Browsing by Author "Liu, Ying"

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    Deep fusion for energy consumption prediction in additive manufacturing
    (Elsevier, 2021-11-26) Hu, Fu; Qin, Jian; Li, Yixin; Liu, Ying; Sun, Xianfang
    Owing to the increasing trend of additive manufacturing (AM) technologies being employed in the manufacturing industry, the issue of AM energy consumption attracts attention in both industry and academia. The energy consumption of AM systems is affected by various factors. These factors involve features with different dimensions and structures which are hard to tackle in the analysis. In this work, a data fusion approach is proposed for energy consumption prediction based on CNN-LSTM (convolutional neural network and long short-term memory) model. A case study was conducted on an SLS system by using the proposed methodology, achieving the RMSE of 8.143 Wh/g in prediction.
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    Feature-level data fusion for energy consumption analytics in additive manufacturing
    (IEEE, 2020-10-08) Hu, Fu; Liu, Ying; Qin, Jian; Sun, Xianfang; Witherell, Paul
    The issue of Additive Manufacturing (AM) energy consumption is attracting attention in both industry and academia, particularly with the trending adoption of AM technologies in the manufacturing industry. It is crucial to analyze, understand, and manage the energy consumption of AM for better efficiency and sustainability. The energy consumption of AM systems is related to various correlated attributes in different phases of an AM process. Existing studies focus mainly on analyzing the impacts of different processing and material attributes, while factors related to design and working environment have not received the same amount of attention. Such factors involve features with various dimensions and nested structures that are difficult to handle in the analysis. To tackle these issues, a feature-level data fusion approach is proposed to integrate heterogeneous data to build an AM energy consumption model to uncover energy-relevant information and knowledge. A case study using real-world data collected from a selective laser sintering (SLS) system is presented to validate the proposed approach, and the results indicate that the fusion strategy achieves better performances on energy consumption prediction than the individual ones. Based on the analysis of feature importance, the design-relevant features are found to have significant impacts on AM energy consumption.
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    Gust alleviation of a large aircraft with a passive Twist Wingtip
    (Wiley, 2015-04-03) Guo, Shijun J.; Espinosa De Los Monteros, Jaime; Liu, Ying
    This paper presents an investigation into the gust response and wing structure load alleviation of a 200-seater aircraft by employing a passive twist wingtip (PTWT). The research was divided into three stages. The first stage was the design and analysis of the baseline aircraft, including aerodynamic analysis, structural design using the finite element (FE) method and flutter analysis to meet the design requirements. Dynamic response analysis of the aircraft to discrete (one-cosin) gust was also performed in a range of gust radiances specified in the airworthiness standards. In the second stage, a PTWT of a length of 1.13 m was designed with the key parameters determined based on design constraints and, in particular, the aeroelastic stability and gust response. Subsequent gust response analysis was performed to evaluate the effectiveness of the PTWT for gust alleviation. The results show that the PTWT produced a significant reduction of gust-induced wingtip deflection by 21% and the bending moment at the wing root by 14% in the most critical flight case. In the third stage, effort was made to study the interaction and influence of the PTWT on the symmetric and unsymmetrical manoeuvring of the aircraft when ailerons were in operation. The results show the that PTWT influence with a reduction of the aircraft normal velocity and heave motion by 1.7% and 3%, respectively, is negligible. However, the PTWT influence on the aircraft roll moment with a 20.5% reduction is significant. A locking system is therefore required in such a manoeuvring condition. The investigation has shown that the PTWT is an effective means for gust alleviation and, therefore, has potential for large aircraft application.
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    An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer
    (Elsevier, 2015-05-21) Liu, Ying; Tiwari, Ashutosh
    The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed. However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed. A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility of the model and the developed algorithm.
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    Passive gust alleviation of a flying-wing aircraft by analysis and wind-tunnel test of a scaled model in dynamic similarity
    (Elsevier, 2021-03-29) He, Shun; Guo, Shijun; Liu, Ying; Luo, Wukui
    An investigation was conducted to evaluate the effectiveness of a passive gust alleviation device (PGAD) installed in a flying-wing aircraft of 62.3m wing span at large swept back angle. It was performed by numerical analysis and validated by wind-tunnel test of a 1:25 reduced scale physical model of dynamic similarity to the full-scale aircraft. The 1-cosine gust model with a range of gust parameters specified in the airworthiness regulation CS-23 was taken in the gust response analysis that led to 7~9% gust alleviation results by employing the PGAD. The gust response dominated by the first three modes of the aircraft was most critical in the frequency close to the first bending mode of the wing. The wind-tunnel test model was designed and manufactured based on dynamic scaling law, and proved to be of excellent dynamic similarity by the deviation of less than 5.5% between the first three modes of the physical model measured by vibration test and the full-scale aircraft model. The wind tunnel test results show that the gust response of the model in the specified range was reduced by 8.3~14.3% according to the measured wing tip deflection associated with the PGAD oscillation amplitude at 4.0°~15.5°. The present study shows that the numerical analysis of gust response and alleviation of a full-scale aircraft installed with PGAD can be validated by wind tunnel test of a scaled physical model with dynamic similarity.
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    Research and application of machine learning for additive manufacturing
    (Elsevier, 2022-02-18) Qin, Jian; Hu, Fu; Liu, Ying; Witherell, Paul; Wang, Charlie C. L.; Rosen, David W.; Simpson, Timothy; Lu, Yan; Tang, Qian
    Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on-demand and decentralized manufacturing. However, a number of challenges stem from not only the complexity of manufacturing systems but the demand for increasingly complex and high-quality products, in terms of design principles, standardization and quality control. These challenges build up barriers to the widespread adoption of AM in the industry and the in-depth research of AM in academia. To tackle the challenges, machine learning (ML) technologies rise to play a critical role as they are able to provide effective ways to quality control, process optimization, modelling of complex systems, and energy management. Hence, this paper employs a systematic literature review method as it is a defined and methodical way of identifying, assessing, and analysing published literature. Then, a keyword co-occurrence and cluster analysis are employed for analysing relevant literature. Several aspects of AM, including Design for AM (DfAM), material analytics, in situ monitoring and defect detection, property prediction and sustainability, have been clustered and summarized to present state-of-the-art research in the scope of ML for AM. Finally, the challenges and opportunities of ML for AM are uncovered and discussed.
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    A review of optimisation techniques used in the composite recycling area: state-of-the-art and steps towards a research agenda
    (Elsevier, 2016-08-11) Liu, Ying; Farnsworth, Michael; Tiwari, Ashutosh
    The increased use of carbon fibre and glass fibre reinforced polymer in industry coupled with restrictions on landfill disposal has resulted in a need to develop effective recycling technologies for composites. Currently, mechanical, thermal and chemical approaches have been use to recycle composites. This paper seeks to examine the applications of engineering optimisation techniques in the composite recycling and re-manufacturing processes and their relevant systems, providing an overview of state-of-the-art. This paper is based on a comprehensive review of literature covering nearly all the research papers in this area. These papers are analysed to identify current trends and future research directions. The composite recycling is a relatively new area, and the modelling and optimisation work for composite recycling and re-manufacturing techniques and their relevant systems is still in its infancy. Currently, the optimisation work developed in composite recycling mainly focus on the applications of design of experiments methods. These approaches have been applied to improve the quality of recyclates such as carbon fibres. Some of the soft-computing algorithms have been applied to optimise the re-manufacturing at the system level. Based on the existing research, the area of optimisation for composite recycling and re-manufacturing haven't been well explored despite the fact that many opportunities and requirements for optimisation exist. This means significant amount of modelling and optimisation work is required for the future research. More significantly, considering optimisation at the early stage of a system development is very beneficial in terms of the long term health of the composite recycling industry.
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    SME-Oriented Collaborative Customer Insights Platform for user-driven RdM (SOC-UDRdM)
    (Cranfield University, 2017-08-23 07:38) Liu, Ying; Han, Liangxiu
    This is one of five reports created as part of the feasibility studies conducted by the RECODE Network
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    Spatial attention-based convolutional transformer for bearing remaining useful life prediction
    (IOP Publishing, 2022-08-02) Chen, Chong; Wang, Tao; Liu, Ying; Cheng, Lianglun; Qin, Jian
    The remaining useful life (RUL) prediction is of significance to the health management of bearings. Recently, deep learning has been widely investigated for bearing RUL prediction due to its great success in sequence learning. However, the improvement of the prediction accuracy of existing deep learning algorithms heavily relies on feature engineering such as handcrafted feature generation and time–frequency transformation, which increase the complexity and difficulty of the actual deployment. In this paper, a novel spatial attention-based convolutional transformer (SAConvFormer) is proposed to establish an accurate bearing RUL prediction model based on raw vibration data without prior knowledge or feature engineering. In this algorithm, firstly, a convolutional neural network enhanced by a spatial attention mechanism is proposed to squeeze the feature maps and extract the local and global features from raw bearing vibration data effectively. Then, the extracted senior features are fed into a transformer network to further explore the sequential patterns relevant to the bearing RUL. An experimental study using the XJTU-SY rolling bearings dataset revealed the merits of the proposed deep learning algorithm in terms of root-mean-square-error (RMSE) and mean-absolute-error (MAE) in comparison with other state-of-the-art algorithms.
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    Task-driven data fusion for additive manufacturing: framework, approaches, and case studies
    (Elsevier, 2023-07-01) Hu, Fu; Liu, Ying; Li, Yixin; Ma, Shuai; Qin, Jian; Song, Jun; Sun, Xianfang; Tang, Qian
    Additive manufacturing (AM) has been envisioned as a critical technology for the next industrial revolution. Due to the advances in data sensing and collection technologies, a large amount of data, generated from multiple sources in AM production, becomes available for relevant analytics to improve process reliability, repeatability, and part quality. However, AM processes occur over a wide range of spatial and temporal scales where the data generally involves different types, dimensions and structures, leading to difficulties when integrating and then analysing. Hence, in what way and how to integrate the heterogeneous data or merge the spatial and temporal information lead to significant challenges in data analytics for AM systems. This paper proposed a task-driven data fusion framework that enables the integration of heterogeneous data from different sources and modalities based on tasks to support decision-making activities. In this framework, the data analytics activities involved in the task are identified in the first place. Then, the data required for the task is identified, collected, and characterised. Finally, data fusion techniques are employed and applied based on the characteristics of the data for integration to support data analytics. The fusion techniques that best fit the task requirements are selected as the final fusion approach. Case studies on different research directions of AM, including AM energy consumption prediction, mechanical properties prediction of additively manufactured lattice structures, and investigation of remelting process on part density, were carried out to demonstrate the feasibility and effectiveness of the proposed framework and approaches.
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    Towards high-quality biodiesel production from microalgae using original and anaerobically-digested livestock wastewater
    (Elsevier, 2020-10-09) Li, Gang; Zhang, Jiang; Li, Huan; Hu, Ruichen; Yao, Xiaolong; Liu, Ying; Zhou, Yuguang; Lyu, Tao
    In this study, we conducted proof-of-concept research towards the simultaneous treatment of livestock wastewater and the generation of high-quality biodiesel, through microalgae technology. Both original (OPE) and anaerobically-digested (DPE) piggery effluents were investigated for the culture of the microalgae, Desmodesmus sp. EJ8-10. After 14 days’ cultivation, the dry biomass from microalgae cultivated in OPE increased from an initial value of 0.01 g/L to 0.33-0.39 g/L, while those growing in DPE only achieved a final dried mass of 0.15-0.35 g/L, under similar initial ammonium nitrogen (NH4+-N) concentrations. The significantly higher microalgal biomass production achieved in the OPE medium may have been supported by the abundance of both macronutrient, such as phosphorus (P), and of micronutrients, such as trace elements, present in the OPE, which may not been present in similar quantities in the DPE. However, a higher lipid content was observed (19.4-28%) in microalgal cells from DPE cultures than those (18.7-22.3%) from OPE cultures. Moreover, the fatty acid compositions in the microalgae cultured in DPE contained high levels of monounsaturated fatty acids (MUFAs) and total C16-C18 acids, which would afford a superior potential for high-quality biodiesel production. The N/P ratio (15.4:1) in OPE was much closer to that indicated by previous studies to be the most suitable (16:1) for microalgae growth, when compared with that determined from the DPE culture medium. This may facilitate protein synthesis in the algal cells and induce a lower accumulation of lipids. Based on these findings, we proposed a new flowsheet for sustainable livestock waste management
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    Unpacking additive manufacturing challenges and opportunities in moving towards sustainability: an exploratory study
    (MDPI, 2023-02-20) Liu, Wen; Liu, Xielin; Liu, Ying; Wang, Jie; Evans, Steve; Yang, Miying
    The global market for Additive Manufacturing (AM) is expected to grow, which may increase the prominence of sustainability aspects in the manufacturing process. A growing number of AM academics and practitioners have started to pay attention to the environmental and societal impacts of AM instead of only focusing on its economic aspect. Yet, AM is still not widely adopted, and the research on AM sustainability is still at the nascent stage. This paper aims to better understand AM’s sustainable adoption and seeks to address three questions: what the sustainability implications of AM are; what challenges may prevent the broad adoption of AM; and what opportunities can enable AM sustainability. The research adopts a multiple case study method to investigate six AM companies that play different roles in the AM ecosystem, including AM design, AM machine, AM material, AM service, AM education, and AM consulting. The results from these studies reveal that AM has the potential to reduce environmental and social impacts; however, it might also cause negative consequences and lead to some rebound effects. We identified 43 categories (synthesized from 199 examples) of key challenges for AM adoption and proposed 55 key solutions in moving AM towards sustainability. It is evident that AM acts as a promising digital technology for manufacturing and has the potential to pave the way for a new era of sustainable manufacturing.