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Abstract Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components.

Abstract Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.

Abstract The integration of the Mobile Crowdsensing (MCS) and Industrial Internet of Things (IIoT) brings enormous volumes of data that generate significant commercial value. However, the data contain a wealth of sensitive information about devices' environmental situation and collective activities, which draws a flock of adversaries and poses an unprecedented security challenge. Furthermore, sensing gadgets deployed in the IIoT device are usually resource‐constrained and often do not have adequate 3C resources (i.e. communication, computing, caching) to run sophisticated privacy‐preserving methods, making them easier targets for attacks in data sharing. Therefore, a risk‐adaptive privacy protection scheme R‐DP for MCS‐enabled IIoT gadgets is proposed, which comprises a closed‐loop risk‐awareness process and an adaptive privacy protection method DP (a dissemination process with perturbation). The closed‐loop process dynamic awareness of risks and threats in MCS task feeds appropriate privacy protection advice to the decision‐makers for the task. In addition, DP was designed as a lightweight and risk‐adaptive privacy protection method to meet the operational needs of 3C resource‐constrained gadgets. The analysis and evaluation show that R‐DP provides satisfactory privacy protection while the availability of statistical features reaches more than 96%, and the time complexity is only O (1) for sensing gadgets.

Abstract The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue.

Abstract In the modern age of Industry 4.0 and manufacturing servitisation, energy saving and environment consciousness are regarded as vital themes in manufacturing processes to reduce carbon tax and achieve sustainable development. For the past 20 years, the concept of green manufacturing has grown from infancy to a fully formed framework agreed upon by world‐leading enterprises. With the unprecedented development of the information technology today, the industrial data collected could assist in the in‐depth study on green manufacturing, which ranges from the operations of machining tools all the way to supply chain management. The wide scope of research promises a tremendous amount of annual publications in this field. To better facilitate follow‐up research work, the present study provides a systematic overview of green manufacturing‐related areas, including research progress and the developed features. The article set retrieved from the Web of Science contains 5989 documents related to green manufacturing. It is revealed that Journal of Cleaner Production is the most productive journal, archiving documents within the scope of green manufacturing. P. R. China tops the list of the number of documents with 1357 documents (22.66%), while Zhejiang University is the most productive institution. As the cooperation network indicates, P. R. China and the United States maintain the strongest collaborative links with other countries/regions. Finally, possible future directions are recommended based on the findings in the study. For instance, additive manufacturing technology and industrial IoT both have a great potential in green manufacturing; the weak link between the disciplines of manufacturing engineering and environmental science is expected to be strengthened, and a stronger international cooperation is believed to be beneficial to the field for the otherwise isolated countries/regions.

Abstract Lithium‐ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium‐ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real‐time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real‐time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins‐based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.

The integration of the Mobile Crowdsensing (MCS) and Industrial Internet of Things (IIoT) brings enormous volumes of data that generate significant commercial value. However, the data contain a wealth of sensitive information about devices' environmental situation and collective activities, which draws a flock of adversaries and poses an unprecedented security challenge. Furthermore, sensing gadgets deployed in the IIoT device are usually resource‐constrained and often do not have adequate 3C resources (i.e. communication, computing, caching) to run sophisticated privacy‐preserving methods, making them easier targets for attacks in data sharing. Therefore, a risk‐adaptive privacy protection scheme R‐DP for MCS‐enabled IIoT gadgets is proposed, which comprises a closed‐loop risk‐awareness process and an adaptive privacy protection method DP (a dissemination process with perturbation). The closed‐loop process dynamic awareness of risks and threats in MCS task feeds appropriate privacy protection advice to the decision‐makers for the task. In addition, DP was designed as a lightweight and risk‐adaptive privacy protection method to meet the operational needs of 3C resource‐constrained gadgets. The analysis and evaluation show that R‐DP provides satisfactory privacy protection while the availability of statistical features reaches more than 96%, and the time complexity is only O (1) for sensing gadgets. [ABSTRACT FROM AUTHOR]

Abstract In the process of detecting materials inside a tray in an automated production line, it is necessary to detect not only the known materials and blank space in the designated area, but also the unknown materials misplaced inside the tray. However, the supervised detection algorithm based on deep learning can only detect the known and blank material areas. Therefore, this paper proposed a phased material detection. The first stage is to detect the tray and then identify the material area in the second stage. In order to improve the tray detection accuracy during the first stage under the condition of a high intersection ratio, an improved YOLOv5s tray detection method is proposed. The structure of YOLOv5s is improved using the SENet. This paper proposes to use the rich geometric information of the shallow network and the high‐level semantic information to integrate the bypass features. MAP@0.5:0.95 of the improved model increased from 95.7% to 96.6% and MAP@0.95 from 78.5% to 90.8%. The challenge of detecting unknown wrong materials on a tray can be resolved through the recognition of material area segmentation images processed by using the improved pre‐detection algorithm, together with the relative position reference between the material area and the tray. The experimental results showed that the improved method proposed meets the industrial detection requirements with an overall recognition accuracy of 91% within a 250 ms detection interval.

Abstract The objective of this study is to ensure the sustainable development of traditional machinery manufacturing industry under limited conditions of non‐renewable energy, and reduce the environmental pollution caused by high energy consumption of machine tools. Here the energy consumption of numerical control machine tools is analysed, and the relevant energy‐saving model is established. The energy consumption system of the numerical control machinery tools, including the cutting parameters, is classified. The relevant energy consumption prediction model is built to evaluate the influence of different cutting parameters, feed per tooth, and the back cutting depth on the energy consumption of numerical control machine tools. The results show that classifying the energy consumption system of numerical control machine tools can effectively predict the energy consumption of machine tools, which provides theoretical support for the subsequent energy‐saving experiments. Larger cutting parameters will increase the energy consumption of numerical control machine tools, improve the energy efficiency of machine tools at the same time. Thus, reducing the feed per tooth and the back cutting depth can effectively decrease its energy consumption. The results of this study provide data support and guidance for the energy‐saving experiments and practical applications of numerical control machine tools.

Abstract In competitive growth and Industry 4.0, construction prediction and management have a key role. To find a way to provide a simulation method for the damage assessment of buildings and Industry 4.0, building information modelling technology is the most suitable choice. This work presents and analyses the building material from design modelling to model information extraction, virtual construction, and an imported virtual simulation engine. A simulation system has been built to understand the force and material collision detection of buildings, and a three‐dimensional (3D) simulation platform is developed based on the Unity3D engine. A 3D display of building model and simulation data is realized in this work based on the simulation software platform. The results show that the building 3D simulation images constructed by the designed system are high definition, take little time, and have excellent performance. The outcomes are realized in terms of the engineering cost ratio and have energy consumption and efficiency values of 20% and 40%, respectively, which are much better than the traditional method. Efficiency has also improved to 76% from the traditional method using the proposed method, which makes it a robust platform for construction prediction and management in industries. The virtual simulation technology is applied to solve problems of building design and damage assessment. The influence of this technology on the overall design of the building is discussed, followed by future development directions for industrial automation.

Abstract Technology and revolutions have powered the growth of Industry 4.0, the fourth industrial uprising. Industry 4.0 inspires progress and expansion through its efficiency capacity, as given in the literature. To improve the design quality and design efficiency of construction engineering units, this study adopts Building Information Modelling technology concepts. The implementation of Building Information Modelling in construction developments includes the digital demonstration of the fleshly and efficient features of the constituents that organise a production project. The centre for International Finance Corporation Standards' research is to achieve the goal of collaborative work in the whole life cycle of buildings and the Building Information Modelling technology building collaboration platform. Current challenges and limitations of the typical Building Information Modelling architectural collaboration platform based on the Building Information Modelling application are discussed in detail with some possible suggestions. The research shows that the development of a Building Information Modelling architectural collaboration platform based on the Building Information Modelling application software is very critical. The construction collaboration platform based on Building Information Modelling technology can achieve a higher degree of data sharing and strengthen the coordination of the work of various agencies in construction engineering.

Abstract As digital technology and connectivity advance rapidly, the premise of bringing supply chain (SC) visibility across multiple tiers of supply, whilst facilitating the velocity to achieve strategic business objectives, is gaining interest. The feasibility and timing for successful adoption and implementation of such technology depend primarily on the readiness level and specific needs of each organisation, making it imperative to exercise insightful judgement as it can be expensive to acquire, develop and master. This research study examines the market pull versus technology push components of the functionalities enabled by digital SC control towers and buildings on the outcome of an extensive survey and expert interviews and proposes an assessment tool to aid decision making for the consideration of their adoption.

Abstract In case of glass tube for pharmaceutical applications, high‐quality defect detection is made via inspection systems based on computer vision. The processing must guarantee real‐time inspection and meet increasing rate and quality requirements. Defect detection in glass tubes is complicated by aspects that hamper the efficiency of state‐of‐the‐art techniques. This paper presents a pre‐processing algorithm which excludes portions of the image where defects are surely absent. The approach decreases the time for defect detection and classification phases (any detection algorithm can be applied), as they are applied only in high‐probability candidate sub‐image. We derive a methodology to get robust values of algorithm's parameters during production. The algorithm relies on detrended standard deviation and double threshold hysteresis, which solve issues related to the misalignment between illuminator and acquisition camera, and enable a robust detection despite rotation, vibration, and irregularities of tubes. We consider Canny, MAGDDA, and Niblack algorithms. The solution keeps the detection quality of such algorithms and reaches a 4.69× throughput gain. It represents a methodology to obtain defect detection in time‐constrained environments through a software‐only approach, and can be exploited in parallel/accelerated solutions and in contexts where a linear camera is utilized on both flat and uneven surfaces.

Abstract Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon‐YOLO (L‐YOLO) is proposed to improve the accuracy and real‐time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L‐YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L‐YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits.

Abstract Studying the influence of imperfect prognostics information on maintenance decisions is an underexplored area. To bridge this gap, a new comprehensive maintenance support system is proposed. First, a survival theory‐based prognostics module employing the Weibull time‐to‐event recurrent neural network was deployed in which prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction with this, a new predictive maintenance (PdM) planning model was framed via a trade‐off between corrective maintenance and time lost due to PdM. This optimises maintenance time based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning for cutting tools within a manufacturing facility. Systematic sensitivity analysis is provided, and the impact of imperfect prognostics information on maintenance decisions is discussed. Results show that uncertainty about prediction declines as time goes on, and as uncertainty declines, the maintenance timing becomes closer to the remaining useful life. This is expected, as the risk of making a wrong decision decreases over time.

Abstract A Digital Twin is a virtual representation of a physical product, asset, process, system, or service that allows us to understand, predict, and optimise their performance for better business outcomes. Recently, the use of Digital Twin in industrial operations has attracted the attention of many scholars and industrial sectors. Despite this, there is still a need to identify its value in industrial operations mainly in production, predictive maintenance, and after‐sales services. Similarly, the implementation of a Digital Twin still faces many challenges. In response, a systematic literature review and analysis of 41 papers published between 2016 and 11 July 2020 have been carried out to examine recently published works in the field. Future research directions in the area are also highlighted. The result reveals that, regardless of the challenges, the role of Digital Twin in the advancement of industrial operations, especially production and predictive maintenance is highly significant. However, its role in after‐sales services remains limited. Insights are offered for research scholars, companies, and practitioners to understand the current state‐of‐the‐art and challenges, and to indicate future research possibilities in the field.

Abstract Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long‐term monitoring of model performance and adaptation of models if surrounding conditions change and the desired prediction accuracy is no longer met. The heterogeneous landscape of application scenarios, machine learning frameworks, hardware‐restricted IIoT platforms, and the diversity of enterprise systems require flexible, yet stable and error resilient solutions that allow the automated adaptation of prediction models. Recommendations are provided for the application and management of predictive models in smart manufacturing. Typical causes for concept drift in real‐world smart manufacturing applications are analysed, and essential steps in data and prediction model management are highlighted, to ensure reliability and efficiency in such applications. For this purpose, recommendations and a reference architecture for model management are provided. In addition, experimental results of two model adaptation strategies on an artificial dataset are shown.

In order to solve the problems of low production efficiency, poor quality of blanks, high labour intensity and inability to meet the market demand with the manual gypsum mould, a novel design of the gypsum mould to be applied into the automatic production of the planks was developed for ceramic handicrafts. This mould inherits the advantages of the manual mould with tenon–mortise connection and adopts six-piece combination. According to the spherical radial movement, the new mould opens and closes, mostly avoiding movement interference and significantly improving service life. The connection technique of ‘six-point positioning principle’ was proposed and adopted to embed the connection device of four side die pieces with manipulator, so as to realise the strength of gypsum die pieces and effectively connect with the manipulator. Based on the group technique, the standard eigenvector and norm for the grouping of geometric elements of target blanks were proposed to realise the grouping of many different kinds of blanks into a few number of groups with all the blanks. Within a group, the gypsum mould keeps the external geometric features and tenon–mortise connection unchanged, while the working cavity of different blanks only alters, for the flexibility of gypsum mould. Thus, the universality of the mould is gained, and the design and manufacturing efficiency is greatly improved, with the considerable reduction of the cost, and the digitisation of design, manufacturing and management processes is realised. The gypsum mould provides a new technological method and technical guarantee for changing the labour-intensive production mode of ceramic handicraft blank cast and forming, realising the intellectualisation and automation of the main production link of ceramic handicraft blank cast and efficiency automatic and intellectualised production of ceramic handicraft blank with high identity.

Biomass fuel production has a variety of methods, and the use of biomass cracking to produce biofuels is an advanced technology used in many countries. In the control of the biofuel production process, how to get the motion law of the heat carrier in the cone relative to the cone becomes a problem that must be solved. This study uses the basic theory of dynamics and computer simulation calculation function to carry out dynamic analysis of each of the above contents, and analyses the response problem of comprehensive factors, and obtains the simulation results in accordance with the experimental results. This analysis provides a detailed design rationale for the optimal design of biomass cone pyrolysis equipment.

Cone pyrolysis liquefaction reactor is the key core device of biomass energy production equipment. The study of the dynamics of the cone is of great significance to the bioenergy conversion technology. Proper control of the movement time of the heat carrier in the cone can prevent it. Biomass superheated carbonisation can increase the ratio of gas and liquid formation. It is a difficult and key technology in the design of the cone pyrolysis liquefaction reactor. The kinetic differential equation of the relative motion of heat carrier particles can be obtained by kinetic analysis and calculation. It can play a key role in the in-depth study of the structural size design of the cone and the optimisation of the structural size parameters. This study conducts a dynamic simulation of the physical simplification model of biomass energy production equipment. The computer simulation software was used to simulate the running process of the simplified cone mechanism model. A reasonable physical prototype that meets the process parameters is designed. In view of the above complex dynamic phenomena, it is necessary to comprehensively analyse various methods of dynamics, and use appropriate numerical analysis and computer-aided design methods to carry out system equipment research and design.

With the rapid development of the electronic industry, the defect detection of printed circuit board (PCB) components is becoming more and more important. The types of PCB components are diverse and accompanied by complex character information, which is difficult to identify. The traditional detection method is inefficient, and it is unable to effectively perform the diversified category detection of PCB components and character recognition in complex scenes. The deep convolutional neural network has obvious advantages in object detection and character recognition, which can be used to implement a PCB component defect detection system. In this study, the authors have established a lightweight PCB type detection model called LD‐PCB, which can perform real‐time detection while improving detection accuracy. In addition, in the character detection of PCB, they have established a fast and robust character recognition model, called CR‐PCB. This model can effectively improve the accuracy of irregular character recognition. Finally, they established and published a dataset of PCB components, and combined with LD‐PCB and CR‐PCB to realise the PCB defect detection system. This system can realise the functions of defect detection, wrong insertion, missing insertion, and character recognition in industrial PCB production. The results show that the method proposed in this study can effectively detect defects on PCB components. [ABSTRACT FROM AUTHOR]

Defect detection is now an active research area for production quality assurance. Traditional visual inspection systems are developed by human beings, which is a time‐consuming, labour‐intensive, and highly error‐prone process, and are therefore unreliable. To overcome these problems, the authors proposed a new method for detecting defects when printing on a 3D micro‐textured surface. They utilise an orientation code as the basis to resist the fluctuations in illumination. Based on the consistency of the pixel pairs, they developed a model called multiple paired pixel consistency to represent the statistical relationship between each pixel pair in defect‐free images. Finally, based on this model, they designed a defect detection method. Even with different defect sizes, illumination conditions, noise intensities, and other characteristics, the performance of the proposed algorithm is extremely stable and highly accurate, and the recall, precision, and F‐measure in most of the results can reach 0.85,0.93, and 0.9, respectively. In addition, the defect detection rate can reach almost 100%. This demonstrates that the authors' approach can achieve state‐of‐the‐art accuracy in real industrial applications. [ABSTRACT FROM AUTHOR]

Evaporation duct is an abnormal refraction atmospheric stratification frequently appearing at sea. Accurately obtaining evaporation duct height (EDH) is significant for the effective application of electromagnetic system equipment. To overcome the limitations of theoretical prediction models, this study proposes a pure data-driven back propagation neural network (BPNN) EDH prediction model (referred to as BPNN model) for the first time, which uses BPNN suitable for the data characteristics of evaporation duct and then conducts two groups of experiments to fully test the proposed BPNN model. The Paulus–Jeske (PJ) model and support vector regression (SVR) model are introduced as the baseline methods in these experiments. The results of the first group of experiments prove that on the EDH prediction in all the experimental areas, the BPNN model has significantly better comprehensive performances than the PJ model and the SVR model. Also, the second group of experiments' results reflects the BPNN model has great generalisation capacity, which makes it suitable for predicting the EDH, and the distributions of EDH have a strong spatial correlation. [ABSTRACT FROM AUTHOR]

In the case of the glass tube for pharmaceutical applications, high‐quality defect detection is made via inspection systems based on image processing. Such processing must be fast enough to guarantee real‐time inspection and to meet the increasing rate and quality required by the market. Defect detection is complex due to specific problems of the production process: vibration, rotation and irregularity of the tube. All these aspects prevent the efficient use of known techniques. The authors present an algorithm that decreases the processing time of the defect detection phase. The algorithm is based on a moving average filter working at row level, that allows to minimize the effects of rotation, vibration, and irregularity of the tube. Luminosity variations due to the tube curvature are cut by the filter and a threshold algorithm can be applied. They made the evaluation considering different solutions taken from literature. The algorithm outperforms, in processing time, all these solutions with increased accuracy. Experimental measures show that the algorithm achieves a throughput gain of 2.6 times with respect to Canny. They develop also a methodology to get the best values for the algorithm parameters directly at the factory, during the change of production batches. [ABSTRACT FROM AUTHOR]

Biomass fuel production has a variety of methods, and the use of biomass cracking to produce biofuels is an advanced technology used in many countries. In the control of the biofuel production process, how to get the motion law of the heat carrier in the cone relative to the cone becomes a problem that must be solved. This study uses the basic theory of dynamics and computer simulation calculation function to carry out dynamic analysis of each of the above contents, and analyses the response problem of comprehensive factors, and obtains the simulation results in accordance with the experimental results. This analysis provides a detailed design rationale for the optimal design of biomass cone pyrolysis equipment. [ABSTRACT FROM AUTHOR]

Cone pyrolysis liquefaction reactor is the key core device of biomass energy production equipment. The study of the dynamics of the cone is of great significance to the bioenergy conversion technology. Proper control of the movement time of the heat carrier in the cone can prevent it. Biomass superheated carbonisation can increase the ratio of gas and liquid formation. It is a difficult and key technology in the design of the cone pyrolysis liquefaction reactor. The kinetic differential equation of the relative motion of heat carrier particles can be obtained by kinetic analysis and calculation. It can play a key role in the in-depth study of the structural size design of the cone and the optimisation of the structural size parameters. This study conducts a dynamic simulation of the physical simplification model of biomass energy production equipment. The computer simulation software was used to simulate the running process of the simplified cone mechanism model. A reasonable physical prototype that meets the process parameters is designed. In view of the above complex dynamic phenomena, it is necessary to comprehensively analyse various methods of dynamics, and use appropriate numerical analysis and computer-aided design methods to carry out system equipment research and design. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low production efficiency, poor quality of blanks, high labour intensity and inability to meet the market demand with the manual gypsum mould, a novel design of the gypsum mould to be applied into the automatic production of the planks was developed for ceramic handicrafts. This mould inherits the advantages of the manual mould with tenon–mortise connection and adopts six-piece combination. According to the spherical radial movement, the new mould opens and closes, mostly avoiding movement interference and significantly improving service life. The connection technique of 'six-point positioning principle' was proposed and adopted to embed the connection device of four side die pieces with manipulator, so as to realise the strength of gypsum die pieces and effectively connect with the manipulator. Based on the group technique, the standard eigenvector and norm for the grouping of geometric elements of target blanks were proposed to realise the grouping of many different kinds of blanks into a few number of groups with all the blanks. Within a group, the gypsum mould keeps the external geometric features and tenon–mortise connection unchanged, while the working cavity of different blanks only alters, for the flexibility of gypsum mould. Thus, the universality of the mould is gained, and the design and manufacturing efficiency is greatly improved, with the considerable reduction of the cost, and the digitisation of design, manufacturing and management processes is realised. The gypsum mould provides a new technological method and technical guarantee for changing the labour-intensive production mode of ceramic handicraft blank cast and forming, realising the intellectualisation and automation of the main production link of ceramic handicraft blank cast and efficiency automatic and intellectualised production of ceramic handicraft blank with high identity. [ABSTRACT FROM AUTHOR]

Interaction models specify the structure and content of the user interface, the allowed user actions, and the corresponding system responses. There is a need to inspect interaction models, as it avoids the propagation of defects to other artefacts. We created two inspection techniques for interaction models, called MoLVERIC Cards (MCards) and MoLVERIC Check (MCheck). MCards employs a gamification mechanism to motivate practitioners during the inspection. MCheck is a simple technique to be used by practitioners in a traditional way. Both techniques have questions, whose answers assist in identifying defects. We performed three studies to verify whether these techniques support the inspection of interaction models. In the first and second studies, we evaluated MCards and MCheck in comparison with an ad‐hoc technique supported by the conventional inspection approach based on defect types. The results of these studies showed that both techniques support the inspection of interaction models. In the third study, we evaluated MCard in comparison with MCheck to understand the participants'; perceptions of both techniques. The study results showed that MCards was considered more suitable for practitioners interested in using dynamic activities, while MCheck was considered more suitable for practitioners who want to use a more traditional technique. [ABSTRACT FROM AUTHOR]

Large amounts of labelled vibration data of rolling bearings are difficult to acquire in full during operating conditions under varying loads. Moreover, a large divergence in data distribution exists between source and target domains for the same state. A multiple‐state identification method for rolling bearings under varying loads is proposed. The deep domain adaptation method integrates the convolutional and pooling theory with the deep belief network (DBN) that enables the construction of a convolutional Gaussian–Bernoulli DBN, which is used to extract the deep generalised features from the frequency‐domain amplitudes of the rolling bearings. The weighted mixed kernel is then used instead of the single kernel to improve the joint distribution adaptation, which is used to process the features of both the labelled source domain and the unlabelled target domain for domain adaptation, and reduce the distribution divergence. Finally, the k‐nearest neighbour algorithm is used for identification. Experimental results show that the proposed method can make full use of unlabelled data, mine the deep features of vibration signals, and reduce the divergence between data of the same state. In resolving the multiple‐state identification of rolling bearings under varying loads, a higher accuracy is attained in the identification. [ABSTRACT FROM AUTHOR]

This study proposes a decentralised methodology to deal with the optimal energy flow (OEF) problem of the electricity–natural gas system (EGS). The OEF problem of EGS is formulated as a multi‐stage dynamic programming (DP) process, and the approximate DP algorithm is used to decompose it into subproblems. The independent decision of each subproblem can be made by solving Bellman's equation, requiring just a moderate interchange of information among different subproblems. Such that the information privacy and dispatch independency of subsystems (including electricity and natural gas (NG) subsystems) can be ensured. Different from most existing decentralised algorithms, the impact of one subproblem's decision on other subproblems can be estimated in the proposed algorithm, and the parameters of the proposed algorithm are not required for tuning, which makes more sense in real applications. Furthermore, an improved linear cut technique is proposed to handle the non‐convexity of the NG network model, so that the tightness of all gas flow constraints can be ensured. Case studies containing small, large and complicated EGSs validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Software product lines (SPL) emulate the industrial production lines that are capable of generating large volumes of products through reuse schemes and mass production. A multi product line (MPL) aims to reuse of several SPL. Feature models are often used to manage the existing resources of SPLs and define valid products through notations and relationships such as mandatory, optional, and alternative features. The main contribution of this study is a method to manage the variability of multiple SPL and generate a new portfolio of products for Internet of Things (IoT). For this, the problem of developing a universal feature model (FM) for an MPL from merging the FMs of the individual SPLs with a Search‐Based Software Engineering (SBSE) technique is addressed. In addition, the authors propose a multi‐objective optimisation model to maximise the reusability and compatibility between features and minimise the development cost. The model facilitates the design of an MPL‐feature model. Authors' empirical results show that the proposed model solved by genetic algorithms allows to configure a variety of software products and to determine the scope of the MPL. [ABSTRACT FROM AUTHOR]

The quality of electric power equipment directly affects and decides the securable and stable operation of the power grid. Revealing the quality problem causes and influencing factors are considered as the key point to improve and guarantee the quality of power apparatus. However, the data of power supplies quality problems have the features of diversity and complexity. It is of great value to take the full advantages of the multi‐source heterogeneous data, especially the possess flow information tracing time and space, to locate vulnerable processes, problem causes and influencing factors. This study put forward a data source system, which includes general information, quality problem information, process flow information and other supplementary information. Furthermore, an improved neural network model utilising knowledge–data fusion method is proposed. In this way, the efficiency and accuracy of analysis for quality problems is available and enhanced. To verify the validity of the knowledge–data fusion model, a case study with 2084 sample data of gas‐insulated switchgear is carried out, proving help to strengthen the management and control measures of power equipment quality problems. [ABSTRACT FROM AUTHOR]

With the speeding up of the industrialisation process, lightweight products are more and more favoured by people, so the engineering plastic is born at the right moment. Engineering plastics are often used for structural materials in engineering. They have high mechanical strength, such as high temperature resistance, and are wear-resisting. With the development of engineering plastics, their advantages have been gradually accepted by people. In recent years, statistics shows that about 35% of the engineering plastics are used to produce auto spare parts. Using plastic products instead of tempered products is a trend of energy saving and environmental protection. However, there are some security risks such as the stability of the plastic riveting process. The quality of the product can be quickly detected by the artificial but some tiny product cracks are hard to detect. In order to make use of the plastic rivet products better, a product quality inspection system combining computer vision with infrared image processing technology is designed in the background of the hot riveting process of some automobile products. Firstly, this paper uses the infrared detector for image acquisition, then the digital images are obtained through the analogue digital conversion circuit.

CNC machine tools play important role in industries. The dynamic characteristics of the machine tools are the main factor which affected the status of a machine tools. A three-dimensional model of a five-axis CNC machine tool was built by using PRO/E. First, the model was imported into ANSYS software in order to analyse the dynamic characteristics of the machine tool; the first six natural frequencies and vibration modes of the machine tool were obtained through modal analysis and harmonic response analysis. The accuracy of the finite element analysis model was verified through the experimental results. Finally, the optimisation suggestions of the machine tool structure were proposed.

It is necessary to test the reliability of the operation of the machine itself for meeting the high-speed, ultra-precision, flexible, and other modern manufacturing and processing needs. In practical applications, the obtained reliability detection information presents a variety of uncertainties such as randomness, imprecision, incompleteness and so on, due to the complexity of the machining environment, the limitations of the sensor itself and the imperfections of the information acquisition technology. The uncertainty reasoning method is proposed to test reliability and accuracy precision. Based on the multi-sensor information characteristic, the rule of the credibility sets is established to achieve the operational reliability analysis. It can realise the uncertainty reasoning for the modal and reliability rules. Examples of analysis show that during the high-end CNC machine tools operational reliability analysis, it has a practical effect to study the uncertainty reasoning method of the modal and reliability propositional rule and the analysis basis can be provided for the precision degradation and fault diagnosis.

The concept of cyber-physical systems (CPSs) is a core technology indispensable to Industry 4.0. The development of an intelligent automatic operational management platform is presented in this study. The platform is used to collect on-site data, provide predictive maintenance, and monitor and control the mobile robots. For predictive maintenance, it is targeted at products from manufacturing big data to cloud computing, and predictive maintenance for all factories around the world. The efficiency of the management system is improved by the performance evaluation strategy. In the experiments, the authors implement and discuss the predictive maintenance platform with generated synthetic data and real-world data collected from a gearbox plant. The results show that the important variables possess large drops according to mean decrease Gini. A warning generation for battery simulation and manufacturing execution is also presented to demonstrate the proposed system design. The results show that the discharge curve drops sharply at the last stage, and the warning value can be set accordingly.

‘S’ specimen through its unique profile features makes it possible to fully mobilise the five-axis linkage function in the five-axis finishing stage. The continuous rotation direction of the rotating axis not only reflects the static error of the machine tool but also the servo system follow error, tool positioning error, profile error etc. The authors studied its influence on the accuracy of each axis of five-axis CNC machine tool by analysing the geometrical characteristics of a S specimen and using UG's CAM environment to process ‘S’ specimen. Finally, the corresponding program forming system for S specimen machining experiments is designed by LabVIEW, and the authors used macro instruction to do NC program trigger system to collect vibration signals segmentally, so that the processing and acquisition system can be synchronised. This provides a more complete basis for the test piece cutting method to evaluate performance of machine tools.

Tool failure is one of the main causes of failure in CNC machine tool machining. The real-time online monitoring technology of CNC machine tool is studied, which is helpful to improve the efficiency of failure-free operation of CNC machine tool and reduce the probability of scrap and equipment failure due to tool failure. Here, the correlation between inverter input current and tool wear condition was studied first, based on which a new method about how to calculate line current on inverter input side was defined, according to definition of current virtual value. Then, a real-time monitoring system for online tool wear is designed and developed. The experimental results show that the system can reflect the tool wear condition and remind to change tool timely.

The new generation Geometrical Product Specifications require consideration of the effects of measurement uncertainty in the product inspection. This study estimated the measurement results and the uncertainty by integrating the statistical production information into the product detection results to rationally and fairly narrow the uncertainty area of qualification determination. Based on the Bayesian information fusion and statistical inference principle, the model of uncertainty evaluation is established. The Bayesian information fusion model integrated measuring information with manufacturing information was built, with which the uncertainty of product inspection was reappraised based on posteriori distribution function. The validity of the proposed method and theory was demonstrated by the example analysis.

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.

Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available.

The separating and screening of cylindrical parts are always difficult in the assembly industry, and the previous devices have the weakness of low working efficiency, low accuracy, and low automaticity. Based on the theoretical analysis, modelling and simulation, a theory of simultaneous separation and equidistant screening is proposed, and the corresponding equipment is established with the core modules of the multi-grooved cylindrical cam mechanism, pneumatic system, sensing, and controlling system. Solidworks software is used for three-dimensional modelling and kinematic simulation, which verify the correctness of the equipment design.

Wool and mohair fibres are both animal‐based fibres and having circular scales on their microscopic images from the longitudinal view. Although they look very similar in their microscopic view, they show different physical/chemical properties which determine their usage area. Thus, in textile industry, they need to be separated carefully from each other. The separation of wool/mohair fibres is an important issue and can be performed with human eye by using the microscopic images, that is not time/cost effective and not objective. The novelty of the presented study is to design an objective, easy, rapid, time and cost‐effective method in order to separate wool fibre from mohair fibre by using a texture analysis based identification method. For this purpose, microscopic images of both wool and mohair fibres were preprocessed as the texture images. Local binary pattern‐based feature extraction process and deep learning were separately used to get determinative information from the fibres. In order to identify the samples, the classification based method was completed. Experimental results indicated that an accurate texture analysis for this kind of animal fibres is possible to identify wool and mohair fibres by using deep learning and machine learning with 99.8% and 90.25% accuracy rates, respectively. [ABSTRACT FROM AUTHOR]

With the speeding up of the industrialisation process, lightweight products are more and more favoured by people, so the engineering plastic is born at the right moment. Engineering plastics are often used for structural materials in engineering. They have high mechanical strength, such as high temperature resistance, and are wear-resisting. With the development of engineering plastics, their advantages have been gradually accepted by people. In recent years, statistics shows that about 35% of the engineering plastics are used to produce auto spare parts. Using plastic products instead of tempered products is a trend of energy saving and environmental protection. However, there are some security risks such as the stability of the plastic riveting process. The quality of the product can be quickly detected by the artificial but some tiny product cracks are hard to detect. In order to make use of the plastic rivet products better, a product quality inspection system combining computer vision with infrared image processing technology is designed in the background of the hot riveting process of some automobile products. Firstly, this paper uses the infrared detector for image acquisition, then the digital images are obtained through the analogue digital conversion circuit. [ABSTRACT FROM AUTHOR]

The new generation Geometrical Product Specifications require consideration of the effects of measurement uncertainty in the product inspection. This study estimated the measurement results and the uncertainty by integrating the statistical production information into the product detection results to rationally and fairly narrow the uncertainty area of qualification determination. Based on the Bayesian information fusion and statistical inference principle, the model of uncertainty evaluation is established. The Bayesian information fusion model integrated measuring information with manufacturing information was built, with which the uncertainty of product inspection was reappraised based on posteriori distribution function. The validity of the proposed method and theory was demonstrated by the example analysis. [ABSTRACT FROM AUTHOR]

Tool failure is one of the main causes of failure in CNC machine tool machining. The real-time online monitoring technology of CNC machine tool is studied, which is helpful to improve the efficiency of failure-free operation of CNC machine tool and reduce the probability of scrap and equipment failure due to tool failure. Here, the correlation between inverter input current and tool wear condition was studied first, based on which a new method about how to calculate line current on inverter input side was defined, according to definition of current virtual value. Then, a real-time monitoring system for online tool wear is designed and developed. The experimental results show that the system can reflect the tool wear condition and remind to change tool timely. [ABSTRACT FROM AUTHOR]

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety. [ABSTRACT FROM AUTHOR]

CNC machine tools play important role in industries. The dynamic characteristics of the machine tools are the main factor which affected the status of a machine tools. A three-dimensional model of a five-axis CNC machine tool was built by using PRO/E. First, the model was imported into ANSYS software in order to analyse the dynamic characteristics of the machine tool; the first six natural frequencies and vibration modes of the machine tool were obtained through modal analysis and harmonic response analysis. The accuracy of the finite element analysis model was verified through the experimental results. Finally, the optimisation suggestions of the machine tool structure were proposed. [ABSTRACT FROM AUTHOR]

It is necessary to test the reliability of the operation of the machine itself for meeting the high-speed, ultra-precision, flexible, and other modern manufacturing and processing needs. In practical applications, the obtained reliability detection information presents a variety of uncertainties such as randomness, imprecision, incompleteness and so on, due to the complexity of the machining environment, the limitations of the sensor itself and the imperfections of the information acquisition technology. The uncertainty reasoning method is proposed to test reliability and accuracy precision. Based on the multi-sensor information characteristic, the rule of the credibility sets is established to achieve the operational reliability analysis. It can realise the uncertainty reasoning for the modal and reliability rules. Examples of analysis show that during the high-end CNC machine tools operational reliability analysis, it has a practical effect to study the uncertainty reasoning method of the modal and reliability propositional rule and the analysis basis can be provided for the precision degradation and fault diagnosis. [ABSTRACT FROM AUTHOR]

'S' specimen through its unique profile features makes it possible to fully mobilise the five-axis linkage function in the five-axis finishing stage. The continuous rotation direction of the rotating axis not only reflects the static error of the machine tool but also the servo system follow error, tool positioning error, profile error etc. The authors studied its influence on the accuracy of each axis of five-axis CNC machine tool by analysing the geometrical characteristics of a S specimen and using UG's CAM environment to process 'S' specimen. Finally, the corresponding program forming system for S specimen machining experiments is designed by LabVIEW, and the authors used macro instruction to do NC program trigger system to collect vibration signals segmentally, so that the processing and acquisition system can be synchronised. This provides a more complete basis for the test piece cutting method to evaluate performance of machine tools. [ABSTRACT FROM AUTHOR]