Exsys corvid download free

Each technique recommended is linked to the corresponding tech- 15 nique guideline within the manual portion of the CD, so that the user can learn more about the recommended technique. The logic flow, or selection methodology, used in Greenbank is described in more detail in Appendix B. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

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Exsys Corvid Ver 5. If — Then rule based system was utilized to accomplish the material selection process whereas a score system was formulated to facilitate the ranking of selected materials. The expert system selected materials that satisfied all constraints and selection results were presented in suitability sequence depending on their scores. The expert system selected polylactic acid PLA as the most suitable material.

The numerous types of biobased polymers coupled with their closely similar properties, often complicates their selection process for any application. Possible risk of neglecting best biobased polymer materials for intended applications may occur, when inaccurate selection decisions are done.

Consequently, the developed product may fail to satisfy its designed purpose and thus, unable to fulfil the demand of end users. It may also contribute to non-compliance with prevailing safety and environmental regulations. In view of these risks, systematic selection procedures are employed to address potential material selection problems. Material selection is the process of picking the best materials for a specific design Thakker et al. It is a significant step in developing new products or enhancing existing products Maleque and Salit, ; Hambali et al.

It contributes a crucial part in the development of products that are efficient, safe, quality and satisfactory to consumers. Material selection also helps to eradicate materials with potential environmental hazards from early stages of product development. Maleque and Salit recommended that one should know what product is to be developed or manufactured, what it does and how it does it. Providing appropriate answers to these questions at the initial stages of product development will enable effective product design and proper material selection.

In order to resolve the issue of material selection for better product performance, many computerized material selection systems have been developed to select the most fitting materials for a certain application Fairuz et al. Exsys Corvid software helps to develop user-friendly expert system for any material selection purpose.

The software assists users in choosing suitable materials from among a definable group of materials. Expert systems are problem solving and decision making systems based on knowledge of their task and logical rules. The selection decisions are based on logical steps that represent expertise of domain experts Maleque and Salit, Previous studies on material selection using various expert systems were reported in literature.

Sapuan and Abdalla conducted material selection for composite pedal box system using rule and frame based expert system. However, the system was incapable to automatically rank the materials after the first selection stage.

Sapuan and Mujtaba developed a computational framework for material selection of natural fibre-reinforced polymer composite materials using neutral network. In their work, the initial stage of material selection process was carried out by neutral network whereas the ranking of selected materials was accomplished manually. Mun et al. Their system could only select all the materials that fitted within the set range of properties. However, it was unable to suggest a single final candidate among the qualified materials.

Base on the above succinct drawbacks, Exsys Corvid expert system was developed in the current study for material selection. This software has the ability to select suitable materials and automatically rank them in a single step Fairuz et al. Binti Ahmad and Yaacob used Exsys Corvid software in the decision making for diagnosis of a suspected transformer faults and the appropriate maintenance actions to be taken based on dissolved gas analysis techniques.

Kowalski et al. Flior et al. Fairuz et al. To the best of our knowledge, this work is the first to investigate biobased polymer material selection for food packaging using Exsys Corvid software. The primary objective of this investigation was to determine the most suitable biobased polymer packaging materials for fruits, dry food and dairy products to extend shelf-life, maintain nutritional and sensory quality, plus provide microbial safety.

Expert systems typically possess three main components which are knowledge base, inference engine and user interface. The relationship between these different components is shown in Fig. The knowledge base of the expert system contains high level information coupled with expert-level heuristic rules for solving material selection problems in a particular domain. The domain knowledge and rules are inserted into the knowledge base by the knowledge engineer.

The knowledge engineer acquires relevant domain information from online database, material handbooks and research publications. Good communication with the domain experts enables the knowledge engineer to identify the factors that influences the selection of materials for a specific design. Consequently, this passed-on knowledge helps the knowledge engineer in formulating accurate rules of thumb to ensure suitable material selection for the known design specifications. The inference engine provides the expert system with a systematic procedure for filtering through the knowledge base and electing the applicable rules in an orderly manner to reach recommended solutions Farag, In other words, the inference engine is considered as a computerized reasoning system that assesses the facts stored in the knowledge base and applies relevant rules to those known facts, and then deduces new facts.

The new facts are communicated back to the knowledge base. The primary role of the inference engine is to distinguish, select and match the rules in knowledge base to obtain optimum solutions. The inference engine serves as the link between the knowledge base and the user interface.

The user interface permits easy interaction between user and inference engine. The users can key in significant parameters for the problem. In return, the user interface provides recommendations. Firstly, biobased polymer materials were sieved out from material handbooks, online database and research articles.

However, data are scarce compared to petroleum-derived polymers. Information on the most crucial properties of biopolymers which determines their suitability for application in the food packaging industry are widely scattered over many sources. The most essential material properties with regard to food packaging were classified into four intrinsic properties which are mechanical, thermal, gas barrier and water vapour barrier properties Weber Compostibility of the materials was also included for environmental safety consideration.

The focus of the selection was on these five mentioned properties. The mechanical and thermal properties were recorded as quantitative data whereas gas and water vapour barrier properties and compostibility of materials were in qualitative data format such as Excellent, Very good, Good, Fair and Poor.

Each qualitative datum was represented by a grade i. The developed expert system Exsys Corvid has a database system feature called MetaBlock. Data of material properties were stored in MetaBlock. Each row and column in the MetaBlock represents a particular biobased polymer i. Material data can be stored in MetaBlock Fig. The second alternative method of storing material data in MetaBlock is by importing data into MetaBlock from external material sources.

In the present work, biobased polymer materials with relevant properties were entered in Microsoft Excel spread sheet and then transferred into the MetaBlock to create a database for food packaging material selection.

Domain experts are able to make complex material selection decisions by considering numerous relevant factors to reach solutions. Interestingly, the expert system captures this decision making logic of domain experts as rules that are processed by the inference engine. The user is then asked to characterize the erosion problem as 1 gullying, 2 erosion or scour by stream flow or wave wash, or 3 mass wasting i.

If the user is uncertain about the nature of the erosion at his or her site, links are provided with text and photos to help the user iden- tify the dominant erosion process es. In the case of erosion or scour by stream flow, the user can also input hydraulic criteria i. A worksheet is pro- vided to assist the user in computing estimates of velocity or shear stress.

The user is also asked to classify the spatial extent of the problem as local or general. If the erosion is general, the user must identify the parts of the stream channel cross section that appear to be eroding: top bank, middle bank, toe, or channel bed. Through this dialog, the user is led to identify the dominant erosion mechanisms operative at the site in question.

Up to 4 erosion mechanisms may be selected from a master list of 12 processes. The user is then asked to spec- ify the maximum acceptable unit cost relative to a riprap blanket. This fea- sibility score takes into account suitability for a particular type of erosion problem, spatial location of the problem, environmental attributes specified as important, and price the.

The top techniques are then output to the user, who may elect to change any or all of his or her pre- vious responses and obtain new recommendations.

Each technique recommended is linked to the corresponding tech- 15 nique guideline within the manual portion of the CD, so that the user can learn more about the recommended technique.