Cutting Tool Condition Recognition in NC Machining Process of Structural Parts Based on Machining Features

Lu, Nanhong; Li, Yingguang; Liu, Changqing; Mou, Wenping

doi:10.1016/j.procir.2016.10.028

Cited by 3 publications

(1 citation statement)

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“…In the manufacturing field, there are specific approaches concerning feature selection. Thus, [11] deals with finding the machining features needed to build relationships between geometry, processing technology and monitoring signals in order to provide a basis for cutting tool condition identification. In [12], nine types of time-domain features are extracted from the milling force signals, after which the sensitive features that can accurately indicate the tool wear states are selected by using the correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Causal Modeling of the Manufacturing System

Frumusanu

Afteni

Epureanu

2021

TFAMENA

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In manufacturing system management, the decisions are currently made on the base of 'what if' analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.

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Section: Introductionmentioning

confidence: 99%