The development of strategies for sequencing slabs through the reheat furnace and rolling mill of a modern steel manufacturing system is complicated by multiple and often conflicting objectives. For example, optimal energy efficiency through the reheat furnace may lead to inefficient rolling sequences or a less than desirable product delivery schedule. Thus, not only the model formulation is complicated, but also the combinatorial nature of the problem precludes an optimal solution. To address these complexities, an agentoriented approach that has originated from distributed artificial intelligence (DAI) is proposed here. It is found that the implementation of agent technology in the steel manufacturing system makes the operations more flexible, economical and energy efficient.

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The drive of this research is to examine the machinability of 100Cr6 bearing steel using advanced C-type cutting tools. Experimental studies investigated the effects of machining variables on the surface quality, chip reduction coefficient and cutting force. Seven advanced coated tools were checked for characterization by micro hardness (VHN), adhesion quality, X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDXS). The experimental trials were planned by Taguchi’s L18 orthogonal array using a mixed-level design. Two numerical machining variables feed rate and cutting speed, and one categorical machining variable tool type was taken into consideration while a constant depth of cut was kept for all trails. A combined Taguchi-Satisfaction function distance measure approach was implemented for multi-response optimization. The most promising machining parameter setting for minimization of surface roughness, cutting force, and chip reduction coefficient was identified. The most important process parameter was found to be tool-type. Ceramics tools are found to be best trailed by WC coated tools under most of the conditions. Lower tool wear was observed in the CBN tool as compared to others.