The development of the fourth industrial revolution (Industry 4.0) in the 2010s has paved the way to a digital transformation, fuelled by increased connectivity and intelligence in the manufacturing industry. This technological driven transformation has led to fast paced developments in a number of engineering fields in the past 10 years. Within higher education institutions, educators are finding difficulties to keep abreast of the latest technologies and developments in the field as complexity increases and the need for cross-disciplinary knowledge is required. This means that educators may not be in an effective position to pass on knowledge to their students who are the workers of future generations. This situation implies that there is a need for both educators and learners to be provided a tool which supports them in addressing their Industry 4.0 skills gaps and mismatches. This research therefore aims to contribute an open and digital training toolbox to educate both current and previous generations of higher education institution educators and learners in Industry 4.0 technologies and concepts.

The paper presents the ICARUS Pedagogical Framework, or Reference model to address development of innovative pedagogical approaches to overcome the actual effectiveness problems in development, acquisition and application of the required knowledge and skills for the concepts related to Industry 4.0. The aim of the Pedagogical Framework is minimum twofold: 1) to define and guide and applications of innovative pedagogical methods to explore and address the needs of HEI educators and learners, and 2) to provide a model of the pedagogical methodology design space for future development and adaptations. In the send part of the paper a contribution to the formalization of the model using set theory approach is presented as well.

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.

The development in the past decade of Industry 4.0 technologies has brought many new opportunities to manufacturers. The increased digitization of manufacturing operations has led to new modes of production and product development. This digitization has also increased the quantity of sensorial data which is easily available and which can be used to support real-time decision making. That said, with the opportunities come as well a number of challenges. Principally amongst these is a skills gap within the workforce. Without the required knowledge organisations will find it difficult and complex not only to employ these technologies, but also to develop the new manufacturing paradigms of tomorrow. Hence an innovative and effective training methodology is required to address this skills and knowledge gap. As part of the development of this methodology, this paper presents the finding of a study carried out to analyse the knowledge and skills gap, preferred learning methods and styles of trainers, current and past students in engineering Higher Education Institutions. This requirements analysis has led to the specifications for a Digital Training Toolbox, which can be utilised to support the implementation of Inudstry 4.0 technologies and organisational concepts.

In the age of digital manufacturing there is a need to elicit and transfer procedural knowledge between humans and machines. Having proper knowledge is essential in decision-making. The more the knowledge, the better decisions are made. To capture experiences and turn them into knowledge is fundamental in learning processes and knowledge development. Knowledge engineering and knowledge management have been subject for research for decades and several concepts about knowledge and knowledge transfer have been introduced, but a functional approach to exploit knowledge efficiently in manufacturing is still missing. In the era of Industry 4.0, humans and machines must be able to collaborate in such way that both can exploit the best abilities of each other in a manufacturing process. This paper introduces a procedural knowledge process (PKP) approach to capturing and defining unexpected events, while a process step is able to perform its required functions and transfer that information as machine-understandable knowledge about a failure mode. Function blocks (FBs), as per the IEC-61499 standard, have been proposed as a way to achieve distributed process planning in which the manufacturing process can adapt itself to runtime conditions, e.g. machine availability, etc. However, FBs are event-driven systems and the approach is limited to work under well-known runtime conditions, e.g. machine configurations and states, or deviations which are impossible to foresee in advance, for instance the outcome of a process failure mode effects analysis (PFMEA). The PKP introduced in this paper, aims at bridging this gap by integrating at runtime an expert operator’s solution based on root cause analysis (RCA) in an FB architecture, making the FB knowledge-driven systems, for further executions of the same without redesigning it. Natural language representations of procedural knowledge blocks (PKBs) allow to transfer procedural knowledge to human operators, i.e. explain the process flow of a machine decision, while machine representations of PKBs allow to embed procedural knowledge that is elicited from expert operators upon unexpected events into the FBs process. The resulting PKP enhances the FBs for smart industrial applications, such as the process planning use case described in this paper.

Process and quality planning are prerequisites for the production of qualified products at competitive cost. Today´s document-based work methods are not effective as valuable time is spent on document creation and document management instead of being spent on innovation and process improvements. Information duplication is an inevitable consequence with current document-based work methods where the same information is described in different ways, at many places, across many different documents. This paper presents a model-driven approach where vital manufacturing information is described once, at one place, and in one way, enabling improved work methods for cross-organizational process and quality planning.

Process planning is the activity of determining the manufacturing operations needed to produce a product. The knowledge work of process planning has been thoroughly investigated. Several ideas to automate process planning have been proposed but their success in practice has not yet been realized. Little attention has been given to design as an inevitable element in process planning and the role of human expertise in process design. From the premise that competent planners are a primary source of productivity this paper discusses process design, the role of human expertise and how CAPP systems could support human decision making.

Besides decisions in design, decisions made in process planning determine the conditions for manufacturing the right quality. Hence systematic process planning is a key enabler for robust product realization from design through manufacturing. Current work methods for process planning and quality assurance lack efficient system integration. As a consequence companies spend unnecessary lot of non-value adding time on managing quality. This paper presents a novel model-based approach to integrate process planning and quality assurance. The presented model enables a more efficient and holistic way for managing quality from design to manufacturing. New possibilities to communicate process design intent and present important quality assurance information in a more structured and comprehensive way is also enabled.

Systematic process planning is a key enabler for robust product realization from design through manufacturing. Every process and operation must be designed in the best possible way to ensure that the overall process chain leads to the right product quality. During the last two decades a shift from inspection of manufactured products to a more holistic approach with quality assurance as an integrated activity throughout the product realization process has emerged in manufacturing industry. The importance of the principles addressed in the methods and tools used in automotive industry for quality management is indisputable. However, the tasks of creating and managing documents for Process Failure Mode and Effect Analysis (PFMEA), Control plans, Initial process studies and Measurement System Analysis (MSA) results in high workload. Also, lack of interoperability between different computer applications used in process planning and quality assurance results in information fragmentation, data duplication and potential data inconsistency. This paper proposes a novel, model driven approach for process planning integrating quality assurance which emphasizes the application of digital models to create, represent and use information of products, processes and resources. By reducing the amount of data and document duplication, the presented model driven approach has potential to radically increase the direct value adding part of manufacturing engineer's daily work also contributing to achieve a more holistic view in interdisciplinary work between different experts in product realization.

Process planning is a central, knowledge extensive and important activityin a manufacturing company. During process planning, countless decisions are made,many times based upon the process planner´s tacit knowledge, based on years ofexperience. The knowledge gap between the expert and the novice is wide.Narrowing this gap, taking the novice towards becoming expert, is an objective ofeducation. This paper presents a solution for model-based interactive learning ofprocess planning, validated through application in master level productionengineering courses.