Object Shape Error Correction using Deep Reinforcement Learning for Multi-Station Assembly Systems

Abstract

The paper proposes a novel approach, Object Shape Error Correction (OSEC), to determine corrective action in order to mitigate root cause(s) (RCs) of dimensional and geometric product shape errors. It leverages Deep Deterministic Policy Gradient (DDPG) algorithm to learn optimal process parameters update policies based on high dimensional state estimates of multi-station assembly systems (MAS). These policies can be interpreted in engineering terms as sequential corrective adjustments of process parameters that are necessary to mitigate RCs of product shape errors. The approach has the capability to estimate adjustments of process parameters related to fixturing and joining while simultaneously accounting for (i) RC uncertainty estimation, (ii) Key Performance Indicator (KPI) improvement, (iii) MAS design architecture; and, (iv) MAS inherent stochasticity. In addition, the OSEC methodology leverages a reward function parameterized by user interpretable functional coefficients for optimal tradeoff involving various corrections requirements. Benchmarking using an industrial, automotive cross-member assembly system demonstrates a 40% increase in the effectiveness of corrective actions when compared to current approaches.