Research on Evaluation of Intelligent Manufacturing Capability and Layout Superiority of Supply Chain by Big Data Analysis

With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing industry, becoming the commanding point of a new round of global manufacturing competition. Based on the literature review of intelligent manufacturing and intelligent supply chain, a total factor production cost model for intelligent manufacturing and its formal expression are proposed. Based on the analysis of the model, 12 first-level indicators and 29 second-level indicators of production line, workshop/factory, enterprise and enterprise collaboration are proposed to evaluate the intelligent manufacturing capability of supply chain. This article also further studies the layout superiority and spatial agglomeration characteristics of intelligent manufacturing supply chain, providing useful reference and support for enterprises and policy makers in the decision-making.

Two-Sided Matching Model of Shared Manufacturing Resources considering Psychological Behavior of Agents

Sharing manufacturing is a new manufacturing mode based on sharing economy, which is one of the pillars of intelligent manufacturing. This paper proposes a two-sided matching model of shared manufacturing resources considering the psychological behavior of agents. We describe the definition of two-sided matching and introduce the concept of the cloud model. The preference information of agents is transformed to values according to the cloud model. We combine prospect theory and grey relational analysis to calculate the prospect values. Furthermore, an optimization model which aims to maximize the overall satisfaction degree of matching agents is established. A numerical example for the matching of providers and demanders is provided to verify the feasibility and effectiveness of the model.

A Novel Intelligent Manufacturing Mode With Human-cyber-physical Collaboration and Fusion in the Non-Ferrous Metal Industry

The non-ferrous metal industry is one of the most important part in China’s process industry, and has an extremely important strategic position in national economy. However, in the smelting process of non-ferrous metal, there still exists many problems: 1) the production process of non-ferrous is long, the utilization rate of resource and energy is low; 2) the large amount of waste discharge in the production process has caused prominent environmental protection issue; 3) product homogeneity is serious and the added value is low. To solve these problems, we constructed a novel intelligent manufacturing mode with human-cyber-physical (HCP) collaboration and fusion from three aspects: 1) intelligent manufacturing system with HCP collaboration and fusion, 2) product service system based on ecosystem platform, and 3) sustainable business model, to realize the safe, efficient and green production for the non-ferrous metal smelting process. In the case study on the largest copper smelting enterprise in the world, the digital twin based manufacturing system collaborative platform architecture, product &service system framework, and sustainable business model canvas are built by the proposed intelligent manufacturing mode with HCP collaboration and fusion.

Human Motion Position Prediction for Human-Robot Collaboration in Manufacturing Considering Point Cloud Completion Of Human Joint

The research of human-robot collaboration for intelligent manufacturing is being paid gradually increasing attention due to high flexibility and high manufacturing efficiency. Comparing with the traditional manufacturing with low flexibility, human-robot collaboration in manufacturing system provides more personalized and flexible way to cover the shortages of traditional manufacturing mode. In human-robot collaboration system, human motion position prediction in the collaborative space is an essential prerequisite for ensuring the safety of workers. In this paper, 3D sensor Kinect is utilized to directly obtain human joint information. A partial circle delimitation method is used to solve the offset phenomenon of human joint obtained by Kinect, so as to achieve accurate estimation of human joint points. On this basis, an algorithm combing multilayer perceptron and long short-term memory network is explored to predict human motion position accurately. It not only helps to avoid complex feature extraction due to its end-to-end characteristic, but also provide natural interaction manner between human and robot without wearable devices or tags that may become a burden for the former. After that, the experimental results demonstrate that the proposed method makes predicting results accurate, and provides the reliable basis for human position prediction in the human-robot collaboration. This research could be applied to the human motion position prediction in human-robot collaboration process.

An OSGi-Based Adaptation Access of Machine Tools in the Cloud Manufacturing Environment

Machine tools (MTs) are the core manufacturing resources for discrete manufacturing enterprises. In the cloud manufacturing environment, MTs are massive, heterogeneous, widely dispersed and highly autonomous, which makes it difficult for cloud manufacturing mode to be deeply applied to support the networked collaboration operation among manufacturing enterprises. Realizing universal access and cloud application of various MTs is an essential prerequisite to solve the above problem. In this paper, an OSGi-based adaptation access method of MTs is proposed. First, the MTs information description model in the cloud manufacturing environment is built. Then, an OSGi-based adaptation access framework of MTs is constructed, and key enabling technologies, including machine tool information acquisition and processing, Bundle and Subsystem construction, are studied. Finally, an application case is conducted to verify the effectiveness and feasibility of the proposed method.

SOFTWOOD COMPOSITES FOR CONSTRUCTION

The article deals with the issues of secondary use of industrial waste from the processing of soft hardwood wood. It is shown that the disposal of man-made waste in landfills is undesirable, and the use of wood waste to generate heat for heating is limited to the autumn-winter period. It is proved that it is most expedient to make composites from the waste of processing soft hardwood wood, since the need for inexpensive building materials is constantly increasing. Wood- cement materials from soft-leaved wood are practically not produced due to the presence of water- soluble saccharides, which worsen the process of hydration of cement. It is proposed to use binders that harden quickly in the production of composites made of soft hardwood. To exclude the negative influence of the extracted substances, it is proposed to use urea-formaldehyde glue as a binder. The mathematical dependence of the compressive strength of a composite made of soft hardwood on the glue consumption, wood consumption and the duration of exposure after molding is obtained. The parameters of the composite manufacturing mode are set: wood consumption-190 ... 195 kg/m3, urea-formaldehyde glue consumption-262...270 kg/m3; the duration of exposure after molding – 6 days. Methods for reducing the release of free formaldehyde from composites have been identified. It was found that in the steam-air mixture after 12 days of exposure of the chip-and- glue composite, there are no previously detected micro-impurities of formaldehyde, and the chip- and-glue composite can be used in construction without restrictions.

Evolutionary game of green manufacturing mode of enterprises under the influence of government reward and punishment

Green production mode is an advanced manufacturing mode. However, due to the environmental externality of green production, it is different for a pure market mechanism to promote the evolution of green operation mode of manufacturing enterprises. Government regulation is very important. This paper establishes an evolutionary game model of whether manufacturing enterprises choose to implement green production mode when the government implements two different mechanisms of reward and punishment. Considering the complexity of strategy selection of enterprises' green production behaviour under market competition, the method constructs the simulation analysis model of enterprises' green product production behaviour with multi-subject participation. We can simulate the influence of these factors on the strategic choice of both parties (enterprises and governments) by changing the different influence factors, and studying the evolutionary law of different government guidance and regulation strategies on the production behaviour of green products. These factors include government incentives, penalties, reputations, costs, differences in the cost of implementing green production on the corporate side, corporate reputation, and false rewards or penalties. By the computer implementation of multi-subject modelling, the results show that enterprises' green product production behaviour needs the government's guidance and regulation. When formulating relevant policies, the government should combine various guidance and regulation strategies and fully consider the influence of market competition.