مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

شبیه‌سازی پروفیل سطح حاصل از فرآیند داخل‌تراشی به وسیله‌ی گردآوری سیگنال ارتعاشی و مدل هندسه‌ی ابزار

نوع مقاله : پژوهشی اصیل

نویسندگان
1 دانشجوی دانشگاه فردوسی مشهد
2 استادیار دانشگاه علوم و فنون هوایی شهید ستاری
3 استاد دانشگاه فردوسی مشهد
چکیده
در گذشته مقالات بسیار زیادی در زمینه‌ی شبیه‌سازی و پیش‌بینی زبری سطح در فرایندهای ماشین‌کاری به خصوص تراش‌کاری و فرزکاری منتشر شده اما تعداد مقالات در مورد فرآیند داخل‌تراشی بسیار محدود بوده و هم­چنین پژوهش ­های موجود در این زمینه نیز اکثرا از روش­ های آماری استفاده نموده­ اند که تعمیم­ پذیری بالایی نداشته و نیاز به انجام آزمون­ های فراوانی دارند. در پژوهش پیش رو شبیه‌سازی زبری سطح در فرآیند داخل‌تراشی با استفاده از سینماتیک و دینامیک فرآیند مورد مطالعه قرار گرفته که با وجود استفاده­ در فرآیندهای تراش­کاری، تاکنون در مورد فرآیند‌های داخل‌تراشی به کار برده نشده است. در روش ارائه شده در این پژوهش ابتدا پروفیل نوک ابزار توسط دستگاه CMM اندازه‌گیری شده و سپس از این پروفیل برای تولید سطح حاصل از سینماتیک فرآیند که مولفه‌ی تناوبی پروفیل زبری است، استفاده می‌شود. در قدم بعدی با توجه به این که در فرآیند داخل­ تراشی به دلیل طول بلند میله ­ی بورینگ ارتعاشات قابل توجهی وجود دارد، این ارتعاشات که در طول آزمایش توسط شتاب­ سنج اندازه­ گیری شده­­ به صورت جابه ­جایی ابزار نسبت به قطعه­کار به پروفیل تناوبی زبری تشکیل شده در مرحله­ ی قبلی اضافه می ­گردد. نتایج آزمایش ­های صحه­ گذاری انجام شده نشان داده است که روش توسعه داده شده در این مقاله خطایی معادل با حداکثر 19.3% را برای پارامترهای زبری تولید می ­کند که با توجه به پیچیدگی­ های فراوان مبحث زبری می­ توان عملکرد روش ارائه شده در این پژوهش را مناسب و کاربردی ارزیابی نمود.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Simulation of Surface Profile in Internal Turning Using Acceleration Signal and Tool Insert Geometry Modelling

نویسندگان English

Ali Pordel 1
Mohammad Kazemi nasrabadi 2
Behnam Moetakef-Imani 3
1 Student in Ferdowsi University of Mashhad
2 Assistant Professor at University of Shahid Sattari
3 Professor at Ferdowsi University of Mashhad
چکیده English

Although there have been several research work published in the field of simulating and predicting the surface roughness of machining processes, most of them are limited to turning and milling operations. A few number of studies concerning the internal turning processes is very limited. Furthermore, the existing publications in this field have implemented statistical approaches which not only clearly lack in generality, but also require a huge amount of experiments. In the current research, the simulation of surface roughness has been investigated by using kinematics and dynamics of the process. Despite the numerous applications of this approach in turning operations, this approach has not applied in the internal turning processes. In order to implement the proposed approach, firstly the insert nose profile of the tool has been measured. Then, the surface profile consisting the periodical component of feed marks has been constructed. In the next step, excessive amount of vibrations imposed by the long boring bar have been measured by an accelerometer, which are then converted to displacements and added to the periodical component of the roughness profile. Results obtained from internal turning experiments show that the developed simulation approach has a maximum error of 19.3% in estimating roughness parameters which can be considered as a reasonably accurate results due to the complicated nature of surface roughness.

کلیدواژه‌ها English

Internal Turning
Roughness
surface profile
Vibrations
simulation
[1] P.G. Benardos, G.C. Vosniakos, Predicting surface roughness in machining: a review, International Journal of Machine Tools and Manufacture, Vol. 43, No. 8, pp. 833-844, 2003.
[2] DIN4760: Form deviations; concepts; classification system. Deutches Institut Fuer Normung, e.V., 1982.
[3] K.A. Risbood, U.S. Dixit, A.D. Sahasrabudhe, Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process, Journal of Materials Processing Technology, Vol. 132, No. 1-3, pp. 203-214, 2003.
[4] H. H. Shahabi, M. M. Ratnam, Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach, International Journal of Advanced Manufacturing Technology, Vol. 48, No. 1-4, pp. 213-226, 2010.
[5] Chen Lu, Study on prediction of surface quality in machining process, Journal of Materials Processing Technology, Vol. 205, No. 1-3, pp. 439-450, 2008.
[6] Y. Beauchamp, M. Thomas, Y. Youssef, J. Masounave, Investigation of cutting parameter effects on surface roughness in lathe boring operation by use of a full factorial design, Computers & Industrial Engineering, Vol. 31, No. 3-4, pp. 645-651, 1996.
[7] B.A.G. Yuvaraju, B.K. Nanda, Prediction of Vibration Amplitude and Surface Roughness in Boring Operation by Response Surface Methodology, International Conference on Emerging Trends in Materials and Manufacturing Engineering, Tiruchirapalli, India, 2017.
[8] M. Munawar, J.C.S. Chen, N.A. Mufti, Investigation of cutting parameters effect for minimization of surface roughness in internal turning, International Journal of Precision Engineering and Manufacturing, Vol. 12, No. 1, pp. 121-127, 2011.
[9] K.V. Rao, B.S.N. Murthy, N.M. Rao, Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network, Measurement, Vol. 51, pp. 63-70, 2014.
[10] G. Balamurugamohanraj, K. Vijaiyendiran, P. Mohanaraman, V. Sugumaran, Prediction of surface roughness based on machining condition and tool condition in boring stainless steel-304, Indian Journal of Science and Technology, Vol. 9, No. 47, pp. 1223-1228, 2016.
[11] K.V. Rao, B.S.N. Murthy, Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM, Journal of Intelligent Manufacturing, Vol. 34, No. 7, pp. 1-11, 2016.
[12] A.N. Sung, W.P. Loh, M.M. Ratnam, Simulation approach for surface roughness interval prediction in finish turning, International Journal of Simulation Modelling, Vol. 15, No. 1, pp. 42-55, 2016.
[13] A. N. Sung, M. M. Ratnam, W. P. Loh, Effect of tool nose profile tolerance on surface roughness in finish turning, International Journal of Advance Manufacturing Technology, Vol. 76, No. 9-12, pp. 2083-2098, 2015.
[14] D.Y. Jang, Y.G. Choi, H.G. Kim, A. Hsiao, Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning, International Journal of Machine Tools and Manufacture, Vol. 36, No. 4, pp. 453-464, 1996.
[15] S.C. Lin, M.F. Chang, A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning, International Journal of Machine Tools and Manufacture, Vol. 38, No. 7, pp. 763-782, 1998.
[16] ISO 3685, Tool Life Testing with Single Point Turning Tools, International Organization for Standardization, 1993.
[17] G.J. Chian, M.M. Ratnam, Determination of tool nose radius of cutting inserts using machine vision, Sensor Review, Vol. 31, No. 2, pp. 127-137, 2011.
[18] K. E. Atkinson, An Introduction to Numerical Analysis, New York: John Wiley & Sons, 1978.
[19] R. Pintelon and J. Schoukens, Real-time integration and differentiation of analog signals by means of digital filtering, 7th IEEE Conference on Instrumentation and Measurement Technology, San Jose, USA, 1990.
[20] S. Han, Measuring displacement signal with an accelerometer, Journal of Mechanical Science and Technology, Vol. 24, No. 6, pp. 1329-1335, 2010.
[21] S. Han and J.W. Chung, Retrieving displacement signal from measured acceleration signal, Proceedings of 20th International Modal Analysis Conference (IMAC '02), Los Angles, USA, 2002.
[22] A. Brandt and R. Brincker, Integrating time signals in frequency domain – Comparison with time domain integration, Measurement, Vol. 58, pp. 511-519, 2014.
[23] A. Brandt, Noise and Vibration Analysis - Signal Analysis and Experimental Procedures, 1st ed., UK: John Wiley & Sons, 2011.
[24] D.M. Meko, Notes on Autocorrelation – Note5 – GEOS585A – 2005, Accessed on 3 December 2018; https://www.ltrr.arizona.edu
/~dmeko/geos585a.html
[25] K. Khalili and M. Danesh, Identification of vibration level in metal cutting using undecimated wavelet transform and gray-level co-occurrence matrix texture features, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 229, pp. 205-213, 2015.
[26] V. Marinov, Manufacturing Technology, fifth edition, Pearson Education Inc, 2006.
[27] P.M. Groover, Fundamentals of Modern Manufacturing Materials, Processes, and Systems, Student edition, Asia: John Wiley & Sons, 2007.
[28] P.V.S. Suresh, P.V. Rao and S.G. Deshmukh, A genetic algorithmic approach for optimization of surface roughness prediction mode, International Journal of Machine Tools and Manufacture, Vol. 42, No. 6, pp. 675-680, 2002.
[29] ISO 11610-21, Geometrical Product Specifications (GPS) - Filteration - Part 21: Linear profile filters: Gaussian filters, International Organization for Standardization, 2011.
[30] J. Raja, B. Muralikrishnan, S. Fu, Recent advances in separation of roughness, waviness and form, Journal of the International Societies for Precision Engineering and Nanotechnology, Vol. 26, No. 6, pp. 222-235, 2002.
[31] ISO 4287, Geometrical Product Specifications (GPS) - Surface texture: Profile method - Terms, definitions and surface texture parameters, International Organization for Standardization, 1997.
[32] M. Fallah, Active Control of Vibration for Stability Improvement in Deep Internal Turning Operations, PhD Thesis, Department of Mechanical Engineering, Ferdowsi University, Mashhad, 2018. (in Persianفارسی )