Klasifikasi Tingkat Kemurnian Bahan Bakar Minyak Berdasarkan Cepat Rambat Gelombang Menggunakan Algoritma K-Nearest Neighbor

The need for fuel oil has increased along with the increase of population, the number of vehicles and industries. An increase in demand for fuel oil is used by some people to make a profit by selling mixed fuel oil at the same price as the price set by the government. The purpose of this study is to create a prototype device that can characterize the type of fuel oil and create a classification system to determine the level of fuel purity with 40 kHz ultrasonic waves based on the parameters of wave velocity using the K-Nearest Neighbor (KNN) algorithm.This device works by using a 40 kHz ultrasonic wave that is connected to an ultrasonic transmitter. The propagated wave will be received by the ultrasonic receiver. The wave received by the receiver will be amplified and connected to the comparator circuit so that it can be processed by a microcontroller. Data obtained using this tool are wave travel time, wave velocity, density, and attenuation. The data used for classification systems using the KNN algorithm is wave velocity.Classification using the KNN algorithm can identify the level of fuel purity based on the parameters of the wave velocity obtained from ultrasonic wave gauges with an accuracy of 72.50%. Wave velocity which is measured using ultrasonic waves is directly proportional to the actual speed with the largest percentage of deviations that is 0.34%

Predicting the Product Classification of Hot Rolled Steel Sheets Using Machine Learning Algorithms

The mechanical properties of the SAPH440 hot rolled steel sheet are mainly controlled to satisfy product specifications. Three mechanical properties including the yield strength, ultimate tensile strength, and elongation are measured and utilized in product classification. Based on these properties, the steel is classified into 3 grades: Class 1 (meets specification), Class 2 (moderate quality), and Class 3 (low). However, various factors can affect the mechanical properties, leading to a long setup time for initial production runs. Therefore, this paper aims to improve the accuracy of these predictions by using machine learning algorithms. The results of experiments showed that the random forest algorithm had the best performance, with an accuracy of 70.0% and a macro average F-1 score of 70.0%. This more accurate prediction can reduce the initial setup time and save 37,000 USD per grade in trial run costs

NASA Microgravity Materials Science Conference

The Microgravity Materials Science Conference was held June 10-11, 1996 at the Von Braun Civic Center in Huntsville, AL. It was organized by the Microgravity Materials Science Discipline Working Group, sponsored by the Microgravity Science and Applications Division at NASA Headquarters, and hosted by the NASA Marshall Space Flight Center and the Alliance for Microgravity Materials Science and Applications (AMMSA). It was the second NASA conference of this type in the microgravity materials science discipline. The microgravity science program sponsored approximately 80 investigations and 69 principal investigators in FY96, all of whom made oral or poster presentations at this conference. The conference's purpose was to inform the materials science community of research opportunities in reduced gravity in preparation for a NASA Research Announcement (NRA) scheduled for release in late 1996 by the Microgravity Science and Applications Division at NASA Headquarters. The conference was aimed at materials science researchers from academia, industry, and government. A tour of the MSFC microgravity research facilities was held on June 12, 1996. This volume is comprised of the research reports submitted by the principal investigators after the conference and presentations made by various NASA microgravity science managers

Numerical Modelling and Simulation of Metal Processing

This book deals with metal processing and its numerical modelling and simulation. In total, 21 papers from different distinguished authors have been compiled in this area. Various processes are addressed, including solidification, TIG welding, additive manufacturing, hot and cold rolling, deep drawing, pipe deformation, and galvanizing. Material models are developed at different length scales from atomistic simulation to finite element analysis in order to describe the evolution and behavior of materials during thermal and thermomechanical treatment. Materials under consideration are carbon, Q&T, DP, and stainless steels; ductile iron; and aluminum, nickel-based, and titanium alloys. The developed models and simulations shall help to predict structure evolution, damage, and service behavior of advanced materials

Bibliography of Lewis Research Center technical publications announced in 1993

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

MC 2019 Berlin Microscopy Conference - Abstracts

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2019", die vom 01. bis 05.09.2019, in Berlin stattfand

Materials sciences programs, Fiscal year 1997

The Division of Materials Sciences is responsible for basic research and research facilities in materials science topics important to the mission of the Department of Energy. The programmatic divisions under the Office of Basic Energy Sciences are Chemical Sciences, Engineering and Geosciences, and Energy Biosciences. Materials Science is an enabling technology. The performance parameters, economics, environmental acceptability and safety of all energy generation, conversion, transmission and conservation technologies are limited by the properties and behavior of materials. The Materials Sciences programs develop scientific understanding of the synergistic relationship among synthesis, processing, structure, properties, behavior, performance and other characteristics of materials. Emphasis is placed on the development of the capability to discover technologically, economically, and environmentally desirable new materials and processes, and the instruments and national user facilities necessary for achieving such progress. Materials Sciences subfields include: physical metallurgy, ceramics, polymers, solid state and condensed matter physics, materials chemistry, surface science and related disciplines where the emphasis is on the science of materials. This report includes program descriptions for 517 research programs including 255 at 14 DOE National Laboratories, 262 research grants (233 of which are at universities), and 29 Small Business Innovation Research Grants. Five cross-cutting indices located at the rear of this book identify all 517 programs according to principal investigator(s), materials, techniques, phenomena, and environment

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

The Fifth National Technology Transfer Conference and Exposition

No abstract availabl