An Industry 4.0 Training Framework Addressing ‘COVID-19 Type’ Disruptions on Manufacturing

Although digitization in the manufacturing industry has been going on for some years, the recent COVID-19 pandemic helped reveal a number of bottlenecks and challenges that still need to be overcome. Joint ongoing research by a number of European Universities aimed at developing a systematic training framework on Industry 4.0 happened to be performed in the midst of the pandemic. COVID-19 meant that suddenly, internal and external workers of different educational backgrounds and in different roles had to rapidly adapt to new working procedures and environments whilst learning to use new technologies. This disruption helped this research group to generate specifications of a Higher Education Industry 4.0 Training Framework (HEI4.0) that is relevant to foster skills and competencies that make manufacturing more resilient to other possible scenarios requiring social distancing limitations. This paper outlines the details of the research performed and contributes the concept and value of establishing what is termed as the ‘flow-cognitive profile chart’ of a manufacturing organization to effectively help it in its transition towards digital manufacturing. Based on this concept, the paper passes on to prescribe a HEI4.0 Training Framework intended to guide manufacturing organizations in addressing ‘COVID-19 type’ manufacturing disruptions that can take place in other future unforeseen circumstances.

Concept for the Urban Production of Pharmaceuticals to Compensate for Local Shortages

Long and complex supply chains are one of the main reasons for drug shortages. The COVID-19 pandemic and abrupt global lockdown have highlighted how precarious the global pharmaceutical market is. This paper presents a concept for pharmaceutical production in greenfield, urban and local areas as a way to mitigate drug shortages around the world. This approach represents a paradigm shift because the production of medicine tablets still happens mostly at big brownfield sites. The concept is based on the VDI 5200 guidelines and procedures used for factory planning at general production plants. The derived methodology takes three phases into account and enables the integration of continuous tablet manufacturing into urban areas to supply the local population with medicine.

Maturity Assessment of Laser Powder Bed Fusion Process Chain Modelling and Simulation

This paper presents case studies of additive manufacturing process chains including laser powder bed fusion and post-processes. The presented case studies are used to assess the maturity of the manufacturing process chains using a Modelling and Simulation Readiness Level Scale. The results from the assessment have shown that the maturity of the modelling and simulation of laser powder bed fusion process chains lies between the stage of applied research and development and the stage of being instrumental, with high reliance on modelling and simulation experts. This means that the laser powder bed fusion (L-PBF) process chain modelling and simulation can support low-risk development, with high reliance on modelling and simulation experts, making them suitable for qualitative assessment, alternative design/solution ranking, defining the design structure, constraining the design space and replacing some experimental trials. This shows that further maturation is required before the modelling and simulation methods and codes are well recognised as best practice in the industry and are part of operational process control at any stage of the supply chain.

An Internal Model-Based PID Control for Smart Shot Peening Operation

Current control systems for the shot peening operation merely rely on old technologies, which often require repetitive processes to obtain pre-validated Almen systems to guide industrial productions. These designs for the manufacturing paradigm are not efficient for complicated workflows in modern manufacturing operation. Thus, in this study, we propose a practical model-based control system to address the issues; especially for a smarter and automated shot peening machine. In particular, the closed-loop control system development utilizes a model-based proportional-integral-derivative (PID) control technology and extreme gradient boosting (XGBOOST) machine learning algorithm. The control system includes an internal process model, a proxy model, a model-based PID controller, and pressure sensors with a low-pass filter for feedback control. The developed control system is integrated into a physical shot peening machine for on-site control validation and demonstration. In both in-silico and on-site control demonstrations, the obtained control performance is stable, robust, and reliable for different operational conditions. The measurement intensities are very close to targeted setting intensities. All the differences are smaller than the industrial threshold of (±0.01 mmA). It implies that the control system can use in industrial peening operations without the need for Almen system development for operational guidance. In other words, the control system can significantly reduce the total cost of the actual production by eliminating the cost, time, and labor of the iterative trials to build the Almen system.

Maximum Utilization in Operations Scheduling for Multiple Machines and Batches in Additive Manufacturing

Scheduling and sequencing are forms of decision making that present a fundamental role in Additive Manufacturing (AM). This work studies the scheduling for a set of three-dimensional (3D) printers when the maximum utilization is used as an indicator. The aim is to establish a criterion that minimizes the use of available resources. A program has been done by using the software package Python and this has allowed studying the effect that the maximum utilization presents on operation scheduling. As evaluated a large number of pieces of various sizes, the analysis is found to be effective in yielding near-optimal solutions. The obtained outcomes have identified aspects of the production such as production planning and control as well as work organization.