To objectively evaluate the resource efficiency and environmental impact of iron and steel enterprises, it is necessary to comprehensively evaluate the greenness of their manufacturing process. In this paper, based on the green evaluation index of iron and steel enterprises, a two-level green evaluation system derived from the manufacturing process is established, and a green evaluation and prediction model of the manufacturing process are established. Firstly, the data in the actual production process of iron and steel enterprises are normalized. The first 75% of the data is taken as the training set, and the last 25% as the test set. Then, the data set is imported into the constructed BP neural network for training. Finally, through the analysis of the training results, it can simulate the experts to evaluate the diagnosis and predict the optimized manufacturing process.
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

In order to realize the green design requirement of 550 mm gate valve in a valve system of an enterprise, the response surface method was used to optimize the body structure by single factor objective. Body wall thickness, height of stiffener, and thickness of stiffener as the objective factor, gate parameterized model is set up, using the finite element analysis software to optimize the former body strength that is numerically simulated, and based on DOE target optimization design for the parameters of optimized body coupling analysis, get the body by the maximum equivalent stress is 103 MPa. The results show that the strength of the improved valve body can apply to the requirements of the actual working condition, and the consumption of raw materials of the product is greatly reduced compared with the original design, which meets the requirements of the green design of valve products.
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

In order to analyze the energy consumption state of machine tools in processing workpieces and improve the workpiece processing technology, an information management system of machine tool energy consumption is developed. The system consists of three functional modules: basic information input module, energy consumption data acquisition module, energy consumption data analysis module and corresponding model database. The system can effectively manage machine tool information, process information and workpiece information, collect energy consumption data of machine tool in processing workpiece, and provide help for process improvement.
© 2020, Springer Nature Singapore Pte Ltd.

Machine tools are the main equipment in the process of mechanical manufacturing, which will consume resources and produce environmental emissions in the process of operation. How to effectively improve the processing technology of machine tools to reduce energy consumption and environmental emissions is one of the focuses of green manufacturing research. Based on the idea of green manufacturing, this paper constructs the resource and environmental attributes database of machine tool manufacturing processes and elaborates the structure and design method of the database. The system can be used to statistically analyze and analyze resource consumption and environmental emissions during machine tool processing, helping to study the machining procedure that need improvement.
© 2020, Springer Nature Singapore Pte Ltd.

Mechanical machining process consumes massive energy and material, which causes amount of carbon emissions. However, due to the complicated relationship of material flow and energy flow in machining process, It is difficult to comprehensively analyze carbon emission sources and quantify carbon emission clearly. Aiming to address this problem, a four-layer framework for quantifying carbon emissions in machining process based on Internet of Things(IoT) and Material and Energy Flow Analysis (MEFA) technology is proposed. Firstly, the carbon emission characteristic of machining process was studied with MEFA, and the carbon emission flow path of the machining process was clear with Business Process Modeling Notation (BPMN). Secondly, carbon emission related data obtained through IoT. Then, a carbon emission quantification model is established, and the results are analyzed and applied. With a case study of a convex boss, the proposed approach was verified, and which can be used to provide decision support for enterprise carbon emission management. © 2020 Elsevier B.V.. All rights reserved.

Remanufacturing plays a vital role in circular economy due to its enormous contribution in promoting resources recycling and utilizing. Disassembly of end of life (EOL) products, as a prerequisite of remanufacturing, is an effective means to improve resource utilization and reduce environmental impact. However, because of the complex quality conditions of EOL products, different disassembly method and sequence for components may lead to different effects. Based on this, a multi-objective disassembly sequence optimization model considering the quality uncertainty of EOL products is proposed in this paper. Firstly, remaining life of each component of an EOL product is calculated by using the Weibull distribution and artificial neural networks (ANN), and then the disassembly modes could be chosen according to their quality conditions. Secondly, a multi-objective disassembly sequence optimization model which takes minimum disassembly time and cost as the objective is established, and the particle swarm optimization (PSO) algorithm is employed to solve this model. Finally, a case study of drum washing machine disassembly is provided to verify the feasibility and superiority of the proposed methodology.
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