The chip-forming precision machining process plays a significant role in the mechanical technology. In planning of machining operation, it is crucial to supply the information about the possible minimal value of the machining allowance. For the technologist, when planning the machining operation, it is important to define the minimal thickness of cutting layer correctly. This article presents a new method of describing the start of decohesion process in a workpiece, meaning the determination of the minimal thickness of cutting layer based on the AE signal generated in the cutting zone. The research conducted on the turning of an alloy steel and the analysis of the AE signal strength confirmed that the proposed method opens new possibilities in quickening the identification of the minimal thickness of cutting layer under normal machining conditions.
Titanium based alloy -Ti6Al4V and nickel based alloy -Inconel 718 belong to the group of difficult-to-cut materials. Thanks to their unique properties they can be used in constructions that need to withstand the high reliability requirements which are required inter alia in the aircraft industry. The physical properties of cutting layer, including residual stresses, play an important role during exploitation of products made out of difficult-to-cut materials. In the article, the method of residual stresses determination is described and the exemplary results of carried out studies are provided. Described method is based on the measurement of the defects in the crystal lattice. The carried out studies show that the state of residual stresses, in a subsurface layer, can be formed by the selection of machining conditions.
Under the influence of the pressure caused by the application of the cutting edge on the surface of the workpiece elastic waves are generated. Waves propagate in the material in every possible direction and can be identify by specialized measuring equipment. Acoustic emission phenomenon was used to determine the beginning of decohesion process. The article presents a new method for determination of the decohesion process during peripheral milling performed with the indexable cutting tools on samples made out of titanium based alloy and nickel based alloy.
The paper presents the influence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term operated in the oil refinery industry. Comparative analysis of AE activity generated by damage process of degraded and new material has been carried out. The different AE parameters were used to detect different stages of fracture process of low alloy steel under quasi-static tensile test. Neural networks with three layers were created with Broyden–Fletcher–Goldfarb–Shanno learning algorithm for a database analysis. The different AE parameters were included in the input layer. Classification neural networks were created in order to determine the stages of material degradation. The results obtained from the carried out studies will be used as the basis for new methodology development of the assessment of the structural condition of in-service equipment.
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