scholarly journals Smart Multi-Sensor Monitoring in Drilling of CFRP/CFRP Composite Material Stacks for Aerospace Assembly Applications

2020 ◽  
Vol 10 (3) ◽  
pp. 758 ◽  
Author(s):  
Roberto Teti ◽  
Tiziana Segreto ◽  
Alessandra Caggiano ◽  
Luigi Nele
Keyword(s):  
Composite Material ◽  
Tool Wear ◽  
Machining Process ◽  
Product Configuration ◽  
Process Conditions ◽  
Drilling Process ◽  
Wear Rates ◽  
Cfrp Composite ◽  
Aerospace Assembly ◽  
Sensor Monitoring

Composite material parts are typically laid out in near-net-shape, i.e., very close to the finished product configuration. However, further machining processes are often required to meet dimensional and tolerance requirements. Drilling, edge trimming and slotting are the main cutting processes employed for carbon fiber-reinforced plastic (CFRP) composite materials. In particular, drilling stands out as the most widespread machining process of CFRP composite parts, chiefly in the aerospace industrial sector, due to the extensive use of mechanical joints, such as rivets, rather than welded or bonded joints. However, CFRP drilling is markedly challenging: due to CFRP abrasiveness, inhomogeneity and anisotropic properties, tool wear rates are inherently high leading to superior cutting forces and detrimental effects on workpiece surface quality and material integrity. Damage such as delamination, cracks or matrix thermal degradation is often observed as the result of uncontrolled tool wear or improper machining conditions. Sensor monitoring of drilling operations is, therefore, highly desirable for process conditions’ optimization and tool life maximization. The development of this kind of automated control technologies for process and tool state evaluation can notably contribute to the reduction of scraps and tool costs as well as to the improvement of process productivity in the drilling of CFRP composite material parts. In this paper, multi-sensor process monitoring based on thrust force and torque signal detection and analysis was applied during drilling of CFRP/CFRP laminate stacks for the assembly of aircraft fuselage panels with the scope to evaluate the tool wear state. Different signal-processing methods were utilised to extract diverse types of features from the detected sensor signals. A machine-learning approach based on an artificial neural network (ANN) was implemented to make smart decisions on the timely execution of tool change, which is highly functional for CFRP drilling process automation.

Tool Wear Analysis of MTConnect Production Data

2021 ◽  
Author(s):  
David Stock ◽  
Aditi Mukhopadhyay ◽  
Rob Potter ◽  
Andy Henderson
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Numerical Control ◽  
Machining Process ◽  
Total Load ◽  
Wear Rates ◽  
Wear Analysis ◽  
Nc Program ◽  
Small Set ◽  
Key Steps

Abstract This paper presents the analysis of data collected using the MTConnect protocol from a lathe with a Computer Numerical Control (CNC). The purpose of the analysis is to determine an estimated cutting tool life and generate a model for calculating a real-time proxy of cutting tool wear. Various streams were used like spindle load, NC program blocks, the mode, execution etc. The novelty of this approach is that no information about the machining process, beyond the data provided by the machine, was necessary to determine the tool’s expected life. This method relies on the facts that a) it is generally accepted cutting loads increase with tool wear and b) that many CNC machines rely on a small set of regularly run CNC programs. These facts are leveraged to extract the total load for each run of each program on the machine, creating a dataset which is a good indicator of tool wear and replacement. The presented methodology has four key steps: extracting cycle metadata from the machine execution data; computing the integrated spindle loads for every cycle; normalizing the integrated spindle loads between different programs; extracting tool wear rates and changes from the resulting dataset. It is shown that the method can successfully extract the signature of tool wear under a common set of circumstances which are discussed in detail.

scholarly journals Experimental Study on Tool Wear and Delamination in Milling CFRPs with TiAlN- and TiN-Coated Tools

Coatings ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 623 ◽  
Author(s):  
Dervis Ozkan ◽  
Peter Panjan ◽  
Mustafa Sabri Gok ◽  
Abdullah Cahit Karaoglanli
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Failure Mechanisms ◽  
Cutting Tools ◽  
Machining Process ◽  
Dynamic Loads ◽  
Fiber Reinforced Polymers ◽  
Machining Parameters ◽  
Coated Tool ◽  
Cfrp Composite

Carbon fiber-reinforced polymers (CFRPs) have very good mechanical properties, such as extremely high tensile strength/weight ratios, tensile modulus/weight ratios, and high strengths. CFRP composites need to be machined with a suitable cutting tool; otherwise, the machining quality may be reduced, and failures often occur. However, as a result of the high hardness and low thermal conductivity of CFRPs, the cutting tools used in the milling process of these materials complete their lifetime in a short cycle, due to especially abrasive wear and related failure mechanisms. As a result of tool wear, some problems, such as delamination, fiber breakage, uncut fiber and thermal damage, emerge in CFRP composite under working conditions. As one of the main failure mechanisms emerging in the milling of CFRPs, delamination is primarily affected by the cutting tool material and geometry, machining parameters, and the dynamic loads arising during the machining process. Dynamic loads can lead to the breakage and/or wear of cutting tools in the milling of difficult-to-machine CFRPs. The present research was carried out to understand the influence of different machining parameters on tool abrasion, and the work piece damage mechanisms during CFRP milling are experimentally investigated. For this purpose, cutting tests were carried out using a (Physical Vapor Deposition) PVD-coated single layer TiAlN and TiN carbide tool, and the abrasion behavior of the coated tool was investigated under dry machining. To understand the wear process, scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS) was used. As a result of the experiments, it was determined that the hard and abrasive structure of the carbon fibers caused flank wear on TiAlN- and TiN-coated cutting tools. The best machining parameters in terms of the delamination damage of the CFRP composite were obtained at high cutting speeds and low feed rates. It was found that the higher wear values were observed at the TiAlN-coated tool, at the feed rate of 0.05 mm/tooth.

Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques

2017 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Patricia Iglesias Victoria ◽  
Rui Liu
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Learning Algorithm ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Process Conditions ◽  
Tool Wear Monitoring ◽  
Audible Sound ◽  
Wear Monitoring

The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.

Evaluation of mechanical properties and surface roughness of cotton–viscose hybrid composite

2020 ◽  
pp. 096739112090905
Author(s):  
Kuppuraj Arunkumar ◽  
Angamuthu Murugarajan
Keyword(s):  
Mechanical Properties ◽  
Surface Roughness ◽  
Composite Material ◽  
Process Parameters ◽  
Water Pressure ◽  
Machining Process ◽  
Optimum Process ◽  
Process Conditions ◽  
Machined Surface ◽  
Reinforced Composite

Natural-fibre reinforced composite material is an emerging material that has great potential to be used in various industrial aspects and applications. The cotton-viscose-reinforced composite is prepared using a compression moulding process. In addition to it, analysis of its mechanical properties was also carried out, such as tensile strength, flexural strength, impact strength and hardness. An attempt was made to process the prepared composite material using abrasive water jet machining (AWJM) under different process parameters (water pressure, nozzle transfer speed and abrasive flow rate) levels to determine the better suitable process conditions to achieve the better surface finish and optimize the machining process. The significance of the optimization process was ensured using the results of the analysis of variance. Morphological analyses of the machined surface were performed using a scanning electron microscope. The surface roughness of 8.28 µm was found to be the optimized process parameter. Optimum process parameters in AWJM are used to improve the surface quality.

Investigation of Chip Formation and Tool Wear in Drilling Process Using Various Types of Vegetable-Oil Based Lubricants

2015 ◽  
Vol 799-800 ◽  
pp. 247-250 ◽  
Author(s):  
M.A. Fairuz ◽  
M.J. Nurul Adlina ◽  
Azwan Iskandar Azmi ◽  
M.R.M. Hafiezal ◽  
K.W. Leong
Keyword(s):  
Stainless Steel ◽  
Tool Wear ◽  
Vegetable Oil ◽  
Vegetable Oils ◽  
Chip Formation ◽  
Chip Thickness ◽  
Coconut Oil ◽  
Machining Process ◽  
Cutting Fluid ◽  
Drilling Process

Cutting fluid is a well-known as one of an important element in machining process. However, the consumption of mineral oils as cutting fluid has been raising concern due to worldwide interest in environmental and health matters. The application of vegetable-oil based lubricant is seen can overcome the problem but requires a research study about the machinability. This research paper represents the machinability of using several possible vegetable oils as cutting fluid in term of chip formation and tool wear during drilling operation on stainless steel, AISI 316. In particular, the performance of the vegetable oils; palm, sesame, olive and coconut oils were compared under minimum quantity lubrication (MQL) technique. The result reported that the coconut oil indicates the best machinability in term of highest and uniform chip thickness and least wear on the drill bit under same condition with others. These performances are followed by palm, olive and sesame oil. In additional, the viscosity measurement indicates that coconut oil has the lowest value which can possesses better fluidity and faster cooling capacity than other oils. Overall, coconut oil is recommended as viable alternative lubricants during drilling of stainless steel.

scholarly journals The Use of Machine Tool Internal Encoders as Sensors in a Process Monitoring System

2013 ◽  
Vol 7 (4) ◽  
pp. 410-417 ◽  
Author(s):  
Tomas Beno ◽  
◽  
Jari Repo ◽  
Lars Pejryd ◽  
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Process Monitoring ◽  
Low Frequency ◽  
Machining Process ◽  
Process Conditions ◽  
Monitoring And Control ◽  
Signal Features ◽  
Process Monitoring And Control ◽  
Cutting Force Components

Tool wear in machining changes the geometry of the cutting edges, which affects the direction and amplitudes of the cutting force components and the dynamics in the machining process. These changes in the forces and dynamics are picked up by the internal encoders and thus can be used for monitoring of changes in process conditions. This paper presents an approach for the monitoring of amulti-toothmilling process. The method is based on the direct measurement of the output from the position encoders available in the machine tool and the application of advanced signal analysis methods. The paper investigates repeatability of the developed method and discusses how to implement this in a process monitoring and control system. The results of this work show that various signal features which are correlated with tool wear can be extracted from the first few oscillating components, representing the low-frequency components, of the machine axes velocity signatures. The responses from the position encoders exhibit good repeatability, especially short term repeatability while the long-term repeatability is more unreliable.

Tool Wear and Surface Roughness Evolution in Hole Making Process of Inconel 718

2016 ◽  
Vol 862 ◽  
pp. 11-17 ◽  
Author(s):  
Martin Eckstein ◽  
Marek Vrabeľ ◽  
Ildikó Maňková
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Inconel 718 ◽  
Experimental Tests ◽  
Machining Process ◽  
Drilling Process ◽  
Industrial Practice ◽  
Engine Industry ◽  
Hole Making ◽  
Super Alloy

The paper focuses on tool wear and surface roughness indicators evaluation associated with hole making of nickel based super alloy Inconel 718 widely used in aero engine industry. Within study of tool wear and surface integrity, series of experimental tests were performed on an Inconel 718 specimen. Special attention was paid to ensure that the cutting conditions correspond to the industrial practice. Two steps of hole making sequence consists of a drilling process applying a twist drill that removes most of the stock. This operation is followed by a second machining process, typically applying a face – cutting finisher (reamer), which removes an additional (radial) stock between 0.1 mm to 0.25 mm per side. Tool wear appeared predominantly as flank wear VBmax and evolution of surface roughness Ra and Rz has a similar trend for drilling and finishing.

Predictive Modeling of Flank Wear in Turning Under Flood Cooling

2006 ◽  
Vol 129 (3) ◽  
pp. 513-519 ◽  
Author(s):  
Kuan-Ming Li ◽  
Steven Y. Liang
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Metal Cutting ◽  
Flank Wear ◽  
Cutting Temperature ◽  
Machining Process ◽  
Analytical Models ◽  
Dynamic Strain ◽  
Process Conditions ◽  
Force Analysis

The objective of this paper is to present physical and quantitative models for the rate of tool flank wear in turning under flood cooling conditions. The resulting models can serve as a basis to predict tool life and to plan for optimal machining process parameters. Analytical models including cutting force analysis, cutting temperature prediction, and tool wear mechanics are presented in order to achieve a thermo-mechanical understanding of the tool wear process. The cutting force analysis leverages upon Oxley’s model with modifications for lubricating and cooling effect of overhead fluid application. The cutting temperature was obtained by considering workpiece shear deformation, friction, and heat loss along with a moving or stationary heat source in the tool. The tool wear mechanics incorporate the considerations of abrasive, adhesion, and diffusion mechanisms as governed by contact stresses and temperatures. A model of built-up edge formation due to dynamic strain aging has been included to quantify its effects on the wear mechanisms. A set of cutting experiments using carbide tools on AISI 1045 steels were performed to calibrate the material-dependent coefficients in the models. Experimental cutting data were also used to validate the predictive models by comparing cutting forces, cutting temperatures, and tool lives under various process conditions. The results showed that the predicted tool lives were close to the experimental data when the built-up edge formation model appropriately captured this phenomenon in metal cutting.

Molecular Dynamics Simulation Study of Tool Wear in Vibration Assisted Nano-Impact-Machining by Loose Abrasives

2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagil James ◽  
Murali M. Sundaram
Keyword(s):  
Molecular Dynamics ◽  
Tool Wear ◽  
Machining Process ◽  
Dynamics Simulation ◽  
Process Conditions ◽  
Wear Process ◽  
Tool Wear Mechanism ◽  
Novel Target ◽  
Tip Radius ◽  
The Impact

Vibration assisted nano-impact-machining by loose abrasives (VANILA) is a novel target specific nano-abrasive machining process wherein, nano-abrasives, injected in slurry between the workpiece and the vibrating atomic force microscope probe, impact the workpiece causing nanoscale material removal. In this study, a molecular dynamics (MD) based simulation approach is used to investigate the tool wear mechanism. The simulation results reveal that the tool wear is influenced by the impact velocity of the abrasive grains and the effective tool tip radius. It is seen that based on the process conditions, the wear process could happen through distinctive mechanisms such as atom-by-atom loss, plastic deformation, and brittle fracture. Experimental results show evidences of tool wear by aforementioned mechanisms in VANILA process.

Influence of Ultrasound on Delamination during Machining of GFRP Composite Material

2017 ◽  
Vol 261 ◽  
pp. 173-178 ◽  
Author(s):  
Marcel Kuruc ◽  
Vladimír Šimna ◽  
Martin Necpal ◽  
Tomáš Vopát ◽  
Jozef Peterka
Keyword(s):  
Composite Material ◽  
Tool Wear ◽  
Machining Process ◽  
Reinforced Polymer ◽  
Glass Fibre Reinforced Polymer ◽  
Gfrp Composite ◽  
Ultrasonic Assisted ◽  
Fibre Reinforced Polymer ◽  
Glass Fibre Reinforced ◽  
Reinforcing Material

In many present applications is requested decreasing of weight of components and increasing of their strength. Often unique properties are required. These properties could be solved by using of composite materials. However, different material properties of matrix and reinforcing material cause issues during machining, such as rapid tool wear and delamination of composite. Therefore there is afford to enhance machining process by different ways to decrease tool wear as well as delamination of composite. This article deals with comparison of conventional milling and ultrasonic assisted milling of glass fibre reinforced polymer (GFRP) composite material by special designed cutting tool.

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