Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and ...cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.
With the deep integration of cyber physical production systems in the era of Industry 4.0, smart workshop dramatically increases the amount of data collected by smart device. A key factor in ...achieving smart manufacturing is to use data analysis methods for evaluating the equipment reliability and for supporting the predictive maintenance of equipment. Based on these insights, this paper proposes a deep learning-based approach that uses time series data for equipment reliability analysis. First, a framework of the TensorFlow-enabled deep neural networks (DNN) model for equipment reliability analysis is presented. Secondly, using time series equipment data, an evaluation strategy of equipment reliability based on deep learning is proposed. Finally, the reliability of a cylinder, an important part of the small trolley in automobile assembly line, is evaluated in a case study. Compared with the traditional reliability analysis method such as PCA and HMM, the prediction results show a significant improvement in prediction accuracy. This work contributes to promoting artificial intelligence algorithms for realizing highly efficient manufacturing.
Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a ...discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the “digital twin + Long-Short-Term Memory Network (LSTM) algorithm”, is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops.
The rapid development in synthesis methodology and applications for covalent organic frameworks (COFs) has been witnessed in recent years. However, the synthesis of highly stable functional COFs ...still remains a great challenge. Herein two-dimensional polyimide-linked phthalocyanine COFs (denoted as CoPc-PI-COF-1 and CoPc-PI-COF-2) have been devised and prepared through the solvothermal reaction of the tetraanhydrides of 2,3,9,10,16,17,23,24-octacarboxyphthalocyaninato cobalt(II) with 1,4-phenylenediamine and 4,4′-biphenyldiamine, respectively. The resultant CoPc-PI-COFs with a four-connected sql net exhibit AA stacking configurations according to powder X-ray diffraction studies, showing permanent porosity, thermal stability above 300 °C, and excellent resistance to a 12 M HCl aqueous solution for 20 days. Current–voltage curves reveal the conductivity of CoPc-PI-COF-1 and CoPc-PI-COF-2 with the value of 3.7 × 10–3 and 1.6 × 10–3 S m–1, respectively. Due to the same Co(II) electroactive sites together with similar permanent porosity and CO2 adsorption capacity for CoPc-PI-COFs, the cathodes made up of COFs and carbon black display a similar CO2-to-CO Faradaic efficiency of 87–97% at applied potentials between −0.60 and −0.90 V (vs RHE) in 0.5 M KHCO3 solution. However, in comparison with the CoPc-PI-COF-2&carbon black electrode, the CoPc-PI-COF-1 counterpart provides a larger current density (j CO) of −21.2 mA cm–2 at −0.90 V associated with its higher conductivity. This cathode also has a high turnover number and turnover frequency, amounting to 277 000 and 2.2 s–1 at −0.70 V during 40 h of measurement. The present result clearly discloses the great potential of 2D porous crystalline solids in electrocatalysis.
Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. ...Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.
Synthesis of functional 3D COFs with irreversible bond is challenging. Herein, 3D imide‐bonded COFs were constructed via the imidization reaction between phthalocyanine‐based tetraanhydride and ...1,3,5,7‐tetra(4‐aminophenyl)adamantine. These two 3D COFs are made up of interpenetrated pts networks according to powder X‐ray diffraction and gas adsorption analyses. CoPc‐PI‐COF‐3 doped with carbon black has been employed to fabricate the electrocatalytic cathode towards CO2 reduction reaction within KHCO3 aqueous solution, displaying the Faradaic efficiency of 88–96 % for the CO2‐to‐CO conversion at the voltage range of ca. −0.60 to −1.00 V (vs. RHE). In particular, the 3D porous structure of CoPc‐PI‐COF‐3 enables the active electrocatalytic centers occupying 32.7 % of total cobalt‐phthalocyanine subunits, thus giving a large current density (jCO) of −31.7 mA cm−2 at −0.90 V. These two parameters are significantly improved than the excellent 2D COF analogue (CoPc‐PI‐COF‐1, 5.1 % and −21.2 mA cm−2).
3D imide‐bonded covalent organic framework (COF) composed of cobalt‐phthalocyanine subunits has been prepared and used in CO2 reduction reaction electrocatalysis, showing the high CO2‐to‐CO conversion Faradaic efficiency. In particular, the active electrocatalytic centers and current density (jCO) of this 3D COF have been significantly improved in comparison to that of the 2D COF electrocatalyst with the same phthalocyanine subunits.
Post‐modification of robust guanine‐quadruplex‐linked 2,2′‐pyridine‐containing HOF‐25 with Ni(ClO4)2 ⋅ 6 H2O followed by exfoliation using sonication method affords hydrogen‐bonded organic framework ...(HOF) nanosheets (NSs) of HOF‐25‐Ni in the yield of 56 %. TEM and AFM technologies disclose the ultrathin nature of HOF‐25‐Ni NSs with thickness of 4.4 nm. STM observation determines the presence of sql segments assembled from HOF‐25‐Ni building blocks at the heptanoic acid/highly oriented pyrolytic graphite interface, supporting the simulated 2D supramolecular framework. ICP‐MS, XAS, and XPS data prove the successful immobilization of atomic nickel sites on the 20 % total 2,2′‐pyridine moieties in crystalline HOF‐25‐Ni. With the aid of Ru(bpy)32+ and triisopropanolamine, 10 wt% HOF‐25‐Ni NSs dispersed on graphene oxide efficiently promotes visible‐light‐driven CO2 reduction, showing a 96.3 % CO selectivity with a prominent conversion rate up to 24 323 μmol g−1 h−1.
Hydrogen‐bonded organic framework (HOF) nanosheets (NSs) of HOF‐25‐Ni have been produced by the post‐modification of robust guanine‐quadruplex‐linked metal‐free 2,2′‐pyridine‐containing HOF‐25 followed by exfoliation. With the help of a sensitizer, 10 wt% HOF‐25‐Ni NSs dispersed on graphene oxide promoted efficient and selective CO2‐to‐CO conversion under visible‐light irradiation.
The simultaneous regulation of production efficiency and equipment maintenance in intelligent production lines poses a challenging problem. Existing approaches addressing this issue often separate ...the regulation of equipment maintenance and load balancing, lacking dynamic indicators to characterize the operational status and equipment workload. Inspired by the cardiac electrical activity recorded from human electrocardiogram (ECG), the electric drive signal of the equipment is proposed as an analogous measure to monitor equipment performance and workload variations. Thereby, the implementation mechanism and working characteristics of equipment ECG (EECG) are put forward for reconfigurable mixed-model assembly. Moreover, the monitoring of equipment performance based on deep learning is explored, leveraging the EECG features combined with multi-source heterogeneous data. The variations of equipment workload are tracked through the construction of a population difference hash analysis of the ECG data flow, along with the characterization of equipment power through electric signals. Additionally, an EECG-driven synchronous mapping approach is proposed to address steady disturbance, considering both workload imbalance and the degeneracy effect of the equipment. The reconfigurability of the intelligent production line enables the proposed mechanism of similarity matching of EECG features through the reconfiguration of the software manufacturing system and hardware physical equipment. Finally, the EECG-based solution is validated on a laboratory-level prototype platform, demonstrating that the robust running of the assembly process can be ensured even in the presence of internal and external disturbances.
In this study, the effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure (CRS) were investigated. The numerical model of the CRS ...embankment was established and validated with the field data. An application programming interface (API) was developed to deal with the data exchanging issue between the numerical model and the spatial variability characterization model. Based on the verified numerical model and the API, the probabilistic analysis with 500 Monte Carlo simulations was automatically computed. Three influencing factors of the retained soil (the mean of the friction angle, the variation of the friction angle and the vertical correlation length of the random field) are analysed by parametric analysis. The results show that the vertical correlation length of the random field is most important in the earth pressure calculation process, while the mean of the friction angle is the factor with least impact. On the whole, the spatial variability of soil properties has minimal impact on the distribution and magnitude of earth pressure behind the retaining structure.
Computer-integrated manufacturing is a notable feature of Industry 4.0. Integrating machine learning (ML) into edge intelligent Industrial Internet of Things (IIoT) is a key enabling technology to ...achieve intelligent IIoT. To realize novel intelligent applications of edge-enhanced IIoT, ML methods are proposed to improve the cognitive ability of edge intelligent IIoT in this article. First, an ML-enabled framework of the cognitive IIoT is proposed. Second, the ML methods are presented to enhance the cognitive ability of IIoT including the ML model of IIoT, data-driven learning and reasoning, and coordination with cognitive methods. Finally, with a focus on the reconfigurable production line, a scenario-aware dynamic adaptive planning (DAP) with deep reinforcement learning (DRL) was conducted. The experimental results show that the DRL-based dynamic adaptive planning (DRL-based DAP) had good performance in an observable IIoT environment. The main purpose of this work is to point out the effects of ML-based optimization methods on the analysis of industrial IoT from the macroscopic view.
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