Non-CFC refrigerants: first and second law efficienies

Concern about the ozone depletion and green house effects caused by refrigerants have initiated and continued studies into more environmentally friendly refrigerants. This study looked into the performance of these refrigerants in terms of second law efficiency, COP, irreversibility, and discharge temperature. A program based on Visual Basic has been developed that can quantify the parameters above and this can be used to guide industrialists in their efforts to build or retrofit systems with new refrigerants. Results from the simulation have shown that R134a is potentially good as a replacement for R12, R402a for R502, and R407c for R22

Robotic arm system with computer vision for colour object Sorting

This study presents the development of robotic arm with computer vision functionalities to recognise the objects with different colours, pick up the nearest target object and place it into particular location. In this paper, the overview of the robotic arm system is first pre-sented. Then, the design of five-degrees of freedom (5-DOF) robotic arm is introduced, followed by the explanation of the image proc-essing technique used to recognize the objects with different colours and obstacle detection. Next, the forward kinematic modelling of the robotic arm using Denavit-Hartenberg algorithm and solving the inverse kinematic of the robotic arm using modified flower pollination algorithm (MFPA) are interpreted. The result shows that the robotic arm can pick the target object accurately and place it in its particular place successfully. The concern on user safety is also been taken into consideration where the robotic arm will stop working when the user hand (obstacle) is detected and resume its process when there is no obstacle

Application of wavelet analysis in tool wear evaluation using image processing method

Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The re-sults showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301

A carnivorous plant algorithm for solving global optimization problems

In this study, a novel metaheuristic algorithm, namely, carnivorous plant algorithm (CPA), inspired by how the carnivorous plants adapting to survive in the harsh environment, was proposed. The CPA was first evaluated on thirty well-known benchmark functions with different characteristics and seven CEC 2017 test functions. Its convergence characteristic and computational time were analysed and compared with seven widely used metaheuristic algorithms, with the superiority was validated using the Wilcoxon signed-rank test. The applicability of the CPA was further examined on mechanical engineering design problems and a real-world challenging application of controlling the orientation of a five degree-of-freedom robotic arm. Experimental simulations demonstrated the supremacy of the CPA in solving global optimization problems

Incorporating environmental elements in property marketing strategy in Kuala Lumpur

Half of the world population all over the countries reside in the cities. By 2050, the world proportion is likely to reach 75%. Malaysia is an urban society with majority people of the country approximately 70% living in the cities. The high demand of accommodation in the cities, and many developers supply the housing unit through condominium complex to fulfil the requirement of accommodation. Every day the number of condominium is increasing in Kuala Lumpur city. The natural green environment is decreasing with destructive impact on physical, mental illness and many problems among the people reside in the city compare to the rural. The modern developers in Kuala Lumpur facing difficulties to influence the target customers due to the lack of green environmental elements in a housing project and marketing strategy are one of the great problems to achieve the high performance of sales. Therefore, incorporate of important environmental elements in a housing project and marketing strategy to achieve the high performance of sales. The level of importance evaluates through quantitative research method with five (5) points Likert types scale. The data collected from Kuala Lumpur city area among condominium users, tenant, owner, management team and developers employees including marketing staff, managers, sales staff, and sales agents altogether 509 respondent. More than 85% respondents are agreed the environmental elements are very important at the condominium complex to have a healthy city life, and it strongly influences customers to buy or rent the apartment units. The green marketing is acting as a mediation to contribute the high performance of sales. As a result, less or no difficulty to reach the high performance of sales. In conclusion, those project has the most demanding environmental elements are more successful projects, compare to less or non-existing environmental facilities projects in Kuala Lumpur, Malaysia

Intelligent approach for processmodelling and optimization on electrical dischargemachining of polycrystalline diamond

Polycrystalline diamond (PCD) is increasingly becomes an important material used in the industry for cutting tools of difficult-to-machine materials due to its excellent characteristics such as hardness, toughness and wear resistance. However, its applications are restricted because of the PCD material is difficult to machine. Therefore, electrical discharge machining (EDM) is an ideal method suitable for PCD materials due to its non-contact process nature. The performance of EDM, however, is significantly influenced by its process parameters and type of electrode. In this study, soft computing technique was utilized to optimize the performance of the EDM in roughing condition for eroding PCD with copper tungsten or copper nickel electrode. Central composite design with five levels of three machining parameters viz. peak current, pulse interval and pulse duration has been used to design the experimental matrix. The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). It was found that copper tungsten electrode gave lower EWR, in comparison with the copper nickel electrode. The predictive model of radial basis function neural network (RBFNN) was developed to predict the MRR and EWR of the EDM process. The prominent predictive ability of RBFNN was confirmed as the prediction errors in terms of mean-squared error were found within the range of 6.47E−05 to 7.29E−06. Response surface plot was drawn to study the influences of machining parameters of EDM for shaping PCD with copper tungsten and copper nickel. Subsequently, moth search algorithm (MSA) was used to determine the optimal machining parameters, such that the MRR was maximized and EWR was minimized. Based on the obtained optimal parameters, confirmation test with the absolute error within the range of 1.41E−06 to 5.10E−05 validated the optimization capability of MSA

Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond

Polycrystalline diamond (PCD) is increasingly becomes an important material used in the industry for cutting tools of difficult-to-machine materials due to its excellent characteristics such as hardness, toughness and wear resistance. However, its applications are restricted because of the PCD material is difficult to machine. Therefore, electrical discharge machining (EDM) is an ideal method suitable for PCD materials due to its non-contact process nature. The performance of EDM, however, is significantly influenced by its process parameters and type of electrode. In this study, soft computing technique was utilized to optimize the performance of the EDM in roughing condition for eroding PCD with copper tungsten or copper nickel electrode. Central composite design with five levels of three machining parameters viz. peak current, pulse interval and pulse duration has been used to design the experimental matrix. The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). It was found that copper tungsten electrode gave lower EWR, in comparison with the copper nickel electrode. The predictive model of radial basis function neural network (RBFNN) was developed to predict the MRR and EWR of the EDM process. The prominent predictive ability of RBFNN was confirmed as the prediction errors in terms of mean-squared error were found within the range of 6.47E−05 to 7.29E−06. Response surface plot was drawn to study the influences of machining parameters of EDM for shaping PCD with copper tungsten and copper nickel. Subsequently, moth search algorithm (MSA) was used to determine the optimal machining parameters, such that the MRR was maximized and EWR was minimized. Based on the obtained optimal parameters, confirmation test with the absolute error within the range of 1.41E−06 to 5.10E−05 validated the optimization capability of MSA

Potential of Palm Oil Fuel Ash (POFA) Layers as Secondary Raw Material in Porcelain Stoneware Application / Azlan Zainudin...[et al.]

Porcelain stoneware is a product produced from kaolinite clay, quartz, and feldspar. This paper studies the potential of POFA layers as a secondary raw material in the porcelain stoneware application. POFA was separated to four layers. Each layer was investigated by using scanning electron microscopy (SEM) and energy dispersive X-Ray spectroscopy (EDX) in order to analyse the microstructure images and elemental analysis of POFA layers. The result was compared to all basic raw materials of porcelain stoneware for a benchmark. The result shows that the fourth layer has the largest silica content. It is the layer that has a characteristic close to the real porcelain stoneware. The microstructure and elemental analysis of POFA layers are compared with previous finding of POFA. There is little published information on POFA layers but certain POFA layers have approached the result of published POFA

Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques

In this study, statistical and soft computing techniques were developed to investigate effect of process parameters on diameter of extruded filament made of polypropylene in hot extrusion. A multi-factors experiment was designed with process parameters of screw speed, roller speed and die temperature. According to the design matrix, twenty four experiments were conducted. The diameter of the extruded plastic filament was measured in each experiment. Subsequently, statistical analysis was used to identify significant factors on diameter of extruded filament. Predictive models of response surface methodology (RSM) and radial basis function neural network(RBFNN)were applied to predict the diameter of extruded filament. The optimal process parameters to maintain the diameter of the filament closest to the target value were identified using the cuckoo search algorithm (CSA), and particle swarm optimization (PSO). Performance analysis demonstrated the superior predictive ability of both models, in which the prediction errors of 0.0245 and 0.0029 (in terms of mean squared error) were obtained byRSM and RBFNN, respectively. Considering the optimization methods, the optimization approaches of using CSA and PSO were promising, in which average relative error of 1.28% was obtained in confirmation tests

Non-CFC refrigerants ; first and second law efficiencies

Concern about the ozone depletion and green house effects caused by refrigerants have initiated and continued studies into more environmentally friendly refrigerants. This study looked into the performance of these refrigerants in terms of second law efficiency, COP, irreversibility, and discharge temperature. A program based on Visual Basic has been developed that can quantify the parameters above and this can be used to guide industrialists in their efforts to build or retrofit systems with new refrigerants. Results from the simulation have shown that R134a is potentially good as a replacement for R12, R402a for R502, and R407c for R22