The Experts below are selected from a list of 24174 Experts worldwide ranked by ideXlab platform
Ramadhan, Fauzi Awal - One of the best experts on this subject based on the ideXlab platform.
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Rancang Bangun Pengontrolan Suhu Pada Sleepingbag Sebagai Tindakan Pencegahan Pada Penderita Hipotermia
2018Co-Authors: Ramadhan, Fauzi AwalAbstract:Maraknya pendakian di Indonesia tidak bisa dipungkiri dengan melonjaknya jumlah pendaki tiap tahunnya. Pendakian banyak dilakukan pada bulan September sampai bulan Desember dimana bulan tersebut menyajikan keindahan alamnya secara maksimal. Namun pada bulan tersebut, Pegunungan memiliki suhu sangat dingin dikarenakan curah hujan yang sangat tinggi. Dimana cura hujan yang sangat tinggi menyebabkan beberapa resiko, terutama yang berhubungan dengan kesehatan pendaki diantaranya Radang dingin, cedera dari tenaga (jantung, dan otot) dan Hipotermia. Kondisi ini kerap menyerang para pendaki yang tidak membawa perlengkapan pendakian yang lengkap, terkena guyuran hujan dan kurang mengkonsumsi kalori atau lainnya. Berdasarkan latar belakang tersebut, penelitian ini merancangan tentang Rancang Bangun Pengontrolan Suhu Pada Sleepingbag Sebagai Tindakan Pertolongan pada Penderita Hipotermia. Pada penelitian ini penulis menggunakan dua komponen yaitu gelang dan sleepingbag. Gelang berfungsi sebagai transformasi untuk mendapatkan data dari tubuh yang merupakan objek lalu dikirim ke sleeepingbag yang berfungsi untuk menghangatkan tubuh penderita hipotermia berdasarkan input yang didapatkan dari gelang. Penelitian skripsi dimaksud untuk merancang dan mengimplementasikan cara pengontrolan suhu pada sleepingbag sebagai tindakan untuk penderita hipotermiah yang sering terjadi di daerah pegunungan serta untuk perkembangan industry dalam negeri pada bidang alat kesehatan. Hasil dari penelitian ini menunjukkan bahwa sistem gelang dan sistem sleepingbag bekerja sesuai kondisi tubuh manusia. Sistem gelang akan memberitahukan data yang didapat dari Sensor lewat tampilan LCD, ketika Sensor Pulse mendapatkan data tubuh kurang dari 60 maka lcd akan menampilakn bahwa anda terdeteksi hipotermiah. Maka user akan menekan button untuk mengrim data ke sleepingbag guna mengolah data dengan metode fuzzy. Dimana jika suhu tubuh kurang dari 35⁰C dan suhu sleepingbag kurang dari 28 maka sistem sleeping bag akan mengluarakan output pemanas berupa kawat nikelin full sesuai rule yang dibuat pada matlab
Rahmi Izzati - One of the best experts on this subject based on the ideXlab platform.
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RANCANG BANGUN ALAT DETEKSI KECEMASAN PADA PASIEN TES DARAH DENGAN METODE FUZZY LOGIC TSUKAMOTO
2021Co-Authors: Rahmi IzzatiAbstract:Penelitian ini bertujuan untuk meminimalisir resiko terjadinya cidera jarum suntik seperti hematoma (bruises), vasovagal dan resiko lainnya yang disebabkan oleh tingkat kecemasan yang berlebihan pada pasien tes darah. Pada penelitian ini diciptakan sebuah sistem monitoring yang dapat menampilkan data berupa denyut nadi, suhu tubuh, konduktivitas kulit serta tingkat emosi pasien tes darah. Sistem ini juga dilengkapi dengan metoda kuisioner STAI yang dapat mendukung hasil deteksi dari alat yang telah dibuat. Terdapat dua komponen utama, yaitu alat pendeteksi kecemasan dan aplikasi mobile. Alat pendeteksi kecemasan terdiri dari mikrokontroler Arduino Uno, Sensor Pulse Heart Rate, Sensor MLX90614, Sensor GSR dan diproses menggunakan metoda Fuzzy Logic Tsukamoto. Setelah hasil akhir didapatkan, data akan dikirim dan ditampilkan pada aplikasi mobile dengan menggunakan media komunikasi Bluetooth HC-05. Dari pengujian diketahui sistem mampu mendeteksi denyut nadi dengan rata-rata selisih sebesar 0,22 BPM, suhu tubuh dengan rata-rata selisih sebesar 1,5℃, konduktivitas kulit dengan rata-rata selisih sebesar 0,03 Volt. Selanjutnya, sistem mampu melakukan pemrosesan input menggunakan metode Fuzzy Logic Tsukamoto dan mengirimkan data secara akurat ke perangkat android dengan keadaan data akan selalu diperbaharui setiap 1 detik
S S Babu - One of the best experts on this subject based on the ideXlab platform.
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investigating the effect of metal powder recycling in electron beam powder bed fusion using process log data
Additive manufacturing, 2020Co-Authors: Sujana Chandrasekar, Jamie B Coble, Sean Yoder, Peeyush Nandwana, Ryan R Dehoff, Vincent C Paquit, S S BabuAbstract:Abstract Recycling metal powders in the Additive Manufacturing (AM) process is an important consideration in affordability with reference to traditional manufacturing. Metal powder recyclability has been studied before with respect to change in chemical composition of powders, effect on mechanical properties of produced parts, effect on flowability of powders and powder morphology of parts. However, these studies involve ex situ characterization of powders after many use cycles. In this paper, we propose a data-driven method to understand in situ behavior of recycled powder on the build platform. Our method is based on comprehensive analysis of log file data from various Sensors used in the process of printing metal parts in the Arcam Electron Beam Melting (EBM) ® system. Using rake position data and rake Sensor Pulse data collected during Arcam builds, we found that Inconel 718 powders exhibit additional powder spreading operations with increased reuse cycles compared to Ti-6Al-4V powders. We substantiate differences found in in situ behavior of Ti-6Al-4V and Inconel 718 powders using known sintering behavior of the two powders. The novelty of this work lies in the new approach to understanding powder behavior especially spreadability using in situ log file data that is regularly collected in Arcam EBM® builds rather than physical testing of parts and powders post build. In addition to studying powder recyclability, the proposed methodology has potential to be extended generically to monitor powder behavior in AM processes.
Ryan R Dehoff - One of the best experts on this subject based on the ideXlab platform.
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investigating the effect of metal powder recycling in electron beam powder bed fusion using process log data
Additive manufacturing, 2020Co-Authors: Sujana Chandrasekar, Jamie B Coble, Sean Yoder, Peeyush Nandwana, Ryan R Dehoff, Vincent C Paquit, S S BabuAbstract:Abstract Recycling metal powders in the Additive Manufacturing (AM) process is an important consideration in affordability with reference to traditional manufacturing. Metal powder recyclability has been studied before with respect to change in chemical composition of powders, effect on mechanical properties of produced parts, effect on flowability of powders and powder morphology of parts. However, these studies involve ex situ characterization of powders after many use cycles. In this paper, we propose a data-driven method to understand in situ behavior of recycled powder on the build platform. Our method is based on comprehensive analysis of log file data from various Sensors used in the process of printing metal parts in the Arcam Electron Beam Melting (EBM) ® system. Using rake position data and rake Sensor Pulse data collected during Arcam builds, we found that Inconel 718 powders exhibit additional powder spreading operations with increased reuse cycles compared to Ti-6Al-4V powders. We substantiate differences found in in situ behavior of Ti-6Al-4V and Inconel 718 powders using known sintering behavior of the two powders. The novelty of this work lies in the new approach to understanding powder behavior especially spreadability using in situ log file data that is regularly collected in Arcam EBM® builds rather than physical testing of parts and powders post build. In addition to studying powder recyclability, the proposed methodology has potential to be extended generically to monitor powder behavior in AM processes.
Sujana Chandrasekar - One of the best experts on this subject based on the ideXlab platform.
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investigating the effect of metal powder recycling in electron beam powder bed fusion using process log data
Additive manufacturing, 2020Co-Authors: Sujana Chandrasekar, Jamie B Coble, Sean Yoder, Peeyush Nandwana, Ryan R Dehoff, Vincent C Paquit, S S BabuAbstract:Abstract Recycling metal powders in the Additive Manufacturing (AM) process is an important consideration in affordability with reference to traditional manufacturing. Metal powder recyclability has been studied before with respect to change in chemical composition of powders, effect on mechanical properties of produced parts, effect on flowability of powders and powder morphology of parts. However, these studies involve ex situ characterization of powders after many use cycles. In this paper, we propose a data-driven method to understand in situ behavior of recycled powder on the build platform. Our method is based on comprehensive analysis of log file data from various Sensors used in the process of printing metal parts in the Arcam Electron Beam Melting (EBM) ® system. Using rake position data and rake Sensor Pulse data collected during Arcam builds, we found that Inconel 718 powders exhibit additional powder spreading operations with increased reuse cycles compared to Ti-6Al-4V powders. We substantiate differences found in in situ behavior of Ti-6Al-4V and Inconel 718 powders using known sintering behavior of the two powders. The novelty of this work lies in the new approach to understanding powder behavior especially spreadability using in situ log file data that is regularly collected in Arcam EBM® builds rather than physical testing of parts and powders post build. In addition to studying powder recyclability, the proposed methodology has potential to be extended generically to monitor powder behavior in AM processes.
