Simulation-Based Thermal Management Framework for Reducing Energy Consumption in Advanced Manufacturing Systems

Abstract

Advanced manufacturing systems consume electrical and thermal energy because production machinery, cooling equipment, ventilation, pumps, auxiliary devices, and heat-rejection systems often operate under variable loads without coordinated thermal control. This study aimed to develop and quantitatively evaluate a simulation-based thermal-management framework for reducing energy consumption by examining how technical, informational, operational, and organizational capabilities influence thermal-management effectiveness and energy-performance improvement. A quantitative, cross-sectional, descriptive, explanatory, and case-based research design was applied across selected advanced manufacturing enterprise cases, including CNC machining, metal additive manufacturing, heat treatment, welding, automated assembly, industrial robotics, and mixed smart-manufacturing facilities. From 350 distributed questionnaires, 300 valid responses were retained, producing an 85.71 percent valid response rate. The model examined Thermal Simulation Capability, Thermal Data Quality and Integration, Real-Time Temperature Monitoring and Sensor Integration, Simulation-Based Process-Parameter Optimization, Cooling-System and Heat-Recovery Integration, Organizational Readiness and Workforce Competency, Thermal-Management Effectiveness, and Energy-Consumption Reduction Performance. Data were analyzed using descriptive statistics, Cronbach’s alpha, KMO and Bartlett’s tests, Pearson correlation, multiple regression, ANOVA, and multicollinearity diagnostics in SPSS. Reliability was strong, with alpha coefficients ranging from .823 to .918, while KMO reached .894 and Bartlett’s test was significant, χ² (780) = 4,128.36, p < .001. Process-Parameter Optimization recorded the highest mean, M = 4.14, SD = 0.54, whereas Cooling and Heat-Recovery Integration recorded the lowest, M = 3.88, SD = 0.64. All predictors significantly improved Thermal-Management Effectiveness, with Process-Parameter Optimization strongest, β = .27, p < .001. Model 1 explained 68.4 percent of its variance, R² = .684, while Thermal-Management Effectiveness strongly predicted Energy-Consumption Reduction Performance, β = .75, p < .001, explaining 56.3 percent of variance. The findings imply that integrated simulation, reliable data, real-time monitoring, optimized parameters, responsive cooling, heat recovery, and workforce capability can jointly strengthen thermal control, reduce avoidable cooling demand, improve energy intensity, and lower operating losses.