Industry 4.0 enabling technologies for increasing operational flexibility in final assembly

The manufacturing industry is facing uncertainties caused by growing competition and increasing customer demands. Simultaneously, the fourth industrial revolution, commonly referred to as Industry 4.0, is helping in modernising the manufacturing industry. In the process of modernising, companies are now capable of building resilience into their systems. This resilience is in the form of higher operational flexibility, which helps cope with the growing uncertainties. The new technologies under the Industry 4.0 umbrella can be used to increase operational flexibility. This article summarises various Industry 4.0 enabling technologies that can increase operational flexibility in final assembl

Variations in cycle-time when using knowledge-based tasks for humans and robots

Operator4.0 was coined in 2016 to create a research arena to understand how the physical, cognitive, and sensorial capabilities of an operator could be enhanced by automation. To create an interaction between operator and robots, there are important factors that needs to be defined. Two important factors are the task and function allocation. Without well-defined tasks it is hard to allocate the tasks between the robot and the human to create resource flexibility. Furthermore, it the tasks are knowledge-based rather than rule-based, the cycle time between operators can differ a lot. Two assumptions are discussed regarding knowledge-based tasks and automation. These are also tested in an experiment. Results show that it is a large variation of the cycle time for both humans (between 1,58 minutes up to 4,40 minutes) and robots (between 1,94 minutes up to 4,49 minutes) when it comes to knowledge-based and machine learning systems

Production Innovation and Effective Dissemination of Information for Operator 4.0

The manufacturing industry is becoming increasingly more complex as the paradigm of mass-production moves, via mass-customization, towards personalized production and Industry 4.0. This increased complexity in the production system also makes everyday work for shop-floor operators more complex. To take advantage of this complexity, shop-floor operators need to be properly supported in order to perform their important work. The shop-floor operators in this future complex manufacturing industry, the Operator 4.0, need to be supported with the implementation of new cognitive automation solutions. These automation solutions, together with the innovativeness of new processes and organizations will increase the competitiveness of the manufacturing industry. This paper discusses three different aspects of production innovation in the context of the needs and preferences of information for Operator 4.0. Conclusively, product innovations can be applied in the manufacturing processes, and thus becoming process innovations, but the implementation of such innovations require organizational innovations

Dual Robot Kit Preparation in Batch Preparation of Component Kits for Mixed Model Assembly

Kitting is a materials supply principle that plays a vital role for performance in mixed model assembly systems. The kit preparation process, whereby component kits are created, is central when kitting is applied. Kit preparation is a form of materials handling and is associated with several ergonomic and quality related issues. Robotics holds a great potential for decreasing the need for human labour, but literature on the topic is scarce. The purpose of this paper is to identify the time efficiency potential of a dual robot application for kit preparation. To address the purpose, a mathematical model is developed that allows dual robot kit preparation to be analysed and compared with manual kit preparation. Furthermore, the model supports identification of a suitable batch size given a lead time requirement from the assembly system. A numerical example shows dual robot kit preparation to be slightly more efficient than its manual ditto for preparation of 2, 3 and 4 kit batch sizes. The paper’s makes a theoretical contribution in terms of the time efficiency model of dual robot kit preparation. This model is also useful for practitioners when evaluating the potential of dual robot arm kit preparation in their own processes

Application of design principles for assembly instructions – evaluation of practitioner use

Production complexity causes assembly errors due to that the demands on the operators are high and there is a need to improve assembly instructions. Design principles for Information Presentation (DFIP) is a method developed to support such improvement and its application was evaluated in three case studies, 152 practitioners. Results indicate that DFIP use help simplifying the information presentation so that complexity can be reduced, and that step 4 is easiest to understand. In addition, the implementation of assembly instructions gave positive results

Low-cost Automation – changing the traditional view on automation strategies using collaborative applications

The labor cost has been one of the main reasons for industry to move some of the production to so called low-cost countries. Research has shown that this issue is more complex than just calculate labor cost as main driver. Organization culture, research and development and technical competence is also important drivers for a successful automation strategy. Another important factor when it comes to automation strategies is what production parameters to consider choosing the right automation. Traditionally five parameters have been considered i.e. Volume, batch sizes, variants, investment cost and labor cost. With new and cheaper solutions for automation these two views on automation and lowcost production need to be considered and changed. This paper will describe three demonstrators using low-cost automation solutions to automate simple tasks in final assembly systems. The stations\u27 investment cost is all below 50,000 euro. The first demonstrators have been set up and tested in a lab environment. The results show a high precision, easiness in programming and high quality. The aim is to test this further in real industrial environment to stress the system and to put it into a tougher environment

How to design a smart factory?

The enabling technologies of today creates a lot of opportunities. Hence, with all the differentchoices the complexity of different hardware and software increases. Furthermore, thecommunication between all the system needs a structure in order to make the whole system moreflexible, proactive and productive factory. The aim of this paper is to demonstrate how a smartfactory can be designed in terms of communication and interoperability between systems. To beable to demonstrate this a drone factory has been built in order to show a smart factory, from inhouselogistics to end-on-line quality check. This paper uses the pathway framework to describethe development of the smart drone factory

The cognitive operator 4.0

While previous Industrial Revolutions have increasingly seen the human as a cog in the system, each step reducing the cognitive content of work, Industry 4.0 contrarily views the human as a knowledge worker putting increased focus on cognitive skills and specialised craftsmanship. The opportunities that technological advancement provide are in abundance and to be able to fully take advantage of them, understanding how humans interact with increasingly complex technology is crucial. The Operator 4.0, a framework of eight plausible scenarios attempting to highlight what Industry 4.0 entails for the human worker, takes advantage of extended reality technology; having real-time access to large amounts of data and information; being physically enhanced using powered exoskeletons or through collaboration with automation; and finally real-time monitoring of operator status and health as well as the possibility to collaborate socially with other agents in the Industrial Internet of Things, Services, and People. Some of these will impose larger cognitive challenges than others and this paper presents and discusses parts of the Operator 4.0 projections that will have implications on cognitive work

A Framework for Extended Reality System Development in Manufacturing

This paper presents a framework for developing extended reality (XR) systems within manufacturing context. The aim of this study is to develop a systematic framework to improve the usability and user acceptance of future XR systems. So that manufacturing industry can move from the “wow effect” of XR demonstrators into the stage whereas XR systems can be successfully integrated and improve the conventional work routines. It is essential to ensure the usability and user acceptance of XR systems for the wider adoption in manufacturing. The proposed framework was developed through six case studies that covered different XR system developments for different application areas of manufacturing. The framework consists of five iterative phases: (1) requirements analysis, (2) solution selection, (3) data preparation, (4) system implementation and (5) system evaluation. It is validated through one empirical case and seven identified previous studies, which partly aligned with the proposed framework. The proposed framework provides a clear guideline on the steps needed to integrate XR in manufacturing and it extends the XR usage with increased usability and user acceptance. Furthermore, it strengthens the importance of user-centered approach for XR system development in manufacturing

Conceptualising Assembly 4.0 through the Drone Factory

This paper aims to discuss the complexity of designing an assembly system according to industry 4.0. This is done by introducing the drone factory as a learning facility at the digital innovation hub SIILab. The paper discusses the areas of Operator-Organisation, Operator-Technologies, Technologies-Product and Product-Organisation in a current state and information support subsystem, IIoT architecture and hardware in the assembly 4.0 context