Work in process

Abstract: In process analysis, production is broken down into distinct activities. This paper offers a stylization of this concept derived from the template provided by an actual production process, the process that dominated the steel industry for over 60 years. The resulting model is then applied to the firm’s short-run supply and demand for labor decisions.

Abstract: Reducing the processing time of instances at critical activities is essential for many application domains. We refer to an activity as being critical if due to restricted resources assigned to the activity, the arrival of a certain number of process instances might lead to a waiting queue. So far, queuing has been adopted for process optimization in a merely static manner, i.e., the strategy in which order the instances are processed from the queue is fixed. We argue that determining the processing strategy for instance queues at runtime (dynamic queuing) offers the potential to reduce the processing time at critical activities. The core idea is that instances arriving at critical activities are first clustered based on similar features and are then distributed to dynamic queues accordingly. The decision on the processing order for the resulting queues requires a state management for allocating the appropriate number of resources during runtime. For this, a configurable performance index is used. The proposed dynamic queuing approach is prototypically implemented and evaluated based on a realistic data set.

Abstract: Additive Manufacturing (AM) technologies have recently gained significance amongst industries as well as everyday consumers. This is largely due to the benefits that they offer in terms of design freedom, lead-time reduction, mass-customization as well as potential sustainability improvements due to efficiency in resource usage. However, conventional manufacturing industries are reluctant to integrate AM within their established process chains due to the unpredictability of the process and the quality of the final parts that are printed. Conventional manufacturing process have the advantage of decades of research in developing process knowledge and optimization, which culminates in accurate process predictability. This gap in process understanding is one that AM will need to cover in a short time. AM does have the benefit of being a digital manufacturing process and with the adoption of advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques in production lines, there may not have been a better industrial age for its implementation. This paper presents a case for actively developing AM processes using ML. Then a method for in-process monitoring of the printing process is presented and discussed. The main benefit from using the proposed system is an increase in the efficiency and final quality of the parts printed, as a result of which there is an increased efficiency in resource usage due to preventing material loss due to failed builds and defected parts.