Computer numerical control (CNC) machining process simulation is increasingly central to intelligent manufacturing, yet its deployment in brownfield environments remains constrained by legacy controllers, heterogeneous data semantics, limited computational resources, and rising cybersecurity requirements. While digital twins (DTs), artificial intelligence (AI), and multi-physics simulation have matured conceptually, practical adoption, particularly among small and medium-sized enterprises (SMEs), continues to lag behind theoretical capability. This paper presents a PRISMA-guided systematic review of peer-reviewed literature, standards, and industrial reports published between 2019 and 2025, focusing on CNC machining simulation, digital twin architectures, interoperability standards, and intelligent optimisation under brownfield constraints. Rather than proposing new simulation algorithms, the review synthesises fragmented evidence into a deployable, standards-aligned integration perspective. The review consolidates prior work into a seven-layer architecture grounded in ISO 23247, explicitly separating sensing, communication, digital twin entities, analytics, and human–machine interaction. It derives practical decision rules for middleware selection, edge-cloud compute placement under latency constraints, and modelling strategy selection, ranging from mechanistic and finite-element methods to hybrid reduced-order and machine-learning surrogates. An SME-oriented implementation and validation roadmap links staged retrofitting to measurable operational indicators, including overall equipment effectiveness, first-pass yield, downtime, cycle time, and energy intensity.

Legacy CNC lathes are essential in brownfield plants but are limited by closed controllers, limited semantics, and ISO 6983 G-code. This paper presents a deployable framework: (i) smart retrofitting with edge sensing and gateways; (ii) interoperable connectivity combining MTConnect semantics with OPC UA messaging; (iii) digital twins (DT) integrated with AI for tool condition monitoring (TCM), predictive maintenance (PdM), and remaining useful life estimation; (iv) feature-based programming through STEP-NC; (v) deterministic communications using Time-Sensitive Networking (TSN) with OPC UA Pub/Sub; and (vi) energy-efficient optimisation of cutting parameters. The findings indicate that these components collectively extend machine lifespan, reduce downtime, and enhance energy efficiency while maintaining human-centred operations. Ultimately, a standards-driven retrofit approach allows SMEs to achieve IR 4.0/5.0 goals without complete replacement. Future research should focus on expanding turning datasets for portable DT/AI models, increasing STEP-NC adoption in lathe controllers, and conducting multi-vendor field validation of TSN quality-of-service impacts.