交通运输业节能减排国际管理手段及启示

交通运输业的能源消费在许多国家中都占有较高比例,经济发展较快的国家更是如此。介绍了能源消费大国英国、美国、欧盟以及日本交通运输业节能减排采取的主要管理手段,包括健全的法律体系、节能技术创新、有效的税费政策等。借鉴其先进经验,为我国交通运输业节能减排管理工作提供启示。     

第三产业清洁生产战略研究

结合国内外清洁生产的定义以及服务业的广泛范围，在第三产业清洁生产取得阶段性成果基础上适时提出第三产业开展清洁生产思路，总结归纳符合中国国情的第三产业清洁生产概念与方案，为我国加入WTO与世界接轨后，第三产业的环境与经济协调发展提供重要参考。     

Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.     

基于模糊决策的风力发电产业评价研究

根据风力发电技术产业的特点,构建改进层次分析与模糊一致矩阵相结合的方案决策数学模型,该模型既可以减少确定判断矩阵的盲目性,又可以避开评价体系中各等级间的隶属度确定问题,使评价结果更加可靠,适合于信息尚不完整、受主观因素影响较强的评价。实例验证了这一模型的合理性、可行性和有效性,具有实用价值。     

Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

If the production process, production equipment, or material changes, it becomes necessary to execute pilot runs before mass production in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Although, artificial neural networks are widely utilized to extract management knowledge from acquired data, sufficient training data is the fundamental assumption. Unfortunately, this is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize bootstrap to generate virtual samples to fill the information gaps of sparse data. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.     

Wire + Arc Additive Manufacturing

Depositing large components (>10?kg) in titanium, aluminium, steel and other metals is possible using Wire + Arc Additive Manufacturing. This technology adopts arc welding tools and wire as feedstock for additive manufacturing purposes. High deposition rates, low material and equipment costs, and good structural integrity make Wire+Arc Additive Manufacturing a suitable candidate for replacing the current method of manufacturing from solid billets or large forgings, especially with regards to low and medium complexity parts. A variety of components have been successfully manufactured with this process, including Ti–6Al–4V spars and landing gear assemblies, aluminium wing ribs, steel wind tunnel models and cones. Strategies on how to manage residual stress, improve mechanical properties and eliminate defects such as porosity are suggested. Finally, the benefits of non-destructive testing, online monitoring and in situ machining are discussed.     

Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains

In the real-world manufacturing/distribution planning decision (MDPD) integration problems in supply chains, the environmental coefficients and parameters are normally imprecise due to incomplete and/or unavailable information. This work presents a fuzzy linear programming approach based on the possibility theory. It applies this approach to solve multi-product and multi-time period MDPD problems with imprecise goals and forecast demand by considering the time value of money of related operating cost categories. The proposed approach attempts to minimize the total manufacturing and distribution costs by considering the levels of inventory, subcontracting and backordering, the available machine capacity and labor levels at each source, forecast demand and available warehouse space at each destination. This study utilizes an industrial case study to demonstrate the feasibility of applying the proposed approach to practical MDPD problems. The primary contribution of this paper is a fuzzy mathematical programming methodology for solving the MDPD integration problems in uncertain environments.     

流程工业CIMS中综合优化问题的研究

文章首先提出了流程工业及其CIMS中的各种优化问题;;小结了各种解决优化问题的求解技术.在此基础上提出了一种基于多级优化的一种流程工业CIMS结构.最后;;作者提出了一种解决流程工业CIMS多目标优化问题的最大熵优化技术     

Effects of training to implement new tools and working methods to reduce knee load in floor layers

The purpose was to measure the effect of a participatory ergonomics implementation strategy consisting of information, education, and facilitation on the use of new tools and working methods in the floor laying trade. Floor layers (n=292) were trained in using new working methods. The effects were evaluated by using questionnaires, interviews, and assessments of quality and productivity. Following the training, 43% had used the new working methods weekly/daily compared to 11% before. There was a reduction in the degree of self-reported pain in the knees among the floor layers using the new working methods weekly or daily compared to those using them never or occasionally. The musculoskeletal complaints did not increase from any other region and the quality and the productivity of the work were not decreased. This indicates that within a 3-months perspective the implementation strategy succeeded.