Directed　energy　deposition　(DED)　represents　a　pivotal　advancement　in　intelligent　manufacturing,　facilitating　efficient　near-net　shape　metal　part　production,　particularly　suited　for　aerospace　and　defense　applications　demanding　high　precision.　Arc-based　DED　relies　on　meticulous　process　and　trajectory　planning,　where　AI-driven　manufacturing　systems　optimize　paths　and　parameters　to　surmount　intricate　physical　phenomena　like　material　melting　and　heat　transfer.　AI　methodologies　such　as　deep　learning　and　big　data　analytics　offer　promising　solutions.　The　exclusive　process　planning　software　for　DED-Arc　(EPPS-DED)　broadens　the　technology＇s　application　domains.　This　paper　comprehensively　outlines　core　algorithms　pertinent　to　EPPS-DED　and　essential　process　strategies　for　meticulous　process　planning,　providing　insights　for　software　development　and　part　quality　enhancement.　Key　topics　covered　include　3D　model　slicing,　path　planning,　printing　efficiency,　and　scanning　order　for　2D　contours　with　diverse　geometries,　alongside　strategies　for　inclined　structures　and　lattices.　Moreover,　it　discusses　the　latest　AI　applications　in　process　planning.　The　paper　concludes　with　current　progress　and　future　outlooks　aimed　at　refining　the　accuracy　and　performance　of　DED-fabricated　components.