Investigation of temperature in orthopaedic drilling using response surface methodology

Rise in temperature is inevitable in orthopaedic drilling. Massive research had been done in the field of orthopaedic drilling to investigate the effect of cutting conditions, bone related parameters, and drill bit geometric parameters on heat generation and minimum surrounding tissues injury. In present research, contradictory conclusions regarding the cutting conditions and drill bit geometric parameters were observed. Minimum temperature of 31°C was achieved at speed of 186 rpm, feed of 0.196 mm/rev, drill diameter of 3.85mm, and drill tip angle of 110°. Response Surface Methodology (RSM) was used to develop a mathematical model to predict the type of relationship between inputs and response. It was concluded that the most influencing parameter was drill diameter

Investigation of electric discharge machining parameters to minimize surface roughness

: Surface roughness during electrical discharge machining (EDM) was determined, in which material is removed by thermo-electric process due to the occurrence of successive discharge between workpiece and electrode. Box-Behnken design (BBD) involving four parameters discharge current (I), Pulse ON time (PON), Pulse OFF time (POFF) and Gap voltage, with three levels was employed to minimize the surface roughness. Other parameters such as Servo speed, Polarity and Die-electric pressure were kept constant throughout the machining. A copper electrode tool was used to machine the holes in AISI 1045 steel work piece. Mathematical models were developed using Response Surface Methodology (RSM), while Analysis of variance (ANOVA) was used to observe individual effect, interaction between parameters, and to check validity of models. Results revealed that pulse on time and discharge current were two main significant parameters that statistically affected surface roughness

Effect of different dielectrics on material removal rate, electrode wear rate and microstructures in EDM

Diesinker electric discharge machining is widely used non-conventional technique for making high precision and complex shaped parts. Dielectrics and electrical parameters were considered as the main factors for EDM performance. In this paper, the effects of pulse-on-time (μs) and current (ampere) were evaluated for performance measures using kerosene and water as dielectrics. A comparison was performed for both dielectrics in terms of material removal rate (mm3/min), electrode wear rate (mm3/min), and microstructures. Aluminum 6061 T6 alloy was used as material for this research due to its extensive use in aerospace and automotive industries. Experiments were designed using Taguchi L9 orthogonal array (OA). Time series graphs were plotted to compare material removal rate and electrode wear rate. Microstructures were taken by scanning electron microscope to analyze the surface produced in terms of cracks, globules and micro-holes. Higher material removal rate and lower electrode wear were achieved with kerosene dielectric. The novelty of this research work, apart from its practical application, is that Aluminum 6061 T6 alloy is used as work material to compare the performance of dielectrics (kerosene and distilled water). Paper presented at: Complex Systems Engineering and Development Proceedings of the 27th CIRP Design Conference Cranfield University, UK 10th – 12th May 2017

CEMENT INDUSTRY PREFERENCES FOR CAPTIVE POWER PLANTS IN PERSPECTIVE OF CURRENT ENERGY CRISES OF PAKISTAN

In the current wake of energy deficiency, senior management of Pakistan Cement Industry is forced to look for non-conventional sources for electricity generation. Comparative study of captive power plant (CPP) options may help the top management in decision making and highlight the industry preferences for installation of new CPPs. This paper presents an Analytical Hierarchy Process (AHP) based multidimensional approach to select the CPP’s for cement industry and to prioritize the factors affecting this selection. The CPP’s shortlisted for this analysis include; Coal Fired CPP (CF-CPP), Refused Derived Fuel CPP (RDF-CPP) and Waste Heat Recovery CPP (WHR-CPP). The AHP routines are modelled in respective software. Data specific models are solved using the data collected from top management of different cement plants in Pakistan. The quantitative data for alternative power plants with respect to each criterion has been collected from different data bases. AHP results show that Pakistan cement industry has a strong demand for non-conventional CPP’s and the top management is giving high priority to factors like ‘Automation’ and ‘Performance’ while installing the CPP’s. Management is not much sensitive about the associated initial costs. The paper concludes with a ranking list in which WHR-CPP is at the top while RDF-CPP and CF-CPP are at the second and third place respectively. The results may help the policy makers of international CPP manufacturing firms and national cement industries in their future strategic decisions

A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines

Assembly Lines (ALs) are used for mass production as they offer lots of advantages over other production systems in terms of lead time and cost. The advent of mass customization has forced the manufacturing industries to update to Mixed-Model Assembly Lines (MMALs) but at the cost of increased complexity. In the real world, industries need to determine the sequence of models based on various conflicting performance measures/criteria. This paper investigates the Multi-Criteria Model Sequencing Problem (MC-MSP) using a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm. To address the multiple criteria, a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm was developed by integrating a priori approach with NEH algorithm. Discrete Event Simulation (DES) was used to evaluate each solution. A mathematical model was developed for three criteria: flow time, makespan and idle time. Further, to validate the effectiveness of the proposed SMC-NEH a case study and Taillard's benchmark instances were solved and a Multi-Criteria Decision-Making (MCDM) analysis was performed to compare the performance of the proposed SMC-NEH algorithm with the traditional NEH algorithm and its variants. The results showed that the proposed SMC-NEH algorithm outperformed the others in optimizing the conflicting multi-criteria problem

Parametric analysis of turning HSLA steel under minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL) : a concept of one-step sustainable machining

Abstract: The requirement of cost-effective and ecological production systems is crucial in the competitive market. In this regard, the focus is shifted towards sustainable and cleaner machining processes. Besides the clean technologies, effective parametric control is required for machining materials (such as High Strength Low Alloy Steels) specifically designed for high strength applications having superior physio-chemical properties. Therefore, the machinability complexities require optimized solutions to reduce temperature elevation and tooling costs and improve machining of these materials. Complying to the market needs, this research examines the effectiveness of nanofluid on tool life, wear mechanisms, surface roughness (Ra), surface morphology, and material removal rate (MRR) in turning of 30CrMnSiA (HSLA) using minimum quantity lubrication (MQL) and SiO2-H2O nanofluids (NF-MQL). A systematic investigation based on physical phenomena involved is carried out considering four process parameters (cutting speed (VC), feed rate (Fr), depth of cut (DOC), and mode of lubrication for machining. Fr is found as the vital parameter for surface roughness while MRR is highly influenced by DOC regardless of lubrication approach. One-step sustainability technique is applied, in which process variables used for roughing conditions are analogous to attain surface comparable to finished machining without compromising process efficiency and demonstrate its feasibility through optimal settings under NF-MQL. Multi-response optimization proved the NF-MQL machining condition as the best alternative which result in 28.34% and 5.09% improvements for surface roughness and MRR, respectively. Moreover, the use of SiO2 is recommended over MQL due to lower energy consumption, low tool wear, and better surface integrity, sustainable liquid, and related costs

Revealing the microstructure and mechanical attributes of pre-heated conditions for Gas Tungsten Arc Welded AISI 1045 steel joints

Gas tungsten arc welding (GTAW) is considered a well-established process in the manufacturing industry. Despite, certain challenges associated with high hardness of heat affected zone and cold cracking susceptibility of joints, are the main barriers for this process to be implemented successfully within high integrity structure. By using a combined procedure of experiments and modelling (response surface methodology (RSM) and multi-objective optimization: multi-objective genetic algorithm (MOGA)) allows obtaining good enhancement over uniform heating, cooling and the heat-affected zone which enable major progress in obtaining high quality welded parts. Therefore, this research study combines the experiments and modelling in a systematic manner considering for the first type the pre-heated treatment and without- pre-heating conditions of GTAW manufacturing. It leads to optimizing the process parameters of GTAW when manufacturing AISI 1045 medium carbon steel. The effects of critical parameters i.e. welding current: WC, welding speed: WS, and gas flow rate: GFR on the mechanical properties (ultimate tensile strength (UTS) and hardness) were investigated and evaluated against the microstructure of weld fracture. The multi-objective genetic algorithm corroborated with experimental observation enables to obtain a maximum UTS of approx. 625 MPa and hardness of 80.19 HRB for preheat condition. The results highlight an improvement in UTS of 0.2% to 6.7% and a decrease in hardness of 0.1% to 21.5% by implementing the preheating conditio

Predicting the tensile strength, impact toughness, and hardness of friction stir-welded AA6061-T6 using response surface methodology

In this research, an attempt has been made to develop mathematical models for predicting mechanical properties including ultimate tensile strength, impact toughness, and hardness of the friction stir-welded AA6061-T6 joints at 95 % confidence level. Response surface methodology with central composite design having four parameters and five levels has been used. The four parameters considered were tool pin profile, rotational speed, welding speed, and tool tilt angle. Three confirmation tests were performed to validate the empirical relations. In addition, the influence of the process parameters on ultimate tensile strength, impact toughness, and hardness were investigated. The results indicated that tool pin profile is the most significant parameter in terms of mechanical properties; tool with simple cylindrical pin profile produced weld with high ultimate tensile strength, impact toughness, and hardness. In addition to tool pin profile, rotational speed was more significant compared to welding speed for ultimate tensile strength and impact toughness, whereas welding speed showed dominancy over rotational speed in case of hardness. Optimum conditions of process parameters have been found at which tensile strength of 92 %, impact toughness of 87 %, and hardness of 95 % was achieved in comparison to the base metal. This research will contribute to expand the scientific foundation of friction stir welding of aluminum alloys with emphasis on AA6061-T6. The results will aid the practitioners to develop a clear understanding of the influence of process parameters on mechanical properties and will allow the selection of best combinations of parameters to achieve desired mechanical properties

Prioritizing Factors of Entrepreneurial University to Inculcate Enterprise Formation Pursuit Among University Graduates Using Analytical Hierarchical Process

The objective of this paper is to prioritize entrepreneurial activities in higher education institutions to inculcate enterprise development pursuit among university graduates. Various phases of enterprise development process are determined along with prominent activities of entrepreneurial universities through literature review. Problem formulation is done by structuring goal, objectives and alternatives into a hierarchical model in order to prioritize factors with the help of Analytical Hierarchical Process (AHP). Experts of academic entrepreneurship are approached for pairwise comparison of factors on the preference scale of nine levels with the help of software tool Expert Choice 11. After recording judgments, preferences of all experts are combined in order to get overall priorities of objectives and alternatives. Results show that Internal Motivation has highest and Business Growth and Sustainability have lowest priority along with priorities of all other objectives of enterprise formation process falling between both. Moreover, University Incubation Center is prioritized among alternatives in order to achieve objectives. Sensitivity analysis of results is carried out with the help of Expert Choice in which weight of a single objective is varied to observe effect on hierarchical model. Research presents a complete picture to academicians and policy makers to determine role of universities for entrepreneurship development in country. It will also help government officials to allocate resources in a prioritized way in order to achieve specific objectives.

Impact of Project Complexity and Environmental Factors on Project Success: A Case of Oil and Gas Sector of Pakistan

Oil and gas industry significantly contribute for economic development of countries enriched with petroleum resources. Mega projects of oil and gas sector usually face many challenges due to environmental issues, high level of risks, huge investments, tight schedules and interdependencies between project activities. Therefore keeping in view, the issues faced by oil and gas sector this study was made to analyze the impact of project complexity and environmental factors on success of oil and gas projects of Pakistan. Based upon hypothetical framework developed for this study, data collection was made from an oil and gas company of Pakistan. After which, data analysis was carried out by using a statistical technique known as structural equation modeling. Project complexity, environmental factors and project success were taken as constructs for model evaluation on AMOS. Analysis of data has concluded that project complexity has negative impact on project success whereas better control over environmental factors enhance the project success rate