State of the art review on process, system, and operations control in modern manufacturing

D Djurdjanovic, L Mears… - Journal of …, 2018 - asmedigitalcollection.asme.org
Dramatic advancements and adoption of computing capabilities, communication
technologies, and advanced, pervasive sensing have impacted every aspect of modern …

Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process

M Correa, C Bielza, J Pamies-Teixeira - Expert systems with applications, 2009 - Elsevier
Machine tool automation is an important aspect for manufacturing companies facing the
growing demand of profitability and high quality products as a key for competitiveness. The …

A uniaxial testing approach for consistent failure in vascular tissues

C Sang, S Maiti, RN Fortunato… - Journal of …, 2018 - asmedigitalcollection.asme.org
Although uniaxial tensile testing is commonly used to evaluate failure properties of vascular
tissue, there is no established protocol for specimen shape or gripping method. Large …

Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling

Á Arnaiz-González, A Fernández-Valdivielso… - … International Journal of …, 2016 - Springer
Industrial demand for models and simulation tools that can predict dimensional errors in
manufacturing processes is vigorous. One example of these processes is ball-end finishing …

Surface roughness monitoring application based on artificial neural networks for ball-end milling operations

G Quintana, ML Garcia-Romeu, J Ciurana - Journal of Intelligent …, 2011 - Springer
Surface roughness plays an important role in the performance of a finished part. The
roughness is usually measured off-line when the part is already machined, although in …

Deep multistage multi-task learning for quality prediction of multistage manufacturing systems

H Yan, ND Sergin, WA Brenneman… - Journal of Quality …, 2021 - Taylor & Francis
In multistage manufacturing systems, modeling multiple quality indices based on the
process sensing variables is important. However, the classic modeling technique predicts …

Data mining for quality control: Burr detection in the drilling process

S Ferreiro, B Sierra, I Irigoien, E Gorritxategi - Computers & Industrial …, 2011 - Elsevier
Drilling process is one of the most important operations in aeronautic industry. It is
performed on the wings of the aeroplanes and its main problem lies with the burr generation …

Using artificial intelligence to predict surface roughness in deep drilling of steel components

A Bustillo, M Correa - Journal of Intelligent Manufacturing, 2012 - Springer
A predictive model is presented to optimize deep drilling operations under high speed
conditions for the manufacture of steel components such as moulds and dies. The input data …

Semi-supervised roughness prediction with partly unlabeled vibration data streams

M Grzenda, A Bustillo - Journal of Intelligent Manufacturing, 2019 - Springer
Experimental data sets that include tool settings, tool and machine-tool behavior, and
surface roughness data for milling processes are usually of limited size, due mainly to the …

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

R Coppel, JV Abellan-Nebot, HR Siller… - … International Journal of …, 2016 - Springer
Nowadays, the miniaturization of many consumer products is extending the use of micro-
milling operations with high-quality requirements. However, the impacts of cutting-tool wear …