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Nowadays, there are various of optimisation methods that have been explored by many researchers to find the appropriate processing parameters setting for the injection moulding process. From the previous researches, it was reported that the optimisation work has improved the moulded part quality. In this study, the application of optimisation work to improve warpage of the front panel housing have been explored. By selecting cooling time, coolant temperature, packing pressure and melt temperature as the variable parameters, design of experiment (DOE) have been constructed by using the rotatable central composite design (CCD) approach. Response Surface Methodology (RSM) was performed to obtain the mathematical model. This mathematical model then will be used in Glowworm Swarm Optimisation (GSO) method in order to determine the optimal processing parameters setting which will optimise the warpage condition. Based on the results, melt temperature is the most significant factor contribute to the warpage condition and warpage have optimised by 39.1% after optimisation. The finding shows that the application of optimisation work offers the best quality of moulded part produced.

Today, there are many of optimisation method have been explored by previous researchers to find the appropriate processing parameters setting for the injection moulding process. From the previous researches, it has been proven that the optimisation work improved the quality of the moulded part. However, the application of optimisation work in conformal cooling channels still lacks. Therefore, in this study, the application of optimisation work to improve warpage of front panel housing with conformal cooling channels moulds have been explored. By choosing cooling time, coolant temperature, packing pressure and melt temperature as the variable parameters, design of experiment (DOE) has been defined by using the rotatable central composite design (CCD) approach. Response Surface Methodology (RSM) was performed to determine the mathematical model. The mathematical model then will be used in Glowworm Swarm Optimisation (GSO) method in order to obtain the optimal processing parameters setting which will optimise the warpage condition. Based on the results, cooling time is the most significant factor contributes to the warpage condition and warpage have optimised by 38.7% after optimisation using the proposed approach. This finding shows that the application of conformal cooling channels with optimisation work will produce better quality of the moulded part.

The work reported herein is about an analysis on the quality (shrinkage) on a thick plate part using Response Surface Methodology (RSM). Previous researches showed that the most influential factor affecting the shrinkage on moulded parts are mould and melt temperature. Autodesk Moldflow Insight software was used for the analysis, while specifications of Nessei NEX 1000 injection moulding machine and P20 mould material were incorporated in this study on top of Acrylonitrile Butadiene Styrene (ABS) as a moulded thermoplastic material. Mould temperature, melt temperature, packing pressure and packing time were selected as variable parameters. The results show that the shrinkage have improved 42.48% and 14.41% in parallel and normal directions respectively after the optimisation process.

The effect of manganese addition and annealing heat treatment on microstructure of austenitic cast irons with high manganese content (Mn-Ni-resist) were investigated. The complex relationship between the development of the solidification microstructures and buildup of microsegregation in Mn-Ni-resist was obtained by using microstructure analysis and EDS analysis. The annealing heat treatment was applied at 700°C up to 1000°C to investigate the effect of the annealing temperature on the microstructure. This experiment describes the characterization of microsegregation in Mn-Ni-reist was made by means of point counting microanalysis along the microstructure. With this method, the differences of silicon, manganese and nickel distribution in alloys solidified in the microstructure were clearly evidenced. The results show microstructure consists of flake graphite embedded in austenitic matrix and carbides. There is segregation of elements in the Late To Freeze (LTF) region after solidification from melting. Manganese positively with high concentration detected in the LTF region. As for heat treatment, higher annealing temperature on the Mn-Ni-resist was reduced carbide formation. The higher annealing temperature shows carbide transformed into a smaller size and disperses through the austenitic matrix structure. The size of carbide decreased with increasing annealing temperature as observed in the microstructure.

This work presents an attempt to study the effect of manganese addition and heat treatment on higher carbon austenitic cast iron to form high manganese austenitic cast iron with reduced nickel content (Mn-Ni-resist) on mechanical properties. The combination on microstructure (microsegregation), mechanical properties and the relationship of heat treatment on the alloy were analyzed. For this purpose Mn-Ni-resist (4.50C, 2.64Si, 6.0 Mn, 10 Ni) was melted and cast in the form of Y-block test pieces. Four different heat treatment procedures were applied to the as-cast to investigate the effect of alloy modifications on Mn-Ni-resist. Optical and scanning electron microscopies were used for microstructure investigation. To determine the mechanical properties tensile test and hardness test were carried out. The result indicates both composition and heat treatment affect the performance of Mn-Ni-resist intensively. Microprobe analysis shows some silicon segregation near the graphite and practically little segregation of manganese. The increase in manganese contents developed some fractions of segregated carbide structures in LTF region located at austenite eutectic cell frame, which caused the tensile properties to drop in a small range. Application of annealing heat treatment gradually changed the carbide formation, so is the material’s strength.

In this work, curing characteristics, tensile and physical properties of epoxidized natural rubber/rice husk (ENR-50/RH) compounds were investigated. Different RH loading (10, 20, 30, 40 and 50 Phr) and size (fine size at 100-300 μm and coarse size at 5-10 mm) were prepared and used. Results indicated that the scorch time (t2) and cure time (t90) became shorter with increasing RH content. In contrast, minimum torque (ML) and maximum torque (MH) increased with increasing RH content in the rubber compounds. Hardness and crosslink density showed improvement with increasing RH content. Tensile strength (Ts) and elongation at break (Eb) decreased slightly as RH content increased. However, the fine size of RH recorded better overall properties compared to the RH coarse size at same loading the rubber compound.

In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA). The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA) method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX) model. The performance of the model is validated using mean squares error (MSE) between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square). Based on the results obtained, MSE produce from Bat Algorithm (BA) is outperformed the Least Square (LS) method.

This study focuses on the optimisation of the injection moulding parameters to maximise the strength ofmoulded parts using a simulation software. The moulded parts were injected with Acrylonitrile- Butadiene-Styrene (ABS) whereas mould temperature, melt temperature, packing pressure and packing time were selected as variable process parameters. The polynomial model obtained using Design of Experiment (DOE) was integrated with the Response Surface Methodology (RSM) and Centre Composite Design (CCD). The RSM was supported with Genetic Algorithm (GA) to anticipate the optimum value of processing parameters with the highest strength. It was found that strength of the parts can be improved 2.2% using the methodology reported herein.

This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS) and differential evolution (DE) algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE). Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.

This paper presents an investigation into the parameter estimation of the damped compound pendulum using Firefly algorithm method. In estimating the damped compound pendulum, the system necessarily needs a good model. Therefore, the aim of the work described in this paper is to obtain a dynamic model of the damped compound pendulum. By considering a discrete time form for the system, an autoregressive with exogenous input (ARX) model structures was selected. In order to collect input-output data from the experiment, the PRBS signal is used to be input signal to regulate the motor speed. Where, the output signal is taken from position sensor. Firefly algorithm (FA) algorithm is used to estimate the model parameters based on model 2nd orders. The model validation was done by comparing the measured output against the predicted output in terms of the closeness of both outputs via mean square error (MSE) value. The performance of FA is measured in terms of mean square error (MSE).

In this study, the influence of Trans- Polyoctylene Rubber (TOR) as a compatibilizer on the cure characteristics, tensile and physical properties of ENR-25/rSC vulcanizate were determined. Five different loading of TOR (2, 4, 6, 8 and 10 phr) were prepared and added into the vulcanizate. Results indicated that the scorch time (t2) and cure time (t90) bacame shorter as TOR increased, while minimum torque (ML) and maximum torque (MH) increased. The incorporation of TOR with the vulcanizates enhanced the tensile strength (Ts), modulus (M100) crosslinking density and hardness values. However, the elongation at break percentage of compatibilized vulcanizates became lower than uncompatibilized vulcanizates.

Selective Laser Melting (SLM) is an Additive Manufacturing (AM) technique that built 3D part in a layer-by-layer method by melting the top surface layer of a powder bed with a high intensity laser according to sliced 3D CAD data. AlSi10Mg alloy is a traditional cast alloy that is broadly used for die-casting process and used in automotive industry due its good mechanical properties. This paper seeks to investigate the requirement SLM in rapid tooling application. The feasibility study is done by examining the surface roughness and dimensional accuracy as compared to the benchmark part produced through the SLM process with constant parameters. The benchmark produced by SLM shows the potential of SLM in a manufacturing application particularly in moulds.

Injection moulding is the most widely used processes in manufacturing plastic products. Since the quality of injection improves plastic parts are mostly influenced by process conditions, the method to determine the optimum process conditions becomes the key to improving the part quality. This paper presents a systematic methodology to analyse the shrinkage of the thick plate part during the injection moulding process. Genetic Algorithm (GA) method was proposed to optimise the process parameters that would result in optimal solutions of optimisation goals. Using the GA, the shrinkage of the thick plate part was improved by 39.1% in parallel direction and 17.21% in the normal direction of melt flow.

Selective Laser Melting (SLM) is one of the most effective powder bed technique in the additive Manufacturing (AM) which able to fabricate functional metal parts directly from 3D Computer Aided Design (CAD) file data. In this paper, the influence of SLM parameters, such as laser power, scanning speed and hatching distance on the density of AlSi10Mg samples are investigated using one factor at a time (OFAT). Furthermore, the optimum results are used to fabricate samples for hardness, tensile strength, and impact toughness test. It is revealed that AlSi10Mg parts fabricated by SLM achieving the best density of 99.13% at the value of 350 watts laser power, 1650 mm/s scanning speed and hatching distance 0.13mm, whereby resulted comparable and even better mechanical properties to those of conventionally HDPC A360F and HDPC A360T6 alloys although without any comprehensive post processing methods.