Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Experimental study and analysis of machinability characteristics of metal matrix composites during drilling
Scopus
Toplam 130 atıf DOI
In this study, the metal matrix composite materials were produced by hot press with various production parameters. The drilling experiments were performed on computer numerical control vertical machining centre without cutting fluid. Analysis of variance (ANOVA) was carried out in order to determine the effects of the production parameters on thrust force and surface roughness of metal matrix composites drilled with different feed rate. The effect of production parameters such as temperature, pressure and reinforcement ratio were investigated, and their effects were presented. The optimal level for each production parameters was determined by ‘Maximize the S/N ratio approach with a Taguchi design’. The test results revealed that the reinforcement ratio was the main factor affecting the surface roughness of the metal matrix composites for both feed rate. However, same singularity was not matter on thrust force due to close contribution rates of production parameters and high error rates of analysis. In literature, an increase on the thrust force and the surface roughness values was reported as the feed rate increased during machining. Nevertheless, in our MMCs system, the thrust force and the surface roughness values were in tendency of declination as the feed rate increased which makes this study more novel research.
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Atıf Yapan Yayın
A predictive model and comparative analysis of machining indicators in turning AA7075 alloy in pursuit of sustainability
Scopus
Havuzumuzda
Forecasting the performance of machining operations heavily rely on the determination of real-life conditions in digital models. Such an approach maximizes productivity and minimizes time and cost outcomes. Aluminum alloys have broad utilization in the metal industry since their lightweight structure and relatively high strength compose an excellent mix. In this context, this paper addresses these critical topics with focusing on the performance assessment of machinability indicators. The evaluation of the performance contributions of cutting mediums that is, dry and MQL were done on machining results. Lastly, machine learning algorithm based on linear regression was tested on the estimation ability of machining outcomes. One of the prominent results from this study is the distinct achievement of MQL method against dry cutting for all experimental lines. Second is the near-excellent structure of the machine learning strategies, which makes it possible to predict machinability characteristics; specifically, decision tree and KNN classifiers achieved testing accuracies of 0.75, 1.00, 1.00, and 1.00 for cutting speed, feed rate, depth of cut, and machining medium, respectively. The paper differs from the previous works by applying various machine learning approaches for achieving maximum machinability for AA7075 alloys under sustainable environment. This paper is expected to analyze the turning Al alloys by using effective models and methods in sustainable and smart manufacturing.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Bursa Teknik Üniversitesi
Bursa, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey