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Additive Manufacturing Process for the Development of Orthosis of Foot
Published in Atul Babbar, Ankit Sharma, Vivek Jain, Dheeraj Gupta, Additive Manufacturing Processes in Biomedical Engineering, 2023
Manak L. Jain, Nalinakash S. Vyas, Anil Mulewa, Sanjay G. Dhande
The additive manufacturing process is a manufacturing process for building a physical prototype in which the three-dimensional (3D) physical part is built by the deposition of physical materials layer over layer. The process is also known as the rapid prototype (RP) system. Here, initially, a computer-aided-design (CAD) model is created virtually on a system and then is imported into an RP system, where a virtual model is converted into a physical prototype 3D model. The goal of RP is to quickly fabricate complex-shaped 3D parts directly from CAD models. Several RP techniques exist, and all employ the same basic five steps; these steps constitute the principle of working with an RP system, in which each physical layer will be placed over the previous one. These five steps follow: Create a CAD model of the design.Convert the CAD model to stereolithography (STL) format.Slice the STL file into thin cross-sectional layers.Construct the model one layer atop another.Clean and finish the model.
Nanomaterial Used in 3D Printing Technology
Published in Ajit Behera, Tuan Anh Nguyen, Ram K. Gupta, Smart 3D Nanoprinting, 2023
Waleed Ahmed, Essam Zaneldin, Amged Al Hassan, Ali H. Al-Marzouqi
The construction industry has one of the slowest automation rates in comparison to other industries, such as the manufacturing industry. As a result of this low automation level, the construction industry has been characterized as having high instability, leading to unsafe work conditions and resulting in dangerously high accident rates [65]. Therefore, various automation methods in construction have been introduced to boost up the pace of the construction processes and increase application of the safety precautions. Additive manufacturing, also described as 3D printing, is a technology that allows building physical elements of a three-dimensional piece at individual layers in a multiple-layered system. In recent years, it is believed to be one of the most rapidly booming fields in the civil engineering sector and one of the critical pillars of the concept of Industry 4.0 [66]. With 3D printing, innovative digital manufacturing technology is now readily available for printing 3D concrete objects and prefabricated building blocks with efficient use of materials [67]. It is pretty clear that there is a global need to increase the construction of homes and meet the required demand and, to achieve this objective, the construction industry needs to use this innovative technology. The increase in the number of projects using 3D printing for construction between the years 1997 and 2018 [68].
Automation in Manufacturing
Published in Edward Y. Uechi, Business Automation and Its Effect on the Labor Force, 2023
Additive manufacturing (also known as 3D printing) is an innovative process in which an object can be made layer by layer with such high degree of precision that allows the object to conform to hitherto new geometric shapes. The material used to make the object can be plastic, metal, or concrete. In the past, wood or plastic models were made into molds. The molds were then made into metal castings from which the finished products were produced. Additive manufacturing removes the steps to produce molds, going directly from conceptual design to finished product. The process would start with a computer-aided design (CAD) drawing of the product. The CAD drawing would then be sent to the 3D printer, which outputs the object layer by layer according to the specifications in the CAD drawing. Then the 3D printer would be able to replicate production by printing a number of copies of the object.
Additive manufacturing of metallic biomaterials: sustainability aspect, opportunity, and challenges
Published in Journal of Industrial and Production Engineering, 2023
Pralhad Pesode, Shivprakash Barve
With the help of 3DP technology, fully customizable 3D models may be created by layering materials and adding more material as needed. Fused deposition modeling (FDM), material jetting, stereolithography (SLA), vat photopolymerization, binder jetting, selective laser sintering (SLS), and powder bed fusion are a few commonly used additive manufacturing techniques. Various materials, including natural and synthetic polymers, biodegradable and non-biodegradable metals, ceramics, and natural and synthetic composites, can be processed using these processes [12]. The development of 3D printing technologies opens up possibilities for changes to the industrial structure. The most recent literature analysis indicates that AM is becoming increasingly and more popular for creating intricate implants including heart valves, blood arteries, and tracheas [13]. Due to noteworthy developments in AM methods in the orthopedic sector, the mobility of bio-implants and medical equipment may have an influence on the supply chain and be sustainable [14]. By doing away with the idea of waste, these advantages could increase the resource efficiency of manufacturing and move the system closer to a circular economy (CE) [15]. By using the production-on-demand (POD) concept, 3D printing can reduce material waste, lower transportation costs, optimize production costs, simplify the supply chain in supply chain management (SCM), and increase environmental sustainability, among other significant opportunities [16].
Detection and characterisation of defects in directed energy deposited multi-material components using full waveform inversion and reverse time migration
Published in Virtual and Physical Prototyping, 2022
Jing Rao, Swee Leong Sing, Joel Choon Wee Lim, Wai Yee Yeong, Jizhong Yang, Zheng Fan, Paul Hazell
Additive manufacturing is a fast growing manufacturing process, which is available to fabricate critical metal parts for aerospace, nuclear and automotive engineering applications (Uriondo, Esperon-Miguez, and Perinpanayagam 2015; Gao et al. 2015; Abe et al. 2001; Smith et al. 2016; Yadroitsev and Yadroitsava 2015). Compared with traditional manufacturing methods, additive manufacturing provides many benefits, such as creating complex geometries, increasing mechanical performances and generating multi-material (MM) metallic components with site-specific properties (Vaezi et al. 2013; Tammas-Williams and Todd 2017). MM additively manufactured (AM) processes could make a superior bonding between multiple metals because there are no weld seams. MM printed components can be applied in severe operating environments (Oyelola et al. 2018) and therefore the quality of the finished product is of prime importance. Corrosion and wear are main reasons to preferentially vary material properties within components. Additionally, selective addition of desirable material properties during the fabrication to areas prone to damage can reduce the cost of creating components (Yamazaki 2016). MM printed components are shown in areas where metal combinations provide benefits in properties which are superior to those obtained from the use of a monolithic metal.
Advanced processing of 3D printed biocomposite materials using artificial intelligence
Published in Materials and Manufacturing Processes, 2022
Deepak Verma, Yu Dong, Mohit Sharma, Arun Kumar Chaudhary
The main advantage of additive manufacturing lies in developing a unique microstructure that differs from the counterpart received from traditional processing methods. Conventionally, professional experience is required for microstructural characterization, but sometimes, it may be biased with some resulting errors .[19] This may be revolutionized by introducing digital image analysis only if microstructural attributes are well defined. Machine learning has played a major role in microstructural studies as it is able to recognize microstructural dendritic morphology for different alloys, groups, various microstructural elements, etc. .[122] On the other hand, CNN requires the micrograph data as an input and takes out typical attributes automatically at the time when the data pass via convolutional layers. However, it is seen that CNN generally requires a considerable data size, which is sometimes impossible to additive manufactured parts of newly developed alloys. Consequently, it may show some reduced accuracy at this stage. This problem can be solved by transfer learning, which will transfer the previously trained data to the CNN model, and ultimately improves the speed and accuracy of the training .[123]