Application of Design for Manufacturing and Assembly: Development of a Multifeedstock Biodiesel Processor

Design for manufacturing and assembly (DFMA) is the method for process and cost optimization of subsystems, whole system as well as the entire manufacturing process. While minimizing assembly operations, it helps in eliminating component redundancy, facilitates assembly and manufacturing of products that are cost effective in terms of material requirements, parts production, labor and overhead. In this study, a multi feedstock biodiesel processor with intelligent systems for control and monitoring was developed using the principles of design for manufacturing and assembly. It consists of a 2 kW variable speed sparkless electric motor, reaction chamber, a thermostatically controlled 3 kW electric heater, saw dust insulation, ball valve, thermostat, funnel, a stirrer of diameter 20 mm with five blades that rotate in the reaction chamber and two baffles to control the splashing. The stirrer is driven by the electric motor. A 2 mm thick galvanized steel was used in the fabrication of the reaction chamber because of its high resistance to corrosion. This work provides a design framework for both small and large scale biodiesel plant for industrial, laboratory and experimental purposes. In addition, the assembly operations of the processor’s components via the principles of DFMA were simplified to reduce ambiguity and redundancy. Hence, the overall processor is cost effective in terms of material requirements, parts production, labor and overhead

Design, simulation and experimental investigation of a novel reconfigurable assembly fixture for press brakes

A reconfigurable assembly fixture is a major and important component of a reconfigurable assembly system. It isrequired for the assembly of a variety of press brake models inorder to reduce the assembly time and overall production time.The stages and requirements for the design of an assembly fixtureand understanding of the assembly process for press brakemodels were used to design a reconfigurable assembly fixture.A detailed design analysis of parts of the fixture and the hydraulicsystem is considered and presented in this article. The stress anddisplacement analysis of the parts is executed using Solidworksexpress simulation. The parameters of the hydraulic componentswere determined from force requirements, and the hydraulic system was modelled physically using Matlab Simscape hydraulics.The response of the hydraulic system was obtained for eachactuator in the system in order to depict the output of the actuators from the spool displacement of the valves. Stress analysisconducted on parts of the fixture showed that it can withstandmaximum stresses that are lesser than the yield strength of thematerial used for the part. It was also established that synchronization of hydraulic actuators can best be achieved by the use of asine input to the electrohydraulic valve. An experimental investigation was done using FESTO hydraulic test bench in order toobserve the synchronized extension and retraction of the hydraulic actuators. The simulation of the hydraulic system, electricsystem and the programmable logic controller was prepared using automation studio. The design is envisaged to provide the industries with relevant information on accurate location and gripping of press brake frames rather than turning and repositioning of the frame in order to fit other parts during assembly. The article provides relevant information on the design analysis of a reconfigurable assembly fixture for press brakes which is novel because articles on reconfigurable assembly fixtures have not considered its application to press brake assembly

Application of the Fourth Industrial Revolution for High Volume Production in the Rail Car Industry

Some recent technological advances in line with the fourth industrial revolution (4IR) are rapidly transforming the industrial sector. This work explores the prospect of robotic and additive manufacturing solutions for mass production in the rail industry. It proposes a dual arm, 12-axis welding robot with advance sensors, camera, and algorithm as well as intelligent control system. The computer-aided design (CAD) of the robotic system was done in the Solidworks 2017 environment and simulated using the adaptive neuro-fuzzy interference system (ANFIS) in order to determine the kinematic motion of the robotic arm and the angles of joint. The simulation results showed the smooth motion of the robot and its suitability to carry out the welding operations for mass production of components during rail car manufacturing. In addition, the ability to fabricate several physical models directly from digital data through additive manufacturing (AM) is a key factor to ensuring rapid product development cycle. Given that AM is embedded in a digitally connected environment, flow of information as well as data processing and transmission in real time will be useful for massive turnout during mass production

APPLICATION OF GROUP GENETIC ALGORITHM FOR GENERATION OF CELLS TO SOLVE A MACHINE LAYOUT PROBLEM

This paper explains the improvement of a layout arrangement as a result of application of Group Genetic Algorithm (GGA) on an excel platform for generaation of cells, in celluar manufacturing to minimize distance travelled and materials handling between workstations. It is based on a case study of ABC (Pvt) Ltd, a privately owned manufacturing company in Zimbabwe. The main objective of the study is to come up with manufacturing cells of machine part matrix generated from chromosomes using GGA. The researchers use the GGA to come up with a machine part matrix which reduces distances between machines which processes related parts. Excel is used in calculating fitness function values and the analysis of the best chromosome is done using the radar and line plots. From the study the first offspring in the second generation (chrom 4) is chosen as the best chromosome which enables best machine layout with 83% machine-part movement minimization and 62% machine utilization and 73% effectiveness

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al4V)

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters

Agent-Based Control System as a Tool towards Industry 4.0: Directed Communication Graph Approach

Agent-based control systems composed of simple locally interacting controller agents with demonstrated complex group behaviour. There have been relatively few implementations of agent-based control systems, mainly because of the difficulty of determining whether simple controller agent strategies will lead to desirable collective behaviour in a large system. The aim of this chapter is to design an agent-based control system for sets of ‘clustered’ controller agents through proposed directed communication graph approach as potent tool for the Industry 4.0. To reach global coordination with focus on real world applications, we use cluster algorithm technique in a set of rules for assigning decision tasks to agents. The outcomes include behavioural pattern, trend of agents and multi-agents usage in rail manufacturing enterprise resource planning and supply chain management. The results of this study showed that the combination of multi-agent system has ability to interact effectively and make informed decision on the type of maintenance actions, resource planning, train arrival times, etc

A case study of product-service integration for train braking systems

A product service system requires coordinated approach from multiple stakeholder groups. Industry, government, and civil society must work together to create and promote the deployment and smooth operation of these systems for a more sustainable economy. The train braking system problem areas such as failure detection, big data collection and sensor-based degradation monitoring have created opportunities for researchers to create jobs in the service sector. The paper aims to design product-service integration train braking system as a big data component with combination of dataset, volume, speed, and data diversity. The big data potentials and analysis using “V” model for train brakes integration and ishikawa diagram for the electro-pneumatic brake system that is applicable to the railcar brakes manufacturing industries fuse railcar’s sensory components innovation to market. This is where advanced analysis to examine the available data and organize it using advanced visualization techniques

Design and simulation of a bearing housing aerospace component from titanium alloy (Ti6Al4V) for additive manufacturing

In evaluating emerging technology, such as additive manufacturing, it is important to analyse the impact of the manufacturing process on efficiency in an objective and quantifiable manner. This study deals with the design and simulation of a bearing housing made from titanium alloy (Ti6Al4V) using the selective laser melting (SLM) technique. The Finite Element Analysis (FEA) method was used for assessing the suitability of Ti6Al4V for aerospace application. The choice of Ti6Al4V is due to the comparative advantage of its strength-to-weight ratio. The implicit and explicit modules of the Abaqus software were employed for the non-linear and linear analyses of the component part. The results obtained revealed that the titanium alloy (Ti6Al4V) sufficiently meets the design, functional and service requirements of the bearing housing component produced for aerospace application. The designed bearing is suitable for a high speed and temperature application beyond 1900 K, while the maximum stress induced in the component during loading was 521 kPa. It is evident that the developed stresses do not result in a distortion or deformation of the material with yield strength in the region of 820 MPa. This work provides design data for the development of a bearing housing for AM under the technique of SLM using Ti6Al4V by reflecting the knowledge of the material behaviour under the operating conditions

A framework for additive manufacturing technology selection : a case for the rail industry

Additive manufacturing is a popular emerging technology of producing parts directly from digital models. This technology has presented benefits such as freedom of design, the ability to customise, and shortened process chains. Presently, there are different additive manufacturing (AM) processes that are available in the market. Often, transport equipment manufacturing companies are faced with challenges while selecting the AM processes that are suited to their needs. Decision-makers need to consider all the underlining factors before a conclusion is reached. This paper proposes an approach that can be used by companies in the rail sector to select AM technologies that are suited to their applications. The approach involves identifying suitable parts, comparing applicable AM technologies, and selecting the most suitable technology. The next stage involves re-designing the parts based on the selected technology. The approach is applied to benchmark parts from the industry. The study provides enlightenment on how AM can be applied in the rail industry.The Open Access Processing fee for this article was paid in full by the Tshwane University of Technology, Pretoria, South Africahttps://www.igi-global.com/journal/international-journal-manufacturing-materials-mechanical/41020hj2023Materials Science and Metallurgical Engineerin