Significant influence factors on the grinding tool wear and cutting mechanisms during grinding of PCBN inserts

Denkena, Berend; Grove, Thilo; Behrens, Leif

doi:10.1007/s11740-015-0599-6

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…Several studies investigated cup wheel grinding of inserts e.g. [Azarhoushang 2017] studied normal and tangential forces, surface roughness, and wheel loading, [Denkena 2014] investigated wheel topography, maximum edge chipping, surface roughness, and grinding forces, [Denkena 2015] studied grinding wheel wear, surface roughness, and cutting edge chipping. These studies investigated specific cost and constraint values.…”

Section: Sensors For Cost and Constraint Measurementmentioning

confidence: 99%

Sensors as an Enabler for Self-Optimizing Grinding Machines

Maier¹,

Gittler²,

Weiss³

et al. 2019

MM SJ

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Today, an operator performs experiments to adaptively select grinding process parameters using observations, expert knowledge, and rules of thumb. Self-optimizing grinding machines cannot use operator observations and must therefore extract enough information out of the grinding process. In this study, a holistic sensor setup as foundation for self-optimizing machines are presented exemplarily for cup wheel grinding machines. In-process detection of grinding burn, based on temperature and gas measurements, is tested and compared. Afterwards, the influence of input variables such as feed rate and cutting speed on grinding cost, grinding burn, and surface roughness are investigated.

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Section: Sensors For Cost and Constraint Measurementmentioning

confidence: 99%

Sensors as an Enabler for Self-Optimizing Grinding Machines

Maier¹,

Gittler²,

Weiss³

et al. 2019

MM SJ

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show abstract

“…The low surface roughness was attributed to the low single grain chip thickness for a small grain size d g . However, the influence of the process parameters cutting speed v c and feed rate v fa on the surface roughness was not significant 8 . Although according to relevant models the single grain chip thickness should also decrease with an increase of the cutting speed 1 , 9 .…”

Section: Introductionmentioning

confidence: 89%

“…In prior studies abrasive layers with diamonds of small grain size d g < 20 µm with a highly porous vitrified bond resulted in high manufacturing quality and low tool wear. Using the small grain size of the abrasive, low roughness values Rz < 1 µm, Ra < 0.4 µm have been achieved on ground surfaces 1 , 8 . The low surface roughness was attributed to the low single grain chip thickness for a small grain size d g .…”

Section: Introductionmentioning

confidence: 99%

Wear-adaptive optimization of in-process conditioning parameters during face plunge grinding of PcBN

Denkena¹,

Krödel-Worbes²,

Müller-Cramm³

2022

Sci Rep

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Polycrystalline cubic boron nitride is a very hard material. Machining of this material is performed by grinding with diamond tools. Due to its high hardness, grinding tools are subjected to severe microscopic and macroscopic tool wear. This wear leads to short tool life and results in high effort in conditioning the abrasive layer. Contrary to the usual conditioning of diamond grinding wheels with diamond dressing tools, this study investigates a conditioning process based entirely on the use of white corundum cup rolls. These conditioning tools allow the in-process face plunge conditioning of vitrified bond diamond grinding tools. The circumferential speed of the conditioning tool and the average grain diameter of the corundum are identified as the main factors influencing the topography of the generated grinding layer. To describe the performance of the conditioning process, a specific conditioning removal rate $$Q_{sd}^{\prime }$$ Q sd ′ is derived. This parameter represents a cumulated variable that allows a comparison of different conditioning strategies. It is shown that an increase in $$Q_{sd}^{\prime }$$ Q sd ′ significantly counteracts microscopic wear on the abrasive layer. Therefore, optimized process parameters enable the process of in-process conditioning to significantly reduce wear on the grinding tool without increasing the process time or the non-productive time.

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“…al investigated that wheel wear influenced the cutting tool roughness negatively [8]. The profitability can be achieved with low cutting speeds and high axial feed rate [9]. Also grain size of grinding wheels affected the process.…”

Section: Introductionmentioning

confidence: 99%

“…Small grains has positive effect of cutting edge chipping. This effect is highly significant [9]. The grinding process and the resulting surface are affected by the grain size of the cemented carbide.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Grinding Process and Drag Finishing on the Milling Tools Macro Geometry

Patoprsty¹,

Pokorný²,

Vozar³

et al. 2019

DAAAM Proceedings

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The paper deals with the comparison of milling tools geometry after drag finishing with various times. The aim was to determine the influence of drag finishing on macro geometry of the solid cemented carbide mills. The article describes tool geometry deviance after grinding process and the influence of drag finishing on rake angle, flank angle and diameter of the tools. The cemented carbide material with chemical composition WC + Co (10%) was used for this research. The tools were manufactured on the Reinecker WZS 60 grinding machine from cemented carbide rod. The tool geometry of cutting tools was measured on ZOLLER genius 3 optical measuring machine. The drag finishing process was carried out on OTEC DF-3 Tools drag finishing machine with OTEC SIX 70/16 grinding granulate. It was found the accuracy of cutting tools manufactured on grinding machine, and the deviation of rake and flank angles was 2° and tool diameter was 0.08 mm.

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Significant influence factors on the grinding tool wear and cutting mechanisms during grinding of PCBN inserts

Cited by 14 publications

References 9 publications

Sensors as an Enabler for Self-Optimizing Grinding Machines

Sensors as an Enabler for Self-Optimizing Grinding Machines

Wear-adaptive optimization of in-process conditioning parameters during face plunge grinding of PcBN

The Influence of Grinding Process and Drag Finishing on the Milling Tools Macro Geometry

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