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Article
Publication date: 21 December 2022

Vimal Kumar Deshmukh, Mridul Singh Rajput and H.K. Narang

The purpose of this paper is to present current state of understanding on jet electrodeposition manufacturing; to compare various experimental parameters and their implication on…

Abstract

Purpose

The purpose of this paper is to present current state of understanding on jet electrodeposition manufacturing; to compare various experimental parameters and their implication on as deposited features; and to understand the characteristics of jet electrodeposition deposition defects and its preventive procedures through available research articles.

Design/methodology/approach

A systematic review has been done based on available research articles focused on jet electrodeposition and its characteristics. The review begins with a brief introduction to micro-electrodeposition and high-speed selective jet electrodeposition (HSSJED). The research and developments on how jet electrochemical manufacturing are clustered with conventional micro-electrodeposition and their developments. Furthermore, this study converges on comparative analysis on HSSJED and recent research trends in high-speed jet electrodeposition of metals, their alloys and composites and presents potential perspectives for the future research direction in the final section.

Findings

Edge defect, optimum nozzle height and controlled deposition remain major challenges in electrochemical manufacturing. On-situ deposition can be used as initial structural material for micro and nanoelectronic devices. Integration of ultrasonic, laser and acoustic source to jet electrochemical manufacturing are current trends that are promising enhanced homogeneity, controlled density and porosity with high precision manufacturing.

Originality/value

This paper discusses the key issue associated to high-speed jet electrodeposition process. Emphasis has been given to various electrochemical parameters and their effect on deposition. Pros and cons of variations in electrochemical parameters have been studied by comparing the available reports on experimental investigations. Defects and their preventive measures have also been discussed. This review presented a summary of past achievements and recent advancements in the field of jet electrochemical manufacturing.

Open Access
Article
Publication date: 20 March 2024

Guijian Xiao, Tangming Zhang, Yi He, Zihan Zheng and Jingzhe Wang

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding…

Abstract

Purpose

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

Design/methodology/approach

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Findings

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

Originality/value

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Details

Journal of Intelligent Manufacturing and Special Equipment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2633-6596

Keywords

Article
Publication date: 23 November 2023

Luciano de Brito Staffa Junior, Dayana Bastos Costa, João Lucas Torres Nogueira and Alisson Souza Silva

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and…

76

Abstract

Purpose

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

Design/methodology/approach

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

Findings

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

Originality/value

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

Details

International Journal of Building Pathology and Adaptation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-4708

Keywords

Article
Publication date: 19 September 2023

Jiazhong Zhang, Shuai Wang and Xiaojun Tan

The light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift…

Abstract

Purpose

The light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift phenomenon and large accumulated error are inevitable when using SLAM. The purpose of this study is to alleviate the accumulated error and drift phenomenon in the process of mapping.

Design/methodology/approach

A novel light detection and ranging SLAM system is introduced based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies conditions of loop-closed.

Findings

The proposed algorithm exhibits competitiveness compared with current approaches in terms of the accumulated error and drift distance. Further, supplementary to the place recognition process that is usually performed for loop detection, the authors introduce a novel dynamic constraint that takes into account the change in the direction of the robot throughout the total path trajectory between corresponding frames, which contributes to avoiding potential misidentifications and improving the efficiency.

Originality/value

The proposed system is based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies condition of loop-closed.

Details

Industrial Robot: the international journal of robotics research and application, vol. 50 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 14 July 2023

Guozhi Xu, Xican Li and Hong Che

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based…

Abstract

Purpose

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Design/methodology/approach

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

Findings

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R2) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

Social implications

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

Originality/value

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Details

Grey Systems: Theory and Application, vol. 13 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 14 December 2023

Hongyan Zhu, Xiaochong Wu, Pengzhen Lv, Yuansheng Wang, Huagang Lin, Wei Liu and Zhufeng Yue

Improvement and optimization design of a two-stage vibration isolation system proposed in this paper are conducted to ensure the device of electronic work effective.

Abstract

Purpose

Improvement and optimization design of a two-stage vibration isolation system proposed in this paper are conducted to ensure the device of electronic work effective.

Design/methodology/approach

The proposed two-stage vibration isolation system of airborne equipment is optimized and parameterized based on multi-objective genetic algorithm.

Findings

The results show that compared with initial two-stage vibration isolation system, the angular vibration of the two-stage vibration isolation system becomes 3.55 × 10-4 rad, which decreases by 89%. The linear isolation effect is improved by at least 67.7%.

Originality/value

The optimized two-stage vibration isolation system effectively improves the vibration reduction effect, the resonance peak is obviously improved and the reliability of the mounting bracket and the shock absorber is highly improved, which provides an analysis method for two-stage airborne equipment isolation design under complex dynamic environment.

Details

Multidiscipline Modeling in Materials and Structures, vol. 20 no. 1
Type: Research Article
ISSN: 1573-6105

Keywords

Open Access
Article
Publication date: 28 February 2023

Luca Rampini and Fulvio Re Cecconi

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM…

Abstract

Purpose

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

Design/methodology/approach

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

Findings

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

Originality/value

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

Details

Construction Innovation , vol. 24 no. 1
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 31 March 2023

Duen-Ren Liu, Yang Huang, Jhen-Jie Jhao and Shin-Jye Lee

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on…

Abstract

Purpose

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

Design/methodology/approach

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

Findings

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

Originality/value

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Details

Data Technologies and Applications, vol. 58 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 12 January 2024

Wei Xiao, Zhongtao Fu, Shixian Wang and Xubing Chen

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this…

Abstract

Purpose

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

Design/methodology/approach

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

Findings

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

Originality/value

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 10 January 2024

Sanjay Saifi and Ramiya M. Anandakumar

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient…

Abstract

Purpose

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management.

Design/methodology/approach

This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed.

Findings

The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making.

Originality/value

The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

Details

International Journal of Disaster Resilience in the Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1759-5908

Keywords

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