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Purpose: This study aimed at implementing lean six sigma to evaluate the productivity and manufacturing waste in the production line of a paper companyMethodology/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 waste. The study considered a real-time problem of customer’s dissatisfaction. The gathered data is based on machine functionality (up time, down time and cycle time); materials and labour flow at every process stages 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 chartFindings: 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 period of time, there are lots of improvements in the production line in terms of all the parameters considered.Research Limitation/ Implications:  The study has demonstrated an 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 learned and implications presented can still be further modeled using some lean based software for validityOriginality/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 concentrate more on discrete based manufacturing.

Cu-Zn alloy (Brass) is widely used as an industrial material because of its excellent characteristics such as high corrosion resistance, non-magnetism and good forging ability. This paper evaluates the mechanical and microstructure properties of α-brass alloy gotten from scrap copper and zinc metal, and compares the properties with normal α-brass billets. Five different compositions of the α-brass alloy (Cu-5%Zn, Cu-10%Zn, Cu-15%Zn, were produced from scraps of copper wire and zinc batteries casing respectively by method of sand casting. The parts of the cast rods were machined to a specification of 60 mm × 100 mm × 300 mm on a lathe to obtain tensile test specimens. After homogenization annealing, the samples were heated in an electric furnace at 500˚C for 3 hours. The samples were etched with ferric chloride solution for 20 seconds and sent for metallographic examination. The result of the hardness test shows variation in hardness of the cast Cu-Zn alloys with increasing zinc content. The ductility and elongation of the α-brass decrease with increasing zinc content. The colouration of the α-brass changed from red to yellow as the zinc content increases. In conclusion, hard brass can be obtained from recycled Cu and Zn as compared to normal brass billets.

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.

Abstract:Modeling of composite curing process is required prior to composite production as this would help in establishing correct production parameters thereby eliminating costly trial and error runs. Determining curing profile temperatures from experiment is a huge challenge which in itself is like re-inventing the wheel of trial and error, when mathematical models of physical, chemical and kinetic properties of the constituent materials could be used in modeling the cure situation to some degree of trust. This work has modeled two types of polymer based composite materials (Aluminum filled polyester and carbon-black filled polyester) representing polymer-metal and polymer-organic composites in order to predict the possible trends during conventional autoclave heating with regards to effect of heating rate on degree of cure of the composites. The numerical models were constructed by taking into account the heat transferred by conduction through the resin/filler mixture, as well as kinetic heat generated by cure reaction. The numerical solution of the mathematical models presented were discretized using forward finite differences of the Runge Kuta Method and finally solved using MATLAB® C programming language. It was observed that Aluminum filled polyester composite responded faster to heat input-induced curing and as such was able to cure faster than polyester -carbon black composite which had much slower cure -heat input response. This implies that in the production process of polymer-organic composites, faster heating rate was necessary to input heat into the process as there was no heat of reaction released during the cure process whereas, polymer-metal composites release heat of reaction contributing to the quick transfer of heat into the metal components causing the metal components to behave as points of adhesion to the polymer matrix thereby necessitating a slower heating rate.