Screw Inspection Optimized System Design and Implementation

In this paper, twelve indicators related to green innovation capability of China's manufacturing enterprises are selected and green innovation capability of twenty-five industries in China's manufacturing industry in 2014 is evaluated and sorted by principal component analysis method. Then the green innovation ability of the industry and enterprise is divided into three categories according to the similarity by using cluster analysis. Finally, the results of principal component analysis and cluster analysis are compared and analyzed. The research shows: The transportation equipment manufacturing industry and electrical machinery and equipment manufacturing industry belong to the high green innovation ccapability of the industry as the first category; chemical fiber manufacturing, general equipment manufacturing industry, ferrous metal smelting and rolling processing industry, equipment manufacturing, non-ferrous metal smelting and rolling processing industry and the agricultural food processing industry belong to middle green innovation capability of the industry as the second categories; the rest of the industry belong to the low green innovation capability of the industry as the third categories.

Data factors are the basic resources, strategic resources and important productivity in the digital economy era. This paper combines two methods of index system and text analysis, and constructs data factor measurement indicators from the provincial macro level and the enterprise micro level. This study found that from 2011 to 2020, the level of development of data factors generally showed a continuous upward trend, of which the upward trend was particularly obvious from 2016 to 2018. Judging from the development situation of various regions, there are still obvious differences in the development levels of data factors in various regions of China. The development level of data factors in the eastern region is the highest and far higher than the national average. The development trend of data factors in the central and western regions is almost the same, and the development level of data factors in the northeast region is the lowest. Judging from the differences in the manufacturing industry, advanced manufacturing industries with high technology intensiveness such as computer, communications and other electronic equipment manufacturing industries, electrical machinery and equipment manufacturing industries, and automobile manufacturing industries have the highest comprehensive index of data factors. The research in this article provides a reference for better evaluating the effectiveness and characteristics of data factors and for the government to formulate relevant policies.

This article explores the digital transformation path of agricultural machinery and equipment manufacturing industry from the perspective of smart agriculture, and focuses on the practical path at the enterprise level. The article analyzes the background of smart agriculture, summarizes the construction experience of smart agriculture at home and abroad, and points out the achievements and shortcomings of China's agricultural machinery and equipment manufacturing industry in digital transformation. Suggestions have been put forward to address the issues, including breakthroughs in key core technologies, popularization of information technology and promotion of agricultural machinery and equipment, and introduction of new agricultural professionals, in order to accelerate the pace of digital rural construction and promote the modernization and intelligent transformation of agriculture.

This paper introduces the virtual water and the virtual water trade. Based on the input-output theory, we analysis the water consumption features and virtual water trade of the national economy and industry of Tianjin. We analysis the water feature by the direct water input coefficient, the total water input coefficient and the water coefficient of added value, and analysis the virtual water trade of Tianjin in 2002, 2005and 2007 in perspective of production and consumption. The results showed that: The electrical machinery and equipment manufacturing industry, the transportation equipment manufacturing and the electronic equipment industry is a net output of virtual water trade, three water consumption coefficients of the three industry are relatively small, so the net output of them is attribute to the large export of them. The virtual water of the agricultural, the chemical and the textile and garment industry is net output in 2002 and net input in 2007, of which the agriculture has a relatively high direct water input coefficient, the chemical and the textile and garment industry have high indirect water input coefficients. The net input industry contains the food manufacturing and tobacco processing industry, the metallurgy industry and so on. Tianjin changed the state of virtual water trade and became a net transferred region from 2002 to 2007. It shows that the adjustment of industrial structure of Tianjin is toward to the direction of water-saving. At the same time, we should focus on the reduction of the virtual water of agriculture, the conservation of industry and should focus on the development of the tertiary industry (service) in the future.

This paper identifies the systemic shocks of total factor productivity (TFP) at the macro level and industry level, and then evaluates the structural stickiness of TFP shocks by using information entropy and industry correlation degree through counterfactual structural simulation based on China’s manufacturing companies. We find that: in the face of TFP systemic shocks, the industries with less structural stickiness include computer communication and other electronic equipment manufacturing, special equipment manufacturing and general equipment manufacturing, indicating that these industries have a strong internal innovation power. The TFP distribution of electrical machinery and equipment manufacturing industry and ferrous metal smelting and rolling industry showed structural differentiation, and the lower tail enterprises are not sensitive to TFP shocks. The industries with strong structural stickiness are non-ferrous metal processing industry and non-metallic mineral products industry, etc., which have weak internal innovation power and need exogenous innovation incentives. In addition, there is a significant positive correlation between industry correlation and information entropy, which emphasizes the radiation effect role of industries with high industry correlation degree. The research provides a new method to evaluate the innovation ability of the industry and a basis for the differentiation of innovation incentive policies in the industry.

Based on the direct consumption coefficient, using the clustering analysis technology, our studies have showed that the contribution of science and Technology Service Industry to six industrial categories of the smelting and mangling process of ferrous metal six industrial categories was lower. The contribution to four industry categories of electrical machinery and equipment manufacturing industry focused on the scientific and technological exchanges and promotion services; while the contribution of technology services to the petroleum processing, coking and nuclear fuel processing industry is not enough, its distribution is more balanced and reasonable. The contribution to the fabricated metal products industry is focused on scientific and technological exchanges and promotion. The contribution of general equipment manufacturing industry is focused on the development of researches and experiments and professional and technical service.

There are many different uses for the data that comes from a measurement. With the advent of new testing methodologies, data has become finely granular—allowing for precise analysis of the properties and performance of materials, geometries, and even simulations. Raw measurement data can be retained and compared directly to the output of complex acoustic models for development, improvement, evaluation, and eventually a form of calibration. Advances in technology are rapidly removing the limitations of computational power needed to create accurate models of acoustic phenomena in a timely and efficient manner. This allows a progression from limited particle simulations on single workstations, to large scale wave-based simulations using cloud-based GPU computing clusters. Performance will be evaluated, comparing their output to laboratory measurements, with the goal of creating better tools for acoustic design, prediction, experimentation, and development.There are many different uses for the data that comes from a measurement. With the advent of new testing methodologies, data has become finely granular—allowing for precise analysis of the properties and performance of materials, geometries, and even simulations. Raw measurement data can be retained and compared directly to the output of complex acoustic models for development, improvement, evaluation, and eventually a form of calibration. Advances in technology are rapidly removing the limitations of computational power needed to create accurate models of acoustic phenomena in a timely and efficient manner. This allows a progression from limited particle simulations on single workstations, to large scale wave-based simulations using cloud-based GPU computing clusters. Performance will be evaluated, comparing their output to laboratory measurements, with the goal of creating better tools for acoustic design, prediction, experimentation, and development.

When making a choice regarding care homes, potential service users or their family members face problems associated with information asymmetry. Given the credence nature of care home services, many aspects of quality cannot be assessed prior to use. In the absence of a mechanism to assess service quality two main problems can arise. First, users are not able to distinguish between good-quality providers and poor-quality providers, and second, providers of good-quality services are unable to credibly provide accurate information to users to distinguish them from poor-quality providers. In such a situation, both users and providers of high-quality services will welcome measures or effective ‘signals’ that would allow them to differentiate service quality. In England, this role is fulfilled by the Care Quality Commission, through inspections and provision of quality ratings. Quality ratings produced by the regulator have a dual purpose. They provide information about quality for potential stakeholders who may be interested in using the service and they provide feedback for care home service providers to encourage service quality improvement. If quality ratings are to effectively remove problems of information asymmetry, they must be effective in both purposes. Using signalling theory as the basis for a range of quantitative methods, this research assesses the effectiveness of quality ratings provided by the regulator for all care homes, as market signals of quality. Based on the assessment of the three dimensions of an effective signal (signal cost, signal observability and signal consistency), the study finds that regulator quality ratings may not be an effective market signal. Whilst the study finds that quality ratings of some service providers improves after inspection, no improvements are made by a greater number of care homes. Furthermore, the study also finds a positive association between quality ratings and demand for care home places, however, the evidence also suggests that changes in demand are caused mostly by the regulator’s own enforcement actions. In addition to having some issues with internal consistency between quality ratings and inspection reports, evidence suggests that regulator quality ratings are mainly fulfilling the role of a feedback signal for regulatory scrutiny. There is a need for the regulator to consider the informational gain from its quality ratings for potential service users of care homes. If service users are to benefit from quality ratings and inspection reports, these must be internally consistent and easily understandable by those accessing the information.

Quality inspection and defect management in industrial building construction is one of the major factors that general contractors have to consider for improving customer satisfaction and gain competitive advantage. This paper describes a web-based system for quality inspection and defect management (SIDEM). The system is derived from the elements of ISO 9001 standard, and assist management in tracking of inspection/test results, defects management, non-conformities analysis, preventive and corrective measures, etc. A Quality Inspection database and a relational database structure were devised to effectively interconnect the essential information such as facility, trades, trade contractors, materials, and defect types required for a computerized quality inspection and defect management for industrial building construction. In SIDEM, all collected and analyzed information is stored into the database of the central server system allowing general contractors to effectively monitor and statistically analyze the statuses and results of the quality inspection and defect management in real time.

Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects.

In this paper, the degree of new energy industry agglomeration is obtained by calculating the location entropy, and the results show that China's new energy industry agglomeration generally presents a pattern of "east > middle > west".Then, based on the panel data of 30 provinces from 2013 to 2019, an econometric model is built to explore the impact of new energy industry agglomeration on economic growth.The regression results show that the new energy industry agglomeration has a significant positive effect on economic growth, and has obvious industrial heterogeneity.The impact of general equipment manufacturing industry agglomeration on economic growth is better than that of electrical machinery and equipment manufacturing industry agglomeration on economic growth.

Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measurement techniques are reliable, they are also time-consuming, costly, and susceptible to environmental and sample changes. Hyperspectral imaging technology combined with deep learning methods can effectively overcome these problems. Compared with human evaluation, automated inspection improves inspection efficiency, reduces subjective error, and promotes the intelligent and precise fruit and vegetable quality inspection. This paper reviews reports on the application of hyperspectral imaging technology combined to deep learning methods in various aspects of fruits and vegetables quality assessment. In addition, the latest applications of these technologies in the fields of fruit and vegetable safety, internal quality, and external quality inspection are reviewed, and the challenges and future development directions of hyperspectral imaging technology combined with deep learning in this field are prospected. Hyperspectral imaging combined with deep learning has shown significant advantages in fruit and vegetable quality inspection, especially in improving inspection accuracy and efficiency. Future research should focus on reducing costs, optimizing equipment, personalizing feature extraction, and model generalizability. In addition, the development of lightweight models and the balance of accuracy, the enhancement of the database and the importance of quantitative research should also be brought to attention. These efforts will promote the wide application of hyperspectral imaging technology in fruit and vegetable inspection, improve its practicability in the actual production environment, and bring important progress for food safety and quality management.

Based on the rapid development of the current big data era, the organic integration of machine manufacturing model and big data technology is directly related to the development of China's machine manufacturing industry. Based on the above background, this article mainly analyzes the application of big data technology in machinery manufacturing. Starting from the connotation of big data technology, combined with the current situation of the machinery manufacturing industry, analyze its application in the machinery manufacturing industry, in order to better formulate the strategy of the machinery manufacturing industry. First, analyze the basic profile of the machine manufacturing company and the production characteristics of its manufacturing process, and point out the problems that the company's workshop manufacturing system faces during the production process. Secondly, on the basis of the production characteristics of the selected machinery manufacturing company and the problems faced by the workshop in the production process, a 4V+ model based on big data evaluation and an artificial neural network as the core of the production equipment are proposed. The integrated operation mode of the workshop manufacturing system. On this basis, in order to support the effective implementation of the integrated operation mode of the workshop, the key systems such as the information support system of the integrated operation mode have been studied, including the overall model of the distributed mechanical manufacturing energy storage aggregation model. Framework design, functional architecture and system implementation principles and steps. The experimental research results show that combined with the production status of the machinery manufacturing company, the 4V + model based on big data evaluation and the CNC equipment in the integrated operation mode of the workshop manufacturing system with the artificial neural network as the core of the production equipment are selected as the implementation objects. The information support system was implemented and applied and achieved very good application effects.KeywordsBig data technologyMechanical manufacturing model4V+ modelArtificial neural network

Using fast Fourier transform analysis for the rapid evaluation of the diffraction fringes of optical components produced through ultraprecision single-point diamond turning

<p>In modern manufacturing, the efficiency and accuracy of quality inspection significantly influence production costs and product quality. With the development of sensing technology, signals of process variables can be collected in high resolution, which can be regarded as multichannel profile data. They have abundant information to characterize the multistage manufacturing process and help with quality inspection tasks. Motivated by the urge to take the place of manual inspection, we target at modeling based on multichannel profile data for data-driven quality inspection. However, quality inspection modeling based on multichannel profile data is challenging due to the complexity of data structures and manufacturing processes. Specifically, in the pipe tightening process, accurate diagnosis of defect types is required, but the available samples for each defect type are limited and imbalanced. Moreover, the profile data is incomplete since the pre-tightening process before the pipe tightening process is unobserved. In the ceramic firing process, the identification of crucial phases and stages is required. Nevertheless, the process data streams from different stages are unsynchronized and high-dimensional, and a hierarchical structure exists between phases and stages. To tackle the challenges, we propose models based on functional data analysis, machine learning, and deep learning. For the pipe tightening process, we propose an innovative classification framework to train on imbalanced datasets based on deep metric learning. A neural network and padding mechanism specially crafted for processing profile data are also proposed to handle incomplete profile data. For the ceramic firing process, we propose a real-time diagnostic system, facilitating the real-time synchronization of unsynchronized and high-dimensional process data streams. We also develop a hierarchical sparse partial functional linear regression model (HSPFLR) and corresponding parameter estimation algorithm, enabling the simultaneous identification of crucial phases and stages. The effectiveness of our frameworks is demonstrated through simulation studies and real-world case studies. The proposed methods can replace traditional manual inspection methods, improving product quality and reducing inspection costs.</p>