Surface finish generated in turning of medium carbon steel parts using conventional and adhesive bonded tools

Jachak, Shrikant; Giri, Jayant; Awari, G. K.; Bonde, Ajay

doi:10.1016/j.matpr.2021.01.127

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Consequently, at the end of the cutting distance, a lower cutting edge radius can be observed in the adhesively bonded tool. These results align with the observations of Darwish et al [17] and Jachak et al [18] in metal cutting.…”

supporting

confidence: 92%

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Temperature and Wear Analysis of Adhesively Bonded and Soldered Cutting Tools for Woodcutting

Stribick,

Pahmeyer

2023

JMMP

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Cutting tools undergo constant development to meet the demands of higher cutting speeds, difficult-to-cut materials and ecological considerations. One way to improve cutting tools involves transitioning from soldering to adhesive bonding in the manufacturing process. However, there is limited research comparing adhesively bonded tools with soldered tools in woodcutting applications. This paper presents a comparison between adhesively bonded and soldered tools in the cutting of medium-density fiberboards over a cutting distance of 1000 m. The results indicate that adhesively bonded tools are well-suited for machining medium-density fiberboards. Additionally, the cutting-edge radii exhibit a slower increase and the tool temperatures are higher compared to soldered tools. Future research could optimize the damping effect through the precise design of the bonding area. Additionally, investigating a cooling concept for the machining process could help minimize ageing effects.

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supporting

confidence: 92%

“…In 2021, Jachak et al presented similar results through the milling of EN8. They pointed out, that an adhesively bonded tool has lower cutting forces, a slower wear rate, and produces better surface quality than a soldered tool [18].…”

Section: Introductionmentioning

confidence: 99%

Temperature and Wear Analysis of Adhesively Bonded and Soldered Cutting Tools for Woodcutting

Stribick,

Pahmeyer

2023

JMMP

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“…Artificial neural networks (ANNs) are computer programs that aim to mimic the way the human nervous system works by replicating its complex network structure [61 -62] that could validate and predict responses from process parameters [63][64]. These networks consist of an input layer, one or more hidden layers, and an output layer, each with multiple neurons, in which the neurons between adjacent layers are fully interconnected [65][66].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Preprint

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High-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for Ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, Machine learning models and algorithms were developed with Support vector machine (SVM), Gaussian process relation (GPR), Adaptive-neuro fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that ANFIS gave the best predictive accuracy of average R, RMSE, and MAPE values of 0.98, 0.06, and 9.98%, respectively, and that of additional validation tests were 0.81, 0.17 and 32.34%, respectively, which are found reasonably accurate. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Ra among the process parameters, with 92% influence, and optimal cutting conditions was found to be cutting speed = 100 m/min, feed = 0.025 mm/rev and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry.

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Practical Implementation and Validation of a Novel Process for Manufacturing Milling Tools Using Adhesive Bonding

Fiedler

Fischer

Droeder

2022

Proceedings in Engineering Mechanics

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Surface finish generated in turning of medium carbon steel parts using conventional and adhesive bonded tools

Cited by 8 publications

References 10 publications

Temperature and Wear Analysis of Adhesively Bonded and Soldered Cutting Tools for Woodcutting

Temperature and Wear Analysis of Adhesively Bonded and Soldered Cutting Tools for Woodcutting

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Practical Implementation and Validation of a Novel Process for Manufacturing Milling Tools Using Adhesive Bonding

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