Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Investigation of progressive tool wear for determining of optimized machining parameters in turning
Scopus
Toplam 177 atıf DOI
On-line monitoring of tool wear and tool breakage are very important to reduce production costs through the optimization of machining parameters. Increasing cutting forces affect workpiece quality and tool condition that is the ultimate aim of production line and progressive tool wear which can trigger the tool breakage. Taguchi method is extensively used for determining number of experiment while variance analysis (ANOVA) deals with which parameter/s is/are effective on output. This study contains experiments and optimization processes during turning of AISI 1050 material with 3 input parameters (cutting speed, feed rate, tool tip) using Taguchi method. In order to determine the condition of the cutting tool, measurement of tangential cutting force and acoustic emission (AE) were carried out during metal removing. ANOVA results showed that cutting speed is the most effective about %45 and tool tip is the second about %35 on tool wear. On the other hand, the effect of feed rate on tangential cutting force (%88) and cutting speed on AE (%80) is remarkably higher than the other two parameters. In order to obtain the minimum tool wear value, the optimum cutting parameters have been selected as v 1 = 135 m/min, f 2 = 0,214 mm/rev, T 2 = P25. By implemented sensor system tool breakage can be successfully detected and used for producing high quality materials with low costs.
Atıf Kaynağı
Atıf Yapan Yayın
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
Scopus
Havuzumuzda 119 atıf almış
Sensors are the main equipment of the data-based enterprises for diagnosis of the health of system. Offering time- or frequency-dependent systemic information provides prognosis with the help of early-warning system using intelligent signal processing systems. Therefore, a chain of data-based information improves the efficiency especially focusing on the determination of remaining useful life of a machine or tool. A broad utilization of sensors in machining processes and artificial intelligence–supported data analysis and signal processing systems are prominent technological tools in the way of Industry 4.0. Therefore, this paper outlines the state of the art of the mentioned systems encountered in the open literature. As a result, existing studies using sensor systems including signal processing facilities in machining processes provide important contribution for error minimization and productivity maximization. However, there is a need for improved adaptive control systems for faster convergence and physical intervention in case of possible problems and failures. On the other hand, sensor fusion is an innovative new technology that makes decisions using multi-sensor information to determine tool status and predict system stability. It is currently not a fully accepted and practiced method. In a nutshell, despite their numerous advantages in terms of efficiency, time saving, and cost, the current situation of sensors used in the industry is not a sufficient level due to the investment cost and its increase with additional signal acquisition hardware and software equipment. Therefore, more studies that can contribute to the literature are needed.
Atıf Yapan Makale Bilgileri
Kurumlar (5)
Opole University of Technology
Opole, Poland
Selçuk Üniversitesi
Selçuklu, Turkey
Shandong University
Jinan, China
Sinop Üniversitesi
Sinop, Turkey
South Ural State University
Chelyabinsk, Russian Federation