Waktu Siklus dan Produktivitas Pada Alat Berat
Waktu Siklus :
· Waktu mengisi bucket
· Waktu putar bermuatan
· Waktu membongkar muatan
· Waktu putar tanpa muatan
Produksi excavator dapat dihitung dengan
persamaan dibawah ini (Rochmanhadi 20:1982 Kapasitas dan Produksi Alat-Alat
Berat ; Ronald C.Smith 38:1986):
𝑸
= 𝟑𝟔𝟎𝟎 𝒙𝒒𝟏𝒙𝑲𝒙𝑬𝒎𝟑𝒋𝒂𝒎.
Rumus waktu siklus
Excavator dapat dihitung dengan persamaan berikut :
Cm = t1 + (2 x t2)+ t3
(detik)
Keterangan : t1 = waktu
gali / waktu muat bucket
t2 = waktu swing
t3 = waktu buang
Motor Scrapper
![Hasil gambar untuk scraper alat berat](https://peguinhitamputih.files.wordpress.com/2016/01/motorized-scrapers.jpg?w=640)
![Hasil gambar untuk scraper alat berat](https://peguinhitamputih.files.wordpress.com/2016/01/motorized-scrapers.jpg?w=640)
Waktu Siklus :
· Waktu pengerukan dan pemuatan material
· Waktu penyimpanan muatan di dalam
bagian bowl
· Waktu gerak scaper menuju tempat
pembongkaran
· Waktu pembongkaran
Produktivitas scraper =
Kapasitas × 60/waktu siklus × job. effencieny,.
Waktu siklus = FT+HT+RT
Motor Grader
![Hasil gambar untuk motor grader](data:image/jpeg;base64,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)
Waktu Siklus :
· Waktu maju atau mundur motor grader
· Waktu pendorongan bidang dengan pisau
· Waktu menurunkan pisau grader
· Waktu pemerataan bidang
· Waktu putar tanpa muatan
Produktivitas = 1000.v.W.E
Dozer
![Hasil gambar untuk dozer](https://4.imimg.com/data4/YW/VV/MY-3224679/dozer-machine-500x500.jpg)
![Hasil gambar untuk dozer](https://4.imimg.com/data4/YW/VV/MY-3224679/dozer-machine-500x500.jpg)
Dozer atau Loder ialah
alat yang umum dipakai pada proyek konstruksi untuk menangani material hasil
penggalian atau untuk membuat timbunan material. Pada bagian dozer terdapat
bucket sehingga alat ini juga disebut front end dozer. Ada 2 jenis tipe dozer
antara lain, menggunakan roda kelabang (Crawler Tractor Dozer) dan Buldozer
yang menggunakan roda karet (Wheel Tractor Dozer).
Waktu Siklus =
Cm=D/F × D/R+Z
Produktivitas persiklus :
q = L × H2 × a
Produktivitas maksimum :
Q = q × N×E
Waktu Siklus :
· Waktu mengisi bucket
· Waktu gerak/putar tanpa muatan
· Waktu bucket di dorongkan pada
material
· Waktu gerak/putar bucket yang
bermuatan
· Waktu pemindahan material
Produktivitas Alat Secara
umum, produktivitas suatu alat berat,dihitung dengan menggunakan rumus
(Rochmanhadi, 84:1992 Alat-alat berat dan penggunaanya) :
Q=qx60xE/ Cm
dimana :
Q = produksi per-jam
(m3/jam) q = produksi persiklus (m3)
E = effisiensi kerja
Cm = waktu siklus (menit)
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