Abstract: We present a standards-aware deep reinforcement learning (S-DRL) approach for AQC in smart manufacturing. The framework represents four heterogeneous process variables on a common standardized scale, uses a ±3σ quality boundary, and incorporates PPO-based reward function deviations from that boundary.