Digital Twin-Driven Adaptive Manufacturing Selection for Multiple Part Families in Reconfigurable Flow Lines

Background: Reconfigurable manufacturing systems (RMSs) enable rapid adaptation of machines, tools, and control software to produce different part families.Objectives: This paper addressed a Multiple Part Family Production (MPFP) problem selecting the best reconfiguration policy (RP) when switching production between part families.RPs are represented as sequences of reconfiguration steps and evaluated by number of setups, total reconfiguration cost, and total reconfiguration time.This work presented a conceptual digital twin (DT) based framework for forming RP applicable to reconfigurable flow line (RFL).Method: This framework describes how a DT can model candidate reconfiguration sequences by using real or historical data to estimate time and cost, and support virtually in what if analyses for policy comparison prior to physical changes.The manuscript focuses on framework design, required data flows, and decision logic rather than on implementation or experimental deployment.Results: This work provides a structured DT architecture which acts as reconfiguration decision making tool for engineering managers and a formal representation of reconfiguration policies and evaluation metrics, and a roadmap for integrating data sources, simulation components, and validation procedures.A worked example is also presented to demonstrate how the proposed framework supports a management decision.Illustrative results show that RP 1 and RP 3 require only one reconfiguration, whereas RP 2 incurs an additional change at Stage 3. Consequently, RP 1 is the most efficient policy, followed by RP 3 , while RP 2 imposes the highest reconfiguration burden.Conclusion: This work proposes a novel DT-driven RP for MPFP, applicable to an RFL, integrating real-time data analytics into RMS to manage reconfigurable machine tool (RMT) configurations across four production stages.The selected RP is intended to improve delivery performance, capacity utilization, production continuity, and system availability by minimizing setups and reconfiguration time/cost, thereby reducing downtime and maintaining a smoother production flow.