34 research outputs found
Design and fabrication of a wrist splint for burn patient rehabilitation using 3D printing technologies.
Get PDFSevere and common injuries involving burns to the hands and wrists can often lead to permanent loss of motion. The issue is exacerbated by the delicate nature of tendons and muscles in the hands, along with the formation of scar tissue. While rehabilitation exercises can help improve the range of motion, early-stage recovery requires additional tension on the affected areas. To address this concern, a novel project was initiated, aiming to develop a specialized splint for later-stage rehabilitation. This innovative splint allows users to carry out their daily tasks while wearing it, constantly applying a beneficial load on the wrist, hand, and digits to enhance range of motion. The development of the splint involved leveraging Fused Deposition Modelling (FDM) 3D printing and medically safe materials for the initial prototype. Finite Element Analysis (FEA) was employed to analyze the design. The process underwent iterative design improvements and parameter adjustments, ultimately resulting in the final prototype. The FEA analysis confirmed the strength and durability of the PLA components, while the TPU digit resistance bands were evaluated using a hyper-elastic model. As a result, the final design effectively applies tension to the digits without compromising day-to-day tasks' usability and wearer's comfort. Future iterations of the splint could focus on enhancing fastening methods, reducing brace movement during usage, creating various sizes to accommodate different arm/hand dimensions, and optimizing mass-manufacturing processes
Effect of short term water exposure on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester nanocomposites.
Get PDFThe influence of short term water absorption on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester hybrid nanocomposites has been investigated. The addition of nano-fillers significantly increased the flexural strength; tensile strength and impact strength in dry and wet conditions. After short term water exposure; the maximum microhardness; tensile; flexural and impact toughness values were observed at 0.1 wt% MLG. The microhardness increased up to 50.3%; tensile strength increased up to 40% and flexural strength increased up to 44%. Compared to dry samples; the fracture toughness and surface roughness of all types of produced nanocomposites were increased that may be attributed to plasticization effect. Scanning electron microscopy revealed that the main failure mechanism is caused by the weakening of nano-filler-matrix interface induced by water absorption. It was further observed that synergistic effects were not effective at concentration of 0.1 wt% to produce considerable improvement in mechanical properties of produced hybrid nanocomposites
Effect of short-term water exposure on the mechanical properties of halloysite nanotube-multi layer graphene reinforced polyester nanocomposites.
Get PDFThe influence of short-term water absorption on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester hybrid nanocomposites has been investigated. The addition of nano-fillers significantly increased the flexural strength, tensile strength, and impact strength in dry and wet conditions. After short-term water exposure, the maximum microhardness, tensile, flexural and impact toughness values were observed at 0.1 wt % multi-layer graphene (MLG). The microhardness increased up to 50.3%, tensile strength increased up to 40% and flexural strength increased up to 44%. Compared to dry samples, the fracture toughness and surface roughness of all types of produced nanocomposites were increased that may be attributed to the plasticization effect. Scanning electron microscopy revealed that the main failure mechanism is caused by the weakening of the nano-filler-matrix interface induced by water absorption. It was further observed that synergistic effects were not effective at a concentration of 0.1 wt % to produce considerable improvement in the mechanical properties of the produced hybrid nanocomposites
Carbon nanotubes after 30 years of research, development and commercialisation.
Get PDFThe commentary discusses the last three decades of research, development and successful commercialisation of Carbon Nanotubes (CNTs) and their related composites. Whilst the number of publications are on the decline and despite of major technical challenges, CNTs continue to emerge as significant materials due to their superlative combination of properties
The processing of epoxy/1 wt%-graphene nanocomposites: effects of ethanol on flexural properties.
Get PDFFour different types of nanocomposites were successfully produced using solution casting technique. Graphene was dispersed in four different ethanol concentrations; 0g, 1g, 2.5g, and 5g. In general, it can be observed that ethanol is an excellent agent for 1 wt% graphene dispersed in the epoxy matrix. The maximum increase in flexural properties, impact strength and microhardness were observed in 1 wt% graphene dispersed in 1g ethanol. The flexural strength and modulus increased by 62% and 61% respectively. The highest impact strength was recorded for 1 wt% graphene dispersed with 1g ethanol, where an improvement of 9.5% was observed. The maximum Vickers microhardness was recorded to improve 3% compared to monolithic epoxy. SEM images revealed that graphene can impede the advancing cracks and significantly change the fracture mode from a straight fracture path to radially emanated path. It is worth to point out that if ethanol is not completely evaporated during processing, it can cause porosity which is unfavourable to the mechanical properties of the nanocomposites
Digital twin framework using real-time asset tracking for smart flexible manufacturing system.
Get PDFThis research article proposes a new method for an enhanced Flexible Manufacturing System (FMS) using a combination of smart methods. These methods use a set of three technologies of Industry 4.0, namely Artificial Intelligence (AI), Digital Twin (DT), and Wi-Fi-based indoor localization. The combination tackles the problem of asset tracking through Wi-Fi localization using machine-learning algorithms. The methodology utilizes the extensive "UJIIndoorLoc" dataset which consists of data from multiple floors and over 520 Wi-Fi access points. To achieve ultimate efficiency, the current study experimented with a range of machine-learning algorithms. The algorithms include Support Vector Machines (SVM), Random Forests (RF), Decision Trees, K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN). To further optimize, we also used three optimizers: ADAM, SDG, and RMSPROP. Among the lot, the KNN model showed superior performance in localization accuracy. It achieved a mean coordinate error (MCE) between 1.2 and 2.8 m and a 100% building rate. Furthermore, the CNN combined with the ADAM optimizer produced the best results, with a mean squared error of 0.83. The framework also utilized a deep reinforcement learning algorithm. This enables an Automated Guided Vehicle (AGV) to successfully navigate and avoid both static and mobile obstacles in a controlled laboratory setting. A cost-efficient, adaptive, and resilient solution for real-time tracking of assets is achieved through the proposed framework. The combination of Wi-Fi fingerprinting, deep learning for localization, and Digital Twin technology allows for remote monitoring, management, and optimization of manufacturing operations
Innovative and sustainable advances in polymer composites for additive manufacturing: processing, microstructure, and mechanical properties.
Get PDFAdditive manufacturing (AM) has revolutionised the production of customised components across industries such as the aerospace, automotive, healthcare, electronics, and renewable energy industries. Offering unmatched design freedom, reduced time-to-market, and minimised material waste, AM enables the fabrication of high-quality, customised products with greater sustainability compared to traditional methods like machining and injection moulding. Additionally, AM reduces energy consumption, resource requirements, and CO2 emissions throughout a material's lifecycle, aligning with global sustainability goals. This paper highlights insights into the sustainability of AM polymers, comparing bio-based and traditional polymers. Bio-based polymers exhibit lower carbon footprints during production but may face challenges in durability and mechanical performance. Conversely, traditional polymers, while more robust, require higher energy inputs and contribute to greater carbon emissions. Polymer composites tailored for AM further enhance material properties and support the development of innovative, eco-friendly solutions. This Special Issue brings together cutting-edge research on polymer composites in AM, focusing on processing techniques, microstructure–property relationships, mechanical performance, and sustainable manufacturing practices. These advancements underscore AM's transformative potential to deliver versatile, high-performance solutions across diverse industries
Transforming manufacturing quality management with cognitive twins: a data-driven, predictive approach to real-time optimization of quality.
Get PDFIn the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through these reactive measures, are often inefficient because of their higher operational cost and delayed defect detection and mitigation. The paper introduces a novel cognitive twin (CT) framework, which is the next evolved version of digital twin (DT). It is designed to advance the current quality management in flexible manufacturing systems (FMSs) through real-time, data-driven, and predictive optimization. This proposed framework uses four data types, namely feedstock quality (Qf), machine degradation (Qm), product processing quality (Qp), and quality inspection (Qi). By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. The cognitive twin optimizes the above quality components. This enables a very proactive decision making through an augmented reality (AR) interface by providing real-time visual insights and alerts to the operators. Thorough experimentation was conducted on the aforementioned FMS. Through the experiments, it was revealed that the proposed cognitive twin outperforms conventional QMSs by a great margin. The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. Furthermore, the overall equipment efficiency (OEE) increased to 93–96%. The overall equipment efficiency increased by 11.8%, on average, from 82% to 93%, and the scrap rate decreased by 33.3% from 60% to 40%. The excellent results showcase the effectiveness of cognitive twin quality management via minimum wastage, continuous quality improvement, and enhancement in operational efficiency in the paradigm of smart manufacturing. This research study contributes to the field of industry 4.0 by providing a comprehensive, scalable, and adaptive quality management solution, thus leading the way for further advancements in intelligent manufacturing systems
The processing of epoxy/multi-layer graphene nanocomposites: effects of acetone on properties.
Get PDFEpoxy/multi-layer graphene nanocomposites prepared with different acetone dosages (0 ml, 25 ml and 50 ml) were successfully produced. This study investigates the effectiveness of short-term dispersion and small dosages of acetone on the properties of nanocomposites. The maximum increase in glass transition temperature (Tg), storage modulus, flexural strength, flexural modulus, fracture toughness and microhardness were observed in the case of epoxy/0.1 wt % MLG dispersed in an epoxy matrix. Scanning Electron Microscopy (SEM) analysis revealed that a good dispersion of MLG in the epoxy matrix has the ability to prevent and stop crack propagation. The cracks became parabolic or emanated radially in comparison to monolithic epoxy samples. For samples prepared with acetone, smooth surfaces can be seen on the fractured samples due to retained acetone that acts as stress raisers, which result in straight crack propagation and consequently reduced mechanical properties of the nanocomposites
Quality of surface texture and mechanical properties of PLA and PA-based material reinforced with carbon fibers manufactured by FDM and CFF 3D printing technologies.
Get PDFThe paper presents the results of mechanical tests of models manufactured with two 3D printing technologies, FDM and CFF. Both technologies use PLA or PA-based materials reinforced with carbon fibers. The work includes both uniaxial tensile tests of the tested materials and metrological measurements of surfaces produced with two 3D printing technologies. The test results showed a significant influence of the type of technology on the strength of the models built and on the quality of the technological surface layer. After the analysis of the parameters of the primary profile, roughness and waviness, it can be clearly stated that the quality of the technological surface layer is much better for the models made with the CFF technology compared to the FDM technology. Furthermore, the tensile strength of the models manufactured of carbon fiber-enriched material is much higher for samples made with CFF technology compared to FDM
