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Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.

It is widely thought that considerable manual rework is a necessity in the production of aerospace composite structures manufactured through automated fiber placement (AFP). However, there is limited availability of information regarding the precise quality control outcomes obtained through manual rework and defect removal. Given the large amounts of cycle time dedicated to the rework process, it may be fruitful to investigate in what ways rework may improve part quality, shift defect distributions, or in some cases, fail at improvement and make the resulting structure worse. To provide insights into these questions, a ply of a doubly-curved part was produced by AFP, inspected using an automated inspection system, manually repaired, and then inspected again. Through a multi-faceted analysis of the inspection data, trends including changes in defect number and significance, repair suggestions, and a previously unreported new defect type associated with manual rework can be observed.

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation, (2) fast and secured industrial communication networks, (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction, (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.

Background: The application of continuum manipulators as assistive robots is discussed and tested through the use of Bendy ARM, a simple manually teleoperated tendon driven continuum manipulator prototype. Methods: Two rounds of user testing were performed to evaluate the potential of this arm to aid people living with disabilities in completing activities of daily living. Results: In the first round of user testing, 14 able-bodied subjects successfully completed the prescribed task (pick-and-place) using multiple control schemes after being given a brief introduction and one minute of practice with each scheme. In the second round of user testing, subjects ( n = 3 ) demonstrated between 29.5 and 48.9 percent improvement in completion time across twelve trials of a peg-in-hole task, and between 8.4 and 33.8 percent improvement across six trials of a task involving opening and closing a drawer. Conclusion: Based on these results, it is posited that continuum manipulators merit further consideration as a safer and more cost-effective alternative to existing commercially available assistive robotic manipulators.

Previous results demonstrated piezoelectricity in 3D printed PVDF, however these results showed near undetectable signal levels and inconsistent piezoelectric behavior. This paper presents far improved sensor performance, and the first characterization of the piezoelectric properties of 3D printed PVDF. A value for d 33 of 0.36 ± 0.13 pC/N was discovered for a 35mm by 35mm single layer wafer.

This paper presents a demonstration of the 3D printed piezoelectric sensors described in the attached document.