A generic approach of integrating 3D models into virtual manufacturing

Various 3D modeling software has been developed for design and manufacturing. Most of the commercially available software uses native file formats, which may not be able to be read or understood by other software. This paper deals with the development of a generic approach of a 3D model conversion program for virtual manufacturing （VM）, using a lexical analyzer generator Lex and the Open Graphic Library （OpenGL）. The program is able to convert 3D mesh data between four universal file formats, i.e., Stereolithography （STL）, Virtual Reality Modeling Language （VRML）, eXtensible Markup Language （XML）, and Object （OBJ）. Simple assembly functions can be applied to the imported models. The quaternion angle is used for object rotation to overcome the problem of gimbal lock or a loss of one degree of rotational freedom. The program has been validated by importing the neutral format models into the program, applying the transformation, saving the new models with a new coordinate system, and lastly exporting into other commercial software. The results showed that the program is able to render and re-arrange accurately the geometry data from the different universal file formats and that it can be used in VM. Therefore, the output models from a VM system can be transferred or imported to another VM system in a universal file format.

Fuzzy-based risk prioritization for a hydrogen refueling facility in Malaysia

Hydrogen is starting to be mentioned as an alternative fuel to replace the fossil fuel in future transportation applications due to its characteristics of zero greenhouse gas emission and high energy efficiency. Before hydrogen fuel and its facilities can be introduced to the public, relevant safety issues and its hazards must be assessed in order to avoid any chance of injury or loss. While a traditional risk assessment has difficulty in prioritizing the risk of failure modes, this paper proposes a new fuzzy-based risk evaluation technique which uses fuzzy value to prioritize the risk of various scenarios. In this study, the final risk of each failure modes was prioritized by using the MATLAB fuzzy logic tool box with a combination of two assessments. The first assessment was concerned with the criteria which affected the actual probability of occurrence.This assessment considered the availability of the standard that was applied to prevent the likelihood of the scenario occurring. On the other hand, the second assessment was focused on evaluating the consequence of the failure by taking into account the availability of detection and the complexity of the failure rather than only the severity of the scenarios. A total of 87 failure scenarios were identified using failure modes and effect analysis(FMEA) procedures on hydrogen refueling station models. Fuzzy-based assessments were performed through risk prioritizing various failure scenarios with a fuzzy value(0 to 1) and risk level(low, medium, and high)while a traditional risk assessment approach presented the risks only in forms of level(low, medium, and/or high). Availability of the fuzzy value enabled further prioritizing on the risk results that fell in the same level of risk. This study concluded that fuzzy-based risk evaluation is able to further prioritize the decisions when compared with a traditional risk assessment method.

Development of a system configuration for a solar powered hydrogen facility using fuzzy logic control

Solar energy is a natural resource which can be harnessed to provide clean electricity for hydrogen production systems.However, this technology is not widely used because of control issues, particularly for hydrogen refuelling stations. At present,direct or DC-DC converter couplings are the most common system configurations for hydrogen refuelling stations. However, these system configurations are costly and suffer from gas shortage at hydrogen refuelling stations. Furthermore, the hydrogen produced by such system configurations varies considerably depending on the levels of solar radiation. In order to address these issues, a new system configuration is proposed, incorporating the feedback signal of the storage level in the control system. The photovoltaic(PV) system, electrolyzer, and storage tank are integrated with a fuzzy logic controller(FLC) to determine the backup current compensation for electrolyzer operation in order to obtain the minimum power required for hydrogen production. The proposed FLC is constructed with three input variables which are the PV current, hydrogen storage level, and the battery state of charge. The rules-based fuzzy inference process is based on the proposed configuration which combines the advantages of direct and DC-DC converter coupling configurations. The simulation results show that the proposed configuration offers better adaptability to variable radiation conditions compared to other methods. This gives a more promising option for ensuring the adequacy of hydrogen supply at hydrogen refuelling stations.

Fuzzy-based risk prioritization for a hydrogen refueling facility in Malaysia

Hydrogen is starting to be mentioned as an alternative fuel to replace the fossil fuel in future transportation applications due to its characteristics of zero greenhouse gas emission and high energy efficiency. Before hydrogen fuel and its facilities can be introduced to the public, relevant safety issues and its hazards must be assessed in order to avoid any chance of injury or loss. While a traditional risk assessment has difficulty in prioritizing the risk of failure modes, this paper proposes a new fuzzy-based risk evaluation technique which uses fuzzy value to prioritize the risk of various scenarios. In this study, the final risk of each failure modes was prioritized by using the MATLAB fuzzy logic tool box with a combination of two assessments. The first assessment was concerned with the criteria which affected the actual probability of occurrence. This assessment considered the availability of the standard that was applied to prevent the likelihood of the scenario occurring. On the other hand, the second assessment was focused on evaluating the consequence of the failure by taking into account the availability of detection and the complexity of the failure rather than only the severity of the scenarios. A total of 87 failure scenarios were identified using failure modes and effect analysis (FMEA) procedures on hydrogen refueling station models. Fuzzy-based assessments were performed through risk prioritizing various failure scenarios with a fuzzy value (0 to 1) and risk level (low, medium, and high) while a traditional risk assessment approach presented the risks only in forms of level (low, medium, and/or high). Availability of the fuzzy value enabled further prioritizing on the risk results that fell in the same level of risk. This study concluded that fuzzy-based risk evaluation is able to further prioritize the decisions when compared with a traditional risk assessment method.