aspg-31-4458-1791686730 20261011024530 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2026 8 2 Khaled Sh. Gaber Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Mahmoud Elshabrawy Mohamed Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Synthetic industrial telemetry can alleviate scarcity and confidentiality constraints, but its value depends on more than distributional similarity. Generated sequences must support downstream engineering analysis, limit disclosure risk, and satisfy operational relations. This paper presents a reproducible three-axis benchmark and a differential-evolution calibration layer for conditional denoising diffusion. Six generators–bootstrap resampling, a shrinkage-Gaussian model, Fourier surrogates, a conditional variational autoencoder, a conditional diffusion model, and its calibrated counterpart–are evaluated on chronologically partitioned manufacturing telemetry. The protocol covers 18,000 generated 80-minute windows and combines train-synthetic/test-real alarm classification, marginal and temporal fidelity measures, record-proximity and membership-inference tests, and five engineering-rule audits. Differential evolution selects four post-generation controls using validation data only. Relative to the uncalibrated diffusion model, calibration reduces the aggregate physical-violation rate by 94.7% and the Wasserstein error by 29.2%, while increasing mean downstream ROC–AUC by 0.009. The improvement is accompanied by a 0.073 increase in membership-inference AUC and a small deterioration in autocorrelation error. Bootstrap resampling provides the strongest mean predictive utility but exactly reproduces 77.9% of its outputs; the conditional variational autoencoder attains the lowest Wasserstein error (0.046) with a 0.020% physical-violation rate. No generator dominates utility, privacy, and plausibility simultaneously. Synthetic industrial data should therefore be selected through deploymentspecific acceptance regions rather than a single realism score. 2026 19 27 4458 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.080203 https://www.americaspg.com/journal/31/article/4458 https://www.americaspg.com/storage/articles/manuscripts/01KY5XV8NZW6T6MPARGW2XEJWE.pdf