Efficiency Of Industrial Production Research Articles

A review of robotic assembly strategies for the full operation procedure: planning, execution and evaluation

The application of robots in mechanical assembly increases the efficiency of industrial production. With the requirements of flexible manufacturing , it has become a research hotspot to accomplish diversified assembly operations safely and efficiently in unstructured environments. In recent years, many advanced robot assembly strategies have been proposed. Fault monitoring and strategy performance evaluation have also attracted more attention. To promote the development of robotic assembly , this paper systematically reviews the recent research in this field. According to the assembly process, the review separates the research contents into target recognition and searching, compliant strategies for fine insertion motion and fault monitoring. The characteristics of each method are summarized. Furthermore, a performance evaluation for assembly strategies is proposed with typical metrics. We surveyed the classical benchmarks to provide support for standardized performance evaluation. Finally, the challenges and potential directions are discussed.

Two-stage dynamic data envelopment analysis measuring the overall efficiency and productivity changes of industry and agriculture in EU countries

Carbon emissions from industrial production affect agricultural production, and forests have the benefit of being carbon neutral. Therefore, this study adopts the dynamic network data envelopment analysis (DEA) model, takes carbon emissions as the link between industry and agriculture, and explores the production efficiency of industry and agriculture. It further incorporates forest area as a carry-over for evaluation and discusses 27 EU members' dynamic overall efficiency and productivity changes in 2014–2018. The empirical results are as follows. 1) The EU countries' industrial stage efficiency is better than their agriculture efficiency, with Germany, Luxembourg, Malta, and the Netherlands exhibiting the best efficiency performance. 2) The overall efficiencies of the Czech Republic, Portugal, and Croatia are the worst, because their misallocation of resources and excessive energy input cannot improve their output value. 3) The agricultural productivity of EU countries is better than that of industry productivity. 4) The EU has made great efforts at planting forests. 5) As energy consumption and carbon emissions are still rising, the EU still needs to do more to achieve the carbon reduction goals of the Paris Agreement and the Eu's 2030 zero carbon emissions.

Natural disasters and regional industrial production efficiency: evidence from pre-war Japan

ABSTRACT In this paper we investigate whether destruction due to natural disasters induces industries to increase their regional production efficiency using the case of pre-war Japan, a setting of frequent disasters and technological upgrading. To this end we compile a regional sectoral dataset of natural disaster destruction and production for machinery and textiles. We then employ a stochastic frontier analysis (SFA) approach to estimate the role of disaster events on changes in production efficiency. Our results show that earthquakes led to increases in efficiency for both machinery and textiles, although they were substantially greater for textiles due to the recovery persisting longer. Overall earthquakes contributed 6.8% of efficiency gains in textiles and 3.1% in machinery. However, allowing events to compound in their impact showed that such gains were dampened when there were damaging earthquakes in subsequent years. In contrast, for climate-related natural disaster events there is only weak, if any, evidence that these played a significant role in determining productive efficiency.

Dynamic econometric analysis on influencing factors of production efficiency in construction industry of Guangxi province in China

China's construction industry has assumed an important role in China's urbanization process, improving China's urban landscape and the level of national production and living facilities, but the productivity of the construction industry in some regions of China is still at a relatively low level. Taking the construction industry in Guangxi province in southwest China as an example, this paper analyzes the relevant indexes affecting the total factor productivity level of the regional construction industry and composes the statistical relationships among the indexes using dynamic measurement methods, and obtains that: (1) The number of employees, enterprises, labor productivity and construction profit have positive influence on the total factor productivity of Guangxi construction industry, but the improvement of regional construction gross product does not drive the improvement of technical equipment rate; (2) There is a dynamic equilibrium relationship between input and output indicators of total factor productivity of Guangxi construction industry, and the positive driving effect of output indicators on input indicators is not obvious; the influence of input indicators on output indicators is greater, and the positive influence is more. Accordingly, this paper also puts forward corresponding suggestions to promote the technical production level of Guangxi's construction industry.

How to reduce PM2.5? Perspective from a spatial autoregressive threshold panel model

Reducing PM2.5 is an important way to ameliorate air quality. Research on driving factors of PM2.5 can provide empirical support for controlling cross-regional linkage air pollution. In this paper, we first propose a new spatial autoregressive threshold panel model and its estimation method, and then apply this methodology to study influencing elements of PM2.5 using the panel data of China during 2000–2019. The results reveal that China's provincial PM2.5 has a significant positive spatial spillover effect, and its major driving factors have significant two regimes threshold characteristic when economic development is divided into low and high levels. Specifically, (1) PM2.5 concentration in adjacent provinces increases 1% can lead to that in observing province increases 0.7741%. (2) The positive impacts of population size, energy structure and humidity on PM2.5 are significant at low economic level, and become not significant at high economic level. (3) The influence of industrialization, economic development, energy intensity, urbanization and wind speed on PM2.5 in two regimes is significantly positive, and elasticity coefficients of industrialization and economic development decrease with improvement of economic level while energy intensity, urbanization and wind speed are on the contrary. (4) The influence of possession of civil vehicles on PM2.5 changes from negative to positive as economic level increases. (5) Temperature has no significant effect on PM2.5 concentration. (6) The influence of precipitation on PM2.5 is significantly positive in regime II and not significant in regime I. Therefore, local governments should strengthen cooperation with adjacent provinces and jointly adopt effective measures to cut down on PM2.5 pollution. The provinces with low economic level should accelerate economic transformation, improve industrial production efficiency, optimize energy structure, and advocate green and low-carbon lifestyle. The provinces with high economic level need to further expand consumer demand, actively develop tertiary industries, promote new energy vehicles and vigorously develop public transport.

The impact of quality of life on industrial and agricultural production and environmental efficiency in China's provinces

AbstractWhile most related topics focus on industrial production, economics, and environmental pollution, few studies discuss industrial production, economic development, environmental pollution, and quality of life together, and those that do fail to recognize how QoL influences agriculture and industry development issues. This research thus takes QoL as an exogenous variable and places agricultural production efficiency and industrial production efficiency as two parallel stages into one stage. The finding is that its addition modifies the overall efficiency of the region, as none of the 30 provinces has reached the efficiency frontier, and there is room for improvement in all of them.

Research on the Production Efficiency of Construction Industry in China's Provinces along the Belt and Road

The proposal of the Belt and Road Initiative (BRI) has had a profound impact on the development of the construction industry. Based on the panel data from 2014 to 2020 in 18 China's provinces along the Belt and Road, we combined the data envelopment analysis (DEA) with the Malmquist productivity index (MPI) model to analyze the static production efficiency of the construction industry in these provinces in 2020 and the changes in production efficiency from 2014 to 2020. The results show that the production efficiency of the construction industry in China’s provinces along the Belt and Road in 2020 is relatively high, and nearly half of the provinces are effective for DEA. The production efficiency of the construction industry in the provinces along the Belt and Road declined from 2014 to 2020. During the period, the technical level of most provinces improved, while the technical efficiency declined. Finally, some suggestions were put forward based on the results.

Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network

In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models.

Measuring Pollution Control and Environmental Sustainable Development in China Based on Parallel DEA Method

The purpose of this study is to explore the impact of pollution control on industrial production efficiency in 31 provinces and cities in the Yellow River and Non-Yellow River basins in China from 2013 to 2017, using the methods of the directional distance function (hereinafter referred to as DDF) and the technology gap ratio (hereinafter referred to as TGR) in parallel, while taking the industrial production sector (labor force, total capital formation, energy consumption and industrial water consumption) and the pollution control sector (wastewater treatment funds and waste gas treatment funds) as input variables. Undesirable outputs (total wastewater discharge, lead, SO2 and smoke and dust in wastewater) and an ideal output variable (industrial output value) are taken as output variables. It is found that the total efficiency of DDF in the Non-Yellow River Basin is 0.9793, which is slightly better than 0.9688 in the Yellow River Basin. Among the 17 provinces and cities with a total efficiency of 1, only Shandong and Sichuan are located in the Yellow River Basin. The TGR values of 31 provinces, cities and administrative regions are less than 1, and the average TGR value of the Yellow River Basin is 0.3825, which is lower than the average TGR value of the Non-Yellow River Basin of 0.5234. We can start by improving the allocation of manpower and capital, implementing the use of pollution prevention and control funds, improving the technical level of industrial production, improving pollutant emission, and increasing output value to improve overall efficiency performance. This study uses the parallel method, taking the industrial production department and the pollution control department as inputs, to objectively evaluate the changes in industrial production efficiency and technology gap in the Yellow River and Non-Yellow River basins, which is conducive to mastering the situation of pollution control and industrial production efficiency, and provides the reference for SDG-6- and SDG-9-related policy making.

Preparation and characterization of an alkali‐pickled preserved egg white heat‐induced gel

AbstractIn recent years, heavy‐metal salts for pickling preserved eggs and the long production period have affected its acceptability and industrialization. In this study, we used the method of short‐term pickling of no‐heavy‐metal salts to obtain gels by alkali‐picked and heat induced. The physicochemical properties, secondary structure and free amino acid composition of egg whites during pickling were determined. Furthermore, the texture and chromatic characters of the preserved egg white gels after heating were analyzed. After 7 days of pickling, the pH and free alkalinity can reach the level of preserved eggs, presenting the value of 11.48 and 184.03 mg/100 g respectively. The α‐helix content decreased from 36.01% to 32.39%. On the other hand, the β‐turns increased from 16.98% to 18.82%, and the Random coils increased from 26.31% to 28.76%. The free sulfhydryl content of preserved egg white increased gradually, whereas the total sulfhydryl content, the Td value, and free amino acid content decreased during the pickling process. The hardness and springiness of the gel remained largely unchanged during the pickling time (5–8 days). The gel's appearance, chromatic, and texture reached the level of traditional preserved egg in a relatively short pickling time, providing a new idea for the rapid picking of preserved egg white gel. The treatment in this study is important for improving the efficiency of industrial production of preserved egg white gels.Practical applicationsHeat treatment is a standard method in food processing, helping improve the texture and quality of products. After heat treatment, the physicochemical properties of the alkali‐pickled eggs can reach the state of traditionally preserved egg whites. Therefore, the method described in this study can help shorten the pickling time of preserved egg white products and provide theoretical support for developing new preserved egg products.

Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint

Along with the rapidly development of economy in recent years, China has also become one of the largest countries for energy consumption and carbon emission. Recently, the Chinese government announced its dual-carbon policy target of achieving carbon peak by 2030 and carbon neutrality by 2060, implying that there exists a fixed-sum constraint on total carbon emissions. As China's important energy sector and source of carbon emissions, the thermal power industry has become the key to meeting the energy needs of China's socio-economic development and to successfully achieving carbon peak and carbon neutrality. In this paper, we consider the thermal power industry as a two-stage process with energy production and energy utilization, which shows a fixed-sum constraint on shared undesirable outputs. To measure and analyze the two-stage energy production-utilization performance with the fixed-sum carbon emission constraint, we propose a novel two-stage data envelopment analysis approach based on a generalized equilibrium efficient frontier method. The equilibrium efficient frontier is obtained by minimizing the total adjustment of carbon emissions and is further used for calculating the two-stage overall energy production-utilization efficiency. Afterward, we develop an efficiency decomposition framework to obtain individual energy production efficiency and energy utilization efficiency. Finally, we apply the approach to empirically analyze the energy production-utilization efficiency of the Chinese thermal power industry at the provincial level from 2015 to 2019. We then provide some analytical findings on the Chinese provincial energy production-utilization performance in the thermal power industry.

Ontological support system of managerial decision-making of production tasks for a food enterprise

Resource and energy efficiency of industrial production, in particular, food production, is a defining requirement that will ensure its functioning without loss of quality and quantity of final products. This is achieved by observing the requirements for the operational parameters of the company’s technological processes and their operational changes. Given the complexity of the functioning of the energy component of the world and Ukraine due to military operations and their consequences, the issue of quality/cost ratio has become more acute. Therefore, for large manufacturing enterprises, the development of systems for supporting management decision-making in accordance with the Industry 4.0 concept becomes relevant. This will contribute to improving the production and economic indicators of the enterprise through coordinated actions of all links of production activities by structuring and processing large amounts of heterogeneous information. The purpose of the study is to develop a decision support system for the task of choosing the structure of an automated control system based on an ontological knowledge base. The developed application ontology uses descriptive logic and is interpreted as part of a digital production double implemented by a single ontological knowledge base and ontological repository. Considering existing international standards, the OWL2 language was chosen for the implementation of the ontological knowledge base. The ontology system architecture contains an ontology server, a Node-Red application, and a user form. A project decision support system that issues recommendations based on requests for the structure of the control system for a technological facility with uncertainties, considering the requirements and restrictions set for each technological process of a food enterprise, reduces the time to choose the appropriate structures, schemes, and methods. Thus, the designer receives the necessary information, supported by knowledge from the subject area, for the synthesis of an effective automated control system. It is also assumed that the ontological system will be expanded by connecting new created applied ontologies that implement related tasks of an industrial enterprise

A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today?s machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and efficiency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%.

Preparation and characterization of high-stability lithium ion-sieves with aluminosilicate framework

The ion exchange adsorption method is the most promising technique for lithium recovery from low-grade brines, and the main adsorbents are titanium-based and manganese-based ion-sieves. Their expensive cost and poor cycling performance, which have limited their industrial applications, need to be solved fundamentally by finding a new ion-sieve. Hence, in this work, a high-stability and low-priced lithium ion-sieve with aluminosilicate framework was successfully synthesized by solid-phase sintering. The cheap silicon and aluminum raw materials make this ion-sieve have a low synthesis cost. The adsorption behavior of ion-sieves fits the Langmuir model and pseudo-second-order kinetics, indicating that the adsorption of lithium is mainly monolayer adsorption, and the process is chemisorption. After ten cycles, the lithium adsorption capacity is still over 27 mg/g. Moreover, it can recover lithium in the brine of West Taijinar Salt Lake in China even at 160 °C due to the excellent stability of the aluminosilicate framework, which can effectively improve its industrial production efficiency. Therefore, it has great potential to extract lithium from liquid lithium resources with a high Mg/Li ratio.

Effects of particle size and liquid film viscosity on characteristics of particles colliding with dry and wet walls

Particle-wall collisions frequently occur in fluidized beds, granulation processes, and the transportation of gas–solid two-phase flows in pipelines. Exploring the rebound characteristics of particles after colliding with the wall will enable more accurate calculation models of collision rebound, enhanced control of the collision–rebound process, and improved efficiency of industrial production. This paper studies the relationship between the energy consumed by the liquid film and the energy consumed in the collision and investigates the effects of different particle sizes and liquid film viscosities on the liquid bridge morphology, coefficient of restitution (COR), and critical capture angle (CCA). The results show that the normal COR is affected by particle size, incident velocity, and viscosity, while the tangential COR is only related to the existence of the liquid film. Additionally, the maximum size of the liquid bridge and the growth rate of COR change suddenly in a transition region. The particle size and liquid film viscosity have significant effects on the CCA of the particle. The numerical results enable us to propose a general form of the normal wet collision model.

SMA: SRv6-Based Multidomain Integrated Architecture for Industrial Internet

With the increasing requirements of industrial production efficiency, the Industrial Internet has played a very important role in the fourth industrial revolution. However, the current Industrial Internet still has many drawbacks, especially in terms of network systems, such as low network expansion, inconvenient troubleshooting, and low data transmission efficiency. For this motivation, a novel SRv6-based multidomain integrated architecture (SMA) for the Industrial Internet has been proposed. Multilayer controllers are deployed in the SMA, and a software-defined network controller that generates the transmission path is replaced by SMA nodes, which realizes the high network scalability and efficient data transmission of the Industrial Internet. The faulty node in the SMA can be quickly and accurately identified through the periodic detection actively sent by the controller node in the domain and the passive feedback of the SMA nodes, and the generated SMA node trusted set (SNTS) can be used for forwarding path generation. A Bellman–Ford algorithm with a hop count constraint based on the total number of SNTS nodes is proposed, which effectively avoids long-path forwarding and improves network resource utilization. Through theoretical analysis, the safety and scalability of the SMA have been fully verified. The simulation results of the SMA on the experimental platform show that the SMA is superior to the existing Industrial Internet network structure in terms of troubleshooting efficiency of faulty nodes, network throughput, and data communication overhead. In the Industrial Internet, when the proportion of SMA nodes reaches 30%, the SMA controller can control nearly 80% of the traffic. In addition, the maximum link utilization rate will be greatly reduced, which means better adjustment of network load balance.

Industrial production evaluation with the consideration of technology accumulation

In actual industrial production, technology shows an accumulative effect that exceeds the corresponding factor endowment in each period. This accumulation effect is accompanied by the whole industrial production and plays an important role in the evolution of industrialization as a bridge. In order to identify the status quo of industrial production more comprehensively, explore the role of technological accumulation in industrial production, and further promote industrialization, this paper constructs a new multi-period dynamic data envelopment analysis model reflecting the effect of technological accumulation. Furthermore, a spatial autocorrelation analysis is conducted to determine the spatial correlation of regional industrial production and to provide a reference for the coordinated development of industry among regions. A case study of China's industries was conducted, and the main results show the following: (1) The average industrial production efficiency is 0.806, and 17% of provinces have a score of less than 0.65. (2) There is an obvious positive spatial autocorrelation evolution trend in regional industrial production; the eastern coastal area is dominated by High-High clustering, and Low-Low clustering is concentrated in the northwest. (3) Technology accumulation aggravates the dispersion degree of regional industrial production efficiency. Finally, policy implications are put forward to promote industrialization and the coordinated development of interregional industry.

Dynamic Modeling of High-Quality Development of Sports Industry Driven by Big Data Digital Economy

The overall structure layout of sports industry is inappropriate, and the effect is not ideal. The improper layout of industrial structure is not only an important factor hindering the healthy and rapid development of sports but also a key factor leading to the unsatisfactory effect of sports industry. In order to solve the problem that the potential of related industries is not brought into full play and the industrial quality is not high, this paper puts forward the research on dynamic modeling of high-quality development of sports industry driven by big data digital economy, understands the dynamic relationship between them through the research on the mechanism of high-quality development of sports industry driven by digital economy, and constructs the evaluation system of high-quality development of sports. The experimental results show that digital economy can give high-quality development power to the sports industry, improve industrial production efficiency, reduce production and transaction costs, promote industrial innovation, and provide diversified and personalized services for consumers. It is also helpful to the quality reform, efficiency reform, and power reform of the sports industry. However, in the initial stage of the integration of sports industry and digital economy, the weight of efficiency reform has not been significantly improved, and the efficiency of all production factors of sports industry needs to be further improved.

Analysis of production and investment efficiency in the Mexican food industry: Application of two-stage DEA

The food industry in Mexico is a precarious sector and lags behind other manufacturing industries, it is made up mainly of small and medium-sized enterprises. Its importance in the food assurance of the country requires strategic monitoring of the yield and efficiency variables that allow successful interventions to improve results. Commonly, the efficiency in the agriculture sector is evaluated as a one-stage data envelopment analysis (DEA) process using a specific set of variables. In this article, we applied a two-stage process to evaluate the efficiency in the Mexican food industry. The first stage evaluates the efficiency of the production, whereas the second stage evaluates the efficiency of investments in the sector. The process is demonstrated on a sample of 1 672 Mexican municipalities using data from 2014 and 2019 Census. The results indicate a growth in production efficiency with significant differences between regions. Moreover, the results also revealed very low investment efficiency in the whole food sector with a negative tendency.

Hydrophobic Outer Membrane Pores Boost Testosterone Hydroxylation by Cytochrome P450 BM3 Containing Cells

The implementation of biocatalytic steroid hydroxylation processes at an industrial scale still suffers from low conversion rates. In this study, we selected variants of the self-sufficient cytochrome P450 monooxygenase BM3 from Bacillus megaterium (BM3) for the hydroxylation of testosterone either at the 2β- or 15β-position. Recombinant Escherichia coli cells were used as biocatalysts to provide a protective environment for recombinant enzymes and to ensure continuous cofactor recycling via glucose catabolism. However, only low initial whole-cell testosterone conversion rates were observed for resting cells. Results obtained with different biocatalyst formats (permeabilized cells, cell-free extracts, whole cells) indicated a limitation in substrate uptake, most likely due to the hydrophilic character of the outer membrane of E. coli. Thus, we co-expressed nine genes encoding hydrophobic outer membrane proteins potentially facilitating steroid uptake. Indeed, the application of four candidates led to increased initial testosterone hydroxylation rates. Respective whole-cell biocatalysts even exceeded activities obtained with permeabilized cells or cell-free extracts. The highest activity of 34 U gCDW−1 was obtained for a strain containing the hydrophobic outer membrane protein AlkL from Pseudomonas putida GPo1 and the BM3 variant KSA14m. Overall, we show that the straightforward application of hydrophobic outer membrane pores can boost whole-cell steroid conversion rates and thus be game-changing with regard to industrial steroid production efficiency.