Digital Manufacturing Process Research Articles

Sustainable Entertainment: Management Strategies for Sustainable Growth in the Television Industry

As it faces the threats and seizes the possibilities presented by sustainability, the television business is experiencing a profound shift. The potential for long-term expansion in the television industry is investigated in this study by looking at sustainable entertainment management practices. The research finds important tactics that production businesses and television networks may use to incorporate social, economic, and environmental sustainability into their operations by looking at existing practices and new developments. Case studies of prominent firms, examination of sustainability reports, and expert interviews make up the research's mixed-methods strategy. The results show that in order to promote sustainability, it is crucial to use energy-efficient technology, decrease waste with digital manufacturing processes, and encourage social responsibility with inclusive content and fair labour standards. In order to achieve sustainable development in the long run, the research stresses the significance of stakeholder involvement and strategic alliances. Stakeholders in this sector may use the suggestions made in this paper to improve their sustainability initiatives and meet the changing needs of the global market in a way that is good for the environment and their bottom line.

Snapshot on 3D printing with alternative binders and materials: Earth, geopolymers, gypsum and low carbon concrete

The rapid development of 3D concrete printing now offers mechanical efficiency and freedom to push the limits of construction design. The digital manufacturing process holds potential for reducing carbon footprints through design optimization. Printable concrete, which is a mix of cement (based on ordinary Portland cement), aggregates, and admixtures, is attractive due to widespread and cost-effective constituents. However, many common formulations omit gravel, requiring higher cement paste volumes and inducing significant embodied carbon. Assessing the potential of low-carbon cements like Limestone Calcined Clay Cement (LC3), calcium aluminate cement (CAC), or magnesium-based cement for 3D printing is a current challenge that can address this issue. Tailoring these construction materials to printing applications and environmental needs now drives scientific exploration. This paper comprehensively reviews alternative materials and binders such as earthen materials, geopolymers, low carbon binders or gypsum-based materials, addressing fresh and hardened properties, developed digital processes, targeted applications, and discussing advantages and drawbacks of each alternative.

Sketch, CAD, or simulate the build? Exploring novice designers’ design representation use in design for additive manufacturing

ABSTRACT The growing adoption of digital tools to support additive manufacturing (AM) calls for designers to rethink their use of design representations in the design process. For example, designers can automate concept generation through generative design, reducing reliance on conceptual sketches. Little research has explored designers’ use of design representations in design for AM (DfAM). We explored this gap through an observational study with novice designers involving an open-ended DfAM task, and their use of design representations was captured using eye-tracking. Overall, participants spent a significant amount of time CAD modeling their designs compared to using other design representations such as sketches and build simulations. Moreover, participants’ design representation usage did not relate to the creativity of their designs. However, participants who generate high-performing designs spent more time CAD modeling and less time sketching – a novel and important finding from our study. Therefore, the advancement of digital design tools and the growing adoption of digital manufacturing processes may result in designers conceptualizing their solutions in embodiment-based tools such as CAD and relying less on low-fidelity tools such as sketching. Our results provide an essential first step toward creating a generalized framework for designers to employ different design representations in DfAM.

3D Printing of Customizable Transient Bioelectronics and Sensors

AbstractTransient electronics have emerged as a new category of devices that can degrade after their functional lifetime, offering tremendous potential as disposable sensors, actuators, wearables, and implants. Additive manufacturing methods represent a promising approach for patterning transient materials, yet examples of fully printed bioelectronic devices are scarce. This study introduces a fully digital 3D printing approach enabling the prototyping and customization of soft bioelectronics made of transient materials. The direct ink writing of poly(octamethylene maleate (anhydride) citrate) (POMaC) as an elastomeric matrix and of a shellac‐carbon ink as a conductor is investigated. Precise and repeatable deposition of both structural and conductive features is achieved by optimizing printing parameters, i.e., the dispense gap, printing speed, and inlet pressure. Multi‐material 3D printing enables the fabrication of functional transient devices. Notably, pressure and strain sensors are shown to operate in ranges relevant to implanted biomechanical monitoring. 3D‐printed transient electrodes are demonstrated to be comparable to state‐of‐the‐art devices in terms of impedance behavior. Finally, physical degradation of the materials is confirmed at physiological conditions. These fully digital additive manufacturing processes enable the monolithic fabrication of customizable transient bioelectronics with adaptable functions and geometries.

Additive manufacturing of Invar 36 alloy

Invar 36 alloy, renowned for its ultra-low coefficient of thermal expansion (CTE), is a functional Fe–Ni alloy that finds wide applications in aerospace and precision instruments. Additive manufacturing (AM), as a rapid digital manufacturing process, can efficiently and flexibly fabricate Invar 36 alloy parts with complex geometric and exceptional properties. Considering the advantages of AM and the increasing interest and demand for Invar 36 alloy parts fabrication by AM in recent years, it is imperative to provide a comprehensive summary of the current research status and technological advancements pertaining to AM Invar 36 alloy. Firstly, an overview of the AM processes currently employed for fabricating Invar 36 alloy is provided, including laser powder bed fusion, selective laser sintering, directed energy deposition, cold spray additive manufacturing, wire arc additive manufacturing, and binder jetting. Additionally, the microstructure, mechanical properties, and CTE of Invar 36 alloy manufactured by AM are summarized. Even eliminating the post heat treatment, the as-printed Invar 36 alloy can achieve excellent low CTE and mechanical properties, comparable to or exceeding traditional manufacturing processes. Moreover, the advantages and research progress of AM Invar 36 alloy composites are introduced. Enhancing mechanical properties while reducing CTE is a common challenge for both AM Invar 36 alloy and Invar composites. Finally, the technical gaps, research trends, and potential applications of AM Invar 36 alloy are summarized and prospected.

3D printing of ultra-thin veneers made of lithium disilicate using the LCM method in a digital workflow: A feasibility study.

This article highlights the feasibility of the additive fabrication of ultra-thin veneers made of lithium disilicate using the lithography-based ceramic manufacturing (LCM) method. An esthetical appealing restoration of anterior teeth with thin ceramic veneers is considered one of the ultimate challenges in restorative dental prosthetics. These sophisticated restorations can be fabricated in different ways. Both analog and digital subtractive manufacturing processes have been used to date. Either of the methods is highly demanding for the dental technician and dental engineering due to the required low ceramic layer thickness. Modern additive manufacturing methods, for example LCM technology, enable the production of ultra-thin lithium disilicate veneers with layer thicknesses of down to 0.2 mm and could therefore represent a viable alternative for this indication in the future. Digital technologies can help streamline workflows, make the outcome more predictable and reproducible, and even further optimize therapeutic restorative options such as highly esthetic veneers for anterior teeth. The reduced material thickness allows for a true non-prep solution or minimally invasive preparation.

Exploration of making removable partial denture by digital technology

To explore the digital manufacturing process of distal extension removable partial denture. From November 2021 to December 2022, 12 patients (7 males and 5 females) with free-ending situation were selected from the Department of Prosthodontics, School of Stomatology, The Fourth Military Medical University. Three-dimensional model of the relationship between alveolar ridge and jaw position was obtained by intraoral scanning technique. After routine design, manufacturing and try-in of metal framework for removable partial denture, the metal framework was located in the mouth and scanned again to obtain the composite model of dentition, alveolar ridge and metal framework. The free-end modified model is obtained by merging the digital model of free-end alveolar ridge with the virtual model with the metal framework. The three-dimensional model of artificial dentition, and base plate was designed on the free-end modified model, and the resin model were made by digital milling technology. The removable partial denture was made by accurately positioning the artificial dentition and base plate, bonding metal framework with injection resin, grinding and polishing the artificial dentition and resin base. Compared with the design data after clinical trial, the results showed that there was an error of 0.4-1.0 mm and an error of 0.03-0.10 mm in the connection between the resin base of artificial dentition and the connecting rod of the in-place bolt and the connection between artificial dentition and resin base. After denturen delivery, only 2 patients needed grinding adjustment in follow-up visit due to tenderness, and the rest patients did not find any discomfort. The digital fabrication process of removable partial denture used in this study can basically solve the problems of digital fabrication of free-end modified model and assembly of artificial dentition with resin base and metal framework.

From patient to maker - a workflow including people with cerebral palsy in co-creating assistive devices using 3D printing technologies

Purpose Digital fabrication, like 3D printing, is a new opportunity for rehabilitation professionals to produce customized assistive devices. It allows for empowerment and collaboration in device procurement, but practical implementations are scarcely described. We describe the workflow, discuss feasibility and propose directions for future work. Methods We showcase a process of co-manufacturing a custom spoon handle together with two people with cerebral palsy. Our digital manufacturing process was centered around videoconferencing to remotely control the processes from design to final 3D printing. Device functionality and satisfaction were assessed using standard clinical questionnaires: the Individual Priority Problem Assessment Questionnaire (IPPA) and the Quebec User Satisfaction Assessment with Assistive Technology (QUEST 2.0). Results IPPA was instrumental in assessing user needs and device effectiveness. QUEST revealed where to focus future design efforts. Conclusion Involving people with disabilities in co-creation of assistive devices opens for new opportunities for healthcare providers that should be explored in depth using the described methodology. There may also be therapeutic benefits and we envisage specific actions to take in order to make it clinically viable.

Building Information Modeling (BIM) and Robotic Manufacturing Technological Interoperability in Construction – A Cyclic Systematic Literature Review

The architectural engineering and construction (AEC) industry is undergoing a digital transformation that progressively improves its performance, productivity, and competitiveness. This digital shift is accelerated through building information modeling (BIM) which facilitates technological integrations. BIM has significantly contributed to digitizing design and management activities. However, it has not yet sufficiently demonstrated its interoperability with digital manufacturing processes, such as robotic manufacturing (RM). It is from this perspective that this work will review the current literature’s stance on the technological interoperability of BIM and RM tools through the systematic literature review (SLR) method. This literature review aims to identify research avenues to operationalize RM through BIM tools in construction. The study conducted in this research is progressive; it builds on the identified research gaps and investigates potential research avenues to be undertaken. The results revealed that computational design (CD) could serve as a bridge between BIM and RM. They also revealed that RM is operationalizable in off-site construction (OSC) through BIM and CD.

A Novel Method of Digital Twin-Based Manufacturing Process State Modeling and Incremental Anomaly Detection

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly.

Integration of Industry 5.0 requirements in digital twin-supported manufacturing process selection: a framework

Process selection has been an integral part of manufacturing design and operation, with a close link to manufacturing process modeling. Throughout the years, it has been enriched with many criteria, such as energy efficiency, emissions, resources, and sustainability in general, increasing its complexity in terms of modeling and optimization. Herein, process selection is coupled with (a) the capabilities of the digital twins, (b) the criteria introduced by Industry 5.0 (I5.0) in terms of sustainability and human inclusion, and (c) the opportunities offered by key enabling technologies. A framework is presented capable to implement automated process selection and potentially scheduling through a specific set of integrated technologies, utilizing the concept of the microfactory as a basis in terms of implementation. A case study for the evaluation of two parts, using Additive Manufacturing and laser welding, in different scenarios is presented. It was concluded that the inclusion of the I5.0 criteria not only increases the well-being of the workers but also the energy and time efficiency of the production line, increasing the profit margin.

Computational study on subsonic impact resistance of lattice structures in 3D printed thin Ti6Al4V parts

Additive manufacturing (AM) is a set of digital manufacturing processes which is gaining momentum in the industries for its flexibility, versatility and ability to manufacture complex geometries with limited human interaction. It has enabled relatively fast materialization of a conceptualized design for testing, inspecting and examining the components. AM is highly suitable for fabricating functional gas turbine components owing to its low volume production requirements, lightweight components and geometrical complexity requirements. Laser powder bed fusion (LPBF), is the most common form of metal AM method used. The shape complexity attribute of LPBF can be taken advantage of to fabricate lattice-induced gas turbine inlet components prone to hail or bird impact. In this study, a computational approach was implemented to assess the behaviour of thin Ti6Al4V parts induced with a single-layer lattice subjected to projectile impact. Homogenization of lattice structures was carried out in nTopology to shortlist them for impact studies based on Young’s modulus distribution. The impact analysis was performed on eight lattice structures, two of which were derived from topology optimization, along with a solid plate and hollow plate acting as benchmarks for comparison. The high-velocity impact (250 m/s) was simulated using the “Explicit Dynamics” module in Ansys. It was observed that the lattice geometry directly below the impact site influenced the deformation, stress and energy values. It was concluded that lattices such as isotruss, kelvin cell and FCC, which have more struts interfacing with the impact region, tend to show deformation comparable to solid plates (4.89E-04 m). The equivalent stress values were within the material's elastic limit, indicating elastic deformation. The reduction in kinetic energy of the projectile for lattice-induced plates was also comparable with the values for solid plates (68 %). A correlation coefficient of −0.807 was obtained between packing factor and minimum kinetic energy, indicating denser lattices resulted in higher deceleration.

Glass art: A discussion of the impact of digital manufacturing processes on craft practice

The traditional glass art industry has changed over time and technological renovation, and its forging and forming methods have been fused with the latest technologies that began to prevail ten years ago 3D printing and laser cutting in terms of production processes. The involvement of machines in the process has greatly saved the time needed to make crafts, and the changes behind it have had a series of impacts on the lowering of the manufacturing threshold of the industry, the liberation of beginners in terms of skill acquisition, The changes behind this have had several implications in terms of the lowering of the manufacturing threshold for the industry, the liberation of beginners in learning skills, and the scarcity of skilled craftsmen in the artisanal field. This paper will focus on the ethical issues and considerations behind the changes and richness of the glass art industry.

Digital manufacturing process quickly produces liquid silicone rubber parts

To support manufacturers, Trelleborg Sealing Solutions has developed and is offering a fast and flexible digital manufacturing process for product development and small-batch production of liquid silicone rubber (LSR) parts. This article briefly looks at what is involved.

Associative Rules-Driven Intelligent Production Schedule Control System for Digital Manufacturing Ecosystem

The paper presents the architecture of a subsystem for processing, storing, visualizing and analyzing of production schedules. It is an important part of an integrated digital manufacturing process control ecosystem. The proposed system allows to process and analyze the production schedule in real time with reference to possible changes in the production situation. The system uses open source big data solutions. The results of the system operation tests with the data from production schedules of a large automotive company are presented. A methodology for updating production schedules based on the realtime predictive models for making control decisions is proposed; the overall system performance is investigated. Identification models for digital twins are offered.

DIE SHEET HYDROFORMING OF A COMPLEX-SHAPED AA2024-W AIRCRAFT SKIN PANEL — FROM CONCEPT TO FINAL COMPONENT

Product design in the early stages requires a more quantitative process to ensure that the simulation study's outcome will meet expectations. The purpose of this study was to determine whether a manufacturing simulation software package could effectively evaluate and validate the conceptualised blank and tooling to manufacture a sizeable complex- shaped aluminium alloy aircraft panel using die sheet hydroforming. Blank and tool concepts were conceptualised, evaluated, and validated digitally using the forming process conceptualisation cycle. Without the need to trial several blank and tool concepts physically, a significant saving was achieved using a resource-efficient development process. Even without modelling the hyperelastic rubber elements, a successful die sheet hydroformed simulation result was conducted. However, the study showed that a rubber-to-metal friction coefficient must be incorporated appropriately. The study further showed the lack of standard metrology methods in commercial simulation packages to evaluate a simulation result accurately. Simulation metrology is a new area of interest that requires further development. This research contributes to improving the sheet metal forming simulation process and, specifically, die sheet hydroforming process simulation. The study will benefit the aerospace and automotive industry and promote the adoption of digital manufacturing processes.

3D/4D Printing of Polymers: Fused Deposition Modelling (FDM), Selective Laser Sintering (SLS), and Stereolithography (SLA).

Additive manufacturing (AM) or 3D printing is a digital manufacturing process and offers virtually limitless opportunities to develop structures/objects by tailoring material composition, processing conditions, and geometry technically at every point in an object. In this review, we present three different early adopted, however, widely used, polymer-based 3D printing processes; fused deposition modelling (FDM), selective laser sintering (SLS), and stereolithography (SLA) to create polymeric parts. The main aim of this review is to offer a comparative overview by correlating polymer material-process-properties for three different 3D printing techniques. Moreover, the advanced material-process requirements towards 4D printing via these print methods taking an example of magneto-active polymers is covered. Overall, this review highlights different aspects of these printing methods and serves as a guide to select a suitable print material and 3D print technique for the targeted polymeric material-based applications and also discusses the implementation practices towards 4D printing of polymer-based systems with a current state-of-the-art approach.

Digital Technologies and Automation: The Human and Eco-Centered Foundations for the Factory of the Future [TC Spotlight

Reports on the factory of the future where automation dominates operations. Digital manufacturing processes have been extensively transformed by ubiquitous connectivity and collaborative robots so much that the industry of the future is pursuing high productivity, efficiency, and customization by becoming increasingly collaborative, connected, and cognitive (IC3). In fact, these new technologies have fostered a remarkable increase in production efficiency and an exceptional economic growth worldwide.

Spatiotemporal Projection‐Based Additive Manufacturing: A Data‐Driven Image Planning Method for Subpixel Shifting in a Split Second

Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.

DEVELOPMENT OF SUITABLE MACHINE LEARNING MODEL FOR A CEMENT PLANT CALCINER

Cement industry is one of the largest CO2 emitters and continuously working to minimize these emissions. Use of Artificial intelligence (AI) in manufacturing helps in reducing breakdowns/ failures by avoiding frequency of startups, reducing fuel fluctuations which will ultimately reduce carbon footprint. AI offers a new mode of digital manufacturing process that can increase productivity by optimizing the use of assets at the fraction of cost. Calciner is one of the key equipment of a cement plant which dissociates the calcium carbonate into calcium oxide and carbon dioxide by taking heat input from fuel combustion. An AI model of a calciner can provide valuable information which can be implemented in real time to optimize the calciner operation resulting in fuel savings. In this study, key process parameters of continuous operation for a period of 3 months were used to train the various machine learning models and a best suitable model was selected based on metrics like RMSE and R2 value. It is found that Artificial neural network is best fitted model for the calciner. This model is able to predict the calciner outlet temperature with high degree of accuracy (+/- 2% error) when validated against real world data. This model can be used by industries to estimate the calciner outlet temperature by changing the input parameters as it is not based on the chemical and physical process taking place in the calciner but on real world historical data.