Dr Shahid Ikramullah Butt - Academia.edu (original) (raw)

Papers by Dr Shahid Ikramullah Butt

Environmental Impact Assessment Review, Sep 1, 2023

Applied Soft Computing, Dec 1, 2021

Abstract In engineering optimization, surrogate model (SM) is widely used to replace the involved... more Abstract In engineering optimization, surrogate model (SM) is widely used to replace the involved time expensive model, due to the expensive model is complex and high precise requirement caused a long calculation cycle. In traditional process of engineering optimization, the separation of the surrogate model static construction stage and dynamic optimization stage depresses the optimization accuracy and efficiency. Moreover, in order to ensure the accuracy of the surrogate model, expensive model had to be intensively invoked to get enough representative samples in the design space for the SM training. In this paper, a surrogate model adjoint refine based global optimization method combining with the multi-stage fuzzy clustering space reduction strategy (MFCPR-SGO) is proposed to improve the optimization accuracy and efficiency. Firstly, the optimal Latin hypercube design method (OLHD) is used to sample in design space to assure the initial sample set with strong space filling property. Then, the design space is subdivided into three tiered subspaces by using the space reduction strategy of multi-stage fuzzy clustering, which has the ability of space focusing, space reduction and jumping out of local optimum. On this basis, the hierarchical optimization method with ADAM gradient descent is proposed to quickly and accurately search the local minimum value of the objective function in each subspaces. At the same time, combined with the extremum sampling and the gaussian process sampling, a dynamic sampling algorithm is given to realize the synchronization of optimization and surrogate model update. Finally, the benchmark test problems in 12 different dimensions are used to verify the proposed method. The results show that the optimization accuracy can be improved by 21.3% and expensive model invoking times are reduced by 31.5% compared with other three heuristic optimization methods and the three recent surrogate-based optimization (SGO) algorithms. It indicated that the optimization precision and efficiency can be greatly improved by synchronizing the dynamic updating of the surrogate model with the engineering optimization search.

Advances in Production Engineering & Management, Dec 24, 2020

Flexible job shop scheduling problem (FJSSP) is a further expansion of the classical job shop sch... more Flexible job shop scheduling problem (FJSSP) is a further expansion of the classical job shop scheduling problem (JSSP). FJSSP is known to be NP-hard with regards to optimization and hence poses a challenge in finding acceptable solutions. Genetic algorithm (GA) has successfully been applied in this regard since last two decades. This paper provides an insight into the actual complexity of selected benchmark problems through quantitative evaluation of the search space owing to their NP-hard nature. A four-layered genetic algorithm is then proposed and implemented with adaptive parameters of population initialization and operator probabilities to manage intensification and diversification intelligently. The concept of reinitialization is introduced whenever the algorithm is trapped in local minima till predefined number of generations. Results are then compared with various other standalone evolutionary algorithms for selected benchmark problems. It is found that the proposed GA finds better solutions with this technique as compared to solutions produced without this technique. Moreover, the technique helps to overcome the local minima trap. Further comparison and analysis indicate that the proposed algorithm produces comparative and improved solutions with respect to other analogous methodologies owing to the diversification technique.

The International Journal of Advanced Manufacturing Technology, Nov 13, 2015

This article deliberates that thin-walled parts are more easily deformed in the machining process... more This article deliberates that thin-walled parts are more easily deformed in the machining process, due to low stiffness, which not only creates elevated machining errors and reduces machining precision. This paper presents a systematic methodology to analyze and control the machining errors caused by machining deformation. With the minimum modulus principle, the kinematics model of the workpiece-fixture system is established and contact force (including friction force) between the fixture components and workpiece are calculated, and further, a model to optimize the clamping force is proposed in maintaining the stability of the workpiece-fixture system. Then, the numerical results obtained from the kinematics model are applied as boundary conditions on the finite element model of the workpiece-fixture system; thus, the deformation value is calculated. Then, the machining error model caused by deformation is used to transfer the deformation value to machining errors. The neural network is employed to establish the highly nonlinear relations between the machining error and the cutting parameter, which facilitates the establishment of the optimization model of cutting parameters to improve machining efficiency and ensure the machining precision. Finally, a case study is used to verify the effectiveness of the proposed method.

The International Journal of Advanced Manufacturing Technology, Jan 10, 2020

In modern manufacturing industries, the importance of multi-objective optimization cannot be over... more In modern manufacturing industries, the importance of multi-objective optimization cannot be overemphasized particularly when the desired responses are differing in nature towards each other. With the emergence of new technologies, the need to achieve overall efficiency in terms of energy, output, and tooling is on the rise. Resultantly, endeavor is to make the machining process sustainable, productive, and efficient simultaneously. In this research, the effects of machining parameters (feed, cutting speed, depth of cut, and cutting condition including dry, wet, and cryogenic) were analyzed. Since sustainable production demands a balance between production quality and energy consumption, therefore, response parameters including specific cutting energy, tool wear, surface roughness, and material removal rate were considered. Taguchi-gray integrated approach was adopted in this study. Multi-objective function was developed using gray relational methodology, and its regression analysis was conducted. Response surface optimization was carried out to optimize the formulated multi-objective function and derive the optimum machining parameters. Concurrent responses were optimized with best-suited values of input parameters to make the most out of the machining process. Analysis of variance results showed that feed is the most effective parameter followed by cutting condition in terms of overall contribution in multi-objective function. The proposed optimum parameters resulted in improvement of tool wear and surface roughness by 30% and 22%, respectively, whereas specific cutting energy was reduced by 4%.

2009 International Conference on Measuring Technology and Mechatronics Automation, 2009

Page 1. An Integrated Methodology for Workpiece-fixture System Stiffness Calculation and Error Co... more Page 1. An Integrated Methodology for Workpiece-fixture System Stiffness Calculation and Error Control Zhang fa-ping Lu ji-ping Yan yan Sun hou-fang School of Mechanical and Vehicular Engineering Beijing Institute of Technology Beijing 100081, PRChina zfpnew@163.com ...

Advances in Materials Science and Engineering, 2017

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competi... more Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

2022 13th International Conference on Mechanical and Aerospace Engineering (ICMAE)

2022 IEEE 13th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT)

2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH)

Environmental Science and Pollution Research

Electronics

Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of ... more Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promi...

2020 IEEE 11th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT)

Sustainability of a manufacturing system defines its efficiency and productivity. In the world of... more Sustainability of a manufacturing system defines its efficiency and productivity. In the world of ever growing energy requirements and depleting resources the need of sustainability is greater than ever. Present research was undertaken to analyze sustainability of turning aerospace alloy Ti-6Al-4V under different cutting conditions including dry, wet and cryogenic. Feed rate, cutting speed and depth of cut were taken as input variables along with cutting condition. Specific cutting energy was analyzed under varying machining parameters. It was found that energy consumption under cryogenic conditions was lower than dry and wet conditions. At speed of 50 m/min cryogenic turning energy consumption was 9% lower than dry cutting and 16% lower than wet cutting.

Health Policy and Planning

Decentralised, person-centred models of care-delivery for drug-resistant tuberculosis (DR-TB) con... more Decentralised, person-centred models of care-delivery for drug-resistant tuberculosis (DR-TB) continue to be under-resourced in high burden TB countries. The implementation of such models – made increasingly urgent by the COVID-19 pandemic – are key to addressing gaps in DR-TB care. We abstracted data of RR/MDR-TB patients initiated on treatment at 11 facilities between 2010 and 2017 in Sindh and Balochistan provinces of Pakistan. We analysed trends in treatment outcomes relating to program expansion to peri-urban and rural areas and estimated driving distance from patient residence to treatment facility. Among the 5586 RR/MDR-TB patients in the analysis, overall treatment success decreased from 82% to 66% between 2010 and 2017, as the program expanded. The adjusted risk ratio for unfavourable outcomes was 1.013 (95% CI 1.005-1.021) for every 20 kilometres of driving distance. Our analysis suggests that expanding DR-TB care to centralised hubs added to increased unfavourable outcome...

Advances in Neuroergonomics and Cognitive Engineering, 2020

Comfortable and conducive class environment is very important for students and teachers for effec... more Comfortable and conducive class environment is very important for students and teachers for effective teaching and learning. Good interaction between teachers and students create positive relationships in classroom and contribute to effective learning. The purpose of this study was to develop a methodology for evaluating thermal comfort, ergonomic and human factors effect on learning performance in university classrooms. Conceptual framework is constructed, and study methodology is developed based on literature.

2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2018

Knowledge plays a significant role during product development process. The knowledge service mech... more Knowledge plays a significant role during product development process. The knowledge service mechanism is the key stage for the organization and usage of design knowledge. This paper approaches the mechanism by BOM (Bill of material) technology to organize and reuse design knowledge. Firstly, the knowledge BOM is put forwards and constructed based on the product BOM, hence the effective organization of knowledge is realized. Secondly, according to the refinement and evolution process of product BOM, a knowledge task meta model is constructed to describe the design task, and the mechanism of knowledge integration into the task is formulized to achieve the knowledge service for product development. Finally, an aircraft part, a wing part structural design process is taken as an example to verify the feasibility of the proposed method.

E3S Web of Conferences, 2021

This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) us... more This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.

This study was designed to determine the physicochemical composition and antimicrobial activity a... more This study was designed to determine the physicochemical composition and antimicrobial activity against different food pathogens obtained from different honey samples of Apis dorsata of different locations of Pakistan, that is, Changamanga (Central Punjab), Multan (Southern Punjab), Mansehra (Upper Khyber Pakhtunkhwa; I) and Islamabad (Federal Area). These samples were analysed for their moisture, ash, nitrogen, hydroxymethylfurfural (HMF), diastase number (DN), reducing sugars, total sugars, sucrose contents, acidity and pH contents. It was observed that there was a significant difference among these tested parameters for these honey samples. The analysis showed moisture contents in the range of 22.87-26.70%, ash contents in the range of 0.03-0.1 g/100 g, nitrogen contents in the range of 0.27-0.39%, sucrose in the range of 2.5-4.57%, reducing sugars in the range of 69.6273.93%, HMF contents in the range of 37.14-46.60%, acidity in the range of 23.67-43 meq/kg, pH in the range of 3...

Intrusion Detection System (IDS) are considered as one of the important network tool in managing ... more Intrusion Detection System (IDS) are considered as one of the important network tool in managing the network security. It is found that network practitioners find difficult to use current IDS. Even security software’s like IDS are working efficiently but user found it difficult to use and understand. As a result user has difficulties in using and judging the quality of the output. Therefore, usability evaluation is important to help users in efficient interaction and enhance usage of IDS. In most of the situation the usability evaluation is done by the usability engineers. In small or large scaled companies software developers are forced to learn different paradigm of usability. This is not easier than training the usability engineers on how to develop software. As a remedy Cognitive Analysis of Software Interface (CASI) system has been designer for software engineer. Moreover this system help software engineer to evaluate the IDS based on user perception and evaluation views. To ev...

Frontiers in Neuroscience, 2020

Cognitive workload is one of the widely invoked human factors in the areas of humanmachine intera... more Cognitive workload is one of the widely invoked human factors in the areas of humanmachine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Realtime four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.