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This paper provides a comprehensive review on the dressing techniques of diamond grinding wheels. The common techniques with different tools were discussed in detail, which included the bonded SiC and diamond abrasive tools, loose abrasives, soft-elastic abrasive belts, and profiled diamond wheels. Meanwhile, laser dressing, electrical discharge dressing (EDD), and electrolytic in-process dressing (ELID) were also addressed. Some critical problems in the above dressing techniques were then analyzed and summarized for further investigation.

This paper provides a state-of-the-art review on the investigations into the residual stresses in metallic structural materials generated by grinding. The materials covered include steels, titanium alloys, and nickel-based superalloys. The formation mechanisms of the residual stresses and their impacts are specifically discussed. Some major influential factors on the residual stresses formation in grinding, such as grinding wheel characteristics, dressing techniques, grinding parameters, cooling conditions, and properties of workpiece materials, are analyzed in detail. These include experimental measurement, modeling, simulation, knowledge-based monitoring, and fuzzy analysis. Finally, the paper highlights some important aspects of grinding-induced residual stresses for further investigation.

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A back-propagation neural network BP model and a genetic algorithm optimizing back-propagation neural network (GA-BP) model are proposed to predict the grinding forces produced during the creep-feed deep grinding of titanium matrix composites. These models consider quantitative and non-quantitative grinding parameters (e.g. up-grinding mode and down-grinding mode) as inputs. Comparative results show that the GA-BP model has better prediction accuracy (e.g. up to 95%) than the conventional regression model and the BP model. Specific grinding energy was calculated against the grinding parameters and grinding modes based on the grinding forces predicted by the GA-BP model.

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