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The need for improved productivity without sacrificing quality, which is in line the prime target of many manufacturing industries. The aim of this study is to investigate the causes of production variation: a case study of the rail manufacturing industry, South Africa. In this study, the six-sigma Define, Measure, Analyse, Improve and Control (DMAIC) phases were applied to enhance the process capability (long term) in the production of bolster compression springs in the main line of bogie secondary suspension system. In every phase of DMAIC method, a combination of both qualitative and quantitative techniques was utilized. First, process capability index Cpk of the current process was computed which was found less than 1. The results obtained indicated that the process capability index values were found to be 1 after the improvement phase. Hence, significant improvement was achieved in the area of reduction in process variation and product quality after taking corrective actions. From outcomes of the study, it can be concluded that process performance of a train manufacturing plant can be improved significantly by implementing six-sigma DMAIC methodology. The novelty of this study lies in the fact that the implementation of the six-sigma DMAIC phases to enhance the process capability (long term) and minimise variations in the production of bolster compression springs has not be sufficiently highlighted by the existing literature. [ABSTRACT FROM AUTHOR]

Purpose: This study aims at implementing lean six sigma to evaluate the productivity and manufacturing wastes in the production line of a paper company Methodology/Approach: The study is a case study in nature. The method illustrates how lean six sigma (LSS) is used to evaluate the existing production process in a paper production company with focus on productivity and manufacturing wastes. The study considered a real-time problem of customer's dissatisfaction. Gathered data were based on machine functionality (up time, down time and cycle time); materials and labour flow at every process stage of the production line. The optimization of the production process was based on lean tools like value stream mapping, process cycle efficiency, Kaizen, 5S and pareto chart Findings: Based on lean six sigma application, it was discovered that the present production performance was below standard and more manufacturing wastes were generated. The present productivity and manufacturing wastes are reported as low process cycle efficiency (23.4 %), low takt time (4.11 sec), high lead time (43200sec), high number of products not conforming to six sigma values, high down time (32.64 %) and excess labour flow (33). After the implementation of the lean six sigma tools for certain periods, there were lots of improvements in the production lines in terms of all the parameters considered. Research Limitations/ Implications: The study has demonstrated application of lean six sigma in the case of solving real-time problems of productivity and manufacturing wastes which have a direct implication on customer's satisfaction. The lesson learnt and implications presented can still be further modeled using some lean based software for validity Originality/Value: The study has contributed to the body of knowledge in the field of LSS with focus to process-based manufacturing, unlike most literature in the field which concentrated more on discrete based manufacturing. [ABSTRACT FROM AUTHOR]

The increasing need to develop renewable sources of energy to complement the energy generated via fossil fuel necessitates this study. This study demonstrates the process control and automation of a biodiesel processing plant. The developed biodiesel processing plant has a total output capacity of 200 litres per day with nine compartments divided into three major sections. The first section (pre-processing section) has the oil storage, pre-treatment, methanol and mixing tanks. The second section (main processing section) comprises of the reactor. The last section (post-processing section) comprises of the water, wash and separating tanks as well as the glycerol and biodiesel storages. The control and automation was achieved with the following components; three 3 kW variable speed electric motor, four 2.5 kW pumps, three 300 kW electric heater that are thermostatically controlled. Other materials used for the control and automation include: real time clock, Atmega328 microcontroller, LM6009 and LM 2596 modules, Liquid Crystal Display (LCD), switches, relay, LED, three temperature probes, 220 V AC alarm, four flow switches, 220 V timer relay, 220 V and 60 A contactor. Sensors are incorporated to measure the temperature, pressure, and flow level in the main reactor and the measured variables compared with the threshold values pre-set on the micro controller for control actions. The results obtained indicated that the performance of the plant was enhanced with the capacity to independently execute control actions during the biodiesel production. Thus, this study adds to the practical knowledge of system's control and automation for biodiesel production. [ABSTRACT FROM AUTHOR]

Welding is a complex process requiring combination of several process parameters. In this study, the effect of the thermal and electrical properties of carbon steel STKM13A during the welding assembly of rail car suspension system was investigated using numerical experiment. The welding parameters for the welding simulation process of the suspension system of the rail car are given as the following: voltage (24 V), current (220 A), speed (0.005 m/s), arc length (0.01 m), and efficiency (0.9). This serves as the input parameters into Simufact software version 7.2 for the simulation of the arc welding process at ambient temperature of 25 °C. Also, in order to study the effect of thermal and electrical conductivity on the welding process, the thermal and electrical modeling was carried out at room temperature to melting temperatures (25–1500 °C) using the average values of three critical parameters, namely specific heat (700 J/kg K), thermal conductivity (40 W/m K), coefficient of thermal expansion (1.5 × 10−5 K−1), electrical conductivity (2.5×10−7 − 1.14 × 10−6Ωm), and resistivity (4.0×10−6 − 8.77 × 105Ωm−1), as input parameters into the software. The statistical analysis of the results obtained brought about the development of a model that correlates electrical resistivity as function of the welding parameters for the optimization of welding process. In addition, the five input parameters are critical as they affect welding processes significantly, hence should be controlled to promote safe, reliable, and efficient welding processes. [ABSTRACT FROM AUTHOR]

Arc welding can be used to weld the components of rail car brackets during assembly operation. In this study, the welding parameters, namely voltage, current, speed, and arc length, were optimized using the Taguchi method and response surface methodology (RSM). A predictive model was thereafter obtained that correlates the weld distortion and hardness as a function of the welding parameters. The experimental design which consists of Taguchi orthogonal array of L9, three level-four factor matrix was used as input parameters to determine the weld distortion and hardness while the RSM was used to study the cross effect (interaction) of various process parameters on the weld distortion. Analysis of variance (ANOVA) was used to further test and validate the developed model for adequacy and the regression model was found to be highly significant at 95% confidence level as correlation coefficients were very close to 1. The results obtained also show high degree of convergence of the welded components with changes in welding parameters indicating that weld distortion is kept within the permissible range. This will reduce the production cost and increase the quality as well as the reliability of weld during welding processes. [ABSTRACT FROM AUTHOR]

Modular arrangements of predetermined time standards (MODAPTS) is an effective and efficient method to measure work and the activities associated with it. The time standard is used all over the world in different industries, but the method is old, slow, and difficult for first-time users to work with. This study applied neural networks (NN) to MODAPTS. Primary training data in the form of MODAPTS keywords were employed. The training data were acquired as raw data in the form of MODAPTS time studies. These data however was then broken and processed to extract the keywords for the training of the NN. The NN was also trained with the data collected using the TensorFlow algorithm assisted by the Keras library. This was achieved by first learning the fundamentals of creating a NN. Thereafter, consolidating several tools, such as the Python programming language and the Keras library, were used to implement the artificial neural network (ANN). The results obtained indicated that 94.7 % of successful predictions were made by the NN while only 5.3 % were manually entered codes to correct the ANN chatbot. The mean difference between the two methods is 0.25 minutes; the t-test was calculated at 95 % confidence level (0.05) and produced a P-value of 0.9663. The computed P-value was greater than 0.05, showing that there is no significant difference between the two generated studies. The MODAPTS-ANN technique demonstrated in this study possesses great potential to improve and refine work measurement. [ABSTRACT FROM AUTHOR]

The need for an efficient welding process which is fast, safe, cost-effective and will produce a welded structure that combines high strength with integrity during railcar manufacturing is necessary due to the increasing demand and complexity of the manufacturing process. In this study, the Taguchi method and response surface methodology (RSM) were used for the numerical experimentation design of a railcar bogie frame. The modelling and simulation of the railcar bogie frame was conducted using the Commercial Abaqus software 2018. Using the Taguchi method, the designed experiment consists of an orthogonal array of four factors varied over three levels with the welding parameters in the following ranges: voltage (22–25 V), current (200–250 A), speed (0.01 m/s) and arc length (0.03–0.07 m) as input variables. The matrix generated nine experimental runs whose weld distortions were determined via the physical experimentations. Also, the RSM was used to study the cross effect of the input welding parameters on the welded structure. The statistical analysis of the results obtained brought about the development of a predictive model for the determination of weld distortion as function of the independent welding input parameters. In addition, the results indicate that the input welding parameters are critical factors that affect welding processes significantly, hence the need for control. This work will assist manufacturers in the quest for effective process control during the welding operation. [ABSTRACT FROM AUTHOR]