Cutting performances of TiCN–HfC and TiCN–HfC–WC ceramic tools in dry turning hardened AISI H13

Song, Jiancheng; Gao, Jiaojiao; Liu, Ming

doi:10.1080/17436753.2020.1765292

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…The use of cutting fluid slightly improved the machinability of the tool steel. In [16], cutting performances of TiCN-HfC (TH) and TiCN-HfC-WC (THW) ceramic tools in dry turning hardened AISI H13 was investigated. A study on continuous turning of hardened AISI 52100 (∼63HRc) using coated and uncoated ceramic Al2O3-TiCN mixed inserts, which are cheaper than cubic boron nitride (CBN) or polycrystalline cubic boron nitride (PCBN) was conducted in [17].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation on Nano Oil Added Fluid Influence for the Machining of Hardened AISI H13 Hot Work Tool Steel

Saleh¹,

Ranjbar²

2022

ENGT

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This paper presents an experimental study on how to enhance the machining processes of the AISI H13 hot work tool steel by implementing a decent cooling and lubrication technic throughout the machining processes. Proper machining conditions and parameters for industrial applications are analysed namely; dry conditions, wet conditions of Boron and nano additive solutions. The flow rate is measured and calculated. The cutting fluid in different concentration is used in the hard-turning experiments. Different cutting speeds (100,150,200m/min), feed rates (0.03, 0.06, 0.09mm/rev), and a constant depth of cut (0.2mm) are considered. The assessments are equated against the machining forces and temperatures, using 12 pieces of PVD coated ceramic type turning insert-cutting tools. The results of experimental investigation show that the wet machining conditions have substantial effects across all the machining standards. It is indicated that the machining temperature is reduced tremendously during the both cases of wet machining conditions. Thus, wet machining by adding additives demonstrats enhanced machining conditions at the medium feed rates and lower cutting speeds by reducing the cutting force, which is a threat to the tool life.

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Section: Introductionmentioning

confidence: 99%

Experimental Investigation on Nano Oil Added Fluid Influence for the Machining of Hardened AISI H13 Hot Work Tool Steel

Saleh¹,

Ranjbar²

2022

ENGT

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“…However, three barriers have been identified by the presented study that challenge the deployment of existing solutions in machine shops around the world. First, the inflexibility of the systems to accommodate different TCPs: tool condition affects different aspects of machining like surface finish [9] and dimensional accuracy [10,11]. For example, a TCP for one tool is when the chatter marks start to appear, while for another tool TCP it is related to a burr on the edge.…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning

Mamledesai

Soriano

2020

Applied Sciences

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Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks.

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Machinability and Surface Integrity Characteristics in Hard Turning of High Hardened Steels Using Different Types of Inserts

Krishnan¹,

Kumar²

2022

Materials, Design and Manufacturing for Sustainable Environment

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Cutting performances of TiCN–HfC and TiCN–HfC–WC ceramic tools in dry turning hardened AISI H13

Cited by 12 publications

References 19 publications

Experimental Investigation on Nano Oil Added Fluid Influence for the Machining of Hardened AISI H13 Hot Work Tool Steel

Experimental Investigation on Nano Oil Added Fluid Influence for the Machining of Hardened AISI H13 Hot Work Tool Steel

A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning

Machinability and Surface Integrity Characteristics in Hard Turning of High Hardened Steels Using Different Types of Inserts

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