مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

تحلیل حساسیت پارامترهای کلیدی و استفاده از امواج صوتی در بهبود کیفیت تراشکاری از طریق فناوری MQL در تراشیدن فولاد SCM440

نوع مقاله : پژوهشی اصیل

نویسندگان
دانشگاه اراک
چکیده
فولاد SCM440 به دلیل ویژگی‌های قابل‌توجه بسیار، در صنعت به طور گسترده مورداستفاده قرار می‌گیرد. بااین‌وجود، این فولاد موجب ایجاد مشکلاتی از جمله ارتعاش و سایش می‌شود. به دنبال راه‌حل جایگزین با هزینه کمتر، فناوری کمینه میزان روان‌سازی (MQL) به‌عنوان مؤثرترین روش جایگزین مورداستفاده قرار می‌گیرد. در این راستا، سیگنال‌های امواج صوتی و ارتعاشات در نظارت بر فرسایش ابزار و زبری سطح مؤثر هستند. همچنین، عوامل نامطلوب در حین ماشین‌کاری به‌عنوان پارامتر در بررسی رفتار فرایند می‌توانند مورداستفاده قرار گیرند. بررسی پارامترهای مختلف با استناد به معادله رگرسیون، موجب فراهم‌شدن شرایطی جهت شناسایی ایرادات و نقاط ضعف در فرایند ماشین‌کاری می‌شود. از پارامترهای مؤثر جهت بهبود کیفیت و بهینه‌سازی فرایند تولید می‌توان استفاده کرد. نتایج تحلیل حساسیت انجام شده نشان دادند که نرخ تغذیه به‌عنوان پارامتر حساس در میزان زبری سطح و عمق برش به‌عنوان پارامتر حساس در میزان سایش لبه شناخته می‌شوند و بیشترین تأثیر را در کیفیت ماشین‌کاری دارند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

ensitivity Analysis of Key Parameters and Utilization of Acoustic Emission for Enhancing Machining Quality through MQL Technology in Turning of SCM440 Steel

نویسندگان English

Arian Asgari
Mohammad Khalili
Sara Khosrobegi
چکیده English

The industry uses SCM440 steel extensively because of its many characteristics. Nevertheless, wear and vibration are two issues that this steel may bring about. Minimum Quantity Lubricant (MQL) technology is being employed extensively as the most efficient substitution approach in pursuit of a cost-effective alternative solution. Vibrations and acoustic emission signals work well for tracking surface roughness and tool wear. It is possible to use undesirable machining factors as parameters to study the behavior of the process. Regression analysis of several factors allows for the discovery of weaknesses and vulnerabilities in the machining process. Optimizing the production process and enhancing quality are possible with the use of effective factors. The two parameters that have the biggest effects on machining quality are feed rate and cutting depth. Feed rate is known to be sensitive to surface roughness, while cutting depth is recognized to be sensitive to tool wear.

کلیدواژه‌ها English

MQL Technology
Sensitivity Analysis
Machining Quality
SCM440
Acoustic emission
1- Zhang N, Komoda R, Yamada K, Kubota M, Staykov A. Ammonia mitigation and induction effects on hydrogen environment embrittlement of SCM440 low-alloy steel. International Journal of Hydrogen Energy. 2022 Apr 19;47(33):15084-93.
2- Kwak JS, Sim SB, Jeong YD. An analysis of grinding power and surface roughness in external cylindrical grinding of hardened SCM440 steel using the response surface method. International journal of machine tools and manufacture. 2006 Mar 1;46(3-4):304-12.
3- Thien NV, Trung DD. Study on model for cutting force when milling SCM440 steel. EUREKA: Physics and Engineering. 2021 Sep 13;5:23-35.
4- Tazoe K, Hamada S, Noguchi H. Fatigue crack growth behavior of JIS SCM440 steel near fatigue threshold in 9-MPa hydrogen gas environment. International Journal of Hydrogen Energy. 2017 May 4;42(18):13158-70.
5- Chen CC, Liu NM, Chiang KT, Chen HL. Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis. The International Journal of Advanced Manufacturing Technology. 2012 Nov;63:797-815.
6- Thirumalai R, Srinivas S, Vinodh T, Kowshik Kumar AL, Kumar MK. Optimization of Surface Roughness and Flank Wear in Turning SCM440 Alloy Steel Using Taguchi Method. Applied Mechanics and Materials. 2014 Sep 5;592:641-6.
7- Thamizhmanii S, Hasan S. Effect of tool wear and forces by turning process on hard AISI 440 C and SCM 440 materials. International Journal of Material Forming. 2009 Aug;2:531-4.
8- Jeong JI, Kim JH, Choi SG, Cho YT, Kim CK, Lee H. Mechanical properties of white metal on scm440 alloy steel by laser cladding treatment. Applied Sciences. 2021 Mar 22;11(6):2836.
9- Kong YS, Cheepu M, Lee JK. Evaluation of the mechanical properties of Inconel 718 to SCM 440 dissimilar friction welding through real-time monitoring of the acoustic emission system. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications. 2021 May;235(5):1181-90.
10- Furuya Y, Matsuoka S, Abe T. A novel inclusion inspection method employing 20 kHz fatigue testing. Metallurgical and Materials Transactions A. 2003 Nov;34:2517-26.
11- Panda D, Kumari K, Dalai N. Performance of Minimum Quantity Lubrication (MQL) and its effect on Dry Machining with the addition of Nano-particle with the biodegradable base fluids: A review. Materials Today: Proceedings. 2022 Jan 1;56:1298-301.
12- Gaurav G, Sharma A, Dangayach GS, Meena ML. Assessment of jojoba as a pure and nano-fluid base oil in minimum quantity lubrication (MQL) hard-turning of Ti–6Al–4V: A step towards sustainable machining. Journal of Cleaner Production. 2020 Nov 1;272:122553.
13- Özbek NA, Çiçek A, Gülesin M, Özbek O. Effect of cutting conditions on wear performance of cryogenically treated tungsten carbide inserts in dry turning of stainless steel. Tribology International. 2016 Feb 1;94:223-33.
14- Tran NH, Park HS, Nguyen QV, Hoang TD. Development of a smart cyber-physical manufacturing system in the industry 4.0 context. Applied Sciences. 2019 Aug 13;9(16):3325.
15- Hozdić E. Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies. 2015 Jan;7(1):28-35.
16- Usca ÜA, Uzun M, Şap S, Kuntoğlu M, Giasin K, Pimenov DY, Wojciechowski S. Tool wear, surface roughness, cutting temperature and chips morphology evaluation of Al/TiN coated carbide cutting tools in milling of Cu–B–CrC based ceramic matrix composites. journal of materials research and technology. 2022 Jan 1;16:1243-59.
17- Wu Q, Chen G, Liu Q, Pan B, Chen W. Investigation on the micro cutting mechanism and surface topography generation in ultraprecision diamond turning. Micromachines. 2022 Feb 27;13(3):381.
18- Tien DH, Thien NV, Pham TT, Nguyen TD. Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL. EUREKA: Physics and Engineering (2023),(1). 2023:86-101.
19- Nguyen D, Yin S, Tang Q, Son PX. Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis. Precision Engineering. 2019 Jan 1;55:275-92.