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Tytuł:
Intelligent machining: real-time tool condition monitoring and intelligent adaptive control systems
Autorzy:
Hassan, M.
Sadek, A.
Attia, M. H.
Thomson, V.
Powiązania:
https://bibliotekanauki.pl/articles/99921.pdf
Data publikacji:
2018
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
adaptive control
tool condition monitoring
intelligent machining
Opis:
Unmanned manufacturing systems has recently gained great interest due to the ever increasing requirements of optimized machining for the realization of the fourth industrial revolution in manufacturing ‘Industry 4.0’. Real-time tool condition monitoring (TCM) and adaptive control (AC) machining system are essential technologies to achieve the required industrial competitive advantage, in terms of reducing cost, increasing productivity, improving quality, and preventing damage to the machined part. New AC systems aim at controlling the process parameters, based on estimating the effects of the sensed real-time machining load on the tool and part integrity. Such an aspect cannot be directly monitored during the machining operation in an industrial environment, which necessitates developing new intelligent model-based process controllers. The new generations of TCM systems target accurate detection of systematic tool wear growth, as well as the prediction of sudden tool failure before damage to the part takes place. This requires applying advanced signal processing techniques to multi-sensor feedback signals, in addition to using ultra-high speed controllers to facilitate robust online decision making within the very short time span (in the order of 10 ms) for high speed machining processes. The development of new generations of Intelligent AC and TCM systems involves developing robust and swift communication of such systems with the CNC machine controller. However, further research is needed to develop the industrial internet of things (IIOT) readiness of such systems, which provides a tremendous potential for increased process reliability, efficiency and sustainability.
Źródło:
Journal of Machine Engineering; 2018, 18, 1; 5-17
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Real-time tool condition monitoring in milling by means of control charts for auto-correlated data
Autorzy:
Colosimo, B. M.
Moroni, G.
Grasso, M.
Powiązania:
https://bibliotekanauki.pl/articles/100212.pdf
Data publikacji:
2010
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
statistical process control
profile monitoring
tool condition monitoring
Opis:
Real time monitoring of tool condirions and machining processes has been extensively studies in tne last decades, but a wide gap is stiil present between research activities and commercial tools. One of the factors which currently limit the utilization of these systems is the low flexibility of off-the-shelf solutions: in most cases they need dedicated off-line training sessions to acquire the reference patterns and thresholds, and/or the need for several input data to be defined a priori by a human operator. Instead of exploiting off-line learning sessions and a prior defined thresholds, this paper proposes an approach for automatic modelling of a cutting process and real-time monitoring of its stability that is based only on data acquired on-line during the process itself. This approach avoids any a-priori assumption about expected signal patterns, and it is characterized by an innovative implementation of well known Statistical Process Control techniques. In particular, with regard to milling processes, the paper proposes the utilization of cross-correlation coefficient between repeating signal profiles as the feature to be monitored, and an EWMA (Exponentially Weighted Moving Average) control chart for auto-correlated data as monitoring tool.
Źródło:
Journal of Machine Engineering; 2010, 10, 2; 5-17
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Towards sustainable and intelligent machining: energy footprint and tool condition monitoring for media-assisted processes
Autorzy:
Dogan, Hakan
Jones, Llyr
Hall, Stephanie
Shokrani, Alborz
Powiązania:
https://bibliotekanauki.pl/articles/24084657.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
machining
deep learning
tool condition monitoring
energy footprint
Opis:
Reducing energy consumption is a necessity towards achieving the goal of net-zero manufacturing. In this paper, the overall energy footprint of machining Ti-6Al-4V using various cooling/lubrication methods is investigated taking the embodied energy of cutting tools and cutting fluids into account. Previous studies concentrated on reducing the energy consumption associated with the machine tool and cutting fluids. However, the investigations in this study show the significance of the embodied energy of cutting tool. New cooling/lubrication methods such as WS2-oil suspension can reduce the energy footprint of machining through extending tool life. Cutting tools are commonly replaced early before reaching their end of useful life to prevent damage to the workpiece, effectively wasting a portion of the embodied energy in cutting tools. A deep learning method is trained and validated to identify when a tool change is required based on sensor signals from a wireless sensory toolholder. The results indicated that the network is capable of classifying over 90% of the tools correctly. This enables capitalising on the entirety of a tool’s useful life before replacing the tool and thus reducing the overall energy footprint of machining processes.
Źródło:
Journal of Machine Engineering; 2023, 23, 2; 16--40
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent cyber-physical monitoring and control of I4.0 machining systems – an overview and future perspectives
Autorzy:
Hassan, Mahmoud
Sadek, Ahmad
Attia, M. Helmi
Powiązania:
https://bibliotekanauki.pl/articles/2052195.pdf
Data publikacji:
2022
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
machining process
artificial intelligence
modelling
optimisation
tool condition monitoring
Opis:
Rapid evolution in sensing, data analysis, and industrial internet of things technologies had enabled the manufacturing of advanced smart tooling. This has been fused with effective digital inter-connectivity and integrated process control intelligence to form the industry I4.0 platform. This keynote paper presents the recent advances in smart tooling and intelligent control techniques for machining processes. Self-powered wireless sensing nodes have been utilized for non-intrusive measurement of process-born phenomena near the cutting zone, as well as tool wear and tool failure, to increase confidence in the process and tool condition monitoring accuracy. Cyber-physical adaptive control approaches have been developed to optimize the cycle time and cost while eliminating machined part defects. Novel artificial intelligence AI-based signal processing and modeling approaches were developed to guarantee the generalization and practicality of these systems. The paper concludes with the outlook for future work needed for seamless implementation of these developments in industry.
Źródło:
Journal of Machine Engineering; 2022, 22, 1; 5-24
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development on AI optimizing technology of NC program using tool free-cutting temperature for turning
Autorzy:
Tanabe, Ikuo
Isobe, Hiromi
Powiązania:
https://bibliotekanauki.pl/articles/2212054.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
NC program
correction
free-cutting temperature
high productivity
tool condition
Opis:
In recent years, manufacturing must not only focus on high precision and productivity, but also on saving energy, resources and the environment. At the same time, there are increasing demands for high quality, high grade, wear resistance, heat-resisting property and high rigidity in machining workpieces. Therefore, for example, when creating NC programmes for machining difficult-to-machine materials such as titanium alloys and nickel alloys used in the aerospace industry, it has been very difficult to determine their optimum cutting conditions and create highly productive NC programmes due to lack of experience. Therefore, an AI optimizing technology of NC program using tool free-cutting temperatures for turning was developed and evaluated. The turning was used for this research and neural networks were used for AI optimizing. The algorithm for optimizing NC program using tool free-cutting temperature was firstly developed. Then the AI optimizing program for NC program was developed by C programming language. Previous NC program can rewrite for optimum condition by using the AI optimizing program. The proposed AI optimizing technology of NC program was evaluated by the several experiments. It is concluded from the results that; (1) The AI optimizing program for NC programmes using tool free-cutting temperatures in turning was developed, (2) The developed program was very useful for high productivity, long tool life and environmentally friendly.
Źródło:
Journal of Machine Engineering; 2023, 23, 1; 57--70
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Time-frequency Representation -enhanced Transfer Learning for Tool Condition Monitoring during milling of Inconel 718
Autorzy:
Zhou, Yuqing
Sun, Wei
Ye, Canyang
Peng, Bihui
Fang, Xu
Lin, Canyu
Wang, Gonghai
Kumar, Anil
Sun, Weifang
Powiązania:
https://bibliotekanauki.pl/articles/24200823.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
tool condition monitoring
time-frequency analysis
Markov Transition Field
transfer learning
Opis:
Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of experiments, which significantly affects the performance of ML models. A time-series dimension expansion and transfer learning (TL) method is developed to boost the performance of TCM for small samples. First, a time-frequency Markov transition field (TFMTF) is proposed to encode the cutting force signal in the cutting process to two-dimensional images. Then, a modified TL network is established to learn and classify tool conditions under small samples. The performance of the proposed TFMTF-TL method is demonstrated by the benchmark PHM 2010 TCM dataset. The results show the proposed method effectively obtains superior classification accuracies for small samples and outperforms other four benchmark methods.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 165926
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Study on Tool Condition Parameters Intended for Smart Tool Management in Filleted End Milling
Autorzy:
Sekine, Tsutomu
Powiązania:
https://bibliotekanauki.pl/articles/2123284.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
optimization
multi-axis CNC machining
filleted end mill
tool condition parameter
machining conditions
Opis:
Filleted end milling is commonly used as a versatile manufacturing process, whereas the optimization for tool condition management is one of the continuously noticed topics. It can also contribute to the achievement of Sustainable Development Goals. A lot of outcomes have been reported so far in the experimental investigations of tool condition parameters. However, the findings and knowledge from theoretical perspective are relatively scarce to advance smart tool management. Hence, the aim of this study is to give theoretical consideration with tool condition parameters. The investigations focus on filleted end milling with some variations of machining conditions. Several tool condition parameters were theoretically proposed after illustrating geometrical modeling with variable description. Then, the demonstrations and discussion were made based on the computational results in filleted end milling. The results visually and numerically ascertained novel findings regarding several characteristics of tool condition parameters
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 3; 108--116
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evaluation of sensor-based condition monitoring methods as in-process tool wear and breakage indices - case study: drilling
Autorzy:
Tsanakas, J. A.
Botsaris, P. N.
Amirids, I. G.
Galeridis, G. G.
Powiązania:
https://bibliotekanauki.pl/articles/329544.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
tool condition monitoring
vibration signals
thermal signatures
spindle motor current
tool wear
statistical analysis
frequency domain analysis
Opis:
Today, effective unmanned machining operations and automated manufacturing are unthinkable without tool condition monitoring (TCM). Undoubtedly, the implementation of an adaptable, reliable TCM and its successful employment in industry, emerge as major instigations over the recent years. In this work, a sensor-based approach was deployed for the in-process monitoring and detection of tool wear and breakage in drilling. In particular, four widely reported indirect methods for tool wear monitoring, i.e. vibration signals together with thermal signatures, spindle motor and feed motor current measurements were obtained during numerous drillings, under fixed conditions. The acquired raw data was, then, processed both statistically and in the frequency domain, in order to distinguish the meaningful information. The study of the latter is influential in identifying the trend of specific signals toward tool wear mechanism. The efficiency of this information as a tool wear and/or breakage index is the feature that determines the effectiveness and reliability of a potential indirect TCM approach based on a multisensor integration. The paper concludes with a discussion of both advantages and limitations of this effort, stressing the necessity to develop simple, fast condition monitoring methods which are, generally, less likely to fail.
Źródło:
Diagnostyka; 2012, 2(62); 3-13
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognozowanie stanu obrabiarek skrawających na podstawie wyników badań ich dokładności
Condition of cutting machine tools predicting on the basis of the results of their accuracy tests
Autorzy:
Styp-Rekowski, Michał
Powiązania:
https://bibliotekanauki.pl/articles/1818836.pdf
Data publikacji:
2021
Wydawca:
Wydawnictwo AWART
Tematy:
cutting machine tool
diagnostic tests
machinetool
condition evaluation
machine-tool condition prediction
obrabiarka skrawająca
testy diagnostyczne
obrabiarki
ocena stanu
prognozowanie stanu technicznego obrabiarki
Opis:
W artykule przedstawiono możliwości wykorzystania standardowych badań dokładności obrabiarek skrawających do prognozowania ich stanu w przyszłości. Takie ujęcie zagadnienia znacznie rozszerza przydatność badań diagnostycznych i może być bardzo przydatnym narzędziem w planowaniu napraw i remontów obrabiarek. W znacznym stopniu ogranicza także niebezpieczeństwo nieprzewidzianych awarii.
This paper presents the possibility of using standard accuracy tests of cutting machine tools to predict their future condition. Such an approach to the issue significantly extends the usefulness of diagnostic tests and can be a very useful tool in planning repairs and overhauls of machine tools. It also significantly reduces the risk of unforeseen failures.
Źródło:
Obróbka Metalu; 2021, 3; 46--51
2081-7002
Pojawia się w:
Obróbka Metalu
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring of tool vibration for magnetorheological fluid controlled bar during turning of hardened AISI4340 steel
Monitorowanie wibracji narzędzia w czasie toczenia na twardo stali AISI4340 przy użyciu tłumika ze sterowanym płynem o właściwościach magnetoreologicznych
Autorzy:
Paul, P. S.
Jazeel, M.
Varadarajan, A. S.
Powiązania:
https://bibliotekanauki.pl/articles/139617.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
hard turning
tool vibration
magnetorheological (MR) damper
tool condition monitoring
acoustic emission
skewness
kurtosis
toczenie na twardo
drgania narzędzia
tłumik magnetoreologiczny (MR)
monitorowanie stanu narzędzia
emisja akustyczna
skośność
kurtoza
Opis:
In recent times, the concept of hard turning has gained awareness in metal cutting as it can apparently replace the traditional process cycle of turning, heat treating, and finish grinding for assembly of hard, wear-resistant steel parts. The major apprehension in hard turning is the tool vibration, which affects the surface finish of the work piece, has to be controlled and monitored. In order to control tool vibration in metal cutting, a magnetorheological fluid damper which has received great attention in suppressing tool vibration was developed and used. Also an attempt has been made in this study to monitor tool vibration using the skewness and kurtosis parameters of acoustic emission (AE) signal for the tool holder with and without magnetorheological damper. Cutting experiments were conducted to arrive at a set of operating parameters that can offer better damping characteristics to minimize tool vibration during turning of AISI4340 steel of 46 HRC using hard metal insert with sculptured rake face. From the results, it was observed that the presence of magnetorheological damper during hard turning reduces tool vibration and there exist a strong relationship between tool vibration and acoustic emission (AERMS) signals to monitor tool condition. This work provides momentous understanding on the usage of magnetorheological damper and AE sensor to control and monitor the tool condition during turning of hardened AISI4340 steel.
W ostatnich latach, w obróbce skrawaniem wzrasta zainteresowanie koncepcją toczenia na twardo, ponieważ może ono zastąpić tradycyjny proces toczenia, utwardzania i szlifowania stosowany przy wykonywaniu twardych, odpornych na zużycie cześci metalowych. Głównym problemem przy twardym toczeniu są wibracje narzędzia, które muszą być monitorowane i kontrolowane, gdyż wpływają na wykończenie powierzchni elementu obrabianego. W celu kontrolowania wibracji narzędzia przy obróbce skrawaniem autorzy zastosowali tłumik z płynem o właściwościach reologicznych sterowanych polem magnetycznym. Podjęto także próbę monitorowania wibracji na podstawie parametrów skośności i kurtozy sygnałów akustycznych (AE) emitowanych przez uchwyt narzędzia, mierzonych w warunkach bez tłumika i z tłumikiem magnetoreologicznym. Przeprowadzono szereg eksperymentów z toczeniem stali AISI4340 o twardości 46 HRC przy użyciu narzędzia z płytką z twardej stali, o geometrycznie kształtowanym ostrzu, firmy Taegu Tec. Otrzymano zbiór parametrów roboczych, wyznaczając na ich podstawie lepsze charakterystyki tłumienia i osiagając minimalizację wibracji narzędzia. Wyniki eksperymentów wskazują, że obecność tłumika magnetoreologicznego redukuje wibracje i że istnieje silna zależność miedzy wibracjami narzędzia i wartością skuteczną sygnału emisji akustycznych (AERMS). Praca przyczynia się do znacznie lepszego zrozumienia funkcji tłumika magnetoreologicznego i czujnika emisji akustycznych przy monitorowaniu stanu narzędzia przy toczeniu utwardzonej stali AISI4340.
Źródło:
Archive of Mechanical Engineering; 2015, LXII, 2; 237-255
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study on Path Interval and Two Tool Condition Parameters in Ball and Filleted End Milling
Autorzy:
Sekine, Tsutomu
Powiązania:
https://bibliotekanauki.pl/articles/2023326.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
path interval
filleted end mill
tool condition parameter
machining conditions
ball end mill
odstęp między ścieżkami
frez końcowy stożkowy
parametr stanu narzędzia
stan obróbki
frezy kulowe
Opis:
Milling is widely used as a versatile, highly automated manufacturing process. The selection of tool and its machining conditions is an ever-present challenge inherent in the process. To provide some findings contributable for the process planning, this study focuses on ball and filleted end milling. After the path interval determinations were explained briefly, the characteristics of each procedure were revealed with experimental verifications. The results of computational procedures showed good agreement with the experimental ones. Then, predominant frictional distance and cutting speed were proposed for effective selection of machining conditions. The discussion was given based on the results obtained from the demonstrations. Moreover, the relationships to path interval were elucidated through the discussion. In conclusion, several outcomes were organized from the experimental and numerical evidences.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 4; 311-320
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
Autorzy:
Zheng, Guoxiao
Sun, Weifang
Zhang, Hao
Zhou, Yuqing
Gao, Chen
Powiązania:
https://bibliotekanauki.pl/articles/2038054.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
tool wear condition monitoring
empirical mode decomposition
variational mode decomposition
fourier synchro squeezed transform
neighborhood component analysis
long short-term memory network
Opis:
Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 4; 612-618
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
New method for determining single cutting edge breakage of a multi-tooth milling tool based on acceleration measurements of an instrumented tool holder
Autorzy:
Ramsauer, Christoph
Bleicher, Friedrich
Powiązania:
https://bibliotekanauki.pl/articles/1428710.pdf
Data publikacji:
2021
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
condition monitoring
sensors integration
tool wear
algorithm
Opis:
In machining applications predominantly for automated machining cells, tool life is often not used to its full extend and cutting tools are exchanged prematurely to avoid tool breakage and thus machine downtime or even damage at work piece or machine. Both effective process monitoring and adequate process control require reliable data from sensors and derived indicators that enable meaningful evaluation. Acceleration measurement by the instrumented tool holder provides signals with high quality from close to the cutting zone. Using the monitoring system, the gained data of the instrumented tool holder can be analyzed especially for the use case of unexpected tool wear, chipping of the cutting edge or breakouts at end mills. This paper describes the data analysis based on the rotational sensor and the corresponding effects on the measurement, an advanced assessment of the spectral distribution in the frequency domain and the experimental results of a test series.
Źródło:
Journal of Machine Engineering; 2021, 21, 1; 67-77
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Disc tool profiling for air compressor screws with complex characteristic curves
Autorzy:
Nguyen, Tuan-Linh
Hoang, Long
Powiązania:
https://bibliotekanauki.pl/articles/1833775.pdf
Data publikacji:
2021
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
disk tool
meshing condition
undercutting
envelope
gearing
Opis:
One of the best practical methods to machine the air compressor screw pairs is milling or grinding by disc tools. The mathematics involved in disc tool profiling is quite complex and requires representing the machined surface by mathematical equations without any singular points and undercutting. From a technical perspective, this article proposes the disc tool profiling integrated solution for machining complex profile screws from their reverse engineering data, which contains crucial issues such as machining deviation evaluation due to undercutting, appropriate tool position setting. The disc tool surface designed by the proposed method is highly accurate, leading the RMS error of the machined surface is less than 0.0201 mm. A difference of the angle, formed by the disc tool axis and the workpiece axis, less than 0.16°, is acceptable in air compressor technology, which leads to a machined surface RMS deviation of less than 0.0186 mm.
Źródło:
Journal of Machine Engineering; 2021, 21, 3; 101--109
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Characterization of band sawing based on cutting forces
Autorzy:
Thaler, T.
Bric, I.
Bric, R.
Potocnik, P.
Muzic, P.
Govekar, E.
Powiązania:
https://bibliotekanauki.pl/articles/99509.pdf
Data publikacji:
2012
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
band sawing
cutting forces
condition monitoring
tool wear
chatter
Opis:
Band sawing is one of the most efficient methods for which in general it is known that uneven tool wear, chatter and cutting blade defects can affect cutting performance significantly. A data acquisition system was arranged on an industrial band saw machine in order to characterize the band sawing process based on measurements of forces. In this paper, the cutting force signals are analyzed in order to demonstrate important relations to workpiece and cutting blade properties. It is shown that cutting forces contain information about in homogeneity of a cut workpiece. Signals of cutting forces also reveal important properties of blade geometry that is related to uneven blade wear. Discontinuities such as blade welding are clearly evident in force signals and it is shown that unevenness of blade backing geometry can cause a significant variation in forces due to wedging between the workpiece and a blade support. An original method for blade shape extraction from force signals is presented in detail. Paper also reports on chatter phenomena observed at specific cutting conditions. Possible solutions to the addressed problems and phenomena are discussed in the conclusion.
Źródło:
Journal of Machine Engineering; 2012, 12, 1; 41-54
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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