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1 |
A critical review for machining positioning based on computer vision
مروری انتقادی برای مکان یابی ماشینکاری بر اساس بینایی ماشین-2021 With the rapid development of science and technology, the manufacturing industry has to cope with increasingly stricter requirements in terms of the quality of processed products. To improve production flexibility and automation, computer vision is widely used in machining due to its safety, reliability, continuity, high accuracy, and real-time performance. In this study, a comprehensive review of positioning methods for workpieces in machining is presented from the perspective of computer vision technology. First, the key technologies in image acquisition are described in detail, and a analysis of different lighting modes is conducted. Second, image preprocessing is described by summarizing enhancement and image segmentation methods. Third, from the perspectives of accuracy and speed, feature extraction methods are compared and evaluated. Next, the existing applications of visual positioning technology in machining are discussed. Finally, the existing problems are summarized, and future research directions technology suggested. Keywords: Visual positioning | Positioning processing | Optical system | Image preprocessing | Feature extraction |
مقاله انگلیسی |
2 |
A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective
یک سایه دیجیتال مبتنی بر دانش برای صنعت ماشینکاری در یک چشم انداز دیجیتال دوتایی-2021 This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision
of Digital Shadow (DS) which would be considered as the core component of a future Digital Twin. Knowledge
generated by experts and artificial intelligence, is transformed into formal business rules and integrated into the
DS to enable the characterization of the real behavior of the physical system throughout its operation stage. This
behavior model is continuously enriched by direct or derived learning, in order to improve the digital twin. The
proposed DS relies on data analytics (based on unsupervised learning) and on a knowledge inference engine. It
enables the incidents to be detected and it is also able to decipher its operational context. An example of this
application in the aeronautic machining industry is provided to stress both the feasibility of the proposition and
its potential impact on shop floor performance. keywords: سایه دیجیتال | دوقلو | داده ها و مدیریت دانش | ماشینکاری | Digital shadow | Digital twin | Data and knowledge management | Machining |
مقاله انگلیسی |
3 |
Analyzing the effects of machining parameters on surface roughness of machined surfaces using vision system
تجزیه و تحلیل اثرات پارامترهای ماشینکاری بر زبری سطح سطوح ماشینکاری با استفاده از سیستم بینایی-2021 Surface roughness measurement of machined components helps in predicting the success or failure of component when it is put into service. Surface roughness measurement is also used to judge capability of manufacturing process. Surface roughness evaluation is also used for monitoring the condition of cutting tool or machine tool. Any change in process parameter or condition of the cutting tool results in changes in surface geometry and texture. Usually, the factors affecting the machining capability are its own inherent properties such as, feed, speed, depth of cut, tools, coolant flow rate etc. Relationship between stylus measured roughness and vision surface roughness (Ra) has been studied. As for as the knowledge of authors no research work has been reported about determining the effects of cutting parameters on surface roughness of milled components. In this context, the present research works assumes special significance. An attempt has been made in the current research work to explore the effect of cutting parameters on surface roughness of milled surfaces using vision system.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Surface roughness | Vision system | Machine tool | Cutting tool | Machining parameters |
مقاله انگلیسی |
4 |
Electrical discharge machining of oxide and nitride ceramics: A review
ماشینکاری تخلیه الکتریکی سرامیک های اکسید و نیترید: مرور-2021 Ceramics are widespread materials for engineering applications due to their properties, such as low wear
resistance and coefficient of thermal expansion. The high hardness and brittleness of ceramics reveal
their difficulties in traditional machining. Electrical discharge machining is induced to be a method for
machining non-conductive ceramics in the present review. The ability to be subjected to electrical discharge machining depends on their electrical properties. Some techniques allow subjecting to electrical
discharge machining insulating materials where impulses are addressed to assisting films or particles.
However, the most promising cutting ceramics’ machining results stayed under expectations, with a
maximum depth of 1651 um. The study is devoted to the systematization of the obtained during the last
decades’ results, analyses of the chemical content of the formed erosion products using assisting electrodes made of Copper subgroup, elements of the second-third period, d-elements in hydrocarbon and
water medium based on the theory of structure and transformation of substances for Al2O3, AlN,
SiAlON, Si3N4, ZrO2, development of the alternative perspectives and unique cross-disciplinary approach
that is critical for transitioning to the sixth technological paradigm associated with the Kondratieff waves.
Keywords: Alumina | Electrical discharge machining | Oxide | Nitride | SiAlON | Zirconia |
مقاله انگلیسی |
5 |
Parametric optimization of electrical discharge machining on AI-TiC composites using Grey relational analysis
بهینه سازی پارامتری تخلیه الکتریکی ماشینکاری در کامپوزیت های AI-TiC با استفاده از تجزیه و تحلیل رابطه خاکستری-2020 The Al-TiC sintered nano composites which are difficult to machining by conventional techniques can be
easily machined with Electrical Discharge Machining (EDM). Machining parameters (Current, pulse-on
time, gap voltage), output parameters [Metal Removal Rate (MRR) & Tool Wear Rate (TWR)] were taken
into consideration & were optimized by Grey relational analysis. Analysis of Variance (ANOVA) is used to
determine the most contributing parameters towards the responses. It exhibits that the performance
characteristics of the EDM process (MRR & TWR) were improved by using the techniques proposed in this
study. The confirmation test was used to evaluate the effectiveness. In a nutshell, the developmental
model has good adequacy, which can be used to obtain the future value with minimum error which
can be confirmed through validation and also find out the craters evolution on EDM process by
Scanning Electron Microscope (SEM) analysis.
2020 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Conference
on Newer Trends and Innovation in Mechanical Engineering: Materials Science. Keywords: ANOVA | EDM | Grey | MRR | TWR |
مقاله انگلیسی |
6 |
Bibliometric analysis of artificial neural network applications in materials and engineering
تجزیه و تحلیل کتابشناسی برنامه های کاربردی شبکه عصبی مصنوعی در مواد و مهندسی-2020 Manufacturing industries in world are under pressure to adopt new technologies in order to sustain their
market reputation and increase their performance. ANN is the widely used approach using in the manufacturing
industries in various machining processes which results in improving the performance of
manufacturing industries. There is need to identify the relationship in cutting parameters in various
machining processes which results in the improving the quality and productivity. These relationships
can be identified with various mathematical modelling techniques in which ANN is widely used at present
time. An Artificial neural network (ANN) is the collection of nodes called artificial neurons, which is
modelled according to neurons in biological brain. ANN approach is used because it has an ability to learn,
can be used to model complex patterns and prediction problems. Here, Scopus database is used for collection
of data with the keywords ‘‘Artificial neural network” and also ‘‘Artificial Neural Network and
machining” is used to determine the trend on machining area, based on collected data bibliometric analysis
has been performed. This analysis is used to determine the popularity, impact of publications and use
of ANN on machining of different materials. This method is to explore the impact of ANN, the impact of
different research areas. A thorough study of statistics of ANN publications by years, research areas, document
types, countries, source titles and authors are conducted in this paper. This paper is for research
evaluation only. Keywords: Bibliometric analysis | ANN | Statistics | Materials | Machining |
مقاله انگلیسی |
7 |
Deep learning enabled cutting tool selection for special-shaped machining features of complex products
یادگیری عمیق انتخاب ابزار برش را برای ویژگی های ماشینکاری خاص محصولات پیچیده امکان پذیر می کند-2019 Each complex product contains many special-shaped machining features required to be machined by the specific
customized cutting tools. In this context, we propose a deep learning based cutting tool selection approach,
which contributes to make it effective and efficiency for and also improves the intelligence of the process of
cutting tool selection for special-shaped machining features of complex products. In this approach, one-to-one
correspondence between each special-shaped machining feature and each cutting tool is first analyzed and established.
Then, the problem of cutting tool selection could be transformed into a feature recognition problem.
To this end, each special-shaped machining feature is represented by its multiple drawing views that contain rich
information for differentiating each of these features. With numbers of these views as training set, a deep residual
network (ResNet) is trained successfully for feature recognition, where the recognized features cutting
tool could also be automatically selected based on the one-to-one correspondence. With the learned ResNet,
engineers could use an engineering drawing to select cutting tools intelligently. Finally, the proposed approach is
applied to the special-shaped machining features of a vortex shell workpiece to demonstrate its feasibility. The
presented approach provides a valuable insight into the intelligent cutting tool selection for special-shaped
machining features of complex products. Keywords: Cutting tool selection | Special-shaped machining features | Complex products | Residual networks | Deep learning |
مقاله انگلیسی |
8 |
Nanoscale measurement with pattern recognition of an ultra-precision diamond machined polar microstructure
اندازه گیری نانو با تشخیص الگوی ریزساختار قطبی ماشینکاری شده با الماس بسیار دقیق-2019 Due to the low resolution of pattern recognition and disorganized textures of the surfaces of most natural objects
observed under a microscope, computer vision technology has not been widely applied in precision positioning
measurement on machine tools, which needs high resolution and accuracy. This paper presents a systematic
method to solve the surface recognition problem which makes use of ultra-precision diamond machining to
produce a functional and polar-coordinate surface named ‘polar microstructure’. The unique characteristic of a
polar microstructure is the distinctive pattern of any locations including rotation in the global surface which
provides the feasibility of achieving precise absolute positions by matching the patterns by utilizing computer
vision technology. A polar microstructure which possesses orientation characteristics is also able to measure the
displacement of rotation angle. A series of simulation experiments including feature point extraction, orientation
detection as well as resolution of pattern recognition was conducted, and the results show that a polar microstructure
can achieve a resolution of 9.35 nm which is capable of providing a novel computer vision-based
nanometric precision measurement method which can be applied in positioning on machine tools in the future. Keywords: Polar microstructure | Computer vision | Ultra-precision machining | Nanoscale measurement | Pattern recognition |
مقاله انگلیسی |
9 |
Cyber Physical System and Big Data enabled energy efficient machining optimisation
سیستم فیزیکی سایبری و بهینه سازی ماشین انرژی توانای داده های بزرگ-2018 Due to increasingly customised manufacturing, unpredictable ambient working conditions in shop floors
and stricter requirements on sustainability, it is challenging to achieve energy efficient optimisation for
machining processes. This paper presents a novel Cyber Physical System (CPS) and Big Data enabled
machining optimisation system to address the above challenge. The innovations and characteristics of
the system include the following four aspects: (1) a novel process of “scheduling, monitoring/learning,
rescheduling” is designed to enhance system adaptability during manufacturing lifecycles; (2) an
innovative energy model to support energy efficient optimisation over manufacturing lifecycles is
developed. The energy model, which is enabled by CPS, Big Data analytics and intelligent learning al
gorithms, considers dynamic and aging conditions of machine tool systems during manufacturing life
cycles; (3) an effective evolutional algorithm based on Fruit Fly Optimisation (FFO), is applied to generate
an adaptive energy efficient schedule, and improve schedule when there are significantly varying
working conditions and adjustments on the schedule are necessary (that is rescheduling); (4) the system
has been successfully deployed into European machining companies to verify capabilities. According to
the results, around 40% energy saving and 30% productivity improvement have been achieved in the
companies. A practical case study presented in this paper demonstrates the effectiveness and great
potential of applicability of the system in practice.
Keywords: Cyber physical system ، Big data ، Energy efficient machining ، Scheduling optimisation |
مقاله انگلیسی |
10 |
A research on the cutting database system based on machining features and TOPSIS
تحقیق بر روی سیستم پایگاه داده برش بر اساس ویژگی های ماشینکاری و TOPSIS-2017 Cutting parameters play a significant role in machining processes. The traditional cutting database
usually neither include all information about part machining nor provide the best alternative of cutting
parameters automatically when several alternatives meet the requirements for retrieval. The paper
presents a cutting database system based on machining features and the Technique for Order of Pre
ference by Similarity to Ideal Solution (TOPSIS) for selecting the best alternative of cutting parameters.
Following the object-oriented idea, machining features are organized by part feature, geometric in
formation, material information, precision information and manufacturing resources information, which
is very convenient for the database to store and manage the necessary machining information. The
multiple criteria decision making matrix D is constructed by spindle speed, feed rate, cutting depth and
cutting width. And the best alternative of cutting parameters is selected according to the closeness
coefficient by TOPSIS. In addition, a prototype system based on Web browsing mode has been developed.
Finally, an example is used to validate that the proposed system is feasible and effective.
Keywords: Machining features | TOPSIS | Cutting parameters | Panel part | Database system |
مقاله انگلیسی |