Prediction of formability for sheet metal component using artificial intelligent technique

2015 ◽  
Author(s):  
M R Bhatt ◽  
S Buch
Keyword(s):  
Sheet Metal ◽  
Metal Component ◽  
Artificial Intelligent ◽  
Intelligent Technique ◽  
Artificial Intelligent Technique ◽  
Sheet Metal Component

Intelligent Classification of Palm Oil Tree Pollination Using E-Nose

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 28-37
Author(s):  
Abdul Rahim Jalil ◽  
Muhammad Sharfi Najib ◽  
Suhaimi Mohd Daud ◽  
Mujahid Mohamad
Keyword(s):  
Palm Oil ◽  
Case Based Reasoning ◽  
Artificial Intelligent ◽  
Male And Female ◽  
Intelligent Technique ◽  
Insect Activity ◽  
Artificial Intelligent Technique ◽  
Case Based ◽  
Odour Profile

The pollination period is one of the crucial steps needed to ensure crop yield increases, especially in palm oil palm plantations. Most of the research has difficulty determining the pollination period of palm oil. Many problems contribute to this problem, such as difficut to reach and depedency of the polination insect as the insect activity is influenced by the surrounding enviroment.E-Nose can help determine the period by classifiy odour pattern of the male and female palm oil flower. The pattern of each of the flowers were classified using cased – based reasoning artificial intelligent technique. This paper shows the research of the palm oil pollination flower odour profile pattern using case-based reasoning (CBR) classifier.

Die Producibility Evaluation Method

1992 ◽  
Author(s):  
Arun Shivaram ◽  
Nagi G. Naganathan
Keyword(s):  
Sheet Metal ◽  
Evaluation Method ◽  
Die Design ◽  
Metal Component ◽  
Initial Design ◽  
Reference Design ◽  
Process Cost ◽  
Improved Design ◽  
Cost Factors ◽  
Sheet Metal Component

Abstract Given the design of a sheet metal component that is to be produced by pressworking, there can be more than one design for the die to produce that component. It will be desirable if a mechanism exists to identify the better of the design alternatives. A method referred to as Die Producibility Evaluation Method (DPEM) is presented in this paper to evaluate the design of a given pressworking die. The method involves identification and classification of the parameters involved in the design and operation of the pressworking die. These parameters are categorized as design cost factors and process cost factors. For a given die design, the design and process cost factors are identified and the results are tabulated in the DPEM table. In order to illustrate the method, a particular sheet metal component is chosen. A set of dies required to produce this component is then designed by a novice designer. This design is referred to as the initial design. The Die Producibility Evaluation Method is applied to the initial design, the DPEM table is updated, and an improved design is identified based on the evaluations recorded in the DPEM table. In order to provide a measure of quality, a second design is carried out under the guidance of an expert designer and is referred to as the reference design. It is shown that the improved design closely matches the reference design, thus illustrating the level of performance and applicability of the Die Producibility Evaluation Method.

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