Abstract
Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values.
Highlights
IntroductionTitanium alloys offer exceptional properties, such as high strength-to-weight ratio, intermediate density, unique resistance to corrosion, low coefficient of thermal expansion and high toughness, which make these alloys extremely interesting for advanced applications in different sectors such as the aerospace, automotive and medical industries [1,2].In the aerospace sector, the property of maintaining high strength at high operating temperatures makes Ti alloys suitable materials for aircraft engine components as well as for airframe structures (where these alloys can stand temperatures >130 ◦ C, over the maximum for aluminium alloys) [2].Ti alloys are characterized by high compatibility with carbon fibre reinforced composite materials and are increasingly employed in modern aircrafts [1].the machinability of Ti alloys is generally poor due to the inherent material properties.Machining is characterised by extremely rapid tool wear and short tool life due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys [2].their high strength preserved at elevated temperature and the low modulus of elasticity, responsible for workpiece bending, further impair Ti alloys machinability [3,4]
A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks for tool wear diagnosis
The tool wear diagnosis performance achieved by the different artificial neural networks (ANN) architectures was estimated in terms of mean squared error, MSE, between the VBmax values predicted by the ANN and the measured VBmax values, calculated as follows: MSE =
Summary
Titanium alloys offer exceptional properties, such as high strength-to-weight ratio, intermediate density, unique resistance to corrosion, low coefficient of thermal expansion and high toughness, which make these alloys extremely interesting for advanced applications in different sectors such as the aerospace, automotive and medical industries [1,2].In the aerospace sector, the property of maintaining high strength at high operating temperatures makes Ti alloys suitable materials for aircraft engine components as well as for airframe structures (where these alloys can stand temperatures >130 ◦ C, over the maximum for aluminium alloys) [2].Ti alloys are characterized by high compatibility with carbon fibre reinforced composite materials and are increasingly employed in modern aircrafts [1].the machinability of Ti alloys is generally poor due to the inherent material properties.Machining is characterised by extremely rapid tool wear and short tool life due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys [2].their high strength preserved at elevated temperature and the low modulus of elasticity, responsible for workpiece bending, further impair Ti alloys machinability [3,4]. Machining is characterised by extremely rapid tool wear and short tool life due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys [2]. Their high strength preserved at elevated temperature and the low modulus of elasticity, responsible for workpiece bending, further impair Ti alloys machinability [3,4]
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