Department of Industrial Engineering - Conference Presentations

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https://hdl.handle.net/20.500.14519/92

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Engineering Design Featuring the Life Cycle Approach for Reconfigurable Machine Tool
(Elsevier, 2019-01-01) Sibanda, Vennan; Mpofu, Khumbulani; Trimble, John; Kanganga, Mufaro
The evolution of design has come of age and the new design approaches that look at the rapid change and short life cycles of products is taking the centre stage. Competitiveness and the cost of products determine the lifeline of an organisation. The traditional machine tools such as the dedicated and flexible machines are designed on the principle of machine design. The development of reconfigurable machines seeks a new approach to design that not only encompasses the existing approaches but should include new design approaches as well. The success of a designed product maybe measured by the capability of the system that produces it. It is critical when designing a product that the organisation’s technological and manufacturing system capabilities be considered as failure to do so may lead to production designs that are not manufacturable. A good design system ensures quick delivery of a quality, reliable and safe product. This research therefore seeks to develop an integrated approach to design that incorporates design for manufacturing, design for assembly, concurrent engineering, eco-design and reconfigurable design as critical components for reconfigurable machine tool. Literature on these systems was reviewed to integrate the different design paradigms. The results of the study give a new approach to a reconfigurable machine design based on the integration of the five design approaches and their impact in the life cycle of a product.
Design and optimization of machining parameters for effective AISI P20 removal rate during milling operation.
(Elsevier, 2019-01-01) Daniyan, I. A.; Tlhabadira, I.; Daramola, O. O.; Mpofu, K.
Tool geometry and selection of appropriate machining parameters are important considerations that determine the quality of surface finish, rate of tool wear, production cycle time, rate and ease of machinability. In this study, the milling process was designed to optimize the effects of machining parameters namely; the width of cut, cutting force, depth of cut and feed rate for effective AISI P20 removal during milling operation. The numerical design was carried out using the Complete Abaqus Environment (CAE) and the Response Surface Methodology (RSM) while the physical experiment was investigated using the DMU 80 CNC milling machine as well as the dynamometer and dynaware data acquisition system. The design of the numerical experiment consists of four factor-two level factorial of 16 experimental runs. Based on the feasible combination of the machining parameters from numerical experiment, the milling operation of AISI P20 was carried out on the DMU 80 milling machine limited to a maximum load of 900 kg. The resulting values of the cutting force, moment of force and machining time were obtained via the data acquisition system. The analysis of the results led to the formulation of a predictive model that correlates the rate of material removal (RMR) as a function of the independent machining process parameters. The results obtained also indicate that the cutting force, width and depth of cut as well as the feed rate are important parameters that influence the rate of material removal during machining operations, hence, the need for proper process design and control of the milling operation process parameters in order to reduce the total manufacturing time and increase the metal removal rate which is a function of productivity. This will in turn reduce the total manufacturing cost without sacrificing product quality with attendant increase in productivity.
Design and modelling of automated reactor for the production of caustic potash from cocoa pod husk.
(Elsevier, 2019-01-01) Daniyan, I. A.; Mpofu, K.; Daniyan, O. L.; Adeodu, A. O.; Uchegbu, I. D.
The need for the waste to wealth conversion triggered the design and modelling of an automated reactor for the production of caustic potash from cocoa pod husk. The system, which was designed for an annual production target of 35,000 tons, comprises of seven units namely; the storage unit, fluidized bed dryer and rotary dryer, roll crusher, reactor, furnace, condenser, and evaporator. The mechanical design and modelling was carried out with the aid of the Auto desk Inventor 2017 while the ASPEN HYSYS was employed as a process modelling tool for the material, energy optimization and plant economics. The design of the system also incorporates an Arduino Uno Microcontroller to a pH, pressure and temperature sensors to monitor the process conditions of the designed reactor. Stainless steel was selected for the fabrication of the reaction because of its excellent corrosion resistance ability, strength and machinability. The results of the Finite Element Analysis (FEA) carried out indicates that the material used for the reactor development is likely to yield when loaded beyond its tensile strength. The successful completion of this work provides design data for scaling the development of KOH reactor and a framework for KOH production from the cocoa pod husk.
Decision analysis for optimal design concept: hybridized fuzzified weighted decision matrix and fuzzy topsis using design for X tools.
(Elsevier, 2019-01-01) Olabanji, Olayinka Mohammed; Mpofu, Khumbulani
The need for designs with extended capabilities and customization calls for a robust conceptual design phase of an equipment or a product. It becomes important to appraise different design concepts using a holistic approach for the comparison process in order to obtain an optimal design. In this article, a decision analysis for selecting optimal design of a Reconfigurable Assembly Fixture (RAF) from a set of alternative design concepts is presented by developing a Multi-Attribute Decision Model (MADM). The MADM developed in this article hybridize the aggregating strength of the fuzzified weighted decision matrix and computational strength of the fuzzy TOPSIS in terms of separation from positive and negative solutions and closeness coefficient to ideal solution. The design for X tools (manufacture, assembly and disassembly, maintenance, reliability, life cycle cost, serviceability and environment) are used to represent the design features and factors or constraints for evaluating the designs with several sub-factors in order to have a robust decision process. Two other tools added to the X-tools are design for reconfigurability and functionality. These two tools were added in order to consider the reconfigurable characteristic features for the design concepts of the RAFs. Results obtained from the decision process proves that there are slight differences in the final values of the design concepts which can be linked to the performance of each design concept in the decision process and the weights or priority of the design features. Also, the close variations in the final values of the design concepts may be due to inclusion of weights of the design attributes in computation of the distances to the positive and negative ideal solutions.
Development and simulation of isotropic hardening for AISI 1035 weld stress prediction during design and welding assembly of lower brackets of rail cars.
(Elsevier, 2019-01-01) Daniyan, I. A.; Mpofu, K.; Fameso, F. O.; Adeodu, A. O.; Bello, K. A.
Residual stresses through plastic deformation during the assembly operation of a rail car can cause cracks, distortion or other associated welding flaws if not checked. This study considers the use of analytical and numerical methods involving the simulation of isotropic hardening models for the prediction of AISI 1035 weld residual stresses during the arc welding assembly of the lower brackets of a rail car. Using the isotropic hardening model as well as the ABAQUS simulation software, the dynamic simulation of the gas metal arc welding process was performed at ambient temperature of 25oC over varying temperatures. The thermal modelling of the weld pass was carried out followed by the determination of the stress and strain values from the combined thermal elastic-plastic analysis. The results obtained indicated that the predictive ability of the isotropic model is highly significant, with the values of the weld stress and corresponding strain agreeing closely with the experimental values over varying ranges of temperatures. Hence, the isotropic hardening model can be used for weld residual stress simulation and prediction. Future work can consider the combination of both the isotropic and kinematic models.
Analysis and performance investigation of a reconfigurable vibrating screen machine for mining and mineral processing industries.
(Elsevier, 2019-01-01) Ramatsetse, Boitumelo; Mpofu, Khumbulani; Makinde, Olasumbo Ayodeji
Reconfigurable Vibrating Screen (RVS) machine is an innovative beneficiation solution designed for screening mineral particle into various sizes and volumes demanded by the customers at any particular time in a cost-effective manner. In order to ensure optimal functionality, reliability and maintain ability of the RVS machine when used in the mining industry. There is a need to investigate the performace of this machine using smart measurement and monitoring technology in order to effective using data acquisition system; DAQ which recover raw perfromance data via the sensor connencted to be machine. Multi-point modelling experiments were conducted in order to measure stress distributions on various subcomponents of the RVS machine at different configurations of 305 mm × 610 mm, 305 mm × 1220 mm and 610 mm × 1220 mm respectively. Furthermore, sets of experiments were conducted to determine the stress distribution experienced on the RVS machine structure using the worst loading conditions. The results of the experimental study revealed that the current stresses on the RVS machine subsystems during the granite run-off particles screening operation are subjected to pressure of 5.01 MPa, 1.25 MPa, 0.55 MPa, 0.37 MPa and 1.76 MPa, which are less than the critical or maximum threshold stress limits of these RVS machine subsystems of 20.8 MPa, 7.4 MPa, 4.3 MPa, 16.3 MPa and 13.6 MPa respectively as determined through simulation.
Design of a Control System for a Vending Machine.
(Elsevier, 2020-01-01) Murena, Eriyeti; Sibanda, Vennan; Sibanda, Solomon; Mpofu, Khumbulani
Vending machines are available in many public places for vending of items like snacks, beverages, newspapers, tickets and smoking cigarettes Recently developed vending machine requires a control system to offer a variety of products to the general public. In this light, this paper, therefore, is aimed at developing a control system for the developed vending machine by developing various inputs required to make the machine function efficiently. The system controls and monitors the vending machine functions, namely: alarm system, product dispensing, refrigeration and payment system. The microcomputer capitalises on the evolution of high-performance processors and stable operating systems to implement control requirements. The project shall use intelligent vending machine input/output board to link other machine peripherals. The control system shall enable the machine to handle coin, mobile and point of sale terminal payment options. Implementation of the control system enhances flexibility in payment, remote machine monitoring and inventory control, and improved user experience through the integration of digital touch screen user interfaces and high-speed transaction processing.
Conceptual design of intelligent reconfigurable welding fixture for rail car manufacturing industry.
(Elsevier, 2020-01-01) Seloane, W.T.; Mpofu, K.; Ramatsetse, B.I.; Modungwa, D.
Dedicated welding fixtures are used in the railcar manufacturing industry to fix and stabilise the welded components during the welding assembly process in order to achieve the highest possible accuracy and repeatability of connected components. Utilisation of dedicated welding fixtures in a railcar manufacturing industry leads to high production setup, high manufacturing costs, low quality standards and storage difficulties. Furthermore, because of lack of ability to disassemble, traditional welding fixtures cost a fortune in storage costs. In order to reduce cost, increase productivity and meet the market demands, there is a growing requirement for the tools to be flexible, reconfigurable and reconstructible. This paper presents the conceptual designs of the intelligent reconfigurable welding fixtures aimed to address the current problems encountered by the railcar manufacturing industry when utilizing dedicated fixtures. Three design concepts of the Intelligent Reconfigurable Welding Fixture (IRWF) were generated using Computer Aided Design (CAD) called Unigraphics. Weight design matrix concept evaluation criteria was used to select the most suitable concept based on functional requirements such as reconfigurability, intelligence, assembly, manufacturability and maintenance ability. Concept 3 was selected because it satisfied most of the functional requirements of the IRWF
Exploring energy efficiency prediction method for Industry 4.0: a reconfigurable vibrating screen case study.
(Elsevier, 2020-06-15) Adenuga, Olukorede Tijani; Mpofu, Khumbulani; Ramatsetse, Boitumelo Innocent
The shift towards energy efficiency (EE) programme in accelerating a resource efficient society requires the development of a portfolio for assessing and demonstrating a solution to support decision-making, planning and policy. EE market entails building capacity for industry initiatives in energy savings towards a culture of greater resource efficiency. Many energy predictions have been research by analysing of a large amount of historical and sensing data with high accuracy of the prediction results. However, most of these researches does not apply in practice to energy efficiency and IoT energy management systems, which involves real-time data performance. The proposed methodology is conceptualise on cyber physical systems and application of directed communication approach through a direct and indirect information exchange between agents. This paper explores prediction of energy demands based on measurement data and a statistical regression model using a variable frequency drive (VFD) to control reconfigurable vibrating screen (RVS) machine fixed-speed electric agitating motor. The model acceptance criteria provide a prediction method of energy costs against usage in mining of materials through load testing with measured data every 2 minutes for 24 hours. The logged data prediction accuracy reached 98.47% according to material screening rate, showing close alignment to the measured model and 96.97% coefficient of determination, showing the percentage of variation of independent variables (energy cost with VFD) that affect the dependent variable EED. The study will assist in reducing the energy consumption in conformance to South Africa Department of Energy (DoE) vision to support sustainability in manufacturing.
Prescriptive modelling system design for an armature multi-coil rewinding cobot machine.
(Elsevier, 2020-01-01) Matenga, Alice; Murena, Eriyeti; Mpofu, Khumbulani
Digital transformation has ushered in the digital economy, powered by digital intelligence and quantum computing. The various winding topologies in rotary machines result from multi-variant design specifications and connection types. Rewinding of rotary machines is a behaviour-based decision making process conducted within the shop floor, as the procedure is dependent on multi-input multi-output variables. Due to high data variability in service remanufacturing of armature windings in rotary machines, data abstraction for intelligent automation and analytics leads to increased operational productivity and new insights into market dynamics. In this light, the aim of the paper is to illustrate the design of a prescriptive modelling system of a symmetrical multi-coil winding machine for armature winding. The proposed system is a hybrid least squares support vector machine and adaptive neuro fuzzy inference system for optimizing and maintaining a copper fill factor at 90.7%. A mixed method research was utilized for qualitative and quantitative for the multivariate parameters. The results show that the system through in-slot repetitive orthocyclic winding process, with multi-spindle concentric layering improves the energy efficiency of the induction motors, which in turn lowers winding faults during the remanufacturing process. Streamlining operations through fog computing further enhances system latency and process reliability towards sustainable industrialization.
Design and simulation of a flexible manufacturing system for manufacturing operations of railcar subassemblies.
(Elsevier, 2021-01-01) Daniyan, Ilesanmi; Mpofu, Khumbulani; Ramatsetse, Boitumelo; Zeferino, Emanuel; Monzambe, Giovani; Sekano, Elvis
System (FMS) for manufacturing operations in the railcar industry will promote flexibility, intelligent coordination of the manufacturing operations, efficient handling and quality control. Some production systems in the railcar industry are non responsive to changes in real time thereby leading to reduction in productivity, hence, the need for a FMS that will cater for the dynamics of manufacturing operation. This work proposes a FMS, which encompasses the assembly line, lean production, logistics and quality assurance. The system comprises of the Radio Frequency Identification Technology (RFID) for components identification and process control, arrays of sensors and cameras, automated material storage and supply, standard interfaces such as the interface for the internet of things (IoT), the robotic welding system as well as a robust intelligent control system. A framework for the implementation of the FMS was developed while the simulation of the designed system was performed using the Anylogic 8.2.3. software. Based on the results obtained, there was an inverse relationship between the operating cycle time of the conveyor and the conveyor speed per cycle when the conveyor’s performance was simulated at a speed of 3 m/s and 7 m/s. The results showed that the system can suitably perform the sequence of assembly and quality assurance operations during the manufacturing of railcar subassemblies with minimal interruptions and human intervention. This will promote production of component parts with high structural and dimensional integrity with significant reduction in the manufacturing cycle time and cost
Artificial intelligence for predictive maintenance in the railcar learning factories.
(Elsevier, 2020-01-01) Daniyan, Ilesanmi; Mpofu, Khumbulani; , Oyesola; Ramatsetse, Boitumelo; Adeodu, Adefemi
The learning factories are platform created to provide an effective learning environment that will bring about human capacity development in a bid to bridge the gap between learning and practice. In this study, the training modules involving the Artificial Intelligence (AI) system, which comprises of the Artificial Neural Network (ANN) with dynamic time series model was developed. This is to train maintenance personnel on how to constantly monitor and analyze data from the Internet of Things (IoT) and other sources in order to predict the state and potential failure of a railcar wheel bearing. The modules for training include the data acquisition, pre-processing, network training, features extraction and predictive model modules which are set up to acquaint the personnel in the maintenance section of the rail industry on the use of AI for condition based monitoring and prediction of wheel bearing failure. The demonstration of the training modules was carried out using the past data of the wheel-bearing temperature from a secondary source which was pre-processed and iteratively trained using the Levenberg Marquardt algorithm in a MATLAB 2018a environment in order to predict future temperature variations, the remaining useful life of the bearing and to obtain a predictive model. The result obtained indicates the feasibility of the AI in the diagnosis of the railcar wheel bearing condition, prediction of the Remaining Useful Life (RUL) of the bearing as well as the determination of the optimum time for maintenance.
Analysis of surface post-processing techniques for improvement of additive manufactured parts in aerospace.
(SAGE, 2019-10-08) Oyesola, M. O.; Mpofu, K.; Mathe, N.; Hoosian, S.; Tlhabadira, I.
Additive manufacturing (AM) is a fast growing innovative technology with attractiveness to transform the manufacturing segment of the aerospace industries due to its ability to produce final usable parts. However, the surface finish of AM produced parts usually fall short of desires when compared to the conventional manufacturing method. Therefore, post-processing is often required for surface finishing as applicable in the aerospace. In this context, the AM post-processing techniques are presented along with application on parts fabricated with special attention to Ti6Al4V of titanium alloy material that is well recognised for manufacturing of aero-based parts. Machining is a popular post-processing method for finalising the surface finishing of a given part. However, machining for Ti6Al4V AM parts are known for certain challenges during process due to its inherent material properties and inadequacy for internal complex geometry parts. In this study, a series of other surface finishing techniques were investigated through testing and evaluation. The effectiveness of each processing technique is evaluated with respect to the surface topography in terms of Ra (arithmetical mean deviation of the profile) reduction value.
Development of a diagnostic and prognostic tool for predictive maintenance in the railcar industry.
(Elsevier, 2020-01-01) Daniyan, I.A.; Mpofu, K.; Adeodu, A.O.
The use of a diagnostic and prognostic tool for predictive maintenance serves as a continuous inspection, detective and predictive tool for making important decisions on maintenance activities before failure occurs. The prediction of failure is important in the railway industry in reducing the maintenance and operating cost, minimizing interruptions, risk, unscheduled maintenance and accidents while enhancing higher productivity and component lifespan. In this study, a diagnostic and prognostic tool was developed to constantly monitor and predict the rate of degradation and Remaining Useful Life (RUL) of a railcar wheel bearing. The tool uses the envelope spectrum and kurtosis analysis, which employs the wheel acceleration data obtained from a primary source and the data was interpreted with the aid of statistical and computational methods. The input data was first pre-processed and important features are extracted in a MATLAB 2018b environment. The extracted features were thereafter integrated into the diagnostic and prognostic tool with a pre-set threshold value or feature for the wheel acceleration for predictive purpose. The results obtained indicate the suitability of the diagnosis and prognostic tool for the determination of the railcar wheel condition, prediction of the Mean Time to Failure (MTTF), as well as the remaining useful life of the railcar bearing.
Artificial intelligence system for enhancing product’s performance during its life cycle in a railcar industry.
(Elsevier, 2021-01-01) Daniyan, Ilesanmi; Muvunzi, Rumbidzai; Mpofu, Khumbulani
The management of the activities which characterize the lifecycle of a product could be challenging, hence, a well-structured Product Lifecycle Management System (PLMS) with Artificial Intelligence (AI) capability can offer a sustainable solution in this regard. This will promote data driven maintenance because AI and data analysis can drive the activities of the entire product’s lifecycle. The aim of this study is to demonstrate the use of AI for enhancing products’ performance during its use phase in the products’ life cycle and to develop a framework for the proposed PLMS and AI capability. The bearing component of a railcar was used as a case study. The temperature data of the bearing component employed were obtained from primary and secondary sources and were pre-processed in order to extract features and indicators for the training and predictive model development. The acquisition of the input data was followed by data pre-processing to remove noise and iterative training to obtain the predictive model. The training was done using the Levenberg Marquardt algorithm in a MATLAB 2018a environment in order to predict future temperature variations and the remaining useful life of the bearing. The results obtained indicated that the AI is suitable for condition based monitoring and prediction of the time to failure as well as the Remaining Useful Life (RUL) of the railcar bearing. It is envisaged that with the AI capability integrated into the PLMS will enhance components performance through effective monitoring during its use phase.
Cellular demanufacturing layout in a rail industry: End-of- life components reusability.
(Elsevier, 2019-01-01) Phuluwa, Humbulani Simon; Mpofu, Khumbulani; Trimble, Johh Alfred
Rail manufacturing industry across the globe has been growing exponential since the inception of rail into transportation systems. Freight and passenger trains manufacturing contributes a greater proportion to the rail industry economic impact. The passenger trains are a mode of transport for both poor and rich people in Africa. Due to lack of appropriate demanufacturing plant layouts suited for recovering end-of-life (Eol) components, most of components are often neglected to the landfills. Most African countries have been importing trains for many years around the globe. Some of these trains have reached or are reaching Eol, which makes it difficult for countries to develop or have appropriate demanufacturing plant layouts or technologies for recovering Eol components. The study reviewed various literature on train life cycle management system and cellular demanufacturing layout configurations applied across various industries in the globe. The study further looked into different hybrid layout modalities and a simulation was used to determine efficient models, which can best suit an African context. The study used a case study of Southern Africa to assess the readiness of countries to gain benefits of circular economy through efficient layouts. The study proposes a sustainable cellular layout conceptual model that will accommodate train components variation.
Life cycle assessment for the milling operation of titanium alloy (Ti6Al4V).
(Elsevier, 2022-01-01) Daniyan, Ilesanmi; Mpofu, Khumbulani; Bello, Kazeem; Muvunzi, Rumbidzai
There is a growing interest in titanium alloy for medical, aerospace, biomedical, automotive and rail applications due to its desirable properties such as its high strength to weight ratio and corrosion resistance ability. However, its low thermal conductivity can affect the energy consumption and rate of material removal thereby affecting the overall sustainability of the cutting operation. Hence, this study employs the Life Cycle Assessment (LCA) model for the investigation of the midpoint and endpoint characteristics of the milling operation of titanium alloy (Ti6Al4V) under the cooling and non-cooling conditions. The input and output of the milling process serve as the life cycle inventory. The Environmental Impact Assessment (EIA) was carried out with the aid of the Umberto NXT universal with database Eco-invent version 3.0. (CML 2001 and Impact 2002+ valuation standards). For the mid-point assessment, the results obtained indicate that the ozone layer depletion and global warming potential were the most significantly impacted potentials during the milling operation. However, the potential impacts were more significant under the cutting operation without cooling. The endpoint assessment for a cutting operation with cooling, contributes 15%, 12% and 9% to the global warming, eco system and human health toxicity respectively. The cutting operation without cooling contributes 18%, 16% and 9% to the global warming, eco system and human health toxicity respectively. This study provides an insight into the sustainability of titanium alloy during the milling process in terms of the energy requirements and environmental friendliness of the process.
Simulation of kinematic hardening model for carbon steel AISI 1035 weld stress prediction during the welding assembly of a railcar.
(Elsevier, 2020-01-01) Daniyan, Ilesanmi; Mpofu, Khumbulani; Fameso, Festus; Ale, Felix
The modelling and dynamic simulation of the welding process and material’s behaviour under different loading and unloading conditions of the thermal, stress and strain is important in order to determine the effect of heat and stress distributions as well as the sustainability of the overall process. This work presents the simulation of kinematic hardening model for AISI 1035 weld stress prediction during the welding assembly of the lower brackets of a railcar. A simplified modelling and simulation of kinematic hardening for AISI 1035 weld stress prediction was carried out using the commercial software code ABAQUS ® Complete Abaqus Environment (CAE) 2017 edition. A combination of the coupled thermal-displacement and static general steps for thermal and mechanical analysis were employed in the Abaqus Standard Implicit module. The weld zone was loaded with the heat flux corresponding to a weld temperature of 900 °C in the thermal step and then propagated into the mechanical loading step where a load of 5 kN was impacted on the top of the weld zone within a step time of 15 milliseconds. The results obtained indicated that the maximum stress from the Von Mises stress exceeds the yield strength of the material. This points to the fact that the material will deform plastically under the application of stress. Beyond this point is the strain hardening point where the material is strengthened by plastic deformation. The findings of this work will assist manufacturers to address the issue of sustainability in terms of the process economics, social and the environmental impacts of the welding process from the design phase.
Process optimization of additive manufacturing technology: A case evaluation for a manufactured railcar accessory.
(Elsevier, 2020-01-01) Daniyan, Ilesanmi; Mpofu, Khumbulani; Oyesola, Moses; Daniyan, Lanre
The Additive Manufacturing (AM) technology of producing materials layers upon layers to make objects from a 3D model data is a manufacturing process which eliminates the use of tools and fixtures. It is highly flexible to design modifications and can reduce material wastage during manufacturing operations. In this study, the process optimization of a 3D printer for manufacturing the accessory of a railcar was carried out. The Response Surface Methodology (RSM) was used for the Design of Experiment (DoE) which consists of independent process parameters in the following ranges: scan speed (50-20 mm/sec), nozzle diameter (0.1-1.0 mm) thickness of layer (0.10-0.50 mm) and bed temperature (60-200℃). Taking the surface roughness as the response of the designed experiments, the four factors were varied over 2 levels and the statistical analysis of the results obtained was used for obtaining a predictive model which correlates the surface roughness of the plastic knob produced as a function of the independent process parameters. The results obtained indicate that the quality of the materials produced, which is a function of the finish requirement, depends on the print quality and the independent process parameters and vice versa. In addition, the rate at which the material run off the nozzle, which is a function of the manufacturing cycle time, is inversely proportional to the diameter of the nozzle. It is envisaged that the findings of this work will assist in the process design for component manufacturing using the additive manufacturing technology.
Computer aided modelling and experimental validation for effective milling operation of titanium alloy (Ti6Al4V).
(Elsevier, 2020-01-01) Tlhabadira, Isaac; Daniyan, Ilesanmi; Masu, Leonard; Mpofu, Khumbulani
Titanium alloy (Ti6Al4V) is characterized with excellent mechanical properties, which makes it suitable for many industrial applications. However, the poor rate of machinability mitigates the use of titanium alloy (Ti6Al4V). This study highlight the various methods to improve the machinability and surface finish of titanium alloy. These methods include; the use of PCD and PCBN tools, cooling systems, process optimization as well as the design and selection of the proper geometry for the cutting tool. The Computer Aided Design, modelling and simulation as well as the Finite Element Analysis of the milling process of Ti6Al4V was carried out using Solidworks 2016. The physical experiments were conducted on a DMU80monoBLOCK Deckel Maho 5-axis CNC milling with the stationary dynamometer (KISTLER 9257A 8-Channel Summation of Type 5001A Multichannel Amplifier) mounted directly to the machine table and the work piece screwed to it. The milling operations was carried out with different combination of cutting parameters while the values of the cutting force for each of the experimental trial was collected through the Data Acquisition System (DAS) connected to the computer. The results obtained show that the process parameters affect the magnitude of the cutting force significantly which in turn affects the rate of material removal. This work finds application in the manufacturing industry as it provides in depth understanding of the machining characteristics and behaviour of titanium alloy.

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