Tool failure recognition using inconsistent data
نویسندگان
چکیده
Data is everything - at least this one of the main messages ongoing industrial revolution. Manufacturing companies all over world are expanding their digital infrastructure and knowledge on data analysis in hope increasing KPIs with help artificial intelligence (AI). Although several well-designed data-driven solutions available, most crucial part, preparation still not fully supported. In paper a framework presented for processing sensor machining processes variable cycle times an unstable environment. Traditional novel AI algorithms tested vulcanization process from automotive industry, namely tire manufacturing’s curing phase. The question consists subprocesses, quality mostly dependent status specific type machine tool. Conventional methods (e.g., examining cured product manually) currently used failure recognition, however examination only feasible after long delay due to extreme level heat, which leads unnecessary unwanted scrap production. Therefore, more sophisticated complex approach required increase score. A combination mathematical proposed combining t-SNE feature representation, convolutional neural network, linear programming optimization. model highly relies tool’s continuous degradation characteristics. threshold given binary classification set by maximizing accuracy detection model. contribution research method inconsistent manipulation supports unique models early recognition.
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ژورنال
عنوان ژورنال: Procedia CIRP
سال: 2022
ISSN: ['2212-8271']
DOI: https://doi.org/10.1016/j.procir.2022.05.132