Huajun Cao

A DECISION-MAKING FRAMEWORK MODEL OF CUTTING FLUID SELECTION FOR GREEN MANUFACTURING AND A CASE STUDY

Abstract Pursuing the green manufacturing (GM) of products is very beneficial in the alleviation of environment burdens. In order to reap such benefits, GM is involved in every aspect of manufacturing processes. During the machining process, cutting fluid is one of the main roots of environmental pollution, with the optimal selection of cutting fluid for GM being an important path to reduce the environmental pollution. The objective factors of decision-making problems in the traditional selection of cutting fluid are usually two: quality and cost; but from the viewpoint of GM, environmental impact (E) should also be considered. In this paper, a multi-object decision-making model of cutting fluid selection for GM is put forward, in which the objects of quality (Q), cost (C) and environmental impact (E) are considered together. In this model, E means to minimize the environmental impact, Q means to maximize the quality, and C means to minimize the cost. Each objective is analyzed in detail also. A case study on a decision-making problem of cutting fluid selection in the hobbing process of a gear is analyzed, the result showing that the model is practical.

A model for analyzing the consumption situation of product material resources in manufacturing systems

Abstract Manufacturing systems transfer useful resources (materials, energy, etc.) into industrial products or living products. On one hand, the systems create great wealth for mankind, but on the other hand, they consume a great amount of resources and discard a lot of waste, which usually causes environmental pollution. In order to minimize the consumption of product material resources, first there is a need to know the consumption situation of the resources. The product material resources’ consumption situation of manufacturing systems is extremely complex. So far, there is no valid way for systematically and quantitatively describing the situation. In this paper, based on a resource-classifying description and a flow model for the resources’ consumption situation of manufacturing systems, a model for analyzing the consumption situation of product material resources in manufacturing systems is put forward. Based on the model, a means of calculation of the total utilization rate and the classified utilization rate of the product material resources in manufacturing processes is presented. A case study and successful application of the above model shows that the model is practical.

A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.

Strength analysis of international Feed-in Tariff promotion of clean energy applications for greenhouse gas emission mitigation

Greenhouse gas emissions from power generations are grave concerns around the world. Clean energy technologies such as Solar Photovoltaic, Wind, etc. have been recognized as viable solutions to greenhouse gas emission mitigation. Due to the high cost of clean energy electricity, various financial incentives such as Feed-in-Tariff (FIT) are available throughout the world to promote the applications of clean energy systems to reduce the environmental footprint of power generations. In this paper, we developed a simple mathematic method to characterize and benchmark the strength of different FITs in seven selected countries including United States (US), Germany (DE), South Africa (SA), China (CH), Italy (IT), Iran (IR), and South Korea (SK). The FITs are applied on the greenhouse gas emission mitigation in the selected seven countries. The benchmarking is made on the mitigation benefits relative to the total amount of incentive input in each country. The results demonstrate that China and South Africa have the highest mitigation potential for FIT promotion of wind power, while Iran and South Africa have the highest mitigation potential for solar.

Reducing Greenhouse Gas emissions of global automotive manufacturing through clean energy supply: Cost benefit analysis

Automotive manufacturing is very energy-intensive [1]. Greenhouse Gases (GHGs) are generated in automotive manufacturing, from both the direct on-site consumption of fossil fuel energy and indirect consumption of purchased electricity. As estimated, a typical vehicle requires approximately 120 Giga Joules of energy input to be manufactured [2]. This study is conducted on assessing the application potential of such clean energy power systems as solar PV, wind and fuel cells in reducing the GHG emissions of global auto manufacturing industry. The study is conducted on those representative clean energy systems of solar PV, wind and fuel cells available on the commercial market at six representative locations of GM global facilities including United States, Mexico, Brazil, China, Egypt and Germany.

Greenhouse gas emission mitigation of global automotive manufacturing through clean energy supply

The automotive industry consumes a significant amount of energy in the manufacturing stage. As estimated, a typical vehicle requires approximately 120 Giga Joules of energy input to be manufactured [1]. The consumed energy, directly or indirectly, generates substantial greenhouse gas emissions and a variety of air pollutants into the atmosphere. With more than two thirds of these greenhouse gas emissions generated from grid power consumptions, an effective way to reduce the environmental footprint of automotive manufacturing is to use clean energies to partially supply the power needs of current automotive manufacturing processes [2]. This research is conducted in collaboration with General Motors (GM) Corporation, to explore the potential of using such clean energies as solar PV, wind and fuel cells to reduce the greenhouse gas emissions of its global manufacturing facilities

A DISASSEMBLY CAPABILITY PLANNING MODEL FOR THE MAKE-TO-ORDER REMANUFACTURING SYSTEM

A disassembly capability planning mathematical model is established to balance the order quantity and the disassembly capability in the make-to-order remanufacturing system. The model is formulated as a newsboy problem with the objective of pursuing maximum profit. Finally, a case in the machine tool remanufacturing system is studied to illustrate the model.

Technological Updating Decision–Making Model for Eco–Factory Through Dynamic Programming

As the key subject of the green manufacturing system, the construction of eco–factory has become an important content in order to achieve the sustainable development of enterprises. In this paper, a technological updating decision-making model is established based on dynamic programming (DP) for eco–factory. Firstly, the evaluation index system for eco–factory is established. Secondly, the local weight and global weight of each index are calculated based on analytic network process (ANP) and Delphi method. Finally, the decision–making model of eco-factory is established to find the optimal investment plan by using the method of logarithmic fitting and DP. The ANP and Delphi method present a great potential in solving complex and ambiguous problems and the decision-making based on DP can obtain the better ecological benefits through an example of a foundry factory, therefore the feasibility of the proposed method is proved.

State space modelling carbon emission dynamics of machining workshop based on carbon efficiency

With increasing pressure to deal with climate change, to reduce carbon emissions has been one major challenge for manufacturing enterprises even though they face unprecedented levels of business competition. This paper proposes a state space model for evaluating carbon emission dynamics of a machining workshop based on carbon efficiency. A general hierarchical analysis framework for carbon emission dynamics for a three-level structure machining workshop is initially established from three perspectives: organisation, control loop and resource coupling input and output flow. Within this framework, carbon efficiency measuring emission dynamics is defined systematically, which reflects the balancing trends between benefit output (desired output) and emission output (undesired output) of any level system in a machining workshop. In order to evaluate the balancing trends, a state space based conceptual model is proposed. A state space model module for individual machine which consists of two sub-models, namely production process state space and emission process state space, forms the basic element of this conceptual model. A carbon emission dynamics profile based on carbon efficiency from machine tool level to workshop level is then determined through module integration. Finally, an experimental study is carried out to illustrate its feasibility and applicability of evaluating carbon emission dynamics through aligning economic and environmental dimension.