Experimental approach for a grinding burn in-process inspection system based on Eddy Current

J.L. Lanzagorta, L. Urgoiti, P. Ruiz Vazquez, D. Barrenetxea, J.A. Sánchez
2020 Procedia CIRP  
In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product
more » ... ferent product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Abstract During grinding some detrimental microestructural changes can appear in the workpiece if the temperature reaches the tempering point in the contact zone. This "thermal damage", commonly known as grinding burn, has negative consequences in the workpiece mechanical properties and its avoidance and detection is crucial to optimize grinding operations. Magnetic methods (Barkhausen noise, Eddy Current) are nowadays applied post process to detect burns automatically and without the subjectivity of the inspector (Acid Etching Method). However, the potential use of these magnetic methods during in-process inspection is a matter that has not been studied in detail yet. The objective of this paper is to evaluate the Eddy Current technology for the grinding burn inspection in combination with other process outputs. Experimental tests have been performed in the machine at different conditions considering different inspection strategies. The results show the benefits of the present approach and serve as a starting point for the development of a future in-process inspection system. Abstract During grinding some detrimental microestructural changes can appear in the workpiece if the temperature reaches the tempering point in the contact zone. This "thermal damage", commonly known as grinding burn, has negative consequences in the workpiece mechanical properties and its avoidance and detection is crucial to optimize grinding operations. Magnetic methods (Barkhausen noise, Eddy Current) are nowadays applied post process to detect burns automatically and without the subjectivity of the inspector (Acid Etching Method). However, the potential use of these magnetic methods during in-process inspection is a matter that has not been studied in detail yet. The objective of this paper is to evaluate the Eddy Current technology for the grinding burn inspection in combination with other process outputs. Experimental tests have been performed in the machine at different conditions considering different inspection strategies. The results show the benefits of the present approach and serve as a starting point for the development of a future in-process inspection system.
doi:10.1016/j.procir.2020.02.011 fatcat:o7rglq6j5ndnrhgmz4twkd4xgu