Robotic manipulation of deformable linear objects (DLOs) is an active area of research, though emerging applications, like automotive wire harness installation, introduce constraints that have not been considered in prior work. Confined workspaces and limited visibility complicate prior assumptions of multi-robot manipulation and direct measurement of DLO configuration (state). This work focuses on single-arm manipulation of stiff DLOs (StDLOs) connected to form a DLO network (DLON), for which the measurements (output) are the endpoint poses of the DLON, which are subject to unknown dynamics during manipulation. To demonstrate feasibility of output-based control without state estimation, direct input-output dynamics are shown to exist by training neural network models on simulated trajectories. Output dynamics are then approximated with polynomials and found to contain well-known rigid body dynamics terms. A composite model consisting of a rigid body model and an online data-driven residual is developed, which predicts output dynamics more accurately than either model alone, and without prior experience with the system. An adaptive model predictive controller is developed with the composite model for DLON manipulation, which completes DLON installation tasks, both in simulation and with a physical automotive wire harness.

Cyber-physical manufacturing systems (CPMS) can be defined by the integration of control, network communication, and computing with a physical manufacturing process. In this work, we present a hybrid model of CPMS combining sensor data, context information, and expert knowledge. We used the identification of global operational states and a multimodel framework to improve anomaly detection and diagnosis. The anomaly detection is based on context-sensitive adaptive threshold limits. Root cause diagnosis is based on classification models and expert knowledge. The proposed approach was implemented using the Internet of Things (IoT) to extract data from a computer numerical control machine. Results showed that using a context-sensitive modeling strategy allowed to combine physics-based and data-driven models for residual analysis to detect an anomaly in the part, machine, or process. The identification of root cause was improved by adding context information in classification models to identify worn or broken tools and wrong material.

This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals. Note to Practitioners-Estimating expected performance of a manufacturing system processing different parts across multiple machines is a complex problem due to the lack of closed-form equations. Existing solutions focus on monitoring stochastic variables such as production or failure rate, or machine dynamics in separate environments often running asynchronous to the real system. This paper addresses the problem of monitoring and assessing the performance of complex manufacturing systems in real time. The proposed framework uses a real-time hybrid simulation of manufacturing at a machine and system level. The hybrid approach is based on a discrete and continuous model of manufacturing equipment integrated to run synchronously with the real plant floor operation. Data from both the virtual and real environments are merged to assess performance. Deviations from expected values represent an error that can trigger a warning signal to production, maintenance, and/or manufacturing personnel at the plant regarding health and productivity of plant operations.