Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Study of Augmented Reality based manufacturing for further integration of quality control 4.0: a systematic literature review

Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Modeling and Intelligent Control for Spatial Processes and Spatially Distributed Systems

Dynamical systems are often characterized by their time-dependent evolution, named temporal dynamics. The space-dependent evolution of dynamical systems, named spatial dynamics, is another important domain of interest for many engineering applications. By studying both the spatial and temporal evolution, novel modeling and control applications may be developed for many industrial processes. One process of special interest is additive manufacturing, where a three-dimensional object is manufactured in a layer-wise fashion via a numerically controlled process. The material is printed over a spatial domain in each layer and subsequent layers are printed on top of each other. The spatial dynamics of the printing process over the layers is named the layer-to-layer spatial dynamics. Additive manufacturing provides great flexibility in terms of material selection and design geometry for modern manufacturing applications, and has been hailed as a cornerstone technology for smart manufacturing, or Industry 4.0, applications in industry. However, due to the issues in reliability and repeatability, the applicability of additive manufacturing in industry has been limited. Layer-to-layer spatial dynamics represent the dynamics of the printed part. Through the layer-to-layer spatial dynamics, it is possible to represent the physical properties of the part such as dimensional properties of each layer in the form of a heightmap over a spatial domain. Thus, by considering the spatial dynamics, it is possible to develop models and controllers for the physical properties of a printed part. This dissertation develops control-oriented models to characterize the spatial dynamics and layer-to-layer closed-loop controllers to improve the performance of the printed parts in the layer-to-layer spatial domain. In practice, additive manufacturing resources are often utilized as a fleet to improve the throughput and yield of a manufacturing system. An additive manufacturing fleet poses additional challenges in modeling, analysis, and control at a system-level. An additive manufacturing fleet is an instance of the more general class of spatially distributed systems, where the resources in the system (e.g., additive manufacturing machines, robots) are spatially distributed within the system. The goal is to efficiently model, analyze, and control spatially distributed systems by considering the system-level interactions of the resources. This dissertation develops a centralized system-level modeling and control framework for additive manufacturing fleets. Many monitoring and control applications rely on the availability of run-time, up-to-date representations of the physical resources (e.g., the spatial state of a process, connectivity and availability of resources in a fleet). Purpose-driven digital representations of the physical resources, known as digital twins, provide up-to-date digital representations of resources in run-time for analysis and control. This dissertation develops an extensible digital twin framework for cyber-physical manufacturing systems. The proposed digital twin framework is demonstrated through experimental case studies on abnormality detection, cyber-security, and spatial monitoring for additive manufacturing processes. The results and the contributions presented in this dissertation improve the performance and reliability of additive manufacturing processes and fleets for industrial applications, which in turn enables next-generation manufacturing systems with enhanced control and analysis capabilities through intelligent controllers and digital twins.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169635/1/baltaefe_1.pd