Fracture Behavior and Characterization of Lead-Free Brass Alloys for Machining Applications

Toulfatzis, Anagnostis; Pantazopoulos, G.; Paipetis, Alkiviadis S.

doi:10.1007/s11665-014-1096-3

Cited by 47 publications

(37 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All data were processed using the professional statistical software MINITAB 17. The microstructure of leaded and lead-free brass alloys and its relationship to mechanical properties, under quasi-static and dynamic loading conditions was studied extensively in previous studies [3,7]. Metallographic evaluation revealed a wide variation of microstructures among the leaded and lead-free brass alloys (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…3b). Plastic deformation of the chip/rake face region was encountered for the three lead-free brasses as a result of strain hardening potential, dictated by the strain hardening exponent (n) indicated by R m /R p0.2 ratio [7]. Further microhardness testing of the produced chips was employed to assess the hardness increase due to plastic deformation caused by the machining process.…”

Section: Discussionmentioning

confidence: 99%

“…However, in recent years the consideration of the health hazard involving the use of lead led to stricter regulations for allowable lead content levels in products and provided a strong motivation and challenge for the development of lead-free brass alloys [6]. Towards this aim, in a previous study the establishment of structure/mechanical properties/machinability relationship of lead-free brass alloys was made [7]. The impetus behind this study is the need of the relevant industry to comply with the aforementioned revisions of the environmental regulations and directives in crucial applications, such as drinking water installations or electrical/ electronic component manufacturing, require the compliance of the industrial sectors which proceed to the harmonization to the relevant standards substituting the conventional brass alloys (e.g., leaded brasses) by lead-free and eco-friendly brass alloys.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Toulfatzis

Pantazopoulos²,

Besseris

et al. 2016

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

The aim of this work was the evaluation of the machinability of leaded brass namely CuZn39Pb3 (CW614N) in comparison to three lead-free brasses alloys namely CuZn42 (CW510L), CuZn38As (CW511L), and CuZn36 (C27450). The machinability of the studied alloys was investigated, based on chip morphology and power consumption, as quality characteristics. Microstructure examination and hardness testing was employed for the characterization of the selected alloys. Design of experiments (DOE) was employed in a multi-non-parametric study in order to identify the critical-to-machinability parameters and obtain their optimum values for high performance machining. The attempted joint screening using a four-level orthogonal array revealed that the depth of cut and the alloy type were the two statistically predominant effects. The chip morphology and the power consumption in a balanced concurrent optimization effort were optimal when the depth of cut was set at 0.5 mm for the alloy type CW614N chip morphology optimization. The optimal chip morphology response was split in equal chances to produce needle or arc chips. The predicted power consumption was confined in a range of values with an upper boundary at 65 W. The findings of the statistical evaluation were experimentally confirmed, validating the DOE approach. Keywords Machinable brass . Lead-free brasses . Machinability . Design of experiments (DOE) Nomenclature ANOVA Analysis of variance ER Error contribution HV final The chip average hardness (Vickers hardness) HV initial The initial surface hardness (Vickers hardness) MANOVA Multivariate analysis of variance M CM G Grand median value of CM data M P G Grand median value of P data mCM DC o Median value of CM for the optimal setting of DC effect mCM M o Median value of CM for the optimal setting of M effect mP DC o Median value of P for the optimal setting of DC effect mP M o Median value of P for the optimal setting of M effect MR Master response (ranked values of ssRS) OA Orthogonal array R 2 Coefficient of determination rCM i (i = 1,2) Ranked values of the response of the chip Morphology replicates rP i (i = 1,2) Ranked values of the response of the power consumption replicates srCM rSM 1 + rSM 2 srP rP 1 + rP 2 rCM Ranked values of srCM rP Ranked values of srP ssRS rCM 2 + rP 2 WH

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Toulfatzis

Pantazopoulos²,

Besseris

et al. 2016

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…The auxiliary electrode was a platinum wire [10,11]. The working electrode was realized in square shape and was assembled in teflon.…”

Section: Characterization By Linear Polarization Studiesmentioning

confidence: 99%

Study on structure and properties of CuZn40Pb alloy

Achiţei

Minciună

Vizureanu

et al. 2016

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Its hardness is 130 HB). Studies concerning the microstructure and machinability of CuZn39Pb3 alloy were reported by Pantazopoulos [56] and Toulfatzis et al [57].…”

Section: Test Materialsmentioning

confidence: 99%

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

Vaxevanidis¹,

Kechagias²,

Fountas³

et al. 2014

TOBCTJ

View full text Add to dashboard Cite

Abstract:The present paper investigates the influence of main cutting parameters on the machinability during turning process for three typical materials namely AISI D6 tool steel, Ti6Al4V ELI and CuZn39Pb3 brass, all three under dry cutting environment. Spindle speed, feed rate and depth of cut were selected for study whilst arithmetic surface roughness average (R a ) and main cutting force component (F C ) were treated as quality objectives characterizing machinability. For the aforementioned materials a full factorial design of experiments was conducted to exploit main effects and interactions among parameters it terms of quality objectives. The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding all three materials. The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization problems.

show abstract

Fracture Behavior and Characterization of Lead-Free Brass Alloys for Machining Applications

Cited by 47 publications

References 14 publications

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Study on structure and properties of CuZn40Pb alloy

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

Contact Info

Product

Resources

About