manufacturing sector. The present study will help industry practitioners, decision makers, and researchers effectively analyze the challenges associated with SAM for its effective implementation. Researchers can utilize the findings of the study for establishing the guidelines for the adoption of SAM.

Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle Search Algorithms

High impact polystyrene (HIPS) material is widely used for low-strength structural applications. To ensure proper function, dimensional accuracy and porosity are at the forefront of industrial relevance. The dimensional accuracy cylindricity error (CE) and porosity of printed parts are influenced mainly by the control variables (layer thickness, shell thickness, infill density, print speed of the fused deposition modeling (FDM) process). In this study, a central composite design (CCD) matrix was used to perform experiments and analyze the complete insight information of the process (control variables influence on CE and porosity of FDM parts). Shell thickness for CE and infill density for porosity were identified as the most significant factors. Layer thickness interaction with shell thickness, infill density (except for CE), and print speed were found to be significant for both outputs. The interaction factors, i.e., shell thickness and infill density, were insignificant (negligible effect) for both outputs. The models developed produced a better fit for regression with an R² equal to 94.56% for CE, and 99.10% for porosity, respectively. Four algorithms (bald eagle search optimization (BES), particle swarm optimization (PSO), RAO-3, and JAYA) were applied to determine optimal FDM conditions while examining six case studies (sets of weights assigned for porosity and CE) focused on minimizing both CE and porosity. BES and RAO-3 algorithms determined optimal conditions (layer thickness: 0.22 mm; shell thickness: 2 mm; infill density: 100%; print speed: 30 mm/s) at a reduced computation time equal to 0.007 s, differing from JAYA and PSO, which resulted in an experimental CE of 0.1215 mm and 2.5% of porosity in printed parts. Consequently, BES and RAO-3 algorithms are efficient tools for the optimization of FDM parts.

A Prediction Model for Additive Manufacturing of Inconel 718 Superalloy

Inconel 718 is a nickel-based superalloy and an excellent candidate for the aerospace, oil, and gas industries due to its high strength and corrosion resistance properties. The machining of IN718 is very challenging; therefore, the application of additive manufacturing (AM) technology is an effective approach to overcoming these difficulties and for the fabrication of complex geometries that cannot be manufactured by the traditional techniques. Selective laser melting (SLM), which is a laser powder bed fusion method, can be applied for the fabrication of IN718 samples with high accuracy. However, the process parameters have a high impact on the properties of the manufactured samples. In this study, a prediction model is developed for obtaining the optimal process parameters, including laser power, hatch spacing, and scanning speed, in the SLM process of the IN718 alloy. For this purpose, artificial neural network (ANN) modeling with various algorithms is employed to estimate the process outputs, namely, sample height and surface hardness. The modeling results fit perfectly with the experimental output, and this consequently proves the benefit of ANN modeling for predicting the optimal process parameters.

Assessment of Manufacturing Parameters for New 3D-Printed Heating Circuits Based on CNT-Doped Nanocomposites Processed by UV-Assisted Direct Write

This work consists of the development of an easy strategy to transform any structure into an efficient surface heater by the application of a low voltage over 3D printed nanocomposite circuits. To this end, the electrical conductivity and self-heating capabilities of UV-Assisted Direct Write 3D printed circuits doped with carbon nanotubes were widely explored as a function of the number of printed layers. Moreover, an optimization of the printing process was carried out by comparing the accuracy and printability obtained when printing with two different configurations: extruding and curing the ink in the same stage or curing the extruded ink in a second stage, after the whole layer was deposited. In this regard, the great homogeneity and repeatability of the heating showed by the four-layer printed circuits, together with their excellent performance for long heating times, proved their applicability to convert any structure to a surface heater. Finally, the deicing capability of the four-layer circuit was demonstrated, being able to remove a 2.5 mm thick ice layer in 4 min and 4 s.

A Systematic Review of Construction 4.0 in the Context of the BIM 4.0 Premise

This paper presents a systematic review of Construction 4.0 in the context of the building information modeling (BIM) 4.0 premise. It comprises a review of the industry in the pre-fourth industrial revolution (4IR) age, the current and anticipated development of the 4IR, Construction 4.0’s origin and applications, and the synergy of its main drivers, i.e., the synergy of BIM with the internet of things (IoT) and big data (BD). The main aim of the paper is to determine the Construction 4.0 drivers and to what extent are they initialized by the 4IR, their development and their synergy with BIM, and the direction of BIM’s implementation in the construction phase. It was found that the main drivers of Construction 4.0, which originated from the 4IR, are BIM, IoT, and BD, but with specific implementations. The results of the analysis of BIM with IoT and/or BD revealed that the integrative approaches combining the aforementioned drivers show signs of project enhancement by providing significant benefits, such as improved real-time monitoring, data exchange and analysis, construction planning, and modeling. Furthermore, it was revealed that the main drivers are mostly applied in the project’s preconstruction phase, which is continuously developing and becoming more automated. The state-of-the-art review presented in this paper suggests that BIM is in transition, adopting Construction 4.0 to become BIM 4.0.

The Economic Aspect of Digital Sustainability: A Systematic Review

In recent years, sustainability and Industry 4.0 have become crucial aspects of the global economy. Numerous studies focus on the link between environmental aspects of sustainability and Industry 4.0. On the contrary, few studies address the issue of the integration of economic sustainability and digital technologies. This paper aims to fill this gap through a systematic analysis of the literature. In particular, 32 articles were selected and following a descriptive analysis to evaluate the evolution of the theme, a content analysis was performed. The findings of this study highlight and categorize the main sustainability metrics associated with digital technologies. Specifically, the digitalization process enhances the connection of products and factories, the value chain and users to achieve a production cycle as sustainable as possible. The new technologies developed allow companies to foster innovation and entrepreneurship, increase the market share, reduce energy waste, recover and reuse the material, etc. Finally, managerial and academic contributions were identified.

Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks

A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the monitoring program, the proposed temporal neural network with a two-stage sliding window strategy (TCN-TS-SW) is utilized to accurately provide the predicted thermal values of the nozzle tip. These estimated thermal values are utilized to be the stimulus of the control system that performs countermeasures to prevent the anomaly that is bound to happen. The performance of the proposed TCN-TS-SW is presented in three case studies. The first scenario is when the proposed system outperforms the other existing machine learning algorithms namely multi-look back LSTM, GRU, LSTM, and the generic TCN architecture in terms of obtaining the highest training accuracy and lowest training loss. TCN-TS-SW also outperformed the mentioned algorithms in terms of prediction accuracy measured by the performance metrics like RMSE, MAE, and R2 scores. In the second case, the effect of varying the window length and the changing length of the forecasting horizon. This experiment reveals the optimized parameters for the network to produce an accurate nozzle thermal estimation.

A review of additive manufacturing applications in ophthalmology

Additive Manufacturing (AM) capabilities in terms of product customization, manufacture of complex shape, minimal time, and low volume production those are very well suited for medical implants and biological models. AM technology permits the fabrication of physical object based on the 3D CAD model through layer by layer manufacturing method. AM use Magnetic Resonance Image (MRI), Computed Tomography (CT), and 3D scanning images and these data are converted into surface tessellation language (STL) file for fabrication. The applications of AM in ophthalmology includes diagnosis and treatment planning, customized prosthesis, implants, surgical practice/simulation, pre-operative surgical planning, fabrication of assistive tools, surgical tools, and instruments. In this article, development of AM technology in ophthalmology and its potential applications is reviewed. The aim of this study is nurturing an awareness of the engineers and ophthalmologists to enhance the ophthalmic devices and instruments. Here some of the 3D printed case examples of functional prototype and concept prototypes are carried out to understand the capabilities of this technology. This research paper explores the possibility of AM technology that can be successfully executed in the ophthalmology field for developing innovative products. This novel technique is used toward improving the quality of treatment and surgical skills by customization and pre-operative treatment planning which are more promising factors.

Additive manufacturing of polyhydroxyalkanoates (PHAs) biopolymers: Materials, printing techniques, and applications

Additive manufacturing (AM) is recently imposing as a fast, reliable, and highly flexible solution to process various materials, that range from metals to polymers, to achieve a broad variety of customized end-goods without involving the injection molding process. The employment of biomaterials is of utmost relevance as the environmental footprint of the process and, consequently, of the end-goods is significantly decreased. Additive manufacturing can provide, in particular, an all-in-one platform to fabricate complex-shaped biobased items such as bone implants or biomedical devices, that would be, otherwise, extremely troublesome and costly to achieve. Polyhydroxyalkanoates (PHAs) is an emerging class of biobased and biodegradable polymeric materials achievable by fermentation from bacteria. There are some promising scientific and technical reports on the manufacturing of several commodities in PHAs by additive manufacturing. However, many challenges must still be faced in order to expand further the use of PHAs. In this framework, the present work reviews and classifies the relevant papers focused on the design and development of PHAs for different 3D printing techniques and overviews the most recent applications of this approach.

The Impact of Additive Manufacturing on the Flexibility of a Manufacturing Supply Chain

There is an increasing need for supply chains that can rapidly respond to fluctuating demands and can provide customised products. This supply chain design requires the development of flexibility as a critical capability. To this end, firms are considering Additive Manufacturing (AM) as one strategic option that could enable such a capability. This paper develops a conceptual model that maps AM characteristics relevant to flexibility against key market disruption scenarios. Following the development of this model, a case study is undertaken to indicate the impact of adopting AM on supply chain flexibility from four major flexibility-related aspects: volume, mix, delivery, and new product introduction. An inter-process comparison is implemented in this case study using data collected from a manufacturing company that produces pipe fittings using Injection Moulding (IM). The supply chain employing IM in this case study shows greater volume and delivery flexibility levels (i.e., 65.68% and 92.8% for IM compared to 58.70% and 75.35% for AM, respectively) while the AM supply chain shows greater mix and new product introduction flexibility, indicated by the lower changeover time and cost of new product introduction to the system (i.e., 0.33 h and €0 for AM compared to 4.91 h and €30,000 for IM, respectively). This work will allow decision-makers to take timely decisions by providing useful information on the effect of AM adoption on supply chain flexibility in different sudden disruption scenarios such as demand uncertainty, demand variability, lead-time compression and product variety.

Industry 4.0 and Sustainability: A Bibliometric Literature Review

Industry 4.0 (I4.0), Sustainability, and the Circular Economy are recently popularized concepts likely to redefine how economies and industries work. This paper, as the opening piece of this Special Issue, consists of a bibliometric study of 393 articles linking the Issue’s key themes: Industry 4.0, Sustainability and the Circular Economy. Given that this is still a recent area of the literature, and the fact that it already commands a fast-growing number of publications, the provision of an updated overview of the relevant scientific production in the field is a relevant contribution.

Carbon footprint of anaerobic digestion combined with ultrasonic post-treatment of agro-industrial organic residues

Anaerobic digestion (AD) of organic waste, although widely practiced, may require suitable accompanying treatments to enhance the degradability of complex materials. Since these may require significant efforts in terms of energy and chemical demand, careful assessment of their overall environmental sustainability is mandatory to evaluate their full-scale feasibility. The study aims to represent the environmental profile of ultrasonication (US) applied as a post-treatment of anaerobic digestion of agro-industrial organic residues. There is an interest in the US treatment for the processing of complex organic materials prior to AD in order to enhance the hydrolysis of complex organic substrates and increase the biogas yield of the biological process. An attributional, process-based life cycle assessment (LCA) study was applied to quantify and compare the potential environmental impacts of an AD plant, the biogas utilization options as well as the different digestate processing alternatives grouped into a set of 16 scenarios. Based on the results, upgrading of biogas and bio-methane use as vehicle fuel instead of energy generation from CHP or fuel cell was recommended due to the lower impact on GWP. Similarly, composting was a suitable option to reduce environmental impacts compared to belt drying. From the uncertainty analysis, AD without US as post-treatment proves to be more sustainable in terms of GWP compared to when US is used, showing net savings in GHG emissions especially when upgrading of biogas is applied. The analysis provides useful indications to policy makers to define sustainable management alternatives for organic residues as well as identify the environmental advantages associated with biogas utilization and digestate treatment and disposal alternatives.

An autonomous framework for interpretation of 3D objects geometric data using 2D images for application in additive manufacturing

Additive manufacturing, artificial intelligence and cloud manufacturing are three pillars of the emerging digitized industrial revolution, considered in industry 4.0. The literature shows that in industry 4.0, intelligent cloud based additive manufacturing plays a crucial role. Considering this, few studies have accomplished an integration of the intelligent additive manufacturing and the service oriented manufacturing paradigms. This is due to the lack of prerequisite frameworks to enable this integration. These frameworks should create an autonomous platform for cloud based service composition for additive manufacturing based on customer demands. One of the most important requirements of customer processing in autonomous manufacturing platforms is the interpretation of the product shape; as a result, accurate and automated shape interpretation plays an important role in this integration. Unfortunately despite this fact, accurate shape interpretation has not been a subject of research studies in the additive manufacturing, except limited studies aiming machine level production process. This paper has proposed a framework to interpret shapes, or their informative two dimensional pictures, automatically by decomposing them into simpler shapes which can be categorized easily based on provided training data. To do this, two algorithms which apply a Recurrent Neural Network and a two dimensional Convolutional Neural Network as decomposition and recognition tools respectively are proposed. These two algorithms are integrated and case studies are designed to demonstrate the capabilities of the proposed platform. The results suggest that considering the complex objects which can be decomposed with planes perpendicular to one axis of Cartesian coordination system and parallel withother two, the decomposition algorithm can even give results using an informative 2D image of the object.

Forecasting Warping Deformation Using Multivariate Thermal Time Series and K-Nearest Neighbors in Fused Deposition Modeling

Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are sometimes different from their desired designs or are inconsistent due to defects. Warping deformation, a defect involving layer shrinkage induced by the thermal residual stress generated during manufacturing processes, is a major factor in lowering the quality and raising the cost of printed products. This study utilized a variety of thermal time series data and the K-nearest neighbors (KNN) algorithm with dynamic time warping (DTW) to detect and predict the warping deformation in the printed parts using fused deposition modeling (FDM) printers. Multivariate thermal time series data extracted from thermocouples were trained using DTW-based KNN to classify warping deformation. The results showed that the proposed approach can predict warping deformation with an accuracy of over 80% by only using thermal time series data corresponding to 20% of the whole printing process. Additionally, the classification accuracy exhibited the promising potential of the proposed approach in warping prediction and in actual manufacturing processes, so the additional time and cost resulting from defective processes can be reduced.

Possible Applications of Additive Manufacturing Technologies in Shipbuilding: A Review

3D printing conquers new branches of production due to becoming a more reliable and professional method of manufacturing. The benefits of additive manufacturing such as part optimization, weight reduction, and ease of prototyping were factors accelerating the popularity of 3D printing. Additive manufacturing has found its niches, inter alia, in automotive, aerospace and dentistry. Although further research in those branches is still required, in some specific applications, additive manufacturing (AM) can be beneficial. It has been proven that additively manufactured parts have the potential to out perform the conventionally manufactured parts due to their mechanical properties; however, they must be designed for specific 3D printing technology, taking into account its limitations. The maritime industry has a long-standing tradition and is based on old, reliable techniques; therefore it implements new solutions very carefully. Besides, shipbuilding has to face very high classification requirements that force the use of technologies that guarantee repeatability and high quality. This paper provides information about current R&D works in the field of implementing AM in shipbuilding, possible benefits, opportunities and threats of implementation.

Environmental Sustainability of Digitalization in Manufacturing: A Review

The rapid development and implementation of digitalization in manufacturing has enormous impact on the environment. It is still unclear whether digitalization has positive or negative environmental impact from applications in manufacturing. Therefore, this study aims to discuss the overall implications of digitalization on environmental sustainability through a literature study, within the scope of manufacturing (product design, production, transportation, and customer service). The analysis and categorization of selected articles resulted in two main findings: (1) Digitalization in manufacturing contributes positively to environmental sustainability by increasing resource and information efficiency as a result of applying Industry 4.0 technologies throughout the product lifecycle; (2) the negative environmental burden of digitalization is primarily due to increased resource and energy use, as well as waste and emissions from manufacturing, use, and disposal of the hardware (the technology lifecycle). Based on these findings, a lifecycle perspective is proposed, considering the environmental impacts from both the product and technology lifecycles. This study identified key implications of digitalization on environmental sustainability in manufacturing to increase awareness of both the positive and negative impacts of digitalization and thereby support decision making to invest in new digital technologies.

Additive Manufacturing Applications for Industry 4.0: A Systematic Critical Review

Additive manufacturing, including 3D printing (3DP), is one of the critical pillars of Industry 4.0 and the next construction revolution. Several countries, including China, have utilized 3DP on larger scales or real projects. However, reviews of the lessons learned from previous large-sized practices of 3DP utilization are scarce. This paper presents a few practical applications of implementing 3DP over the past decade and suggests a direction for future research work. Recent publications on 3DP practices are systematically reviewed using an interpretivist philosophical lens, and more specifically, the nozzle characteristics are focused upon. The Scopus and China National Knowledge Infrastructure (CNKI) journal databases are utilized, resulting in the examination of 54 English and 62 Chinese papers. The selected practices from Mainland China, Hong Kong, Taiwan and Macao are considered for this review. A content critical review approach is adopted, and the identified papers are critically reviewed. These papers reported key challenges and advantages from their reported practices, such as limitations in aggregate sizes, nozzle sizes, standards, post-occupancy satisfaction, final product quality, productivity challenges and other associated risks. The paper reports upon prominent limitations and signposts directions for future investigations.

Design for Additive Manufacturing: A Systematic Review

The last few decades have seen rapid growth in additive manufacturing (AM) technologies. AM has implemented a novel method of production in design, manufacture, and delivery to end-users. Accordingly, AM technologies have given great flexibility in design for building complex components, highly customized products, effective waste minimization, high material variety, and sustainable products. This review paper addresses the evolution of engineering design to take advantage of the opportunities provided by AM and its applications. It discusses issues related to the design of cellular and support structures, build orientation, part consolidation and assembly, materials, part complexity, and product sustainability.

Strategy Development for the Manufacturing of Multilayered Structures of Variable Thickness of Ni-Based Alloy 718 by Powder-Fed Directed Energy Deposition

In this study, a manufacturing strategy, and guidelines for inclined and multilayered structures of variable thickness are presented, which are based on the results of an own-developed geometrical model that obtains both the coating thickness and dilution. This model is developed for the powder-fed directed energy deposition process (DED) and it only uses the DED single-track cladding characteristics (height, width, area, and dilution depth), the overlap percentage, and the laser head tilting-angle as inputs. As outputs, it calculates both the cladding geometry and the dilution area of the coating. This model for the Ni-based alloy 718 was improved, based on previous studies of the single clad working both vertically and at an inclined angle, adding the equations of the single clad characteristics with respect to the main process parameters. The strategy proposed in this paper for multilayered cladding consisted of both adding an extra clad at the edges of the layer and using a variable value of the overlap percentage between clads for geometric adaptations. With this strategy, the material deposition is more accurate than otherwise, and it shows stable growth. Manufacturing a multilayered wall of wider thicknesses at higher heights was utilized to validate the strategy.

Integration of Additive Manufacturing, Parametric Design, and Optimization of Parts Obtained by Fused Deposition Modeling (FDM). A Methodological Approach

The use of current computer tools in both manufacturing and design stages breaks with the traditional conception of productive process, including successive stages of projection, representation, and manufacturing. Designs can be programmed as problems to be solved by using computational tools based on complex algorithms to optimize and produce more effective solutions. Additive manufacturing technologies enhance these possibilities by providing great geometric freedom to the materialization phase. This work presents a design methodology for the optimization of parts produced by additive manufacturing and explores the synergies between additive manufacturing, parametric design, and optimization processes to guide their integration into the proposed methodology. By using Grasshopper, a visual programming application, a continuous data flow for parts optimization is defined. Parametric design tools support the structural optimization of the general geometry, the infill, and the shell structure to obtain lightweight designs. Thus, the final shapes are obtained as a result of the optimization process which starts from basic geometries, not from an initial design. The infill does not correspond to pre-established patterns, and its elements are sized in a non-uniform manner throughout the piece to respond to different local loads. Mass customization and Fused Deposition Modeling (FDM) systems represent contexts of special potential for this methodology.

Sustainability Outcomes of Green Processes in Relation to Industry 4.0 in Manufacturing: Systematic Review

Green processes are very important for the implementation of green technologies in production to achieve positive sustainability outcomes in the Industry 4.0 era. The scope of the paper is to review how conventional green processes as a part of Industry 4.0 provide sustainability outcomes in manufacturing. The paper is based on the methodology of systematic literature review through the content analysis of literary resources. Twenty-nine studies were included in our content analysis. The results show the main focus of current literature related to Industry 4.0, sustainability outcomes and green processes. The authors present a conceptual Sustainability Green Industry 4.0 (SGI 4.0) framework that helps to structure and evaluate conventional green processes in relation to Industry 4.0 and sustainability. The study summarizes which technologies (big data, cyber-physical systems, Industrial Internet of Things and smart systems) and green processes (logistics, manufacturing and product design) are important for achieving a higher level of sustainability. The authors found that the most often common sustainability outcomes are energy saving, emission reduction, resource optimalization, cost reduction, productivity and efficiency and higher economic performance, human resources development, social welfare and workplace safety. The study suggests implications for practice, knowledge and future research.

High-Complexity WO3-Based Catalyst with Multi-Catalytic Species via 3D Printing

Three-dimensional (3D) printing has recently been introduced into the field of chemistry as an enabling tool employed to perform reactions, but so far, its use has been limited due to material and structural constraints. We have developed a new approach for fabricating 3D catalysts with high-complexity features for chemical reactions via digital light processing printing (DLP). PtO2-WO3 heterogeneous catalysts with complex shapes were directly fabricated from a clear solution, composed of photo-curable organic monomers, photoinitiators, and metallic salts. The 3D-printed catalysts were tested for the hydrogenation of alkynes and nitrobenzene, and displayed excellent reactivity in these catalytic transformations. Furthermore, to demonstrate the versatility of this approach and prove the concept of multifunctional reactors, a tungsten oxide-based tube consisting of three orderly sections containing platinum, rhodium, and palladium was 3D printed.

Detection of Material Extrusion In-Process Failures via Deep Learning

Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.

Powder-Based 3D Printing for the Fabrication of Device with Micro and Mesoscale Features

Customized manufacturing of a miniaturized device with micro and mesoscale features is a key requirement of mechanical, electrical, electronic and medical devices. Powder-based 3D-printing processes offer a strong candidate for micromanufacturing due to the wide range of materials, fast production and high accuracy. This study presents a comprehensive review of the powder-based three-dimensional (3D)-printing processes and how these processes impact the creation of devices with micro and mesoscale features. This review also focuses on applications of devices with micro and mesoscale size features that are created by powder-based 3D-printing technology.