Combining probabilistic algorithms, Constraint Programming and Lagrangian Relaxation to solve the Vehicle Routing Problem

This paper presents an original hybrid approach to solve the Capacitated Vehicle Routing Problem (CVRP). The approach combines a Probabilistic Algorithm with Constraint Programming (CP) and Lagrangian Relaxation (LR). After introducing the CVRP and reviewing the existing literature on the topic, the paper proposes an approach based on a probabilistic Variable Neighbourhood Search (VNS) algorithm. Given a CVRP instance, this algorithm uses a randomized version of the classical Clarke and Wright Savings constructive heuristic to generate a starting solution. This starting solution is then improved through a local search process which combines: (a) LR to optimise each individual route, and (b) CP to quickly verify the feasibility of new proposed solutions. The efficiency of our approach is analysed after testing some well-known CVRP benchmarks. Benefits of our hybrid approach over already existing approaches are also discussed. In particular, the potential flexibility of our methodology is highlighted.     

Improvement of Water Status Methodology for Leafy Vegetables Reduces Consumption of Time, Skilled Labor, and Laboratory Resources

When determining the water status of leafy vegetables with oven-drying techniques, consumption of time and resources is inevitable. The aim of this work was to evaluate the performance of both relative water content (RWC) and free water and bound water content (FW–BW) techniques to obtain the water content index (WC) of different leafy vegetables in order to dispense with the traditional methodology for water content determination. By doing this, not only consumption of time is reduced, but also skilled labor and laboratory resources. Butterhead lettuces harvested at different growing stages and stored at different postharvest conditions were evaluated. In all cases, water content values obtained with RWC technique were statistically equivalent to those obtained with the traditional methodology. Instead, water content values obtained with FW–BW technique were considerably higher than the original WC. Consequently, using RWC technique data to obtain water content, the time required to determine water status in butterhead lettuce is reduced by 25%. In addition, fresh plants of swiss chard, akusay (chinese cabbage), and romaine lettuce were evaluated in order to validate the simplified methodology. Results obtained were similar to those observed for butterhead lettuce.     

高炉冶炼钒钛矿过程中炉缸沉积物的形成原因

高炉冶炼钒钛矿时炉缸会产生大量的沉积物,影响高炉正常生产。对炉缸沉积物进行了检测,对高炉渣的冶金性能进行了试验研究,结果表明:沉积物的成分复杂,其中一些沉积物的铝、硅含量较大;沉积物形成的主要来源为TiC包裹金属铁的沉降聚集,渣铁对耐火材料的侵蚀,含高熔点物质的高黏度炉渣在炉底堆积。高铝中钛渣的黏度过大,炉渣碳含量较高是导致沉积物形成的主要原因,因此,改善高铝中钛渣的冶金性能可以有效抑制沉积物的形成。     

基于EBSD的金属变形激活滑移系的确定方法

针对多晶金属力学变形过程中的激活滑移系,提出了一种基于电子背散射衍射(Electron Back-Scatter Diffraction,EBSD)技术的滑移系确定方法。借助材料表面晶体取向和滑移带形貌分析,计算并测量滑移带与加载应力轴之间的夹角,通过两者比较确定滑移面;计算该滑移面对应的可能滑移系的Schmid因子,根据Schmid定律确定滑移方向,从而判断实际被激活滑移系。以奥氏体不锈钢为例,试验证实了该方法的可行性。与其他方法相比,该方法可获得表面损伤形貌及其晶体取向分布,更适用于多晶材料力学变形后的滑移系分析。     

氯元素对高炉冶炼的影响分析及展望

在分析高炉内氯元素的主要来源和相关化学反应的基础上,综述了氯对高炉炉料、耐火材料、风口以及设备的影响。指出氯元素对高炉原燃料的作用机理,提出降低氯元素对高炉耐火材料侵蚀的措施,寻找有效降低净煤气中氯含量的方法,以及研究氯元素在高炉渣铁形成过程中的分配规律,指出找到适宜的高炉渣排氯的操作条件是今后研究的重点,可为降低氯元素对高炉冶炼过程的影响提供理论依据。     

Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations

Surface roughness plays a key role in the performance of machined components??specially dies and moulds??manufactured for the aerospace and automotive industries, among others. However, roughness can only be measured off-line after the part has been machined, when cutting conditions may no longer be adjusted to surface roughness requirements. A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors, cutting conditions, dynamic factors and lubricant type. An intensive comparison with an artificial neural network approach for the same dataset is included, to reveal the improvements of the new technique over other well-established ones for this industrial problem. This comparison shows that ensemble learning can by-pass the time-consuming task of tuning neural network parameters and can also improve prediction model accuracy, both of which are features that could lead to greater use of these kinds of prediction models in real workshops. Finally, a methodology, based on this new approach, is presented, in order to illustrate how the prediction model can be used in workshops to optimize cutting conditions in terms of their surface quality and productivity.     

On-line 3-D system for the inspection of deformable parts

In spite of the development of automated tolerance inspection systems for manufactured parts over the years, there are still processes that inevitably require manual intervention making full automation impossible in most cases; in particular when dealing with deformable parts. In most current industrial inspection systems, a deformable part under inspection must first be mechanically constrained on a rigid support or jig so as to be able to compare it with its nominal shape. This paper presents a new system to perform real-time surface inspection of deformable parts that does not require fixturing. Instead, the proposed system applies virtual forces to the part??s CAD model as if the part was installed in the fixturing device. Normally, a precise finite element method (FEM) simulation should be used to approximate the deformation that appends when the part is installed in the device. Even with a fast parallel computer, FEM is far from being real-time and cannot be used for on-line inspection. In the proposed system, a radial basis function approximation of the FEM simulation is trained off-line and used to speed-up the simulation by an order of magnitude. Experimental evaluation of the proposed system is presented for three plastic parts. Using the proposed scheme, an approximation of 0.25?mm compared with the real deformation was obtained. In this paper, statistical results are presented such as the average deviation, standard deviation, and processing time between the approximations obtained with the proposed method and with the finite element method applied to the full CAD model.     

Experimental study and modelling of tool temperature distribution in orthogonal cutting of AISI 316L and AISI 3115 steels

Cutting tool temperature distribution was mapped using the IR-CCD technique during machining of carbon steel AISI 3115 and stainless steel AISI 316L under orthogonal cutting conditions using flat-face geometry inserts. The effect of work material treatment on tool temperature was investigated, and the results showed that AISI 3115 in heat-treated state displayed higher tool temperature than the as-rolled state. Stainless steel 316L with high sulphur content (0.027?wt.%) and calcium treatment displayed lower cutting tool temperature than the variant with low sulphur (0.009?wt.%). The experimental results were compared with theoretical tool temperature distributions based on a modified version of Komanduri and Hou??s analytical model. In particular, variable frictional heat source and secondary shear were introduced and modelling of the tool stress distribution on rake surface was also considered.