Mechanical properties and microstructure of composites produced by recycling metal chips

Aslan, Abdullah; Güneş, Aydın; Salur, Emin; Şahi̇n, Ömer Sinan; Karadağ, Hakan Burak; Akdemir, Ahmet

doi:10.1007/s12613-018-1658-8

Cited by 26 publications

(25 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The increasing demand for new generation materials with a high strength/weight ratio in parallel with technological developments pushes researchers to do more studies [1][2][3][4]. Composite materials that can meet this demand are increasingly employed among a variety of industries in the mechanical engineering field [5,6]. The main purpose in manufacturing composite materials is to improve some characteristic features of the produced part according to service conditions [7,8].…”

Section: Introductionmentioning

confidence: 99%

Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy

et al. 2021

View full text Add to dashboard Cite

The features of composite materials such as production flexibility, lightness, and excellent strength put them in the class of materials that attract attention in various critical areas, i.e., aerospace, defense, automotive, and shipbuilding. However, the machining of composite materials displays challenges due to the difficulty in obtaining structural integrity. In this study, Cu/Mo-SiCP composite materials were produced by powder metallurgy with varied reinforcement ratios and then their machinability was investigated. In machinability experiments, the process parameters were selected as cutting speed (vC), feed rate (f), depth of cut (aP), and reinforcement ratio (RR). Two levels of these parameters were taken as per the Taguchi’s L8 orthogonal array, and response surface methodology (RSM) is employed for parametric optimization. As a result, the outcomes demonstrated that RR = 5%, f = 0.25 mm/rev, aP = 0.25 mm, vC = 200 m/min for surface roughness, RR = 0%, f = 0.25 mm/rev and aP = 0.25 mm and vC = 200 m/min for flank wear and RR = 0%, f = 0.25 mm/rev, aP = 0.25 mm, vC = 150 m/min for cutting temperature for cutting temperature and flank wear should be selected for the desired results. In addition, ANOVA results indicate that reinforcement ratio is the dominant factor on all response parameters. Microscope images showed that the prominent failure modes on the cutting tool are flank wear, built up edge, and crater wear depending on reinforcement ratio.

show abstract

Section: Introductionmentioning

confidence: 99%

Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Considering the inhomogeneity in the material structure and the lack of rigidity in the system, unexpected developments occur inevitably. The repetitive loads on the tool during metal cutting lead to the deprivation of material and the change of tool geometry over time [ 7 , 8 ]. The resulting deformation causes a gradual breakdown of the tool, and deteriorates its cutting [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

show abstract

“…Cost optimization and productivity became the main purpose in industry [ 25 , 26 ]. Breakdowns depend on tool breakage cause cutting tool-related downtime and expense which possess a significant place in overall cost [ 27 ].…”

Section: Tool Condition Monitoring Systemsmentioning

confidence: 99%

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

Kuntoğlu

Aslan

Pimenov

et al. 2020

Sensors

Self Cite

177

View full text Add to dashboard Cite

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

show abstract

Mechanical properties and microstructure of composites produced by recycling metal chips

Cited by 26 publications

References 10 publications

Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy

Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

Contact Info

Product

Resources

About