Experience Capture in Shipbuilding through Computer Applications and Neural Networks

TL;DR

This paper explores capturing and transferring shipbuilding expertise using neural networks and digital tools to preserve traditional knowledge and improve training across generations.

Contribution

It proposes a neural network-based system combined with digital tools to effectively capture and transfer traditional shipbuilding knowledge across generations.

Findings

Neural networks can recognize patterns in shipbuilding expertise.

Digital tools facilitate knowledge sharing across shipyards.

Traditional knowledge can be preserved and utilized for future improvements.

Abstract

It has always been a severe loss for any establishment when an experienced hand retires or moves to another firm. The specific details of what his job/position entails will always make the work more efficient. To curtail such losses, it is possible to implement a system that takes input from a new employee regarding the challenges he/she is facing and match it to a previous occurrence where someone else held his/her chair. This system could be made possible with input through the ages from the array of individuals who managed that particular job and processing this data through a neural network that recognizes the pattern. The paper is based on data collected from traditional wooden dhow builders and some of the modern day unconventional shipyards. Since the requirements for successful implementation in such scenarios seems too steep at the moment, an alternate approach has been suggested by implementation through the design processes across multiple shipyards. The process entails the traditional value passed down through generations regarding a particular profession and analysis has been done regarding how this knowledge/experience can be captured and preserved for future generations to work upon. A series of tools including SharePoint, MATLAB, and some similar software working in tandem can be used for the design of the same. This research will provide valuable insight as to how information sharing can be applied through generations for effective application of production capabilities.