Manufacturers and operators of aircraft and equipment produce large numbers of technical manuals for their air and ground crews. These manuals generally cover all phases of aircraft operation - from assembly and disassembly, operation, and maintenance of the various systems and subsystems, to the storage of the equipment. These manuals are constantly revised as and when the customer makes new procedures or modifications to the existing models.
Two main challenges arise in this context:
- Manual checking
Manual checking is a tedious task, which usually results in errors. Authors tend to waste much time on checking countless pages of instructional manuals.
- Prone to human errors
The chances of having human errors are increase technical authors have to spend more time proofreading while creating technical manuals. Chances of missing out on essential data are high when it comes to manual checking, therefore leading to errors.
How TWEET helps to improve technical writing
TWEET (Technical Writing Efficiency Enhancement Tool) is an integrated web application, that supports multiple domain users in minimising human efforts through Machine Learning and AI. It is mainly designed for maintenance manual authors, namely for quality check.
Since TWEET highlights all the errors based on STE, the chances of errors are minimized to 99%. Along with the STE, English grammar & spelling validation, TWEET also serves the following purpose when it comes to publishing technical manuals:
- The torque extraction feature helps authors by visualizing specific task-related torque values from Engineering drawings and Bill of Materials.
- Co-relate callouts between corresponding illustrations & task texts and identify missing callouts.
- Line Maintenance Process (LMP) – TWEET helps in the segregation of IPL pages into highest assembly and sub-assembly figures thereby making it easier for the line maintenance personnel to execute their duties effectively.
With TWEET, clients do not have to spend hours validating the manuals for errors. Instead, TWEET automates this process and highlights the errors based on STE, English grammar & spelling validation. This saves human effort on the part of the client by up to 60-70% and significantly improves the authoring process quality.
Also, TWEET retrieves the LMP-list and CMM from the database, segregates IPL pages into highest assembly and sub-assembly figures, and groups the Non-FINs items into specific sub-assemblies based on IPL structure.