Existing Learning Factories Research Articles

Implementation of a Prototype Production Line based on concept of Industrial Digitalization in an existing Learning Factory environment

Implementation of a Prototype Production Line based on concept of Industrial Digitalization in an existing Learning Factory environment

Evaluation of a Level-Based Industry 4.0 Competency Model by the Example of an Internationally Distributed Learning Factory

This paper presents a competency model for mediating Industry 4.0 in Learning Factories. It allows assessing existing Learning Factories towards their readiness for Industry 4.0 competencies differentiated by social, technical, methodological, and personal aspects. Industry 4.0 has long since ceased to be an issue reserved solely for research. Companies are pursuing a wide variety of strategies in order to gain the advantages predicted by digitalisation and thus be able to stand up to global competition. In the meantime, there is also a general awareness in industry that the real implementation of such a strategy goes beyond the implementation of technologies and that above all the qualification of employees determine this success. Different models show the dimensions of Industry 4.0 that should be addressed in the qualification of employees. Here, Learning Factories are already recognized vehicles for the transfer of the respective competencies, which represent the link between theoretical knowledge and the practical ability of the learner. Nevertheless, there is hardly more detailed information available about the level of competencies and to which extend this level is beneficial along the stages of implementation of Industry 4.0 according to a comprehensive company strategy. On the example of a concept for an internationally distributed Learning Factory between Universities in Germany, Turkey, and Vietnam, the competency model is applied. We assess this Learning Factory network and show how and to what extend Industry 4.0 competencies can be achieved. Based on this analysis potential development stages for higher levels of Industry 4.0 competencies for the exemplarily chosen distributed Learning Factory are illustrated in order to cover the core requirements for the implementation of Industry 4.0 in industry.

Approach for Implementing New Topics in Learning Factories – Application of Product-specific Carbon Footprint Analysis

Due to the constant changes in the production environment and the increasing relevance of sustainability and digitalisation in the industrial sector, there is a corresponding need for constant development of learning factories and the offered range of courses. To achieve this, not only learning content but also the technical infrastructure for knowledge transfer must be expanded in learning factories. Approaches that exist so far focus on the development of new learning factories to implement new or existing topics, although the already existing learning factories could be expanded and changed to these varied needs. Since a systematic approach is lacking so far, the competency-based design for the development of learning factories was used as a basis, developed further with focus on the technical infrastructure expansion. Subsequently, this approach is applied to the ETA Learning Factory at Technical University of Darmstadt. The ETA use case focuses on the topic of product-specific carbon footprint calculation. Therefore, technical infrastructure such as traceability systems, dashboards for visualisation or newly implemented efficiency measures on machine tools is added. The approach can be applied to a variety of topics.

Managing the Systemic Transition from a Learning Factory for Lean Production (LSP) to an Innovation Lab for Smart & Lean Processes at iwb (TUM)

In order to be able to meet the demands of an ever more competitive business environment, implying an increasing number of product variants and smaller production batches, the strengthening of a company's own market position will prospectively largely depend on the successful integration of disruptive technologies into the value chain. The focus of manufacturing companies, which has in tendency been rather oriented towards optimization of existing processes, should therefore be expanded towards the inclusion of disruptive technologies since this seems inevitable to sustainably maintain competitive advantages essential for long-term survival. This expanded innovation focus requires all market participants to be highly dynamic, responsive and flexible in their innovation management and usually cannot be realized without an increased willingness of economic risk-taking and an additional expenditure of resources. In order to relieve the burden on small and medium-sized enterprises (SMEs) in the manufacturing sector and to develop synergies from a collaborative innovation approach between academic science and industry, this paper shall present a systemic concept elaborating, how an existing learning factory can be enhanced to an innovation lab simulating the digitization of production processes. This process development shall be supported by the morphological reference framework and procedure model developed and further illustrated by means of the established Learning Factory for Lean Production (LSP) at the Institute for Machine Tools and Industrial Management (iwb), as part of the Technical University Munich (TUM).

Machine Vision and Radio-Frequency Identification (RFID) based Real-Time Part Traceability in a Learning Factory

Visual inspection-based quality control systems are deployed to meet the growing demand of high-quality products. In this paper, an inexpensive RFID (Radio-Frequency Identification) technology and a machine vision system have been integrated within an existing learning factory. RFID technology is used to trace a product/part from its origin, enabling the visibility of the product/part’s entire movement in the value chain. The goals of the paper are to track the workpieces in real time (i) to provide immediate feedback, through visibility, to operators and floor managers and (ii) to develop a database to trace parts, in future, backward through value chain. The system can be used to predict the probability of defective parts in the value chain using machine learning algorithms. This will help the manufacturers to trace the parts in value chain at any point of time including aftersales. Traceability has been a pain point for manufacturers during product recalls.

Corporate Learning Factories – Benefits, Challenges, and Success Factors of Learning Factories in Industry

In today's fast-changing and volatile business environment, human capital has become a major economic asset to creating business value and gaining competitive advantage as a business. Human capital can be advanced through training and education. Training in learning factories has proven to be an effective approach for industrial learning, especially in the field of manufacturing. Some companies have recognized this and have developed learning factories for their businesses, in this paper also referred to as corporate learning factories. Employee training and personnel development are time-intensive and costly processes for companies. With the learning factory approach, expenses and training time can be reduced significantly. However, in literature, there is a lack of how to develop and especially implement learning factories in a corporate structure. Above that, studies regarding the benefits, challenges, and success factors of learning factories in industries are lacking. The aim of this paper is to give an overview of the importance of learning factories in personnel development, provide an overview of existing learning factories in industry, and present results conducted from interviews with three companies on benefits, challenges as well as success factors of learning factories in industry.

Industry 4.0 Learning Factory Phased Development

Over the last decade, Learning Factories literature has evolved significantly. Existing Learning Factories definitions and morphology are already well established. Regarding the implementation process, a few models and several case studies are provided. The existing models and cases are mainly based on the Learning Factory implementation as a single integrated project initiative. However, the necessary resources and budget for a comprehensive Learning Factory deployment may not be fully available upfront in some contexts with limited means. In these cases, a phased Learning Factory implementation may be considered. In a phased implementation, the demonstrated results from one phase may support budget, resources, and partnership acquisition for the following stages. Hence, this paper presents a detailed case study conducted between 2015 and 2020 of a Learning Factory phased implementation that continuously and iteratively evolved in terms of resources, operation, partnerships, and teaching applications. The evidence from the case study is considered to expand the current understanding of Learning Factories implementation pathways.

Evaluation of Factory Elements for the Configuration of Learning Factories

Learning factories for lean production represent the majority of the learning factories worldwide. Learning factories for lean production represent the majority of them. For the design of learning factories, the organisational framework, the organisational targets, the target groups and the intended competencies should be clarified, so factory elements can be preselected. Examples of factory elements in learning factories are machines, equipment, assembly lines or logistics systems. The selection of factory elements is a complex task since various restrictions must be considered such as the budget or layout constraints. Configuration systems can simplify the selection process. Since the learning factory developer wants to choose the best factory elements, it is necessary to define how to evaluate the utility of the preselected factory elements. Both competency-based criteria based on the learning targets, and general evaluation criteria for learning factories such as the degree of changeability play a role. In this paper, an evaluation method is presented which allows an individual evaluation of factory elements for learning factories with the focus on lean production. The method is based on a utility value analysis with previously researched evaluation criteria. For each evaluation criterion, a fixed classification is made into strong, medium, weak and non-evaluable. Since the evaluation criteria are different for each learning factory, they can be weighted individually according to the specific use case. This evaluation scheme can also be used to evaluate existing learning factory configurations. As a case study, factory elements of the process learning factory CiP of the TU Darmstadt are examined. However, new learning factories to be developed can also be configured on this basis.

A FabLab as integrative part of a Learning Factory

The maker movement was born in the early 2000s when the first FabLab in the USA was opened at MIT. Since then a dynamic, worldwide growth of FabLabs has been recognized and nowadays they are important for education and research. The FabLab, which was opened at FH JOANNEUM in 2018 as an integrated part of the Learning Factory, offers heterogeneous user groups the opportunity to explore a wide range of issues in the context of Industry 4.0 and digitalization in an open high-tech factory. After twelve months of experience with the FabLab operation as an integrative component in the Learning Factory, first findings on a scientific and organizational level can be derived. This paper answers the questions of how a Learning Factory with integrated FabLab works and what benefits can be generated for the Learning Factory and the FabLab. First, the development of FabLabs and the motivation for implementing a FabLab at FH JOANNEUM will be discussed. This is followed by a description how a FabLab can be integrated as a sub-lab in an existing Learning Factory. In addition to this concept, the FabLab process is described and shows the effects on different user groups and the university. Further on the advantages and potentials of this concept are elaborated.

Design Approach for a Learning Factory to train Services

The automation of warehouse processes, the increased complexity due to e-commerce and the development towards paperless warehousing are one of the severe challenges the logistics industry is currently facing. Due to changing markets new service opportunities have opened up, thus to stay competitive innovative services are vital. Various service products are offered by intralogistics system providers to assist customers but the lack of understanding the services and the costs involved, causes customers to refuse acceptance of the services. Therefore, content of new services needs to be trained and awareness for service innovations need to be built which offers the opportunity to increase customer satisfaction and further develop customer relationships. The demand for practice-oriented training and education of customers is key to master upcoming challenges of warehousing facilities. This paper intents to develop a learning factory for services that promise 1) customers a better operation of their warehouses by correct use of services; 2) innovation opportunities for the service provider; and 3) further research opportunities between industry and academia. For this reason, in the first phase existing learning factories and services of an intralogistics company have been analyzed. Based on that service-learning factory concepts have been developed. In a third phase, a concept was chosen, further detailed and training contents were created. Based on this concept a prototype was built at the LEAD Factory of Graz University of Technology and tested.

Implementation of a cyber-physical cooling storage station in a learning factory

Abstract Learning factories are established means for learning production and process-engineering relevant topics and improving holistic system understanding. Learning factories integrate real-world applications into small-scaled factories to teach students, employees or researchers. Connecting the physical world with virtual (cyber) models to develop cyber-physical systems has become attractive due to low cost, high performance IT infrastructure. However, learning factories and cyber-physical systems have been rarely combined. In this paper, a cyber-physical cooling storage station is presented, which is integrated into an existing learning factory and its potential for engineering education is analysed. In addition, an innovative visualisation enables user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises.

Learning Factory for Industry 4.0 to provide future skills beyond technical training

The paper will describe the design of a learning factory for Industry 4.0 that addresses the growing demand for future skills of production staff. Existing learning factories often focus on the technical skills whereas this learning factory also trains decision making, group work and performance monitoring skills. The paper refers to the existing categorizes of learning factories and unveils its numerous features. The conceptual design includes theoretical and practical parts, which prove to be successful in a German learning factory that was realized by the authors. Especially, for the industry 4.0 environment, the layout consists of three stages of a production system, from manual to automatized manufacturing. The practical tasks cover the introduction of smart devices, connection of information flows as well as monitoring of performance. The didactical design of the training program provides a sustainable approach by not only realizing training courses but also includes preparation with management, mid-term coaching and success monitoring after the training. The learning factory is a part of a whole research institute for intelligent manufacturing in China including consultancy and application support. One of the underlying goals of the learning factory is to enable production staff for change management, decision making and innovation.

SEPT Learning Factory for Industry 4.0 Education and Applied Research

The existing learning factories cover a variety of learning environments. Each implementation of a learning factory looks differently and is used for a different purpose. Several of the newer learning factories have a focus on Industry 4.0 and demonstrate different implementation aspects, but some of them lack a significant hands-on component. Only a few learning factories implement several Industry 4.0 components and have a strong hands-on aspect. Any new implementation needs to identify the aspects on Industry 4.0 that are addressed and to decide the ratio between demonstrations and hands-on processes that answer the goal of the learning factory. A new implementation of a learning factory that uses several technologies included in the Industry 4.0 vision and has a strong experiential learning approach is presented. This implementation provides modern design and simulation, prototyping, manufacturing processes, including 3D metal printing resources, and automated assembly and testing systems that incorporate Industry 4.0 implementation strategy technologies such as Cyber-Physical Systems, Internet of Things, and Industrial Internet of Things.

Classification of a Hybrid Production Infrastructure in a Learning Factory Morphology

Current market trends confront industrial production with significant challenges: Volatility increases, uncertainty mounts, complexity grows and ambiguity escalates. While flexible and changeable organizational structures sufficed to combat present-time increasingly dynamic demands, modern disruptive times call for agility.Agile learning will require expedited and well-structured training within industrial organizations: Learning factories enable new technologies to be put on trial, they capacitate integrated work and learning environments and they provide a valuable framework to instruct and educate new employees.Numerous existing learning factories are geared towards education in engineering and didactics of industrial production. Defined distinctions are yet to be thoroughly discussed and will further the evolution of terminology and good learning factory practices.This article portrays various use cases from real manufacturing operations and their accompanying indirect processes within the Demonstration Factory on the RWTH Aachen Campus. These cases shall be assorted to relevant characteristics of a morphologic description model for learning factories presented by Abele et al. and derive a critical reflection and enhancement of this framework. The applicability of said morphology shall be examined for the holistic Demonstration Factory approach with its aim to manufacture marketable goods in a genuine continuous industrial small batch production.

Experiencing Closed Loop Manufacturing in a Learning Environment

Closed-loop manufacturing reintegrates components and/or materials from the end-of-life products as input flows to produce new products. Today, most learning factories focus on the actual manufacturing system. Some learning factories extend the perspective and include the product development and product use phase. To enlarge the learning perspective towards end-of-life an existing learning factory is extended by a modular, self-designed recycling process chain enabling closed-loop manufacturing. The modular process chain consists of crushing end-of-life products and extruding new filament out of the shredded material. The filament is then utilized to create new products with 3D-printers substituting virgin material. The developed recycling process chain is implemented in an existing learning environment which is equipped with 3D-printers. Further didactic courses are designed considering the ageing of the material and the environmental impact along the product's life cycle.

Development of the Industrial IoT Competences in the Areas of Organization, Process, and Interaction Based on the Learning Factory Concept

Lately, first implementation approaches of Internet of Things (IoT) technologies penetrate industrial value-adding processes. Within this, the competence requirements for employees are changing. Employees’ organization, process, and interaction competences are of crucial importance in this new IoT environment, however, in students and vocational training not sufficiently considered yet. On the other hand, conventional learning factories evolve and transform to digital learning factories. Nevertheless, the integration of IoT technology and its usage for training in digital learning factories has been largely neglected thus far. Existing learning factories do not explicitly and properly consider IoT technology, which leads to deficiencies regarding an appropriate development of employees’ Industrial IoT competences. The goal of this contribution is to point out a didactic concept that enables development and training of these new demanded competences by using an IoT laboratory. For this purpose, a design science approach is applied. The result of this contribution is a didactic concept for the development of Industrial IoT competences in an IoT laboratory.

The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems

The acquisition of data and the development of different options in production system and factory planning requires up to 2/3rds of the total needed time resources. The digitization of production systems offers the possibility of automated data acquisition. Nevertheless, approaches concerning fully automated data acquisition systems are not widely spread among SME (small and medium sized enterprises). On the one hand, this is caused by the heterogeneous databases, on the other hand by insufficient data processing systems. Furthermore, the advantages of The Digital Twin are not sufficiently known due to the lack of competence in SME concerning matters of Industry 4.0. In order to transfer knowledge about the benefits of digitalization, the development of demonstrating platforms is crucial. This paper introduces a learning factory based concept to demonstrate the potentials and advantages of real time data acquisition and subsequent simulation based data processing. Therefore, an existing learning factory will be upgraded regarding both, multi-modal data acquisition technologies as well as a locally independent optimization environment. Thereby the requirements of SME concerning flexible, easy to use, scalable and service oriented digitization applications are met. The approach is part of a concept for the realization of a Cyber Physical Production System (CPPS) in SME that ensures the development of an image of the production with the aid of a multi-modal data acquisition.

Learning factories for future oriented research and education in manufacturing

Learning factories present a promising environment for education, training and research, especially in manufacturing related areas which are a main driver for wealth creation in any nation. While numerous learning factories have been built in industry and academia in the last decades, a comprehensive scientific overview of the topic is still missing. This paper intends to close this gap by establishing the state of the art of learning factories. The motivations, historic background, and the didactic foundations of learning factories are outlined. Definitions of the term learning factory and the corresponding morphological model are provided. An overview of existing learning factory approaches in industry and academia is provided, showing the broad range of different applications and varying contents. The state of the art of learning factories curricula design and their use to enhance learning and research as well as potentials and limitations are presented. Conclusions and an outlook on further research priorities are offered.

Learning Factories for Sustainable Manufacturing - A Generic Design Approach

A learning factory is a learning environment that promotes the competent development of people. Highly qualified employees are a basic prerequisite for competitive and future-oriented production. Therefore, companies and universities increasingly tend to develop and operate learning factories. So far, existing learning factories are mainly used to mediate subjects as energy- and lean-management. In addition to these topics, it becomes increasingly important for many companies to sustainably produce now and in the future. However this topic is rarely taught in learning factories. This paper presents a concept of how companies and universities can qualify people in learning factories for the new challenges arising from a sustainable manufacturing strategy. For this, content and methods will be identified which affect the three dimensions of sustainability. Secondly, the feasibility to apply these contents and methods in a learning factory is assessed.

The State-of-the-Art and Prospects of Learning Factories

Changeability of manufacturing systems is an important enabler for offering large variety of competitive products to satisfy customers’ requirements. Learning factories, as teaching and research environments, can play a key role in developing new solutions for changeability, transferring them to the industry and using them in educating engineers. The results of a survey of existing learning factories and their characteristics are presented. Their use in research, teaching and industrial projects is analyzed. A novel scheme to classify those systems with regard to their design, products and their changeability characteristics is outlined. Conclusions about the future of learning factories are drawn.