An intelligent manufacturing system is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. This kind of intelligent system is called a human–cyber–physical system (HCPS). In terms of technology, HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing. It can be concluded that the essence of intelligent manufacturing is to design, construct, and apply HCPSs in various cases and at different levels. With advances in information technology, intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing, and is evolving toward new-generation intelligent manufacturing (NGIM). NGIM is characterized by the in-depth integration of new-generation artificial intelligence (AI) technology (i.e., enabling technology) with advanced manufacturing technology (i.e., root technology); it is the core driving force of the new industrial revolution. In this study, the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs, and the implications, characteristics, technical frame, and key technologies of HCPSs for NGIM are then discussed in depth. Finally, an outlook of the major challenges of HCPSs for NGIM is proposed.
The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPS) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware.
To achieve zero-defect production during computer numerical control (CNC) machining processes, it is imperative to develop effective diagnosis systems to detect anomalies efficiently. However, due to the dynamic conditions of the machine and tooling during machining processes, the relevant diagnosis systems currently adopted in industries are incompetent. To address this issue, this paper presents a novel data-driven diagnosis system for anomalies. In this system, power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis. To facilitate the analysis, preprocessing mechanisms have been designed to denoise, normalize, and align the monitored data. Important features are extracted from the monitored data and thresholds are defined to identify anomalies. Considering the dynamic conditions of the machine and tooling during machining processes, the thresholds used to identify anomalies can vary. Based on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO) algorithm to achieve more accurate detection. Practical case studies were used to validate the system, thereby demonstrating the potential and effectiveness of the system for industrial applications.
State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives.
Industrial big data integration and sharing (IBDIS) is of great significance in managing and providing data for big data analysis in manufacturing systems. A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks. First, a task flow graph (TFG) is designed to model the data analysis process. The TFG is composed of several tasks, which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy. Second, the function of Fog-IBDIS to enable data integration and sharing is presented in five modules: TFG management, compilation and running control, the data integration model, the basic algorithm library, and the management component. Finally, a case study is presented to illustrate the implementation of Fog-IBDIS, which ensures raw data security by deploying the analysis tasks executed by the data generators, and eases the network traffic load by greatly reducing the volume of transmitted data.
In this research, an auxiliary illumination visual sensor system, an ultraviolet/visible (UVV) band visual sensor system (with a wavelength less than 780 nm), a spectrometer, and a photodiode are employed to capture insights into the high-power disc laser welding process. The features of the visible optical light signal and the reflected laser light signal are extracted by decomposing the original signal captured by the photodiode via the wavelet packet decomposition (WPD) method. The captured signals of the spectrometer mainly have a wavelength of 400–900 nm, and are divided into 25 sub-bands to extract the spectrum features by statistical methods. The features of the plume and spatters are acquired by images captured by the UVV visual sensor system, and the features of the keyhole are extracted from images captured by the auxiliary illumination visual sensor system. Based on these real-time quantized features of the welding process, a deep belief network (DBN) is established to monitor the welding status. A genetic algorithm is applied to optimize the parameters of the proposed DBN model. The established DBN model shows higher accuracy and robustness in monitoring welding status in comparison with a traditional back-propagation neural network (BPNN) model. The effectiveness and generalization ability of the proposed DBN are validated by three additional experiments with different welding parameters.
With the development of modern information technology—and particularly of the new generation of artificial intelligence (AI) technology—new opportunities are available for the development of the intelligent machine tool (IMT). Based on the three classical paradigms of intelligent manufacturing as defined by the Chinese Academy of Engineering, the concept, characteristics, and systemic structure of the IMT are presented in this paper. Three stages of machine tool evolution—from the manually operated machine tool (MOMT) to the IMT—are discussed, including the numerical control machine tool (NCMT), the smart machine tool (SMT), and the IMT. Furthermore, the four intelligent control principles of the IMT—namely, autonomous sensing and connection, autonomous learning and modeling, autonomous optimization and decision-making, and autonomous control and execution—are presented in detail. This paper then points out that the essential characteristic of the IMT is to acquire and accumulate knowledge through learning, and presents original key enabling technologies, including the instruction-domain-based analytical approach, theoretical and big-data-based hybrid modeling technology, and the double-code control method. Based on this research, an intelligent numerical control (INC) system and industrial prototypes of IMTs are developed. Three intelligent practices are conducted, demonstrating that the integration of the new generation of AI technology with advanced manufacturing technology is a feasible and convenient way to advance machine tools toward the IMT.
Intelligent technologies are leading to the next wave of industrial revolution in manufacturing. In developed economies, firms are embracing these advanced technologies following a sequential upgrading strategy—from digital manufacturing to smart manufacturing (digital-networked), and then to newgeneration intelligent manufacturing paradigms. However, Chinese firms face a different scenario. On the one hand, they have diverse technological bases that vary from low-end electrified machinery to leading-edge digital-network technologies; thus, they may not follow an identical upgrading pathway. On the other hand, Chinese firms aim to rapidly catch up and transition from technology followers to probable frontrunners; thus, the turbulences in the transitioning phase may trigger a precious opportunity for leapfrogging, if Chinese manufacturers can swiftly acquire domain expertise through the adoption of intelligent manufacturing technologies. This study addresses the following question by conducting multiple case studies: Can Chinese firms upgrade intelligent manufacturing through different pathways than the sequential one followed in developed economies? The data sources include semistructured interviews and archival data. This study finds that Chinese manufacturing firms have a variety of pathways to transition across the three technological paradigms of intelligent manufacturing in nonconsecutive ways. This finding implies that Chinese firms may strategize their own upgrading pathways toward intelligent manufacturing according to their capabilities and industrial specifics; furthermore, this finding can be extended to other catching-up economies. This paper provides a strategic roadmap as an explanatory guide to manufacturing firms, policymakers, and investors.
Additive manufacturing (AM) is gaining traction in the manufacturing industry for the fabrication of components with complex geometries using a variety of materials. Selective laser melting (SLM) is a common AM technique that is based on powder-bed fusion (PBF) to process metals; however, it is currently focused only on the fabrication of macroscale and mesoscale components. This paper reviews the state of the art of the SLM of metallic materials at the microscale level. In comparison with the direct writing techniques that are commonly used for micro AM, micro SLM is attractive due to a number of factors, including a faster cycle time, process simplicity, and material versatility. A comprehensive evaluation of various research works and commercial systems for the fabrication of microscale parts using SLM and selective laser sintering (SLS) is conducted. In addition to identifying existing issues with SLM at the microscale, which include powder recoating, laser optics, and powder particle size, this paper details potential future directions. A detailed review of existing recoating methods in powder-bed techniques is conducted, along with a description of emerging efforts to implement dry powder dispensing methods in the AM domain. A number of secondary finishing techniques for AM components are reviewed, with a focus on implementation for microscale features and integration with micro SLM systems.
Additive manufacturing (AM), also known as 3D printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situ monitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area.
To design microstructure and microhardness in the additive manufacturing (AM) of nickel (Ni)-based superalloys, the present work develops a novel data-driven approach that combines physics-based models, experimental measurements, and a data-mining method. The simulation is based on a computational thermal-fluid dynamics (CtFD) model, which can obtain thermal behavior, solidification parameters such as cooling rate, and the dilution of solidified clad. Based on the computed thermal information, dendrite arm spacing and microhardness are estimated using well-tested mechanistic models. Experimental microstructure and microhardness are determined and compared with the simulated values for validation. To visualize process–structure–properties (PSP) linkages, the simulation and experimental datasets are input to a data-mining model—a self-organizing map (SOM). The design windows of the process parameters under multiple objectives can be obtained from the visualized maps. The proposed approaches can be utilized in AM and other data-intensive processes. Data-driven linkages between process, structure, and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.
A three-dimensional laser absorption model based on ray tracing was established to describe the coupled interaction of a laser beam with particles in the powder layers of pure tungsten (W) material processed by selective laser melting (SLM). The influence of particle size on the powder-to-laser absorptivity and underlying absorption behavior was investigated. An intrinsic relationship between the absorption, distribution of absorbed irradiance within the powder layers, and surface morphology and geometric characteristics (e.g., contact angle, width and height of tracks, and remelted depth) of the laser scanning tracks is presented here. Simulation conclusions indicate that the absorptivity of the powder layers considerably exceeds the single powder particle value or the dense solid material value. With an increase in particle size, the powder layer absorbs less laser energy. The maximum absorptivity of the W powder layers reached 0.6030 at the particle size of 5 μm. The distribution of laser irradiance on the particle surface was sensitive to particle size, azimuthal angle, and the position of the powder particles on the substrate. The maximum irradiance in the powder layers decreased from 1.117×10–3 to 0.85×10–3 W·μm–2 and the contour of the irradiance distribution in the center of the irradiated area gradually contracted when the particle size increased from 5 to 45 μm. An experimental study on the surface morphologies and cross-sectional geometric characteristics of SLM-fabricated W material was performed, and the experimental results validated the mechanisms of the powder-to-laser-absorption behavior that were obtained in simulations. This work provides a scientific basis for the application of the ray-tracing model to predict the wetting and spreading ability of melted tracks during SLM additive manufacturing in order to yield a sound laser processability.
In the electron beam selective melting (EBSM) process, the quality of each deposited melt track has an effect on the properties of the manufactured component. However, the formation of the melt track is governed by various physical phenomena and influenced by various process parameters, and the correlation of these parameters is complicated and difficult to establish experimentally. The mesoscopic modeling technique was recently introduced as a means of simulating the electron beam (EB) melting process and revealing the formation mechanisms of specific melt track morphologies. However, the correlation between the process parameters and the melt track features has not yet been quantitatively understood. This paper investigates the morphological features of the melt track from the results of mesoscopic simulation, while introducing key descriptive indexes such as melt track width and height in order to numerically assess the deposition quality. The effects of various processing parameters are also quantitatively investigated, and the correlation between the processing conditions and the melt track features is thereby derived. Finally, a simulation-driven optimization framework consisting of mesoscopic modeling and data mining is proposed, and its potential and limitations are discussed.
Photocatalytic water purification is an efficient environmental protection method that can be used to eliminate toxic and harmful substances from industrial effluents. However, the TiO2-based catalysts currently in use absorb only a small portion of the solar spectrum in the ultraviolet (UV) region, resulting in lower efficiency. In this paper, we demonstrate a MoS2/ZIF-8 composite photocatalyst that increases the photocatalytic degradation of ciprofloxacin (CIP) and tetracycline (TC) hydrochloride by factors of 1.21 and 1.07, respectively. The transformation products of CIP and TC from the catalysis processes are tentatively identified, with the metal organic framework (MOF) being
considered to be the main active species with holes being considered as the main active species. The hydrogen production rate of the MoS2/ZIF-8 nanocomposites is 1.79 times higher than that of MoS2. This work provides a novel perspective for exploring original and efficient 1T/2H-MoS2/MOF-based photocatalysts by optimizing the construction of surface nano-heterojunction structures. The composite photocatalyst is found to be durable, with its catalytic performance being preserved under stability testing. Thus, 1T/2H-MoS2/MOF-based photocatalysts have excellent prospects for practical antibiotic-degradation engineering.
In this research, a methodology named whole-process pollution control (WPPC) is demonstrated that improves the effectiveness of process optimization. This methodology considers waste/emission treatment as a step of the whole production process with respect to the minimization of cost and environmental impact for the whole process. The following procedures are introduced in a WPPC process optimization: ① a material and energy flow investigation and optimization based on a systematic understanding of the distribution and physiochemical properties of potential pollutants; ② a process optimization to increase the utilization efficiency of different elements and minimize pollutant emissions; and ③ an evaluation to reveal the effectiveness of the optimization strategies. The production of ammonium paratungstate was chosen for the case study. Two factors of the different optimization schemes—namely the cost-effectiveness factor and the environmental impact indicator—were evaluated and compared. This research demonstrates that by considering the nature of potential pollutants, technological innovations, economic viability, environmental impacts, and regulation requirements, WPPC can efficiently optimize a metal production process.
Three-dimensional (3D) bioprinting is a rapidly growing technology that has been widely used in tissue engineering, disease studies, and drug screening. It provides the unprecedented capacity of depositing various types of biomaterials, cells, and biomolecules in a layer-by-layer fashion, with precisely controlled spatial distribution. This technology is expected to address the organ-shortage issue in the future. In this review, we first introduce three categories of 3D bioprinting strategies: inkjet-based printing (IBP), extrusion-based printing (EBP), and light-based printing (LBP). Biomaterials and cells, which are normally referred to as “bioinks,” are then discussed. We also systematically describe the recent advancements of 3D bioprinting in fabricating cell-laden artificial tissues and organs with solid or hollow structures, including cartilage, bone, skin, muscle, vascular network, and so on. The development of organs-on-chips utilizing 3D bioprinting technology for drug discovery and toxicity testing is reviewed as well. Finally, the main challenges in current studies and an outlook of the future research of 3D bioprinting are discussed.
An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles, as it provides the main power source for the transmission system. However, manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration. This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution, which further lead to reduction of cycle life and battery capacity over time. To solve this problem, this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance. Experiments were first performed by dismantling battery modules for the measurement of performance parameters. The k-means clustering and support vector clustering (SVC) algorithms were then employed to produce battery modules composed of 12 cells each. Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period, while air cooling was provided. It was found that the SVC-clustered battery module in Category 3 exhibited the best performance, with a maximum observed temperature of 32 ℃. By contrast, the maximum observed temperatures of the other battery modules were higher, at 40 ℃ for Category 1 (manufacturer), 36 ℃ for Category 2 (manufacturer), and 35 ℃ for Category 4 (k-means-clustered battery module).
Synthetic biology is moving in the direction of larger and more sophisticated design, which depends heavily on the efficient assembly of genetic modules. Conventional evaluation of the DNA assembly efficiency (AE) requires transformation, and the whole process requires up to 10 h and is susceptible to various interferences. To achieve rapid and reliable determination of the AE, an alternative transformation-independent method was established using a modified quantitative polymerase chain reaction (qPCR) assay. The AE is represented by the proportion of the ligated fragment, which can be determined within 3 h. This qPCR-based measurement was tested by the commonly used restriction ligation, Golden Gate assembly, and Gibson assembly for the assembly of two or more DNA pieces; the results correlated significantly with the AEs represented by the counting of the colony-forming units (CFUs). This method outperformed the CFU-based measurement by reducing the measuring bias and the random deviations that stem from the transformation process. The method was then employed to investigate the effects of terminal secondary structures on DNA assembly. The results revealed the major effects of the overall properties of the overlap sequence and the negative effects of hairpin structures on the AE, which are relevant for all assembly techniques that rely on homologous annealing of the terminal sequences. The qPCR-based approach presented here should facilitate the development of DNA assembly techniques and the diagnosis of inefficient assemblies.