Modelling and controlling variation propagation in mechanical assembly of high speed rotating machines

Assembly plays a vital role in the quality of a final product and has a great impact on the manufacturing cost. The mechanical assemblies consist of parts that inevitably have variations from their ideal dimensions. These variations propagate and accumulate as parts are assembled together. Excessive amount of variations in an assembly may cause improper functionality of the product being assembled. Improving assembly quality and reducing the assembly time and cost are the main objectives of this thesis. The quality of an assembly is determined in terms of variations in critical assembly dimensions, also known as Key Characteristics (KCs). Key Characteristics are designated to indicate where excess variation will affect product quality and what product features and tolerances require special attention. In order to improve assembly quality and reduce assembly time and cost, it is necessary to: (1) model non-ideal parts based on tolerances defined in design standards or current industrial practice of component inspection, (2) model assemblies and their associated assembly processes to analyse tolerance stack-up in the assembly, (3) develop probabilistic model to predict assembly variation after product assembly, and (4) implement control strategies for minimising assembly variation propagations to find optimum configuration of the assembly. Two assembly models have been developed, a linear model and a fully non-linear model for calculating assembly variation propagations. The assembly models presented in this thesis also allows for inclusion of geometric feature variation of each assembly component. Methods of incorporating geometric feature variations into an assembly variation model are described and analysis techniques are explained. The assembly variation model and the geometric variation models have been developed for 20 and 3D assemblies. Modelling techniques for incorporating process and measurement noise are also developed and described for the nonlinear assembly model and results are given to demonstrate the calculation of assembly variations while considering part, process and measurement errors. Two assembly case studies originating in sub-assemblies of aero-engines have been studied: Case Study 1, representing the rotating part (rotor) of an aero-engine, and Case Study 2, representing non-rotating part (stator) of an aero-engine. A probabilistic method based on the linear model is presented as a general analytical method for analysis of 3D mechanical assemblies. Probability density functions are derived for assembly position errors to analyse a general mechanical assembly, and separate probability functions are derived for the Key Characteristics (KCs) for assembly in Case Studies 1 and 2. The derived probability functions are validated by using the Monte Carlo simulation method based on the exact (full non-linear) model. Results showed that the proposed probabilistic method of estimating tolerance accumulation in mechanical assemblies is very efficient and accurate when compared to the Monte Carlo simulation method, particularly if large variations at the tails of the distributions are considered. Separate control strategies have been implemented for each case study. Four methods are proposed to minimise assembly variations for Case Study 1, and one error minimisation method is suggested for assemblies of Case Study 2. Based on the developed methods to optimise assembly quality, the two case studies were investigated, and it was found that the proposed optimisation methods can significantly improve assembly quality. The developed optimisation methods do not require any special tooling (such as fixtures) and can easily be implemented in practice

Aerial identification of flashed over faulty insulator using binary image classification

Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%

Comparison of the Restoration of Breast Feeding after Percutaneous Aspiration vs Incision and Drainage for Management of Breast Abscess

BACKGROUND: Mastitis is not uncommon among lactating woman. This is caused by local inflammation and/or infection of terminal lactiferous ducts. Incision and drainage followed by postoperative antibiotics is the treatment of choice for such abscesses depending on culture and sensitivity. Ultrasound guided needle aspiration is also effective for breast abscesses. OBJECTIVE: To compare the outcome of percutaneous aspiration with incision drainage for management of breast abscess among lactating mothers. METHODOLOGY: This comparative study was carried out at Jinnah Postgraduate Medical Centre, Karachi, Pakistan from August 2015 to August 2016. A total of 90 women with breast abscess were included according to inclusion and exclusion criteria. These women were randomly allocated into two groups. Forty-five in group A, treated with percutaneous aspiration, and 45 patients in group B treated with incision and drainage for breast abscess. Percutaneous aspiration was done after radiological estimation of size, site and shape. After the intervention, all the patients were observed for the outcome parameter i.e. early restoration of breast feeding, as if the mother starts feeding the baby within 24 hours after the procedure All the information was collected on a specially designed Performa. RESULTS: Rate of early restoration of breast feeding was significantly high in group A (percutaneous aspiration) as compared to group B (drainage for breast abscess) [82.2% vs. 57.8%); 0.011]. CONCLUSION: We conclude that percutaneous aspiration of breast abscess is a minimally invasive, simple outpatient procedure without the need of general anesthesia with superior cosmetic results and high satisfaction rate

Protocol optimization for deoxyribonucleic acid (DNA) extraction from dried, fresh leaves, and seeds of groundnut (Arachis hypogaea L.)

Consistent isolation of best quality deoxyribonucleic acid (DNA) from peanut (Arachis hypogaea L.) is particularly problematic due to the presence of phenolic compounds and polysaccharides. Inconsistencies in extraction results can be attributed to the age and growth stages of the plant material analyzed. Mature leaves have higher quantities of polyphenols, tannins and polysaccharides that can contaminate DNA during isolation. In this study, we used fresh and dried leaves as well as seeds for optimization of high quality DNA isolation protocols from A. hypogaea. The DNA extracted with three different methods cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and cesium chloride (CsCl) density gradient) were comparatively studied by polymerase chain reaction (PCR) analysis in terms of quantity and quality. High quality genomic DNA was obtained from fresh leaves by modified CTAB methods. The DNA obtained ranged from 1 to 2.5 ng/μl. DNA obtained by this method was strong and reliable showing its compatibility for simple sequence repeat (SSR) analyses. The SDS based methodology give large quantities of DNA contaminated with polysaccharides. Fresh leaves also gave best result in SDS method. The quantity and quality of DNA obtained was very poor in all the tested methods in case of dried leaf tissues. The current protocol will probably be useful for the extraction of high-molecular weight DNA from other plant materials containing large amounts of secondary metabolites and essential oils.Key words: Polysaccharides, polyphenols, tannins, cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), cesium chloride (CsCl), secondary metabolites, SSR

Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy

Abstract: Artificial intelligence and industrial internet of things (IIoT) have been rejuvenating the fault diagnosis systems in Industry 4.0 for avoiding major financial losses caused by faults in rotating machines. Meanwhile, the diagnostic systems are provided with a number of sensory inputs that introduce variations in input space which causes difficulty for the algorithms in edge devices. This issue is generally dealt with bi-view cross-domain learning approach. We propose a soft real-time fault diagnosis system for edge devices using domain adaptation training strategy. The investigation is carried out using deep learning models that can learn representations irrespective of input dimensions. A comparative analysis is performed on a publicly available dataset to evaluate the efficacy of the proposed approach which achieved accuracy of 88.08%. The experimental results show that our method using long short-term memory network achieves the best results for the bearing fault detection in an IIoT environmental setting. © 2021 Elsevier Inc. All rights reserve

Essential oil and leaves from Lantana camara significantly ameliorate different cancer cell lines by suppressing the NF-κB pathway

The anticancer and anti-inflammatory activities of wild-sage (Lantana camara) leaves essential oil were evaluated against various human cell lines. In this regard, steam hydro-distillation technique was used for oil extraction. Chemical characterization of L. camara essential oil (LCEO) was done by GC-MS, and forty one chemical components (98.69%) were detected in leaves of LCEO. The major constituents were caryophyllene oxide (19.63%), caryophyllene (6.68%), β-phellandrene (6.48%), humulene epoxide II (5.68%), spathulenol (4.17%), α-pinene (3.27%), eucalyptol (3.09%), α-cyclocitral (2.89%), β-pinene (2.38%), and nerolidol II (2.33%). Moreover, the antioxidant and antimicrobial potential of LCEO were also examined. LCEO also showed good anti-inflammatory activity. Interestingly, it was found that the L. camara essential oil was active against a set of microbial strains along with promising antioxidant activity. The MTT assay showed that LCEO possessed good anticancer potential against U-266, A-549, HCT-116, SCC-4, MiaPaCa 2, and KBM-5 cancer cell lines. In these cell lines, it was depicted that the LCEO blocked the tumor cell proliferation via NF-κB pathway suppression. The promising and potent bioactivities of LCEO support the candidature of these essential oils as an anticancer agent

Modelling and controlling variation propagation in mechanical assembly of high speed rotating machines

Assembly plays a vital role in the quality of a final product and has a great impact on the manufacturing cost. The mechanical assemblies consist of parts that inevitably have variations from their ideal dimensions. These variations propagate and accumulate as parts are assembled together. Excessive amount of variations in an assembly may cause improper functionality of the product being assembled. Improving assembly quality and reducing the assembly time and cost are the main objectives of this thesis. The quality of an assembly is determined in terms of variations in critical assembly dimensions, also known as Key Characteristics (KCs). Key Characteristics are designated to indicate where excess variation will affect product quality and what product features and tolerances require special attention. In order to improve assembly quality and reduce assembly time and cost, it is necessary to: (1) model non-ideal parts based on tolerances defined in design standards or current industrial practice of component inspection, (2) model assemblies and their associated assembly processes to analyse tolerance stack-up in the assembly, (3) develop probabilistic model to predict assembly variation after product assembly, and (4) implement control strategies for minimising assembly variation propagations to find optimum configuration of the assembly. Two assembly models have been developed, a linear model and a fully non-linear model for calculating assembly variation propagations. The assembly models presented in this thesis also allows for inclusion of geometric feature variation of each assembly component. Methods of incorporating geometric feature variations into an assembly variation model are described and analysis techniques are explained. The assembly variation model and the geometric variation models have been developed for 20 and 3D assemblies. Modelling techniques for incorporating Modelling and Controlling Variation Propagation in Mechanical Assemblies process and measurement noise are also developed and described for the non- linear assembly model and results are given to demonstrate the calculation of assembly variations while considering part, process and measurement errors. Two assembly case studies originating in sub-assemblies of aero-engines have been studied: Case Study 1, representing the rotating part (rotor) of an aero-engine, and Case Study 2, representing non-rotating part (stator) of an aero-engine. A probabilistic method based on the linear model is presented as a general analytical method for analysis of 3D mechanical assemblies. Probability density functions are derived for assembly position errors to analyse a general mechanical assembly, and separate probability functions are derived for the Key Characteristics (KCs) for assembly in Case Studies 1 and 2. The derived probability functions are validated by using the Monte Carlo simulation method based on the exact (fully non-linear) model. Results showed that the proposed probabilistic method of estimating tolerance accumulation in mechanical assemblies is very efficient and accurate when compared to the Monte Carlo simulation method, particularly if large variations at the tails of the distributions are considered. Separate control strategies have been implemented for each case study. Four methods are proposed to minimise assembly variations for Case Study 1, and one error minimisation method is suggested for assemblies of Case Study 2. Based on the developed methods to optimise assembly quality, the two case studies were investigated, and it was found that the proposed optimisation methods can significantly improve assembly quality. The developed optimisation methods do not require any special tooling (such as fixtures) and can easily be implemented in practice.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

حاجی لق لق ۔پاک فوج کا ایک درخشندہ ادبی ستارہ

Pakistan army is not only the defender of the boundries but in the field of literature, it has its own recognition also. The rankers of infantory, navy and airforce has sought help of pen for their creative thoughts. In any form of literature, our defender's contributions are not less than others. In this connection, a well known name is Haji Laq Laq (Bao Ata Muhammad Chishti). He retired from army as a captain. In the age of sixty four he died, but he left the legacy of poetry, prose and humour, which is quite enough to keep his name alive. Haji Laq Laq wrote fifteen books. Besides this, he served as a journalist in different newspapers and magazines. He earned name and fame. His contemporaries also appreciated his creative qualities. The titles of his books are very attractive. His work consists of every field of literature. The language used in the books is very simple and interesting