Lean philosophy: implementation in a forging company

This research addresses the implementation of lean philosophy in a forging company with a focus on radial forging production flow lines. Here, the prime motive is to evolve and test several strategies to eliminate waste on the shop floor. In this research, a systematic approach is suggested for the implementation of lean principles. This paper describes an application of value stream mapping (VSM). Consequently, the present and future states of value stream maps are constructed to improve the production process by identifying waste and its sources. Furthermore, Taguchi’s method of design of experiments is pursued here to minimize the forging defects produced due to imperfect operating conditions. A noticeable reduction in set-up time and work-in-process (WIP) inventory level is substantiated. Finally, we conclude with a discussion of managerial implications and the future scope of research.     

Influence of ZnO nanoparticles on the cure characteristics and mechanical properties of carboxylated nitrile rubber

Zinc oxide (ZnO) nanoparticles assembled in one dimension to give rod‐shaped morphology were synthesized. The effect of these ZnO nanoparticles (average particle size ～ 50 nm) as the curing agent for carboxylated nitrile rubber was studied with special attention to cure characteristics, mechanical properties, dynamic mechanical properties, and swelling. These results were compared with those of the conventional rubber grade ZnO. The study confirmed that the ZnO nanoparticles gave a better state of cure and higher maximum torque with a marginal decrease in optimum cure time and scorch time. The mechanical properties also showed an improvement. There was an increase in tensile strength by ～ 120%, elongation at break by ～ 20%, and modulus at 300% elongation by ～ 30% for the vulcanizate cured with ZnO nanoparticles, as compared with the one containing rubber grade ZnO. Dynamic mechanical analysis revealed that the vulcanizates exhibited two transitions—one occurring at lower temperature due to the T_g of the polymer, while the second at higher temperature corresponding to the hard phase arising due to the ionic structures. The second transition showed a peak broadening because of an increase in the points of interaction of ZnO nanoparticles with the matrix. The tan δ peak showed a shift towards higher T_g in the case of ZnO nanoparticle‐cured vulcanizate, indicating higher crosslinking density. This was further confirmed by volume fraction of rubber in the swollen gel and infrared spectroscopic studies. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Biodiesel development from high acid value polanga seed oil and performance evaluation in a CI engine

Non-edible filtered high viscous (72 cSt at 40 °C) and high acid value (44 mg KOH/gm) polanga (Calophyllum inophyllum L.) oil based mono esters (biodiesel) produced by triple stage transesterification process and blended with high speed diesel (HSD) were tested for their use as a substitute fuel of diesel in a single cylinder diesel engine. HSD and polanga oil methyl ester (POME) fuel blends (20%, 40%, 60%, 80%, and 100%) were used for conducting the short-term engine performance tests at varying loads (0%, 20%, 40%, 60%, 80%, and 100%). Tests were carried out over entire range of engine operation at varying conditions of speed and load. The brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE) were calculated from the recorded data. The engine performance parameters such as fuel consumption, thermal efficiency, exhaust gas temperature and exhaust emissions (CO, CO₂, HC, NO_x, and O₂) were recorded. The optimum engine operating condition based on lower brake specific fuel consumption and higher brake thermal efficiency was observed at 100% load for neat biodiesel. From emission point of view the neat POME was found to be the best fuel as it showed lesser exhaust emission as compared to HSD.     

Spatio-temporal polygonal clustering with space and time as first-class citizens

Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application.     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

Adaptive state-based multi-radio multi-channel multi-path routing in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are envisioned to seamlessly extend the network connectivity to end users by forming a wireless backbone that requires minimal infrastructure. Unfortunately for WMNs, frequent link quality fluctuations, excessive load on selective links, congestion, and limited capacity due to the half-duplex nature of radios are some key limiting factors that hinder their deployment. To address these problems, we propose a novel Adaptive State-based Multi-path Routing Protocol (ASMRP), which constructs Directed Acyclic Graphs (DAGs) from each Mesh Router (MR) to Internet Gateways (IGWs) and effectively discovers multiple optimal path set between any given MR-IGW pair. A congestion aware traffic splitting algorithm to balance traffic over these multiple paths is presented which synergistically improves the overall performance of the WMNs. We design a novel Neighbor State Maintenance module that innovatively employs a state machine at each MR to monitor the quality of links connecting its neighbors in order to cope with unreliable wireless links. We also employ a 4-radio architecture for MRs, which allows them to communicate over multiple radios tuned to non-overlapping channels and better utilize the available spectrum. Through extensive simulations using ns-2, we observe that ASMRP substantially improves the achieved throughput (～5 times gain in comparison to AODV), and significantly minimizes end-to-end latencies. We also show that ASMRP ensures fairness in the network under varying traffic load conditions.     

Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

Data-driven models have been constructed for Dual Phase (DP) and Interstitials Free (IF) steels using an evolutionary approach. DP steel data are utilized from an existing database, while for the IF steels, data generated at an integrated steel plant have been used. The objective function for Ultimate Tensile Strength (UTS) and % elongation, created as data-driven models, is simultaneously optimized for an optimum strength-ductility balance and the results indicate the possibilities of developing steels with better mechanical properties than what are known to have been existing so far.     

Novel route maintenance protocols for the Bluetooth ad hoc network with mobility

Bluetooth technology is specially designed for the wireless personal area networks to replace cable. Several challenges exist in Bluetooth scatternet formation and routing, since nodes can arrive and depart at arbitrary times. In this paper, novel route maintenance algorithms are proposed for the Bluetooth ad hoc networks, where nodes can enter or exit from the piconets time to time. Our protocols guarantee the connectivity among nodes and reconstruct the routes dynamically by considering location information of the nodes. Besides, it is proposed how to reduce the number of hops and to form the shortest route between the source and the destination due to addition of new nodes to a piconet. Performance analysis of our protocols show that they outperform in terms of end to end transmission delay, bandwidth consumption and average hop counts as compared to similar Bluetooth routing protocols that we have considered.     

Solving shortest path problem using particle swarm optimization

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process is proposed for particle representation in PSO. Simulation experiments have been carried out on different network topologies for networks consisting of 15–70 nodes. It is noted that the proposed PSO-based approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high certainty for all the tested networks. It is observed that the performance of the proposed algorithm surpasses those of recently reported genetic algorithm based approaches for this problem.