No abstract

Sensing of both gradual and catastrophic tool failure is a key aspect in producing high quality parts on fully automated machine tool systems. Acoustic emission provides a means of sensing tool failure, since it is generated from the processes that cause tool failure (e.g., tool wear, tool fracture). A linear discriminant function-based technique for detection of tool wear, tool fracture, or chip disturbance events is developed using the spectra of signals generated by these sources. In addition, a methodology for determining the feature dimensionality, the selection of best features, and the minimum training sample size is presented. The concepts of classification error minimization and manufacturing cost minimization have been applied to design classifiers using a hierarchical decision strategy to improve the performance of tool failure sensing. Results of an application indicate an 84 to 94 percent reliability for detecting tool failure of any type.

Earlier work has shown tool failure monitoring using frequency-based pattern recognition analysis of acoustic emission signals to be feasible while machining under fixed cutting conditions. However, cutting conditions change quite frequently in industrial production, and since AE signals are affected by varying conditions, a model is developed based on Taylor’s expansion using experimental data obtained for various process variables and their output AE spectra, and is used to filter the influence of varying conditions on signal classification. The experimental study involved a fractional factorial experimental design which delineated effects of variables on AE generation during machining. Normalized AE spectra within the 100 to 1000 kHz range were used as system output along with the AE power. While the normalized spectra were found unaffected by changes in the depth of cut, the total AE power was also little affected by the mixed flank and crater wear, and feed rate changes. Using the model and filter designed, performance of the tool wear, chip noise and tool breakage classifier improved to 83, 99, and 97 percent classification, respectively.

The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

No abstract