Effect of bake-hardening on the structure-property relationship of multiphase steels for the automotive industry

The effect of a bake-hardening (BH) treatment on the microstructure and mechanical properties has been studied in C-Mn-Si TRansformation Induced Plasticity (TRIP) and Dual Phase (DP) steels after: (i) thermomechanical processing (TMP) and (ii) intercritical annealing (IA). The steels were characterized using X-ray diffraction, transmission electron microscopy (TEM) and three-dimensional atom probe tomography (APT). All steels showed high BH response. however, the DP and trip steels after IA/BH showed the appearance of upper and lower yield points, while the stress-strain behavior of the trip steel after TMP/BH was still continuous. This was due to the higher volume fraction of bainite and more stable retained austenite in the TMP/BH steel, the formation of plastic deformation zones with high dislocation density around the "as-quenched” martensite and “TRIP” martensite in the IA/BH DP steel and IA/BH TRIP steel, respectively.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups. © 2014 The Natural Computing Applications Forum.