Tool Condition Monitoring While Using Vegetable Based Cutting Fluids During Milling of Inconel 625

Shankar, S.; Mohanraj, T.; Pramanik, Alokesh

doi:10.1142/s0219686719500306

Cited by 49 publications

(18 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The dull tool lost its effective cutting edge, and which enlarged the toolworkpiece contact area, and friction, results in elevation of vibration. During machining of Inconel 625 (Shankar et al, 2019a, Krishnakumar et al, 2015, the similar trend was noticed. From the above Figures, it was evident that raise in flank wear leads to rise in vibration.…”

Section: Figure 4 Resultant Vibration Signatures For Different Toolssupporting

confidence: 73%

See 1 more Smart Citation

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

show abstract

Section: Figure 4 Resultant Vibration Signatures For Different Toolssupporting

confidence: 73%

“…Initial cutting sound was little high owing to initial interaction of cutting tool & workpiece. In dull tool, contact area was increased due to increase in flank wear and it produced the maximum sound pressure (Shankar et al, 2019a).…”

Section: Figure 3 Sound Pressure Values For Different Toolsmentioning

confidence: 99%

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

show abstract

“…ere are sixteen experiments in total. Experiments are carried out after the OA has been defined, and the S/N for every experiment is calculated [34].…”

Section: Doementioning

confidence: 99%

Parameter Optimization and Machining Performance of Inconel 625 with Nanoparticles Dispersed in Biolubricant

Mohanraj

Radhika

Nanda

et al. 2022

Advances in Materials Science and Engineering

Self Cite

View full text Add to dashboard Cite

Productivity and cost-effectiveness are essential components of any long-term manufacturing system. While quantity and quality are linked to productivity, the economy focuses on energy-efficient processes that produce a high output-to-input ratio. Hard-to-cut materials have always been difficult to machine because of more significant tool wear and power losses. Inconel 625 is a hard material used in aerospace and underwater applications and is milled using biolubricants with nanoparticles. Palm oil is considered a biolubricant, and titanium dioxide (TiO2) and copper oxide (CuO) are selected as nanoparticles. When the combination of biolubricants and nanoparticles is added to the workpiece’s surface, it enhanced some properties while machining. Experiments involving four factors with four levels were carried out using the Taguchi design of experiments (DoE). The feed, depth of cut, speed, and coolant with nanoparticle additives were all factors. The responses were surface roughness, spindle vibration along X, Y, and Z axes, and material removal rate. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to alter the multiresponse optimization problem to a single-response optimization problem. The S/N of TOPSIS closeness coefficients was calculated, and the optimal machining conditions were determined. Surface roughness, material removal rate, and spindle vibration were reduced by 3.10%, 6.14%, 7.54% (Vx), and 6.78% (Vz), respectively, due to the TOPSIS optimization.

show abstract

“…Instead, a datadriven approach, which estimates the RUL of a cutting tool based on analysis of monitored signals from sensors mounted on a machine or a cutting tool, shows a greater potential for practical applications. Some earlier designs of such the approach depend on support vector machine (SVM) [15], support vector regression (SVR) [2], artificial neural networks (ANN) [31], fuzzy logic [28], etc., for analysing and estimating tool conditions and lives. However, those "conventional" intelligent algorithms still need tedious manual operations to define features to be extracted from monitored signals, and the pre-set features might not be consistent throughout an actual machining process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM

Zhang

et al. 2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

To enhance production quality, productivity and energy consumption, it is paramount to predict the remaining useful life (RUL) of a cutting tool accurately and efficiently. Deep learning algorithm-driven approaches have been actively explored in the research field though there are still potential areas to further enhance the performance of the approaches. In this research, to improve accuracy and expedite computational efficiency for predicting the RUL of cutting tools, a novel systematic methodology is designed to integrate strategies of signal partition and deep learning for effectively processing and analysing multi-sourced sensor signals collected throughout the lifecycle of a cutting tool. In more detail, the methodology consists of two sub-systems: (i) a Hurst exponentbased method is developed to effectively partition complex and multi-sourced signals along the tool wear evolution, and (ii) a hybrid CNN-LSTM algorithm is designed to combine feature extraction, fusion and regression in a systematic means to facilitate the prediction based on segmented signals. The system was validated using a case study with a large set of databases with multiple cutting tools and multi-sourced signals. Comprehensive comparisons between the proposed methodology and some other mainstream algorithms, such as CNN, LSTM, DNN and PCA, were carried out under the conditions of partitioned and unpartitioned signals. Benchmarks showed that, based on the case study in this research, the prediction accuracy of the proposed methodology reached 87.3%, which is significantly better than those of the comparative algorithms.

show abstract

Tool Condition Monitoring While Using Vegetable Based Cutting Fluids During Milling of Inconel 625

Cited by 49 publications

References 38 publications

Decision support system for tool condition monitoring in milling process using artificial neural network

Decision support system for tool condition monitoring in milling process using artificial neural network

Parameter Optimization and Machining Performance of Inconel 625 with Nanoparticles Dispersed in Biolubricant

Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM

Contact Info

Product

Resources

About