Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model.

Industrial robots contribute to a considerable amount of energy consumption in manufacturing. However, modeling the energy consumption of industrial robots is a complex problem as it requires considering components such as the robot controller, fans for cooling, the motor, the friction of the joints, and confidential parameters, and it is difficult to consider them all in modeling. Many authors investigated the effect of operating parameters on the energy consumption of industrial robots. However, there is no prescriptive methodology to determine those parameter values because of the challenges in the modeling of industrial robots. This article investigates an industrial robot and the manufacturing process together and proposes a black-box model-based energy consumption optimization approach. Our contribution to the research is the new online and data-efficient methodology, prescriptive algorithm, and the analysis of operating parameters' effects on industrial robots' energy consumption. The proposed methodology is tested using two real FANUC industrial robots in three industrial settings.

Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components - Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.

In this work, we optimize manufacturing configurations, i.e., the set of necessary assets, using the known capabilities and capacities of manufacturing equipment. In particular, the work provides an Object-Oriented data model and the translation of the Object-Oriented data model into a Mathematical data model for efficiently utilizing optimization algorithms. The main contribution is developing an optimization strategy for adapting to varying demand periods with the requested capability and capacity. The proposed methodology is validated in optimizing the planning for small-box hinged product assemblies in aerospace manufacturing, which can be assembled in a reconfigurable environment with common pick and place, drilling, fastening, and inspection procedures.