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Self-assessment model for digital retrofitting of legacy manufacturing systems in the context of industry 4.0
Get PDFIn the current competitive landscape, digital retrofitting of legacy manufacturing systems is crucial for maintaining a competitive edge. Digital retrofitting involves adapting existing systems to modern technologies to enhance efficiency and capabilities. To select the most appropriate retrofitting approach, an assessment model is required due to the diversity of legacy systems and the varying readiness levels of organizations. In this study, a comprehensive assessment model is designed for Small and Medium Manufacturing Enterprises (SMMEs) to facilitate digital retrofitting strategies. The model starts with a questionnaire to evaluate existing systems, followed by a classification of maturity levels, and then provides tailored recommendations, aiming to guide SMMEs in successfully integrating Industry 4.0 technologies. The methodology includes a literature review and surveys, which were used to develop, test, and refine the model. The model assesses 21 items across four dimensions, Strategy and Organization, Development of the Workforce, Smart Factory, and Smart Process, using a five-point scale. Furthermore, the model introduces a four-level maturity classification for
digital transformation in manufacturing and offers customized recommendations. The validation through the surveys involves content validity testing with 18 industry practitioners and a pilot study with a sample of 32 SMMEs.International Journal of Industrial Engineering and Managemen
Analytical modelling for prediction and prevention of overflow occurrence in wire-based additive manufacturing
Get PDFIncreasing deposition rate is essential for higher productivity of additive manufacturing (AM). However, a high deposition rate usually requires high heat input to fully melt the fast-fed material, which could lead to defects due to molten material overflow. This paper presents a thermo-capillary-gravity model for predicting the overflow occurrence based on the analytically calculated reciprocal Bond number, 1/Bo. Comprehensive experiments show that when the 1/Bo is no greater than 0.74, or the bead height is no less than 1.16 times the capillary length, overflow is highly likely to occur. Two different steel wire-based AM processes were employed to validate the model, demonstrating an overall accuracy of 84%-93%. It is found that both energy and material inputs per unit length significantly affect the molten material overflow, and hence they can be adjusted to prevent overflow. The validated analytical modelling approach enables efficient prediction and control of overflow for a high deposition rate wire-based AM process.This work was supported by Engineering and Physical Sciences Research Council: [Grant Number EP/R027218/1].Virtual and Physical Prototypin
Developing a scheduling framework for line maintenance service checks in airline maintenance, repair and overhaul
No full textIn today's aviation industry, aircraft must undergo regular maintenance checks,
such as transit/ramp-checks, A-checks (line maintenance) and C/D-checks
(heavy maintenance), in order for Maintenance Repair and Overhaul (MRO)
operators to meet regulatory requirements. These maintenance checks can be
planned or unplanned such when an accident occurs, with planned maintenance
scheduled according to an aircraft’s flight hours, or flight cycles, or by the
calendar. In order for airline MRO (AMRO), and MRO company operators to
generate long-term accurate and achievable maintenance schedules which
ensure the safety and reliability of aircraft, it is essential that aircraft maintenance
is conducted on time and according to relevant regulatory requirements. Effective
and efficient scheduling of aircraft maintenance will maximise both the time an
aircraft can remain in the air and customer satisfaction.
This thesis introduces a novel framework for the scheduling of aircraft
maintenance checks, combining mathematical modelling and computer
simulation subject to both (i) known constraints such as not permitting the
performance of certain tasks simultaneously to avoid adversely affecting safety
requirements and avoiding causing injury to personnel, and (ii) maintenance
resources such as tools are unexpectedly unavailable. Real data from
commercial MROs was used for validation and comparisons; a UK AMRO and a
Libyan MRO. Both model and simulated processes and results were subjected to
review by five experts in the field, who generally agreed that within its constraints
the predictions/simulations were in good agreement with what the experts had
found in practice.
The scheduling-framework encompasses mathematical and simulation models
(in a unified framework). The proposed unified scheduling-framework combining
the mathematical and simulation models offers a comprehensive view of the list
of tasks to be undertaken and the relationships between them. This approach
takes into account the time and efficiency gains that can be achieved when the
order of tasks is rearranged, which has traditionally been overlooked in the
literature. The model also highlights the benefits of rescheduling in terms of the
time needed to complete an activity. Moreover, the simulation model can account
for the effect of missing tools, thus providing a clear illustration of the correlation
between the number of missing tools and the additional time required to complete
the project. Consequently, this approach allows airline MRO operators to
evaluate and optimize their project scheduling, thus saving time and money for
their organization, while increasing customer satisfaction.PhD in Manufacturin
Optimised obstacle detection and avoidance model for autonomous vehicle navigation
Get PDFDriven by cutting-edge research in AI vision, sensor fusion and autonomous
systems, intelligent robotics is poised to revolutionise Aviation hangars, shaping
the "hangars of the future" by reducing inspection time and improving defect
detection accuracy. Many hangar environments, especially in maintenance,
repair and overhaul (MRO) operations, rely on manual processes and algorithms
that need to be optimised for the increasing complexity of these settings. These
include varied obstacle structures, often low-light conditions, and frequent
changes in the scene.
The application of mobile robot solutions demands enhanced perception,
accurate obstacle avoidance, and efficient path planning, essential for effective
navigation in the busy hangar environment and aircraft inspections. The
application of ROS navigation stack has been at the center of most solutions and
is mostly efficient in static settings while limited in complex environments. These
systems are often computationally intensive and require pre-configuration of
environmental parameters, making them less efficient in changing environments
with real-time demand. Deep learning models and ROS integration have shown
promising improvements, leveraging experiential learning and large datasets.
However, accurately detecting obstacles of different shapes and sizes, especially
in varying lighting conditions, poses a significant challenge and affects safe
navigation.
To overcome these challenges in complex environments, this research proposes
a novel solution for enhanced obstacle detection, avoidance and path planning.
Our system leverages LiDAR and camera data fusion with a real-time and
accurate YOLOv7/YOLOv5 object detection model for robust identification of
diverse obstacles. Additionally, we proposed a combination with ROS planners,
including Dijkstra, RRT and DWA, for path planning optimisation to enable
collision-free navigation. The system was validated with ROS Gazebo and real-
Turtlebot3 robot. It achieved zero collisions with YOLOv7 and RRT integration, a
2.7% increase in obstacle detection accuracy, and an estimated 2.4% faster
navigation speed than the baseline methods.PhD in Transport System
Application of fibre lasers in fabrications and processing of thin gauge alloys for engineering applications
Get PDFMicro-joining of thin metallic sheets has been growing due to the product weight
reduction. Several methods are used to join aluminium and iron-based alloys, but
most are limited on the workpiece dimensions, processing time and joint strength.
Laser welding was selected as the joining tool for this study as a non-contact,
productive and highly flexible process in spatial and temporal resolution of energy
application for medical, automotive and aerospace applications.
The digital control of the latest multi-pulse pulsed-wave (MPPW) fibre lasers
allows different spatial and temporal resolutions to apply low pulse energy at a
high repetition rate and narrow pulse width with high precision. However, it isn't
easy to control each parameter's effect on the weld profile without understanding
the underpinning science of the laser-material interaction. This study aims to
predict the material response and establish a relationship between the total
applied energy over a spot, the pulse energy, average peak power and pulse
duration. The fundamental laser-material interaction parameters (FLMIP), which
have proven to characterise the process in continuous-wave (CW) laser welding,
have also been investigated in MPPW seam welding. The performance of MPPW
and CW laser modes was compared under like-to-like conditions to correlate
penetration and melting efficiency, productivity, joining flexibility and defects
generation in the similar and dissimilar joining of 5251 H22 aluminium alloy and
304L austenitic stainless steel. In addition to this, an empirical model was applied
in both laser modes to achieve a specific weld profile independent of the beam
diameter. In MPPW mode, the weld pool profile could be correlated to the power
density and interaction time considering the inter-pulse thermal losses. CW
processing was revealed to have better flexibility to control the weld shape and
joint strength, higher melting efficiency and productivity when compared to
MPPW processing.PhD in Manufacturin
Effect of different shielding conditions, thermal cycles and post- deposition treatments on melting behaviour and mechanical properties of additively built components
Get PDFAdditive manufacturing (AM) offers many advantages as compared to traditional
manufacturing routes such as machining and forging thanks to its capability of
reducing lead times, enhanced design flexibility and material saving. However,
many challenges still must be overcome before this relatively novel technology
can be implemented in the production of critical components. It is known that to
achieve satisfactory performance of additively built parts it is important to ensure
the absence of volumetric defects such as pores and the presence of a suitable
microstructure that will offer the required mechanical properties. Many process
variables such as shielding gas composition, thermal histories and post-
deposition heat treatments can control these aspects. This work, focuses on the
role of shielding gas composition on melting behaviour during laser powder bed
fusion and on the microstructural evolution of stainless steel and the effect of
different thermal cycles on two age hardenable alloys during Wire and Arc
Additive Manufacture deposition. The objective of this thesis was to investigate
how critical these variables can be in achieving the desired properties of 3D-
printed parts for specific processes and alloys. The material interaction with the
AM heat source is a complex phenomenon and for this reason, a wide range of
advanced characterisation techniques were used in this work including high-
speed imaging, scanning electron microscopy, chemical analysis, fractography,
electron back-scattered diffraction, among others. It was possible to conclude
that shielding gas composition is key to ensuring stability during laser powder
melting of stainless steel. Additionally, the sensitivity of the microstructural
features to different thermal cycles inherent to the Wire and Arc Additive
Manufacture (WAAM) process was established for two age-hardenable alloys,
17-4PH (martensitic stainless steel) and Ti-5553 (near β titanium alloy). The
effectiveness of standard post-deposition heat treatments to optimise the final
mechanical properties of these two alloys was also identified. Finally, it was also
possible to find how sensitive the developed microstructure of WAAM Ti-6Al-4V
is to different levels of interstitial elements concentration. This is of great use for
further applications as the incorporation of oxygen and its potential adverse effect
on mechanical performance remains one of the main concerns for the processing
of titanium alloys.PhD in Manufacturin
Machine learning-driven sensor array based on luminescent metal–organic frameworks for simultaneous discrimination of multiple anions
Get PDFDue to the high correlation of anions in waters to environmental quality and human health, thus there is urgent need for developing simple and effective sensors to discriminate multiple anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent metal–organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for simultaneous discrimination of five anions (F−, PO43−, ClO44−, NO3−, and SO42−). Wherein, UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green fluorescence emission, possessing good fluorescence property. Interestingly, the anions could effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate diverse fluorescence responses and unique fingerprints, which could be utilized to develop a fluorescence sensor array for the rapid identification of five anions. Under the optimized conditions, the proposed sensor array showed good performance for identifying multiple anions and their mixtures with satisfactory sensitivity. More importantly, the integration of machine learning algorithm and sensor array has successfully achieved accurate identification and prediction of five anions in real water samples, affirming its practicability in actual samples. Our findings provided a promising tool for detecting multiple anions, and inspired potentials of the combination of sensor arrays and machine learning algorithm for pollution control in real waters.This work was supported by the National Natural Science Foundation of China (Grants No. 22176075, 22406068), Natural Science Foundation of Jiangsu Province (BK20240884).Chemical Engineering Journa
Safe faecal sludge emptying and transport: compliance challenges and models for a public good
Get PDFIn the 81 countries where most urban dwellers rely on faecal sludge (FS) emptying and transport, services are frequently provided by a heterogeneous private sector. Considering the responses of service providers is essential to ensuring that the regulatory frameworks put into place achieve their intended outcomes and safeguard public and environmental health. Combining a literature review and expert practitioner input, we identify priority challenges for scaling safe FS emptying and transport (E&T) services and use these to adapt a holistic model of business compliance. We confirm well-documented challenges such as cost structures for compliance with regulation, the perception of services as low status, and an inadequate enabling environment. We identify the importance of trust in building voluntary compliance as a novel issue for sanitation but widely discussed in the regulation literature. We also identify a distinct role for the regulator as a catalyst for change. The role of disgust as a policy barrier and the application of behavioural theory to building compliance are areas warranting further research. This is the first paper to explicitly consider the regulation of FS E&T through a compliance lens, linking established areas of the regulation literature to new findings in urban sanitation.This research was funded by the UKRI Engineering and Physical Sciences Research Council (EPSRC grant number EP/S022066/1) through the Center for Doctoral Training in Water and Waste Infrastructure and Ser vices Engineered for Resilience (WaterWISER)H2Open Journa
Age estimation using CT images of the pubic symphysis of Lebanese living individuals
Get PDFWhile the Suchey-Brooks method for age estimation is generally accepted in forensic anthropology, its accuracy varies among different populations. This retrospective cross-sectional study aims to test the reliability of the Suchey-Brooks method using Computed Tomography (CT) scans of pubic symphyses of 155 Lebanese living individuals (76 males and 79 females) aged 17 to 98 years. This study reveals that 94.9 % of the sampled individuals fell within the range of 2 standard deviations from the reference mean for predicted age. Additionally, the study assesses phase assignation, overall bias of 1.29, and overall inaccuracy of 8.09, along with strong intra and inter-observer reliability with weighted Cohen’s Kappa (k) 0.901 and 0.82, respectively. Transition analysis was also used to generate new Lebanese age references. The new reference proposed in this study improves the accuracy of age-at-death estimation compared to the Suchey-Brooks method when applied to the Lebanese population.The study presented was supported by the Wenner-Gren Foundation under the Wadsworth International Fellowship, Gr. WIF-286.Legal Medicin
Influence of dynamic load and temperature on guided wave ultrasonic damage detection in thin plates
Get PDFStarr, Andrew - Associate SupervisorLong-thin metallic materials are essentially used in constructing structures of high
economic importance, but their service life is shortened by damage such as
cracks, corrosion, cavities, notches, and dents. Damage is an inevitable condition
of metallic structures over time and, when not detected, could result in a
catastrophic breakdown. In the past decades, high interest has been developed
in using the guided wave ultrasonic technique (GWUT) to monitor the health of
structures and detect damage due to its long-distance coverage potential with
little attenuation and cost-effectiveness. Most guided wave ultrasonic studies
have focused on detecting and characterising empty cracks or notches. Limited
literature is available to explain the behaviour of guided waves while travelling in
thin plates exposed to damage filled with debris, which is more likely possible in
long-thin structures such as pipelines for oil, water or gas transportation. Debris-
filled damage leads to corrosion processes, particularly inducing pitting corrosion.
This form of corrosion is localised and difficult to detect. It has contributed to many
structural failures, particularly in oil and gas pipelines. Hence, early detection and
characterisation of this form of damage is vital to avert catastrophic failure. This
study explored the detection of damage filled with different proportions of debris
in thin plates using guided wave ultrasonic techniques. The captured response
signals underwent analysis through various signal-processing methods in
MATLAB. Additionally, the research examined how temperature variations and
low-frequency vibrations impact the guided wave responses, aiming to simulate
the effect of environmental operation conditions. Through the analysis, an
empirical model was developed to predict debris-filled damage and differentiate
it from empty damage and the health state of the structure. The predictive model
has an average error of about 1.34. Also, the analysis revealed that cross-
correlation of the detrended response and reference signals could demonstrate
a quick way to visualise and spot debris-filled damage in the structure.
Additionally, a model called Olisa-Khan low-vibration mitigation architecture
(Olisa-Khan LMA) was created to counteract the severe effects of varying low-
frequency vibrations and improve the performance of the damage detection
technique. The average percentage deviation of the model response signal and
static response signal was about 1.64 %, suggesting the two signals are very
close. The slight deviation could be attributed to the signal loss due to clipping
and imperfection in the system. In characterising debris that filled the damage,
an excitation signal with a central frequency of 80KHz was found optimal because
the deviation of each state of damage differs from the other and decreases from
an empty case to a debris-filled case and continues as fluid-filled viscosity
increases. The study's merit cannot be overemphasised as it establishes models,
especially for predicting novel damage of debris-filled and characterising different
debris that filled the damage even in severe environmental operation conditions.
Hence, the study would be useful for continuously monitoring long-thin structures
of high economic values for possible damage detection and characterisation.PhD in Manufacturin