Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.
The wire electrical-discharge machining process (WEDM) is a widely-used technology which can achieve the requirements needed by industry, such complex geometries and precision. Cutting process control in WEDM becomes essential in terms of surface finish and dimensional tolerances. When a cut is made in a variable workpiece thickness, the process becomes very unstable in the transition zone. The difference of energy related to the WEDM regime cannot be easily accommodated when changing to another thickness. In this work a study of the most important variables that take part in the process is presented. It has been found that the workpiece thickness determination can be accomplished from the analysis of the variable Gap error, which is built as a combination of Ionization time (Td) and Servo voltage (Servo). An industrial system has been developed to determine the workpiece thickness with less than 10% error in comparison with the nominal value of part thickness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.