Journals / Bilimsel Madencilik Dergisi / 2018 / Cilt: 57 - Sayı: 1

PERFORMANCE PREDICTION OF CHAIN SAW MACHINES USING SCHMIDT HAMMER HARDNESS

SCHMIDT ÇEKİCİ SERTLİĞİ KULLANILARAK ZİNCİRLİ KESME MAKİNELERİNİN PERFORMANS TAHMİNİ

Pages
25–33
Publication Date
2018-03-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results of this study indicated that the CSPI of CSMs could be reliably predicted by empirical model using RL .

📌 3 Key Takeaways

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Objective & Hypothesis:Schmidt hammer hardness (RL) provides a quick and inexpensive measure of surface hardness that is widely used for estimating the mechanical properties of rock material such as strength, sawability, cuttability and drillability.
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Key Findings & Data:In this study, RL as predictors, which is thought to be a useful, simple and inexpensive test particularly for performance prediction of chain saw machine (CSM), is suggested.
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Conclusion & Impact:Results of this study indicated that the CSPI of CSMs could be reliably predicted by empirical model using RL .
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Zincirli kesme makineleri · Spesifik enerji · Schmidt çekici sertliği

Abstract

Schmidt hammer hardness (RL) provides a quick and inexpensive measure of surface hardness that is widely used for estimating the mechanical properties of rock material such as strength, sawability, cuttability and drillability. In this study, RL as predictors, which is thought to be a useful, simple and inexpensive test particularly for performance prediction of chain saw machine (CSM), is suggested. This study aims to estimate CSM performance from RL values of rocks. For this purpose, rock cutting and rock mechanics tests were performed on twenty four different natural stone samples having different strength values. In this study, Chain Saw Penetration Index (CSPI) has been predicted based on RL which is one of the two models previously used for performance prediction of CSMs. The RL values were correlated with UCS, CSPI and SE using simple regression analysis with SPSS 15.0. As a result of this evaluation, RL has a strong relation with UCS and SE. It is statistically proved that the model based on RL for predicting CSPI is valid and reliable for performance prediction of CSM. Results of this study indicated that the CSPI of CSMs could be reliably predicted by empirical model using RL .

Özet

Schmidt çekici sertliği (RL ) kayaların dayanım, kesilebilirlik (doğrusal ve dairesel) ve delinebilirlik gibi mekanik özelliklerini belirlemek için yaygın olarak kullanılan ucuz ve kolaylık sağlayan bir yüzey sertliği ölçüsüdür. Bu çalışmada, özellikle zincirli kesme makinesinin performans tahmininde, kullanışlı, basit ve ucuz bir test olan Schmidt çekici sertliği değişken olarak önerilmiştir. Bu çalışmada amaç, kayaların Schmidt sertliklerinden zincirli kesme makinelerinin performansını tahmin etmektir. Bunun için, farklı dayanım özelliklerine sahip 24 farklı doğal taş numunesi üzerinde kesme ve kaya mekaniği testleri yapılmıştır. Bu çalışmada, zincirli kesme makinelerinin performans tahmini için daha önce kullanılan iki modelden biri olan Zincirli Kesme Penetrasyon İndeksi (CSPI) RL baz alınarak öngörülmüştür. RL değerleri ile tek eksenli basınç dayanımı, zincirli kesme indeksi ve spesifik enerji değerlerinin korelasyonu SPSS 15.0 istatistik programı kullanılarak yapılmıştır. Bu değerlendirme sonucunda; RL değerleri ile tek eksenli basınç dayanımı ve spesifik enerji değerleri arasında güçlü korelasyon olduğu belirlenmiştir. Buna göre; zincirli kesme indeksini tahmin etmek için RL ’ye dayanan modelin zincirli kesme makinesinin performans tahmini için geçerli ve güvenilir olduğu istatistiksel olarak kanıtlanmıştır. Bu çalışmanın sonuçları, zincirli kesme makinelerinin zincirli kesme indeksini, RL değerleri kullanılarak oluşturulan görgül modeller ile güvenilir bir şekilde tahmin edilebileceğini göstermiştir.

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Cite This Article

DURSUN, A. E. (2018). PERFORMANCE PREDICTION OF CHAIN SAW MACHINES USING SCHMIDT HAMMER HARDNESS. Bilimsel Madencilik Dergisi, Cilt: 57 - Sayı: 1, 25–33. https://ajindex.com/articles/58564

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