A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy

Niaki, Farbod Akhavan; Mears, Laine

doi:10.1016/j.jmapro.2017.09.016

Cited by 76 publications

(24 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Research has shown that tool wear has a significant effect on machined surface roughness. [21][22][23][24] For example, Jose et al 21 studied the effect of tool wear and surface roughness in turning D2 steel. They reported that as tool wear increases machined surface roughness increases considerably.…”

Section: Cutting Conditionsmentioning

confidence: 99%

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Fang

Pai

2020

Journal of Testing and Evaluation

View full text Add to dashboard Cite

Tool edge radius plays a significant role in affecting the surface integrity of machined products. The vast majority of existing research, however, takes no account of the effect of tool edge radius in the evaluation and modeling of machined surface roughness, an essential indicator of surface integrity. The present study fills this important research gap and has performed a total of 45 turning experiments on Unified Numbering System (UNS) A92024-T351 aluminum alloy with carefully selected cutting tools with three levels of tool edge radii. This article describes the experimental setup and measurements of tool edge radius and machined surface roughness. Machined surface roughness was evaluated using five parameters, including average roughness, root-mean-square roughness, peak roughness, maximum roughness height, and five-point average roughness. The experimental evidence presented in this article shows that the tool edge radius has a profound effect on machined surface roughness, cutting forces, and cutting vibrations. Based on the experimental data, three types of predictive models are developed, including a multiple regression model, multilayer perceptron neural network model, and radial basis function neural network model. The prediction accuracy of the three models is compared based on average mean squared errors. The results show that different models lead to different prediction accuracy for different surface roughness parameters.

show abstract

Section: Cutting Conditionsmentioning

confidence: 99%

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Fang

Pai

2020

Journal of Testing and Evaluation

View full text Add to dashboard Cite

show abstract

“…Despite the rather active study of the properties of CPZ, which occurs in different types of combinations of AE [3,4,5,6,7], the problem of influence of the chemical composition heterogeneity of the zone's space when measuring the microhardness of the material is relevant. So, we have complicated cases that are very precursor to the production technology [8].…”

Section: Introductionmentioning

confidence: 99%

Specifics of Influence of the Chemical Composition of Abstract Object’s Presence Zone on Accuracy of Determination of Surface’s Coordinates

Skytsiouk¹,

Klotchko²,

Bulyk³

2019

BNTUU"KPI".SIM

View full text Add to dashboard Cite

Introduction. An important problem in ensuring the accuracy of measuring the microhardness of a material is the determination of the coordinates of detail's surface investigated. In this case, the occurrence of chemical compounds in the space between the indenter of the measuring device and the surface being investigated can be observed. Actual technological processes, in particular using control and measuring operations, for example, when measuring the microhardness parameter of detail's material, are accompanied by the formation of a chemical presence zone (CPZ) that occurs in the near-surface space of detail. The chemical composition and form of distribution of such a zone of presence has its own characteristics and affects the accuracy of the measurement process. Consequently, we have complex cases that are very precursor to the technology of production. Modeling the shape of the chemical zone of the presence of a technological object. In the real situation, we obtain non-steady concentric circles of concentration, and closed curves of irregular shape. Moreover, they have a well-defined wave character. Since our task is to determine the distribution of the chemical, the following parameters of the chemical zone of presence are important: first, it is the accuracy of determining the geometry at the boundary of the power of the zone, and for this CZP is considered as a separate abstract entity with its pandanous zone and presence zone; secondly, the accuracy of the geometry of the CKD contour and, as a result, its durability; Thirdly, the propagation of matter in space is influenced by the accompanying processes. The paper presents the models of CWP that take into account the shape of the space of the zone. Conclusions. Consequently, we must conclude that the chemical zone of the presence of an abstract object can be formed in three main ways: the neutral distribution of matter in a medium without chemical interaction, but with the presence of collision mechanics of atoms and molecules; active proliferation due to chemical reactions with the formation of new chemical compounds; distribution of the secondary chemical presence zone, which is the product of interaction with the environment of the primary substance.

show abstract

“…Several aspects of machining processes can be supervized using monitoring techniques. In recent years, numerous publications have focused on the analysis of tool condition [1,2,3,4] and chatter [5,6,7,8], whereas other aspects such as surface finish [9,10,11,12], dimensional precision [11,12,13,14], and chip formation [15,16] have received less attention. The appropriate selection of sensors is crucial for monitoring techniques to be efficacious.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems

Plaza

López

González

2018

Sensors

View full text Add to dashboard Cite

Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

show abstract

A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy

Cited by 76 publications

References 30 publications

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Specifics of Influence of the Chemical Composition of Abstract Object’s Presence Zone on Accuracy of Determination of Surface’s Coordinates

Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems

Contact Info

Product

Resources

About