Dissertations/Theses - Department of Industrial and Production Engineering
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Item Effect of high-pressure coolant on milling Ti-6Al-4V alloy with external rotary liquid applicator(Department of Industrial & Production Engineering,( IPE) BUET, 2023-08-09) Nazma Sultana, Mst.; Nikhil Ranjan Dhar, Dr.An effective cooling approach with appropriate process parameters can enhance the machinability as well as productivity. In this regard, this study focuses on enhancing the machinability of widely used Ti-6Al-4V alloy with the use of high-pressure coolant jets compared to dry milling. A novel rotary applicator has been designed and developed to feed high-pressure coolant jets without any drastic change of solid end mill tool. In order to estimate the optimum design parameters for the proposed cooling approach, Taguchi based DoE has been integrated with CFD analysis. The optimum design parameters for the rotary applicator have been selected as 80 bar pressure, 3.0 L/min oil flow rate, and 0.50 mm nozzle dia. while keeping other factors such as inclination angle, radial distance, and jet inlet to tool distance constant. Average cutting temperature, resultant cutting force, mean surface roughness, and tool wear are all taken into account in evaluating the machinability of various speed-feed combinations. Compared to dry milling, in rotary high-pressure cooling (RHPC), cutting temperature, cutting force, and surface roughness are reduced by 37.56 %, 21.5 %, and 41.3 %, respectively that may be attributed by the cooling and better lubrication effect. Similarly, flank wear is considerably decreased with extended tool life (9 min). Regarding to multi-response optimization, 32 m/min cutting velocity, 26 mm/min table feed rate, and 0.60 mm depth of cut has been selected as the optimal process parameters. Beside this, the complicated milling process has been simulated by using ABAQUS/EXPLICIT 6.14, which took into account the process's dynamic behavior such as variable boundary conditions, Johnson-Cook plastic flow stress model and damage model, thermo-mechanical coupling, film thickness coefficients, load constraints, Coulombs’ friction law with hard contact behavior for the detail analysis of temperature distribution. Finally, the simulated results are cross-checked with the real-world experimental results for the validation of the used model. A satisfactory agreement has been obtained with a margin of error of 14.8%. Predicting cutting forces is essential in such machining process since it affects significantly to better surface integrity as well as longer tool life. To assess cutting forces during milling Ti-6Al-4V alloy, an integrated analytical model based on Armarego-Oxley's predictive machining theory and modified Johnson-Cook's material law is proposed incorporating the cooling effect of HPC. The proposed model's viability is confirmed by the real experiment data in milling Ti-6Al-4V alloy. Experimental studies have demonstrated strong agreement. Lastly, a cost analysis is conducted to determine whether the suggested cooling method is economicallyItem Risk assessment in LPG chain: a case study(Department of Industrial & Production Engineering (IPE), BUET., 2023-08-05) Munsur, Zisan; Ali, Dr. Syed MithunLiquified petroleum gas (LPG) plays a vital role inthe energy sector throughout the world and in Bangladesh. However, the Post-COVID-19 era and the ongoing Russia-Ukraine war caused energy supply disruption globally. The LPG supply chain is also exposed to various risk factors. Therefore, this study aims to identify the major risk factors and prioritize them to improve the resilience of the LPG supply chain. In the study, Multi-criteria decision-making (MCDM) techniques including Analytic Hierarchy Process (AHP),Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods were used. AHP-TOPSIS and AHP-PROMETHEE, these two integrated combinations were used to verify the robustness of the study result. The study reviewed the literature to find the risk prioritizing criteria and the risk factors, and also take input from the experts. This study selected six criteria and 17 risk factors. Using the experts' opinion, the AHP found "Impact on business" and "Probability of occurrence" are the two most weighted criteria for LPG supply chain risk prioritization. "Price fluctuations due to changes in demand or supply, geopolitical events, or regulatory changes", "Cybersecurity breaches including hacking, malware, and other types of cyber-attacks", and "Economic or financial crises leading to changes in demand and supply" have been identified as the topmost three risks in both AHP-TOPSIS and AHP-PROMETHEE combination. This study provides insights for managers and policymakers to improve LPG supply chain resilience in the post-pandemic era and during the global political crisis.Item Automation of repetitive manual process work using robotic process automation at a mobile network operator(Department of Industrial & Production Engineering, 2023-04-01) Sifat Hossain, Md.; Zaman, Dr. Prianka BinteAt present, all the enterprise businesses both from global and local perspective, especially telecommunication industry are facing challenge to keep pace with evolving technology. Thus, business organizations in today’s world aim for better optimization of financial costs and knowledge resources. To improves efficiency and productivity Robotic Process Automation (RPA) is the crucial answer to unlock such openings of possibilities. About cost, an RPA software license may cost between 1/3 and 1/5 of the price of a full-time employee, even with many inherent advantages. Therefore, enterprises and businesses throughout different industries have already started adopting RPA for their optimization and scalability. For example, in Banking, Insurance, Software company, Retail, Manufacturing, Healthcare and even a few Telecom industries. With four major telecom operators in Bangladesh, something like Robotic Automation Process (RPA) has hardly been discovered to optimize man-hour costs. In this case study, one repetitive manual task has been selected from a sample size operational work to automate with UiPath. The outcome of this observation reflected that telecommunication industry may gain higher productivity and efficiency by using Robotic Process Automation (RPA) in regular day-to-to repetitive manual tasks. The findings can be extended to similar other telecommunication industries ie. mobile operators in a larger scaleItem Investigation of physico-mechanical properties of areca-cotton fiber reinforced polypropylyne composite(Department of Industrial & Production Engineering,(IPE), BUET, 2023-07-30) Roy, Gourab Kumar; Dhar, Dr. Nikhil RanjanThe goal of the current research is to fabricate fiber reinforced composite with comparatively easy to get materials, which comes as byproduct and determine the mechanical, physical, chemical and thermal characteristics of a proposed hybrid polymer composite which consist of a polypropylene matrix and a mixture of cotton and areca fibers. Hybrid composites are usually used as a combination of properties of different types of fibers and a polymer matrix. The advantages of natural fibers are their continuous supply, easy and safe handling, and biodegradable nature. This research work investigated the influence of fibers loading, volume fraction of cotton and areca fiber and different percentage of alkali treatment on the aforementioned characteristic of the composites. Composites with volumetric amounts of fibers up to 15% were fabricated. Various tests like, tensile test, flexural test, impact test, hardness test etc. were carried out for mechanical characterization. The thermal stability of the composite was investigated using thermogravimetric analysis (TGA), the crystallinity of the composite was determined using an Xray diffraction (XRD) test, the surface morphology was examined using Scanning electron microscope (SEM), and the functional groups that were present in the composite were investigated using Fourier transform infrared (FTIR). 15 composites and a composite that had not been treated were created using a mixture that was designed by response surface methodology (RSM). Through desirability function, the optimum fabrication combination was identified. The precision of the prediction model was assessed by comparing the predicted value with the experimental value.Item Developing a machine learning model for predicting purchasing behavior of F-commerce-based customer(Department of Industrial & Production Engineering (IPE)., 2022-11-12) Mehnaz, Fatema; Sarwar, Dr. FerdousThe modern tech-based lifestyle has become much easier with online purchases. Social media platforms like Facebook, Instagram and Twitter have taken it to another level. Online business through Facebook is now a significant topic worldwide, so are the challenges. With the easy access of internet and free use of Facebook, doing business through Facebook platform is not a big deal. But as anyone, anywhere can start a small business with little capital using this giant platform due to its easiness, the competition and long-time survival is also the biggest of all challenges. Hence the aim of this study is to build a model that would help anyone get acknowledged with the satisfaction level of F-commerce users and target those pleased consumers in the future to serve better and minimize the survival challenges. The objective of this project is to predict the F-commerce based customer’s satisfactory level and next preferable purchase. In this study, a survey method has been used to get real time customer data and a machine-learning model has been made that would help to cluster those customers together who prefer to purchase similar categories of product from Facebook. This project then also aims to classify and predict the satisfaction level of those customers in their purchase behaviour with real-world data by using K-medoid with the PCA (Principal Component Analysis) algorithm and SVM (Support Vector Machine).Item Exploration of the drivers of solar energy development in Bangladesh: a muticriteria decision making approach(Department of Industrial & Production Engineering (IPE)., 2022-09-13) Zahidul Anam, Md.; Bari, Dr. A. B. M. MainulEnergy demand in Bangladesh is consistently rising due to the country’s rapid population growth and economic expansion. As a result, solar energy holds substantial potential in the Bangladeshi energy portfolio. This study aims to identify and evaluate the key drivers behind the sustainable development of solar energy in Bangladesh, an emerging economy in South Asia. This is done by adopting an integrated methodology. First, through a literature review and expert feedback, 12 drivers of solar energy development are identified. This study then employs the best-worst method (BWM) to rank the drivers based on their significance and uses the Interpretive Structural Modeling (ISM) along with Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodology to analyze the interrelationships among them. The findings indicate that favorable geographical location in terms of solar irradiation, government policy toward sustainable renewable energy, the need to reduce greenhouse gas emissions, and large bodies of water constitute the most significant drivers behind the sustainable development of solar energy in Bangladesh. This research contributes to the literature on sustainable solar energy development in a systematic way that benefits both decision-makers and end-users.Item Supply chain risk assessment in the refrigerator industry of Bangladesh: a case study(Department of Industrial & Production Engineering (IPE)., 2021-09-26) Mohasin Sarder, Mohammad; Ghosh, Dr. ShuvaRisk exists in various fields of research like finance, manufacturing, healthcare, supply chain management, etc. However, insufficient understanding of uncertain regional differences and various industry trends, have increased firm exposure to supply chain risks. In today’s scenario, organizations are becoming more vulnerable in their supply chain due to irregularities of material supply, product demand, skills, and equipment requirements. Supply chain risks consist of complex, uncertain, and vague information, but risk assessment techniques have been unable to handle complexity, and vagueness. Therefore, managing of risk has become important to tackle such kinds of disturbances from a supply chain context. As refrigerator manufacturing industry is emerging sector in Bangladesh so supply chain in this sector is yet to be stabilized, so there are lots of unseen risks come up which is pretty difficult to predict. Even in regulatory level several functions are yet to be defined. Supply Chain Risk Management (SCRM) have enormous effects on the firm’s performance. Therefore,it is necessary to develop strategies appropriate for coping with risks and maintaining the firm’s performance level In this thesis the risks were identified through a combination of literature review and expert opinions from manufacturing field as well as academic field. Research methodology is developed using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and then Analytical Hierarchy Process (AHP), popular method for multiple criteria decision making (MCDM). Then the proposed methodology is practically implemented through a case study on refrigerator manufacturing industry. Severity and probability is selected as a decision making criteria for TOPSIS. Moreover 6 major risk and 24 sub risk under major risks were ranked using TOPSIS and AHP and rank differences were identified. Results suggests supply related problems are major risk factor in refrigerator industry. This research work can assist practitioners and industrial managers in the refrigerator manufacturing industry in taking proactive action to minimize its supply chainrisks. To the end, a sensitivity analysis test, which gives an understanding of the stability of ranking of risks.Item Study on mechanical and machining performance of carbon nanotube reinforced aluminum metal matrix composite(Department of Industrial & Production Engineering (IPE)., 2023-04-16) Md. Sazzad Hossain Ador, Md.; Dhar, Dr. Nikhil RanjanIn modern material science, engineers are constantly attracting and striving to develop nanohybrid Aluminum based Metal Matrix Composite (AMMC) materials due to their outstanding tribological, microstructural and mechanical qualities like lightweight, ductile, highly conductive, superior malleability, high strength and high specific modulus. Moreover, the demand for Aluminum based Metal Matrix Composite is increasing day by day because of their massive applications in various automobile, military, aviation, aerospace, structural, transportation, marine and other manufacturing industries due to their high stiffness, high strength-to-volume portion, deterioration resistance, and exceptional wear resistance. Nano particles like CNTs, Silicon Carbide and Alumina have created a great impact to produce advanced engineering composites. The mechanical and thermal property upgrades accomplished by expansion of CNT in Aluminum metal lattice frameworks. The addition of Carbon Nanotubes potentially helps in further improving the tensile strength of the metal matrix composite. So, Metal matrix composite with nano tubing provide enhanced mechanical features compared to traditional reinforcement. In this research work, mechanical properties and machinability of carbon nanotube reinforced aluminum metal matrix composite has been compared with traditional aluminum metal matrix. Moreover, turning operation of carbon nanotube reinforced aluminum metal matrix composite was performed under both dry and MQL cooling condition. Cutting speed, feed rate and depth of cut have been considered as input cutting parameters whereas resultant outputs are cutting temperature, surface roughness, cutting force and tool wear. It is found that application of MQL resulted in maximum 16.62%, 31.28%, and 27.58% lesser cutting temperature, surface roughness, and cutting force by than machining without any fluid. Using response surface methodology, optimum cutting condition has been found while machining fabricated composite under MQL condition, the optimum cutting parameters which yielded the desired surface roughness Ra = 1.03µm, is follows: 1 mm of t, 168 m/min of Vc and 0.103 mm/rev of feed rate. Finally, A predictive model of surface roughness was developed using artificial neural network (ANN) which has been validated against the experimentally found results. For ANN developed model, regression value is found to be 0.98 for carbon nanotube reinforced aluminum metal matrix composite under MQL condition which is very close to 1, thus justifying the efficacy of the developed model.Item Identification and analysis of barriers of TPM implementation using total interpretive structural modeling approach: a case study(Department of Industrial & Production Engineering (IPE)., 2022-09-28) Miftahul, Jannat Chowdhury; Parveen, Dr. SultanaBangladesh is already entered in the era of modern industrialization, even preparing to enter the 4IR (4th Industrial Revolution). So, we are becoming more export oriented. Above 80% of our export products are ready-made garments. Now a day’s world’s manufacturing industries are going through a very competitive time due to Covid-19 situation. Surviving has become quite difficult. Many industries are trying to adapt some globally used tools and techniques, such as TPM (Total Productive Maintenance), to survive in the competitive market. This is a philosophical tool which mainly focused on to start an autonomous system that combine the manufacturing and maintenance which will prevent losses, reduce costs, and develop a system to properly use the capacity of machines. Some industries are trying their heart and soul to implement TPM. However, most of the industries are not being concern about the barriers of implementing; hence, they fail to take the proper countermeasure and fails to implement. The aim of this research is to identify barriers that are impeding TPM implementation. Initially fourteen barriers were identified and then reduced to ten through analysis and which are mostly related to the RMG sector. After that, a structural model of barriers is suggested using Total Interpretive Structural Modeling (TISM) technique. This model will help to understand contextual relationship among barriers and determined their interdependency. Lastly, MICMAC analysis has been done to determine the importance of barriers based on their driving power and dependency. The findings from this research shows that lack of top management’s involvement and not implementing pilot-study, are the most important among barriers. Some other barriers like lack of education and training, no SOP, lack of KPI based analysis etc. also considered to be significant. These findings and its visual presentation through TISM model will help industrial managers to concentrate on barriers to prevent failure of TPM implementation. They will easily understand where to focus on and what preventive measure could be taken to effectively implement TPM in RMG sector.Item Investigation of the effects of eco-friendly nanofluids through minimum quantity lubrication in grinding metal matrix composite(Department of Industrial & Production Engineering (IPE)., 2023-04-29) Ashab Shakur, Md.; Dhar, Dr. Nikhil RanjanThe unique physical features and reliability of advanced composite materials have attracted interest in recent years. Aluminum MMCs reinforced with SiC particles (Al/SiC-MMC) exhibit a yield strength increase of up to 20%, a greater modulus of elasticity, a lower coefficient of thermal expansion, and are more resistant to wear than the corresponding unreinforced matrix alloy systems. Despite having many benifits, Al/SiC-MMC shows poor machinability. Process such as grinding is crucial for the material to obtain aquality finish and damage-free surfaces. But soft aluminum alloys have poor grindability due to chip adherence, thereby clogging the wheel. Periodic dressing is required to avoid the aforementioned issues, which makes the grinding process inefficient. An effective cooling approach thus needs to be used with optimal process parameters that will enhance the grindability of the Al/SiC-MMC. The present work investigates the effects of the application of ecofriendly ZnO-deionized water nanofluid on the grindability of Al/SiC-MMC by CBN grinding wheel in respect of chip morphology, grinding temperature, surface roughness, wheel wear, and grinding ratio. A suitable MQL set-up has been designed and fabricated to deliver variable MQL flow rate continuously at the critical zones during surface grinding of the workpiece. In order to prepare the nanofluids, 0.5% volume of ZnO & Sodium dodecyl sulphate (SDS) surfactant are dispersed in the deionized water performing ultra-sonication & magnetic stirring for thirty minutes each. Experiments are designed using central composite design and empirical models are developed to predict grinding temperature, wheel wear, and surface roughness through RSM for flood cooling & MQL. Based on the experimental data, empirical model for predicting surface roughness has been developed using artificial neural network. Application of the produced nanofluids through MQL significantly reduces the cutting temperature, surface roughness, & wheel wear of the material and improves the grinding ratio compared to conventional flood cooling method. Significant reduction inclogging of the workpiece material into the CBN grinding wheel is observed for the MQL compared to dry grinding and flood cooling. Spindle speed 3000 rpm, infeed 10 µm, and environment nMQL have been selected using RSM based composite desirability approach to be the desired optimal combination for enhancing the grindability of Al/SiC-MMC.