Manganese is a standard alloy element in many wrought steels, offering improved strength and hardenability and, in Hadfield steels, also a unique combination of strength, ductility and work hardening behaviour. In sintered steels Mn has been used rarely so far because of two reasons: one is the high oxygen affinity of Mn which promotes oxygen pickup during heat treatment and inhibits removal of oxygen in part through the 'internal getter' effect. The other reason is the high vapour pressure of Mn which results in Mn loss during sintering. In this presentation, these problems are discussed on Mn-alloyed sintered steels with transformation-induced plasticity behaviour. It is shown that by suitable alloying techniques, combined with adjustment of the manufacturing parameters, the problems can be overcome and the benefits of Mn addition can be reaped, obtaining PM steels for which potentially surface densification and hardening can be attained in one run.

Acoustic emission monitoring (AEM) is a method for verifying the structural integrity of heavily stressed parts or key components while the plant is in operation. The condition of the monitored component can be determined by repeated short- or continuous long-term AEM. The selection of damage-relevant measurement data and the resulting evaluation serves to determine the actual condition of the test object. Unlike for conventional acoustic emission testing, no shutdown of the plant is required for the measurements. The results can then be used as a quality assurance measure according to maintenance requirements of the owner/operator or as a supplement to the testing and inspection program. This contribution describes the general monitoring concept developed and established by the TÜV AUSTRIA Group as well as two selected industrial use cases.

This paper describes the performance of a laboratory burst test on a test object with a severe real-world crack, the interpretation and evaluation of the acoustic emission (AE) data in general and in particular the results of further AE data trend analysis utilising the failure forecast method at different sections in testing time. The objective of this exercise is to gain experience in application of this method and to check whether or not it could be employed for testing and/or monitoring purposes. The burst pressure prediction as well as the ratio between the actual test pressure and the burst pressure prediction, the failure convergence, are consistent with the usual AE data evaluation throughout the test. Test sequences with a strong progressive increase of activity trend in AE data are highlighted by a gradually increasing failure convergence. This method has the potential to complement state-of-the-art evaluation techniques for testing applications, in particular with activity trend analysis. Due to the good results obtained already with a rather simple approach, remaining useful life prediction for on-line monitoring applications seems to be within reach.