Decomposition of Manufacturing Processes: A Review

Manufacturing is a global activity that started during the industrial revolution in the late 19th century to cater for the large-scale production of products. Since then, manufacturing has changed tremendously through the innovations of technology, processes, materials, communication and transportation. The major challenge facing manufacturing is to produce more products using less material, less energy and less involvement of labour. To face these challenges, manufacturing companies must have a strategy and competitive priority in order for them to compete in a dynamic market. A review of the literature on the decomposition of manufacturing processes outlines three main processes, namely: high volume, medium volume and low volume. The decomposition shows that each sub process has its own characteristics and depends on the nature of the firm’s business. Two extreme processes are continuous line production (fast extreme) and project shop (slow extreme). Other processes are in between these two extremes of the manufacturing spectrum. Process flow patterns become less complex with cellular, line and continuous flow compared with jobbing and project. The review also indicates that when the product is high variety and low volume, project or functional production is applied

Multi-Objective Optimisation of CNC Milling Process for Al 6061 using Modified NSGA-II

Computer numerical controlled (CNC) growth has revolutionised the manufacturing sectors by changing the way people work. In milling process, it has contributed to the higher productivity and better quality of the products. Although a lot of researches have been done on how to improve the process, the process improvement does not stop there because of evolving materials, methods and technologies. This paper presents a multi-objective optimisation of CNC milling process in order to achieve desired surface roughness and minimise machining time for Al 6061. A full factorial experiment has been conducted to model surface roughness by controlling three variables; spindle speed, feed rate and depth of cut. Multi-objective optimisation has been performed using modified Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with two levels crossover. The optimisation result concluded that the modified NSGA-II was able to converge to Pareto-optimal, but having difficulties to spread solutions in wider range

Development of a high pressure compressed natural gas mixer for a 1.5 litre CNG-diesel dual engine

[Abstract]: The Computational Fluid Dynamics (CFD) analysis software was used to study the flow behaviour of compressed natural gas (CNG) and air in a CNG-air mixer to be introduced through the air inlet of a CNG-Diesel dual fuel stationary engine. The results of the simulation show that the Venturi mixer with more holes gives superior engine performance compared to the 4-hole Venturi mixer. Further analysis is done on the different holes mixer to investigate the effect of engine speed on the mass flow rate of CNG and the equivalence ratio Lambda. The second part of the paper represents a comparison results between the performances of a single cylinder research Compression Ignition CI engine fuelled with CNG-diesel system and conventional CI engine fuelled by conventional diesel. The engine was equipped with the simulated Venturi mixer, the result showed significant reduction in the exhaust gas emission compared to the conventional diesel engine. The average power output generated by dual fuel engine was slightly higher than that diesel one at different engine speeds

RULA: Postural Loading Assessment Tools for Malaysia Mining Industry

The ergonomics and environment factors have been the core issue for the mining industry for many years, and its profiles are rising. To ensure an ergonomics work environment, it is possible to require specific attention especially in this industries sector. It is becoming increasingly difficult to ignore the essential issue in Malaysia due to lack of ergonomics knowledge and low awareness among the engineers in the mining sector. The focus of this study is to evaluate and validate the physical risk factor associated with work-related musculoskeletal disorder (WMSDs) by using Rapid Upper Limb Assessment (RULA) among mining industry workers. All the physical risk factors involved the main body regions such as upper arm, lower arm, wrist, trunk, neck and leg that has been identified associated with WMSDs. There were 18 subjects selected to involve in this study. Those subjects were chosen according to their job task. To increase the reliability of the result, each subject was evaluated thrice in the trials. From the analysis, the average of final score of the RULA is 7 indicates high risk and calls for engineering/or work method changes to reduce or eliminate muscular disorder risk. The results of the analysis were used to improve the process of work, design of workstation and also improving the work posture to enhance the comfort level of operators. This study is crucial among the mining industry that is a lack of the information and research about the ergonomics issues in the industry. The overall finding indicated that the whole process of selected work task will contribute to musculoskeletal disorder either for a short or long time exposure

Analysis of measurement and calculation of MSD complaint of chassis assembly workers using OWAS, RULA and REBA method

Car assembly is a combination of all components that form one completed vehicle unit. The work process is manually done and repeatedly, which contributes to a risk of musculoskeletal disorders (MSD). Chassis assembly is a job with a high level of complexity and associated with MSD risk for its employees. This study consists of 30 assembly activities divided into six groups based on posture and working methods used during the work process. Group A consists of 7 assemblies, Group B consists of 8 assemblies, Group C consists of 5 assemblies, Group D consists of 2 assemblies, Group E consists of 5 assemblies, and Group F consists of 3 assemblies. This study aims to compare the measurement and calculation of the risk level of MSD workers by using the RULA, REBA, and OWAS methods. The results of the measurements and computations acquired using these three approaches yielded the same risk category: 83.33 % medium risk/dangerous in working groups A, C, D, E, and F, and 16.67 % very high risk/highly hazardous in working group B. These six groups, particularly group B, requires immediate attention to reduce worker complaints of MSD

Hybrid flow shop scheduling with energy consumption in machine shop using moth flame optimization

Hybrid flow shop with energy consumption (HFS-EC) combine the flow shop scheduling and parallel machine scheduling problem with the aim to optimize energy utilization, besides regular makespan in the production scheduling. This paper optimizes an HFS-EC case study using Moth Flame Optimization (MFO). The case study has been conducted in a machine shop concentrating on three machining types; lathe, milling and deburring. The objectives were to optimize makespan and total energy consumption in the machine schedule. Optimization using MFO has been conducted and the results was compared with well-established algorithm like Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization. The results were also compared with relatively recent algorithm such as Whale Optimization Algorithm and Harris Haws Optimization. Based on the optimization results, the MFO outperformed other comparison algorithms for the mean fitness and also the best fitness. Although there were other solutions with better individual optimization objectives, but results obtained by MFO compromised between minimum makespan and energy consumption. The proposed HFS-EC model and MFO algorithm has a great potential to be implemented in other scheduling case study due to benefit of reducing carbon emission and at the same time maintain the production output

Hybrid Knowledge-Based System for Collaborative Green Automotive Manufacturing Management

The objective of this research paper is to demonstrate the application of hybrid knowledge-based system, gauging absences of pre-requisites (GAP), and analytic hierarchy process (AHP) approaches for selecting the improvement programs for Collaborative Green Manufacturing Management (CGMM) system. In this research, a generic knowledge-based system is developed to measure the level of CGMM adoption in automotive manufacturers compared to the ideal system. Using the GAP and AHP tools, the key green manufacturing improvement programs can be prioritized and demonstrated with an illustrative example

Increasing T-method accuracy through application of robust M-estimatior

Mahalanobis Taguchi System is an analytical tool involving classification, clustering as well as prediction techniques. T-Method which is part of it is a multivariate analysis technique designed mainly for prediction and optimization purposes. The good things about T-Method is that prediction is always possible even with limited sample size. In applying T-Method, the analyst is advised to clearly understand the trend and states of the data population since this method is good in dealing with limited sample size data but for higher samples or extremely high samples data it might have more things to ponder. T-Method is not being mentioned robust to the effect of outliers within it, so dealing with high sample data will put the prediction accuracy at risk. By incorporating outliers in overall data analysis, it may contribute to a non-normality state beside the entire classical methods breakdown. Considering the risk towards lower prediction accuracy, it is important to consider the risk of lower accuracy for the individual estimates so that the overall prediction accuracy will be increased. Dealing with that intention, there exist several robust parameters estimates such as M-estimator, that able to give good results even with the data contain or may not contain outliers in it. Generalized inverse regression estimator (GIR) also been used in this research as well as Ordinary Lease Square Method (OLS) as part of comparison study. Embedding these methods into T-Method individual estimates conditionally helps in enhancing the accuracy of the T-Method while analyzing the robustness of T-method itself. However, from the 3 main case studies been used within this analysis, it shows that T-Method contributed to a better and acceptable performance with error percentages range 2.5% ~ 22.8% between all cases compared to other methods. M-estimator is proved to be sensitive with data consist of leverage point in x-axis as well as data with limited sample size. Referring to these 3 case studies only, it can be concluded that robust M-estimator is not feasible to be applied into T-Method as of now. Further enhance analysis is needed to encounter issues such as Airfoil noise case study data which T -method contributed to highest error% prediction. Hence further analysis need to be done for better result review

Experimental of multi-holes drilling toolpath using particle swarm optimization and CAD-CAM software on PCB

A multi-holes drilling process is widely used in electronics industry to produce printed circuit board (PCB). Nowadays, millions of PCB need to be produced in a single day to support the technological growth in all aspects of life. In this industry, the most time-consuming process is to drill the holes on the board. According to a survey, the tool movement in multi-holes drilling process spent up to 70% of the machining time. Various approaches have been proposed to optimize the toolpath in multi-holes drilling process. Previously, a computational experiment has been conducted to identify the best meta-heuristic algorithm to optimize this problem. The finding shows that Particle Swarm Optimization (PSO) has outperformed other comparison algorithm to generate the best toolpath. This paper aim to validate the PSO performance through an experiment. For this purpose, the experiment consist of nine drilling problems has been conducted to compare the toolpath that generated by PSO and commercial CAD-CAM software. The results indicated that the PSO generated toolpath is consistently faster than CAD-CAM generated toolpath, with 5% average difference. This finding confirmed that PSO has a great potential to be used in this process

Simulation of shear and bending cracking in RC beam: material model and its application to impact

This paper presents a simple and reliable non-linear numerical analysis incorporated with fully Lagrangian method namely Smoothed Particle Hydrodynamics (SPH) to predict the impact response of the reinforced concrete (RC) beam under impact loading. The analysis includes the simulation of the effects of high mass low-velocity impact load falling on beam structures. Three basic ideas to present the localized failure of structural elements are: (1) the accurate strength of concrete and steel reinforcement during the short period (dynamic), Dynamic Increase Factor (DIF) has been employed for the effect of strain rate on the compression and tensile strength (2) linear pressure-sensitive yield criteria (Drucker-Prager type) with a new volume dependent Plane-Cap (PC) hardening in the pre-peak regime is assumed for the concrete, meanwhile, shear-strain energy criterion (Von-Mises) is applied to steel reinforcement (3) two kinds of constitutive equation are introduced to simulate the crushing and bending cracking of the beam elements. Then, these numerical analysis results were compared with the experimental test results