Browsing by Author "Qin, Jian"
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Item Open Access Automated interlayer wall height compensation for wire based directed energy deposition additive manufacturing(MDPI, 2023-10-16) Qin, Jian; Vives, Javier; Raja, Parthiban; Lasisi, Shakirudeen; Wang, Chong; Charrett, Thomas O. H.; Ding, Jialuo; Williams, Stewart; Hallam, Jonathan Mark; Tatam, Ralph P.Part quality monitoring and control in wire-based directed energy deposition additive manufacturing (w-DEDAM) processes has been garnering continuous interest from both the academic and industrial sectors. However, maintaining a consistent layer height and ensuring that the wall height aligns closely with the design, as depicted in computer-aided design (CAD) models, pose significant challenges. These challenges arise due to the uncertainties associated with the manufacturing process and the working environment, particularly with extended processing times. To achieve these goals in an industrial scenario, the deposition geometry must be measured with precision and efficiency throughout the part-building process. Moreover, it is essential to comprehend the changes in the interlayer deposition height based on various process parameters. This paper first examines the behaviour of interlayer deposition height when process parameters change within different wall regions, with a particular focus on the transition areas. In addition, this paper explores the potential of geometry monitoring information in implementing interlayer wall height compensation during w-DEDAM part-building. The in-process layer height was monitored using a coherent range-resolved interferometry (RRI) sensor, and the accuracy and efficiency of this measurement were carefully studied. Leveraging this information and understanding of deposition geometry, the control points of the process parameters were identified. Subsequently, appropriate and varied process parameters were applied to each wall region to gradually compensate for wall height. The wall height discrepancies were generally compensated for in two to three layers.Item Open Access Data supporting "A novel cold wire gas metal arc (CW-GMA) process for high productivity additive manufacturing"(Cranfield University, 2023-06-30 11:53) Wang, Chong; Wang, Jun; Bento, João; Ding, Jialuo; Rodrigues Pardal, Goncalo; Chen, Guangyu; Qin, Jian; Suder, Wojciech; Williams, StewartThis is a supplementary figure, showing the experimental setup for building the large-scale component with the CW-GMA process: (a) experiment setup, and (b) monitors for thermal camera and process camera.Item Open Access Data: Automated interlayer wall height compensation for wire based directed energy deposition additive manufacturing(Cranfield University, 2024-05-08 16:28) Qin, Jian; Vives, Javier; Rajan, Parthiban; Lasisi, Shakirudeen; Wang, Chong; Charrett, Tom; Ding, Jialuo; Williams, Stewart; Hallam, Jonathan; Tatam, RalphExperimental dataset to support the publication. The data includes all the test and measurement records for the experiment.Item Open Access Data: Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling(Cranfield University, 2023-10-26 15:38) Yin, Yi; Tian, Yingtao; Ding, Jialuo; Mitchell, Tim; Qin, JianElectron beam probing data: beam characteristics of raidii at welding direction and cross-section direction. Experiments setup: 40–60 kV for the accelerating voltage, 25–45 mA for the beam current, and a welding speed of 500–700 mm/min.Item Open Access Deep fusion for energy consumption prediction in additive manufacturing(Elsevier, 2021-11-26) Hu, Fu; Qin, Jian; Li, Yixin; Liu, Ying; Sun, XianfangOwing 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.Item Open Access Design optimization of laminated composite structures using artificial neural network and genetic algorithm(Elsevier, 2022-11-26) Liu, Xiaoyang; Qin, Jian; Zhao, Kai; Featherson, Carol A.; Kennedy, David; Jing, Yucai; Yang, GuotaoIn this paper, an efficient method for performing minimum weight optimization of composite laminates using artificial neural network (ANN) based surrogate models is proposed. By predicting the buckling loads of the laminates using ANN the use of time-consuming buckling evaluations during the iterative optimization process are avoided. Using for the first time lamination parameters, laminate thickness and other dimensional parameters as inputs for these ANN models significantly reduces the number of models required and therefore computational cost of considering laminates with many different numbers of layers and total thickness. Besides, as the stacking sequences are represented by lamination parameters, the number of inputs of the ANN models is also significantly reduced, avoiding the curse of dimensionality. Finite element analysis (FEA) is employed together with the Latin hypercube sampling (LHS) method to generate the database for the training and testing of the ANN models. The trained ANN models are then employed within a genetic algorithm (GA) to optimize the stacking sequences and structural dimensions to minimize the weight of the composite laminates. The advantages of using ANN in predicting buckling load is proved by comparison with other machine learning methods, and the effectiveness and efficiency of the proposed optimization method is demonstrated through the optimization of flat, blade-stiffened and hat-stiffened laminates.Item Open Access Developing a framework leveraging building information modelling to validate fire emergency evacuation(MDPI, 2024-01-08) Wang, Bin; Ren, Guoqian; Li, Haijiang; Zhang, Jisong; Qin, JianIn fire emergency management, a delayed execution will cause a significant number of casualties. Conventional fire drills typically only identify a certain percentage of evacuation bottlenecks after the building has been constructed, which is hard to improve. This paper proposes an innovative framework to validate fire emergency evacuation at the early design stage. According to the experience and knowledge of fire emergency evacuation design, the proposed framework also introduces a seamless two-way information channel to embed fire emergency evacuation simulations into a BIM-based design environment. Several critical factors for fire evacuation have been reviewed in relevant domain knowledge, which is used to build virtual characters to test in experimental scenarios. The results are analyzed to validate fire emergency evacuation factors, and the feedback knowledge is stored as a knowledge model for further applications.Item Open Access Fault diagnosis of industrial robot based on dual-module attention convolutional neural network(Springer, 2022-06-01) Lu, Kaijie; Chen, Chong; Wang, Tao; Cheng, Lianglun; Qin, JianFault diagnosis plays a vital role in assessing the health management of industrial robots and improving maintenance schedules. In recent decades, artificial intelligence-based data-driven approaches have made significant progress in machine fault diagnosis using monitoring data. However, current methods pay less attention to correlations and internal differences in monitoring data, resulting in limited diagnostic performance. In this paper, a data-driven method is proposed for the fault diagnosis of industrial robot reducers, that is, a dual-module attention convolutional neural network (DMA-CNN). This method aims to diagnose the fault state of industrial robot reducer. It establishes two parallel convolutional neural networks with two different attentions to capture the different features related to the fault. Finally, the features are fused to obtain the fault diagnosis results (normal or abnormal). The fault diagnosis effect of the DMA-CNN method and other attention models are compared and analyzed. The effectiveness of the method is verified on a dataset of real industrial robots.Item Open Access Feature-level data fusion for energy consumption analytics in additive manufacturing(IEEE, 2020-10-08) Hu, Fu; Liu, Ying; Qin, Jian; Sun, Xianfang; Witherell, PaulThe 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.Item Open Access A machine learning-based approach for elevator door system fault diagnosis(IEEE, 2022-10-28) Liang, Taiwang; Chen, Chong; Wang, Tao; Zhang, Ao; Qin, JianThe door system is the core part of the elevator. An accurate diagnosis of the door system can aid engineers in troubleshooting and reduce maintenance costs. However, the research of fault diagnosis based on elevator operation and maintenance data is still in its infancy. With the development of the industrial Internet-of-things, real-time monitoring data of elevator can be collected and used for fault diagnosis modeling. This paper investigates a machine learning-based approach to achieve accurate elevator door fault diagnosis. An experimental study was conducted based on the monitoring data collected from the real-world elevator door system. The experimental results revealed that XGBoost algorithm can accurately identify the fault type of the elevator door.Item Open Access Model-agnostic meta-learning for fault diagnosis of industrial robots(IEEE, 2023-10-16) Liu, Yuxin; Chen, Chong; Wang, Tao; Cheng, Lianglun; Qin, JianThe success of deep learning in the field of fault diagnosis depends on a large number of training data, but it is a challenge to achieve fault diagnosis of multi-axis industrial robots in the case of few-shot. To address this issue, this paper proposes a method called Model-Agnostic Meta-Learning (MAML) for fault diagnosis of industrial robots. Its goal is to train an effective industrial robot fault classifier using minimal training data. Additionally, it can learn to recognize faults in new scenarios with high accuracy based on the training data. Experimental results based on a six-axis industrial robot dataset show that the proposed method is superior to traditional convolutional neural network (CNN) and transfer learning, and that the diagnostic results with the same amount of data in few-shot cases are better than existing intelligent fault diagnosis methods.Item Open Access A novel cold wire gas metal arc (CW-GMA) process for high productivity additive manufacturing(Elsevier, 2023-07-01) Wang, Chong; Wang, Jun; Bento, João; Ding, Jialuo; Rodrigues Pardal, Goncalo; Chen, Guangyu; Qin, Jian; Suder, Wojciech; Williams, StewartWire-arc directed energy deposition (DED) is suitable for depositing large-scale metallic components at high deposition rates. In order to further increase productivity and efficiency by reducing overall manufacturing time, higher deposition rates are desired. However, the conventional gas metal arc (GMA) based wire-arc DED, characterised by high energy input, normally results in high remelting and reheating at relatively high deposition rates, reducing the process efficiency and deteriorating the mechanical performance. In this study, a novel wire-arc DED process with the combination of a GMA and an external cold wire, namely cold wire-gas metal arc (CW-GMA), was proposed for achieving high deposition rate and low material remelting. The maximum deposition rates at different levels of energy input were investigated, with the highest deposition rate of 14 kg/h being achieved. An industrial-scale component weighing 280 kg was built with this process at a high deposition rate of around 10 kg/h, which demonstrated the capability of the process for high productivity application. It was also found that, due to the addition of the cold wire, the remelting was reduced significantly. The working envelope and geometric process model for the CW-GMA process was developed, which can be used to avoid defects in parameter selection and predict the geometry of single-pass wall structures. Moreover, the addition of the cold wire in the CW-GMA process reduced the specific energy density, leading to a reduction in both grain size and anisotropy, which improved the mechanical properties with increased strength and reduced anisotropy.Item Open Access Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning(Springer, 2022-07-24) Qin, Jian; Wang, Yipeng; Ding, Jialuo; Williams, StewartIn the last decade, wire + arc additive manufacturing (WAAM), which is one of the most promising metal additive manufacturing technologies, has been attracting high interest from both academia and industry. WAAM systems are increasingly employed in the industry and academia, but there are still several challenges and barriers to process stability control. The process stability is highly dependent on how the molten feed wire is added into the melt pool, which is known as the droplet transfer mode. To ensure a stable WAAM deposition process, it is essential to maintain the transfer mode in a suitable stable status. Without an effective transfer mode control method, the operators need to determine and control the transfer mode based on their experience using manual adjustment, which is difficult to achieve in a long period of production process. In this paper, a deep learning-based technology was proposed for the control of the droplet transfer mode based on the data collected from the WAAM process. A long short term memory neural network was applied as the core transfer mode classification model. A time-series data, arc voltage, was collected and statistical and frequency features were extracted, which included 11 relevant features, as the inputs of the classification model. Then, the distance between the melted wire and the melt pool was adjusted based on the determined transfer mode to keep a suitable stability of the process. A case study was used to evaluate the proposed approach and to show its merit. The proposed approach was compared to three commonly used machine learning algorithms, k-nearest neighbours, support vector machine, and decision tree. The proposed method obtained the highest accuracy in determining the transfer mode, which was over 91%. The performance of the proposed approach was also evaluated by the single-pass and oscillated wall building. The proposed deep learning based approach improved the process stability in real-time, which resulted in better deposition qualities, in terms of geometry size and processing cleanliness compared to without control. Furthermore, this data-driven method could be applied to other WAAM processes and materials.Item Open Access Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling(MDPI, 2023-10-24) Yin, Yi; Tian, Yingtao; Ding, Jialuo; Mitchell, Tim; Qin, JianThe necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.Item Open Access Refining microstructure of medium-thick AA2219 aluminium alloy welded joint by ultrasonic frequency double-pulsed arc(Elsevier, 2023-02-14) Wang, Yipeng; Li, Hong; Li, Zhuoxin; Zhang, Yu; Qin, Jian; Chen, Guangyu; Qi, Bojin; Zeng, Caiyou; Cong, BaoqiangThe increasing demand for achieving high-efficiency and high-quality medium-thick aluminium alloy welded structures, especially for large scale aerospace components, presents an urgent challenge to the conventional TIG arc welding process. This work proposed a novel double-pulsed variable polarity tungsten inert gas (DP-VPTIG) arc, in which the variable polarity square wave current was simultaneously modulated into ultrasonic frequency (20–80 kHz) and low frequency (0.5–10 Hz) pulses. Full penetration welds of 6 mm thick AA2219 aluminum alloy were successfully obtained by using this process. The microstructure and mechanical properties of the weld produced by DP-VPTIG arc were investigated, taking the conventional VPTIG arc as a comparative study. Results show that the microstructure of weld zone by DP-VPTIG arc showed an alternating distribution of fine equiaxed grain band and slightly coarse equiaxed grain band. Compared to VPTIG arc, the grain structure was effectively refined in the weld zone with DP-VPTIG arc, showing a significant reduction of average grain size by 51.2% along transverse section and 61.3% along longitudinal section. The morphology of α-Al+θ-CuAl2 eutectics transformed from continuously distributed netlike shape to separately distributed granular shape, and segregation of Cu solute element was obviously improved. The average microhardness of weld zone was increased by about 8.7% and 5.6% along transverse section and along longitudinal section. The tensile properties of ultimate tensile strength, yield strength and elongation were increased by 6.6%, 10.6% and 20.5%, respectively. The results provide a valuable basis for improving welding efficiency and joint quality through a hybrid pulsed arc.Item Open Access 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, QianAdditive 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.Item Open Access A review of WAAM for steel construction – manufacturing, material and geometric properties, design, and future directions(Elsevier, 2022-08-29) Evans, Sian I.; Wang, Jie; Qin, Jian; He, Yongpeng; Shepherd, Paul; Ding, JialuoThis paper provides a review of the capabilities of WAAM for manufacturing steel components for use in the construction industry, with a focus on the structural stability and design of WAAM builds. Manufacturing techniques that can be used for WAAM construction are first discussed. This is followed by a detailed review of the material and geometric properties, and the resulting structural stability performance of WAAM steel structures to date. To exploit the advantage of WAAM in building free-form shapes, structural optimisation techniques suitable for WAAM construction are discussed. Lastly, conclusions and future research directions are provided.Item Open Access 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, JianThe 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.