The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keyword frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.
Liquid leakage from pipelines is a critical issue in large-scale process plants. Damage in pipelines affects the normal operation of the plant and increases maintenance costs. Furthermore, it causes unsafe and hazardous situations for operators. Therefore, the detection and localization of leakages is a crucial task for maintenance and condition monitoring. Recently, the use of infrared (IR) cameras was found to be a promising approach for leakage detection in large-scale plants. IR cameras can capture leaking liquid if it has a higher (or lower) temperature than its surroundings. In this paper, a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant. Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid, it is applicable for any type of liquid leakage (i.e., water, oil, etc.). In this method, subsequent frames are subtracted and divided into blocks. Then, principle component analysis is performed in each block to extract features from the blocks. All subtracted frames within the blocks are individually transferred to feature vectors, which are used as a basis for classifying the blocks. The k-nearest neighbor algorithm is used to classify the blocks as normal (without leakage) or anomalous (with leakage). Finally, the positions of the leakages are determined in each anomalous block. In order to evaluate the approach, two datasets with two different formats, consisting of video footage of a laboratory demonstrator plant captured by an IR camera, are considered. The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos. The proposed method has high accuracy and a reasonable detection time for leakage detection. The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.
A person's eye gaze can effectively express that person's intentions. Thus, gaze estimation is an important approach in intelligent manufacturing to analyze a person's intentions. Many gaze estimation methods regress the direction of the gaze by analyzing images of the eyes, also known as eye patches. However, it is very difficult to construct a person-independent model that can estimate an accurate gaze direction for every person due to individual differences. In this paper, we hypothesize that the difference in the appearance of each of a person's eyes is related to the difference in the corresponding gaze directions. Based on this hypothesis, a differential eyes' appearances network (DEANet) is trained on public datasets to predict the gaze differences of pairwise eye patches belonging to the same individual. Our proposed DEANet is based on a Siamese neural network (SNNet) framework which has two identical branches. A multi-stream architecture is fed into each branch of the SNNet. Both branches of the DEANet that share the same weights extract the features of the patches; then the features are concatenated to obtain the difference of the gaze directions. Once the differential gaze model is trained, a new person's gaze direction can be estimated when a few calibrated eye patches for that person are provided. Because personspecific calibrated eye patches are involved in the testing stage, the estimation accuracy is improved. Furthermore, the problem of requiring a large amount of data when training a person-specific model is effectively avoided. A reference grid strategy is also proposed in order to select a few references as some of the DEANet's inputs directly based on the estimation values, further thereby improving the estimation accuracy. Experiments on public datasets show that our proposed approach outperforms the state-of-theart methods.
Cloud manufacturing is one of the three key technologies that enable intelligent manufacturing. This paper presents a novel attribute-based encryption (ABE) approach for computer-aided design (CAD) assembly models to effectively support hierarchical access control, integrity verification, and deformation protection for co-design scenarios in cloud manufacturing. An assembly hierarchy access tree (AHAT) is designed as the hierarchical access structure. Attribute-related ciphertext elements, which are contained in an assembly ciphertext (ACT) file, are adapted for content keys decryption instead of CAD component files. We modify the original Merkle tree (MT) and reconstruct an assembly MT. The proposed ABE framework has the ability to combine the deformation protection method with a content privacy of CAD models. The proposed encryption scheme is demonstrated to be secure under the standard assumption. Experimental simulation on typical CAD assembly models demonstrates that the proposed approach is feasible in applications.
Although new technologies have been deeply applied in manufacturing systems, manufacturing enterprises are still encountering difficulties in maintaining efficient and flexible production due to the random arrivals of diverse customer requirements. Fast order delivery and low inventory cost are fundamentally contradictory to each other. How to make a suitable production-triggering strategy is a critical issue for an enterprise to maintain a high level of competitiveness in a dynamic environment. In this paper, we focus on production-triggering strategies for manufacturing enterprises to satisfy randomly arriving orders and reduce inventory costs. Unified theoretical models and simulation models of different production strategies are proposed, including time-triggered strategies, event-triggered strategies, and hybrid-triggered strategies. In each model, both part-production-triggering strategies and product-assembly-triggering strategies are considered and implemented. The time-triggered models and hybrid-triggered models also consider the impact of the period on system performance. The results show that hybrid-triggered and time-triggered strategies yield faster order delivery and lower inventory costs than event-triggered strategies if the period is set appropriately.
Intelligent process planning (PP) is one of the most important components in an intelligent manufacturing system and acts as a bridge between product designing and practical manufacturing. PP is a nondeterministic polynomial-time (NP)-hard problem and, as existing mathematical models are not formulated in linear forms, they cannot be solved well to achieve exact solutions for PP problems. This paper proposes a novel mixed-integer linear programming (MILP) mathematical model by considering the network topology structure and the OR nodes that represent a type of OR logic inside the network. Precedence relationships between operations are discussed by raising three types of precedence relationship matrices. Furthermore, the proposed model can be programmed in commonly-used mathematical programming solvers, such as CPLEX, Gurobi, and so forth, to search for optimal solutions for most open problems. To verify the effectiveness and generality of the proposed model, five groups of numerical experiments are conducted on well-known benchmarks. The results show that the proposed model can solve PP problems effectively and can obtain better solutions than those obtained by the state-ofthe- art algorithms.
The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks. The proposed framework includes three layers: a perception and execution layer, a logistics layer, and a configuration layer. In addition to the collaboration among these three layers, this study addresses the collaboration among devices, edge servers, and cloud services. For effective logistics in industrial parks, task collaboration is achieved through four functions: environmental perception and map construction, task allocation, path planning, and vehicle movement. To dynamically coordinate these functions, device–edge–cloud collaboration, which is supported by 5G slices and V2X communication technology, is applied. Then, the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks. Finally, a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.
There has been an increasing demand for bioengineered blood vessels for utilization in both regenerative medicine and drug screening. However, the availability of a true bioengineered vascular graft remains limited. Three-dimensional (3D) bioprinting presents a potential approach for fabricating blood vessels or vascularized tissue constructs of various architectures and sizes for transplantation and regeneration.
In this review, we summarize the basic biology of different blood vessels, as well as 3D bioprinting approaches and bioink designs that have been applied to fabricate vascular and vascularized tissue constructs, with a focus on small-diameter blood vessels.
In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
Waste plastics, such as waste polyethylene terephthalate (PET) beverage bottles and waste rubber tyres are major municipal solid wastes, which may lead to various environmental problems if they are not appropriately recycled. In this study, the feasibility of collectively recycling the two types of waste into performance-increasing modifiers for asphalt pavements was analyzed. This study aimed to investigate the recycling mechanisms of waste PET-derived additives under the treatment of two amines, triethylenetetramine (TETA) and ethanolamine (EA), and characterize the performances of these additives in modifying rubberized bitumen, a bitumen modified by waste tyre rubber. To this end, infrared spectroscopy and thermal analyses were carried out on the two PET-derived additives (PET–TETA and PET– EA). In addition, infrared spectroscopy, viscosity, dynamic shear rheology, and multiple stress creep recovery tests were performed on the rubberized bitumen samples modified by the two PET-derived additives. We concluded that waste PET can be chemically upcycled into functional additives, which can increase the overall performance of the rubberized bitumen. The recycling method developed in this study not only helps alleviate the landfilling problems of both waste PET plastic and scrap tyres, but also turns these wastes into value-added new materials for building durable pavements.
Membrane bioreactors (MBRs) have been and will continue playing an important role in industrial wastewater treatment and reuse in China. The sustainable development of MBR technology in its mature-application stage requires reciprocal interactions between engineering and research participants. Thus, in this study, a total of 182 large-scale MBR projects treating industrial wastewater (with individual treatment capacities ≥ 5000 m3·d−1) commissioned and under construction from 2003 to 2019 were analyzed comprehensively. Fast growth of the cumulative treatment capacity was observed, with extension to diverse industries, and the super large-scale was enhanced recently. The treatment processes, pollutant removal efficiencies, and actual operational parameters were summarized regarding the particularity of industrial wastewater compared to municipal wastewater. Economic features including the total investment costs of the projects, their total footprint, and their operational energy consumption were analyzed as well. A vigorous MBR market has formed in China with the fast development of membrane elements and engineering suppliers, continuously increasing official oriented projects, and responsive and innovative business modes. MBR technology has been mostly applied in specific economic zones and water-deficient areas, but its widespread use all over China is foreseeable considering the vast future market for industrial wastewater treatment and recycling. The policy–economy and market–technology driving forces revealed that MBR is consistent with the national development demand. According to the survey and analysis, prospective development in both engineering and research aspects of MBR is proposed to maintain its competitive edge.
Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional (2D) to three-dimensional (3D) data with a broad range of capture. However, if there are insufficient data for training, the data-driven approach will fail. We propose a feature-based transfer-learning method to extract features from fluoroscopic images. With three subjects and fewer than 100 pairs of real fluoroscopic images, we achieved a mean registration success rate of up to 40%. The proposed method provides a promising solution, using a learning-based registration method when only a limited number of real fluoroscopic images is available.