Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Koohestani, Afshin; Mo, John P.T.; Yang, Sam

doi:10.1080/10910344.2014.863638

Cited by 6 publications

(6 citation statements)

References 54 publications

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“…For a stable milling process, the trajectory points at the same angular position will lie close to one another, while for an unstable process, this trajectory becomes chaotic, and the bisection points do not lie close to one another [217]. The Poincaré section approach employing OPRS has been applied to analyse different signal types, such as vibration acceleration [217,220,265], displacement [312] and force signals [325,326], and several indicators can be extracted. The once-per-tooth period sampling has been used to identify and characterize stable milling, secondary Hopf (quasiperiodic chatter) and period-n instabilities [265,327,328], as shown in Fig.…”

Section: Time Domain Analysismentioning

confidence: 99%

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Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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Section: Time Domain Analysismentioning

confidence: 99%

“…Tran et al [159] utilized a convolutional neural network (CNN) to process scalograms. Meanwhile, Koohestani et al [220] employed image processing in the Poincaré sections. The bidimensional analysis allows quantifying properties and to assemble the information into a monitoring algorithm in a more flexible way.…”

Section: Additional Analysis Approachesmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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“…The quest to develop smart products which will meet functional requirements using emerging materials within a short manufacturing cycle time has led to increase in energy requirement of the manufacturing processes with environmental impact and cost consequences ( Priarone, 2016 ). Typical examples of such emerging materials are the nickel-based super alloys, for instance, the Inconel 718, Grade EA1N steel, Gamma and Beta titanium based alloys amongst others ( Koohestani et al., 2014 ; Fratila, 2014 ; Daniyan et al., 2020a ).…”

Section: Introductionmentioning

confidence: 99%

“…They find suitable applications in the biomedical, railway, aerospace, automobile manufacturing industries most especially in areas where special properties are required to meet the service or functional requirement of a component or sub assembly ( Kara and Li, 2011 ; Mhamdi et al., 2012 ; Pervaiz et al., 2013 ; Koohestani et al., 2014 ; Fratila, 2014 ; Daniyan et al., 2020b ). The limitation being the low thermal conductivity which causes the material to retain heat rather than dissipating it, thus, making the manufacturing process less sustainable ( Rashid et al., 2012 ; Shokrani et al., 2016 ; Ayed et al., 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

Daniyan

Mpofu

Ramatsetse

et al. 2021

Heliyon

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The life cycle models are critical in the assessment of the performance of a product from the design phase to its end of life (EoL). With the quest for manufacturing sustainability with respect to energy, process, material, and environment friendliness as well as the clamour for circular economy which emphasizes zero tolerance for waste, there is a need for a critical review of the life cycle of machine tool employed for machining operations and product development. The objective of this study is to evaluate the efficient way of managing the machine tools throughout its lifecycle. Several studies have been conducted in analysing the life cycle of the machine tools and different strategies were employed for its design, manufacture, use, maintenance and recovery at the end of life. The common approach to ensure environmental sustainability was established when comparing the literature studied. From the articles reviewed 60% applied life cycle assessment (LCA) methodology to reduce energy consumption and enhance environmental sustainability, while 40% employed other assessment tools. In this study an integrated life cycle and cyber physical machine tool model is proposed.

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“…It finds an extensive range of applications in different industries, such as biomedical, aerospace, automotive, marine, railway etc. [1,2]. Its ease of formability via extrusion often makes it a preferred choice for the development of complex and intricate profiles and its outstanding strength-toweight ratio often promotes energy and environmental sustainability.…”

Section: Introductionmentioning

confidence: 99%

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

et al. 2020

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Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

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Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Cited by 6 publications

References 54 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

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