An　acoustic　signal　acquisition　experiment　platform　was　constructed　to　gather　the　acoustic　signals　throughout　the　formation　of　35　single-tracks　of　a　120　mm　length　copper-tin　alloy　in　order　to　monitor　and　precisely　manage　the　selective　laser　melting　(SLM)　forming　process　and　enhance　overall　quality.　The　monitoring　of　the　SLM　forming　process　includes　the　analysis　of　the　time　and　frequency　domains,　the　extraction　of　the　SLM　process　features　using　linear　prediction　techniques,　and　the　development　of　support　vector　machine　(SVM)　model,　back-propagation　(BP)　neural　network　models,　and　convolutional　neural　network　models.　The　results　show　that　the　over-melted　state　can　be　identified　by　extracting　time　and　frequency-domain　features　over　a　given　range,　but　the　normal　and　unmelted　states　are　difficult　to　distinguish.　The　convolutional　neural　network　model　had　a　recognition　rate　of　99%,　the　BP　neural　network　had　an　effective　recognition　rate　of　90%,　and　the　SVM　model　had　a　combined　classification　rate　of　83.14%　for　the　three　states　after　optimization.　In　contrast,　the　convolutional　neural　network　model　performs　best　in　monitoring　and　offers　a　framework　and　point　of　reference　for　acoustic　signal　analysis　and　online　SLM　quality　monitoring.　©　2024　Author(s).