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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.

The PID classic control systems are often employed for rail car systems to reduce the vibrations and disturbance rate during movement. In this study, the dynamic modeling and simulation of PID controls for rail car systems were carried out. Using 9 degrees of freedom, the modeling process comprises the representation of the rail car system and the rail track followed by the generation of equations of motion as well as differential equations for the rail car body, wheel sets and bogie. The represented systems are simulated in the MATLAB Simulink 2018 environment based on the equations of motion generated, and subsequently vibration analysis was carried out. The PID control system tuned according to the Nichols-Ziegler rule was introduced to minimize the vibrations and disturbance rate. The performance of the control and the rail car system in terms of the input step response, bandwidth, frequency, phase margin, frequency and input and output rejections was evaluated. The control system demonstrated significant robustness in providing the required active control for the system, while there was improved stability and reduction in noise and vibration under control action of the PID, thus improving ride comfort.

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.