Department of Mechanical engineering

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Application and future prospects of additive manufacturing in dermatology
(OUP, 2022-06) Mathew, Nitin Tom
The article discusses the additive manufacturing/3D printing of human skin for advanced applications. Even though this is still in its infancy, additive manufacturing has the potential to revolutionize the field of dermatology and cosmetology.
A Blockchain Technology based Framework for Environmental and Social Impact Authenticity of a 3D Printed Product
(Elsevier, 2023) Sangwan, Kuldip Singh; Dua, Amit
This paper proposes a conceptual framework for implementing blockchain technology to enhance traceability, transparency, and authenticity of a 3D printed product. An implementation framework is developed using blockchain technologies to record and trace critical attributes during the various life cycle phases of a 3D printing value chain, viz. raw material extraction, chemical processing, polymerization, filament production, 3D printing, and end-of-life recycling of the product. The information on critical attributes of carbon footprint, workers' age, and material flow during the entire value chain is captured to provide authentic output of carbon footprint and labour age during any of the value chain activity. The uniqueness of the current work lies in offering a series of immutable transactions using blockchain technology to comprehend the circularity of 3D printing material and account for the overall carbon footprint produced by a 3D printed product considering its whole value chain. This would improve the traceability and visibility of the material supply chain for 3D printing. On the hindsight, the proposed framework is expected to assist the manufacturing firms to act as responsible manufacturers by providing the authentic data for the computation of environmental assessment as well as social issues of child labour throughout the value chain.
A cyber physical production system framework for online monitoring, visualization and control by using cloud, fog, and edge computing technologies
(Taylor & Francis, 2023-03) Sangwan, Kuldip Singh
Cyber physical production system (CPPS) is an essential prerequisite to facilitate online monitoring, visualization, and control in a smart manufacturing system. The goal of this paper is to provide a proof of concept by proposing a CPPS framework, based on complementing cloud, fog and edge computing technologies, to demonstrate the real-time online monitoring, visualization, and control of a conventional 3D printer. The proposed methodology was successfully used for online detection and control of part defects, filament runouts, and smoke generation. The proposed methodology was also used to acquire, monitor, and visualize energy consumption, relative humidity, temperature, volatile organic compounds, particulate matters, and acceleration during 3D printing. The present work utilizes the computing technologies where they do not compete, but instead complement each other to enable intelligent capabilities and online sharing of resources which are scalable, reliable, and efficient. The proposed CPPS framework can be extended to provide low-cost solutions to micro, small and medium enterprises to convert their traditional critical equipment for real-time monitoring, visualization, control, and analytics
Development and comparison of machine-learning algorithms for anomaly detection in 3D printing using vibration data
(Springer, 2023-06) Sangwan, Kuldip Singh
3D printing is an emerging technology that converts digital models directly into physical objects. However, abnormal vibrations during the 3D printing process significantly affect the product quality, and also lead to possible failures of the printer components. This paper aims at developing machine-learning algorithms for anomaly detection or abnormal behavior of a 3D printer using vibration data. The proposed algorithms utilize vibration data from a sensor mounted on the printer. Data are then trained and validated developing four machine-learning algorithms to detect anomalies due to the structural or mechanical defects of the printer. Performances of the proposed four algorithms were evaluated and compared. It was found that the proposed long short-term memory (LSTM) algorithm has the best accuracy of 97.17% as compared to other algorithms. The novelty of the present work lies in detecting anomalies with high accuracy due to structural or mechanical faults in 3D printers using a low-cost sensor. The significance of the current work lies in its ability to achieve error-free 3D printing, resulting in less material waste, reduced human intervention and costs, and improved product quality by detecting potential anomalies during printing. The proposed algorithm terminates the printing if any anomaly is detected.
Development of a cyber physical production system framework for 3D printing analytics
(Elsevier, 2023-10) Sangwan, Kuldip Singh
3D printing technology is considered one of the emerging areas to deal with global sustainability challenges and to facilitate the Industry 4.0 adoption. However, 3D printing technology is still immature due to several limitations and negative perceptions about its quality and performance. The goal of this paper is to propose a cyber physical production system (CPPS) framework for a 3D printer to (i) monitor the process, parameters, and carbon footprint, (ii) predict the nozzle’s remaining useful life (RUL), and (iii) prescribe optimum 3D printing parameters for minimizing carbon footprint and printing time, simultaneously at the targeted surface quality. Experiments were designed based on Taguchi L-27 orthogonal array to investigate the relationship between printing parameters and performance characteristics. The usefulness of the proposed framework has been demonstrated for a 3D printer to predict the remaining useful life of the printer nozzle (prognostic model), and to find an optimal combination of printing parameters for the simultaneous optimization of sustainability and productivity at the targeted surface quality (prescriptive model). Layer height was found to have a statically significant impact on the specific carbon footprint followed by scale and bed temperature. Layer height is the only statically significant contributor to the surface roughness of 3D printed parts. The scale and layer height followed by infill have significant effect on the printing time. The significance of the present work lies in enhancing the performance of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. The usefulness of the proposed CPPS framework is demonstrated as a decision support tool for a 3D printer real-time monitoring, visualization, and control. The proposed CPPS framework and its application for prognostic and prescriptive analytics is generic in nature, and is transferable and applicable to other FDM 3D printers, irrespective of brand and size.
Development of Machine Learning Algorithm for Characterization and Estimation of Energy Consumption of Various Stages during 3D Printing
(Elsevier, 2022) Sangwan, Kuldip Singh
Energy usage in industries is one of the major contributors for climate change, biodiversity loss and resource scarcity. Technological advancements in digitalization led by Industry 4.0 facilitates affordable energy monitoring systems. This allows comprehensive understanding of the primary energy needs and improvement in the areas of inefficiency of a modern manufacturing system. Machine learning has the potential to reveal untapped insights, providing decision support for sustainable manufacturing by improving environmental performances, significant savings, and operational opportunities. The objectives of this research paper are to develop a machine learning algorithm for characterization, and to estimate the energy consumption of various stages in 3D printing. Machine learning model is developed using long short-term memory algorithm, and is trained, validated, and deployed for the classification of various stages during 3D printing process. Furthermore, energy consumption in each stage is estimated based on Simpson’s rule.
Live Life Cycle Assessment Implementation using Cyber Physical Production System Framework for 3D Printed Products
(Elsevier, 2022) Sangwan, Kuldip Singh
Sustainable manufacturing aims to deal with the challenges such as climate change, biodiversity loss and resource scarcity by minimising the environmental impacts due to product, processes, and systems through optimal use of energy and resources. Life cycle assessment (LCA) has become an important tool to identify, evaluate and assess the environmental impact of a product, process, or system, along with all the stages of the life cycle of a product. However, several drawbacks such as complexity, uncertainty and impreciseness are also associated with this methodology. Therefore, live LCA as a plausible solution, is gaining popularity with the advancements in Industry 4.o tools and techniques, enhancing ability to collect and analyse live data from various processes, interpret results, identify hotspots, trade-offs, and present better ideas about the environmental impacts in-line with the process. This paper proposes a cyber-physical production system framework for live LCA to estimate the environmental impact of 3D printed products with different combinations of design and process parameters. Also, optimum settings of these parameters are determined, and validated to minimize the environmental impacts. This enables real-time monitoring of the environmental impacts, driving prompt decision support for useful insights to the operator, project manager, business manager, or customer thereby improving the visibility and transparency.
Parametric Optimization of FDM Process for Fabricating High-Strength PLA Parts
(Springer, 2021-02) Mishra, Radha Raman
Polylactic acid (PLA) is a biodegradable polymer that can be 3D printed to develop various complex shape parts for industrial use. However, achieving higher tensile strength in 3D-printed PLA parts is challenging. In the present work, the tensile properties of 3D-printed PLA have been analyzed using the Taguchi method. The tensile samples were 3D printed using the fused deposition modeling (FDM) technology using different variable parameters—layer heights (0.2, 0.4, and 0.6 mm), nozzle speeds (5, 10, and 15 mm s−1), and infill patterns (line, zig-zag and concentric). The tensile testing was accomplished following the test standard ASTM D-638. The study revealed that the tensile strength of 3D-printed samples largely depends on the density of the samples. The tensile strength of sample 7 (layer height–0.6 mm, nozzle speed–5 mm s−1, and infill pattern—concentric) was found 54.437 MPa, which is the highest among the developed samples, whereas the extension in sample 6 (layer height–0.4 mm, nozzle speed–15 mm s−1, and infill pattern—line) was 13.19% which is the highest among all the 3D-printed samples.
Process window identification for 3D printing low melting point alloy (LMPA) using fused deposition modelling (FDM)
(Emerald, 2022-10) Kala, Prateek
Fused deposition modelling (FDM) has gained popularity owing to its capability of producing complex and customized profiles at relatively low cost and in shorter periods. The study aims to extend the use of FDM printers for 3D printing of low melting point alloy (LMPA), which has applications in the electronics industry, rapid tooling, biomedical, etc.