Surface Roughness Model Based on Force Sensors for the Prediction of the Tool Wear

In this study, a methodology has been developed with the objective of evaluating the surface roughness obtained during turning processes by measuring the signals detected by a force sensor under the same cutting conditions. In this way, the surface quality achieved along the process is correlated to several parameters of the cutting forces (thrust forces, feed forces and cutting forces), so the effect that the tool wear causes on the surface roughness is evaluated. In a first step, the best cutting conditions (cutting parameters and radius of tool) for a certain quality surface requirement were found for pieces of UNS A97075. Next, with this selection a model of surface roughness based on the cutting forces was developed for different states of wear that simulate the behaviour of the tool throughout its life. The validation of this model reveals that it was effective for approximately 70% of the surface roughness values obtained.

• Publication year

  2014

• Venue

  Italian National Conference on Sensors

• Publication date

  2014-04-01

• Fields of study

  Medicine, Materials Science, Computer Science, Engineering

• Identifiers
  DOI 10.3390/s140406393PMID 24714391PMCID 4029658
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Advanced monitoring of machining operations

  2010cited by this paper
• Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method

  2009cited by this paper
• Prediction and control of surface roughness in CNC lathe using artificial neural network

  2009cited by this paper
• Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

  2008cited by this paper
• Development of an online machining process monitoring system : Application in hard turning

  2007cited by this paper
• Surface Roughness Characterisation Using Cutting Force Analysis, Regression and Neural Network Prediction Models

  2006cited by this paper
• Surface roughness of AA7050 alloy turned bars Analysis of the influence of the length of machining

  2005cited by this paper
• Advancing Cutting Technology

  2003cited by this paper
• An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling Operations

  2003cited by this paper
• Turned Aluminium Alloys Surface Finish Monitoring through Cutting Force Measurements

  2002cited by this paper
• In-Process Tool Fracture monitoring in Face Milling Using Spindle Motor Current and Tool Fracture Index

  2001cited by this paper
• On-line metal cutting tool condition monitoring.: I: force and vibration analyses

  2000cited by this paper
• Real‐Time Monitoring of Tool Fracture in Turning Using Sensor Fusion

  1999cited by this paper
• An intelligent sensor system approach for reliable tool flank wear recognition

  1998cited by this paper
• On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion

  1997cited by this paper

• Herbal Plant Leaves Classification Using Convolutional Neural Network Models: A Literature Review

  2025cites this paper
• Bilinear LSTM with Bayesian Gaussian Optimization for Predicting Tomato Plant Disease Using Meteorological Parameters

  2024cites this paper
• Tool Condition Monitoring Methods Applicable in the Metalworking Process

  2023cites this paper
• A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

  2022cites this paper
• Classification of Plant Leaves Using New Compact Convolutional Neural Network Models

  2021cites this paper
• Repairing Hybrid Mg–Al–Mg Components Using Sustainable Cooling Systems

  2020cites this paper
• Experimental Study for Improving the Repair of Magnesium–Aluminium Hybrid Parts by Turning Processes

  2018cites this paper
• Estimation of Surface Roughness in Turning by Considering the Cutting Tool Vibration, Cutting Force and tool Wear

  2017cites this paper
• Estimating the remaining useful tool life of worn tools under different cutting parameters: A survival life analysis during turning of titanium metal matrix composites (Ti-MMCs)

  2016cites this paper
• Machinability of titanium metal matrix composites (Ti-MMCs)

  2015cites this paper
• The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation

  2015cites this paper
• Predictive Modelling of Cutting Force and its Influence on Surface Accuracy in Ultra-high Precision Machining of Contact Lenses

  2015cites this paper
• In-situ work piece surface roughness estimation in turning

  2014cites this paper