New Challenges for Quality Assurance of Manufacturing Processes in Industry 4.0

2017 ◽  
Vol 261 ◽  
pp. 481-486 ◽  
Author(s):  
Béla Illés ◽  
Péter Tamás ◽  
Péter Dobos ◽  
Róbert Skapinyecz

Nowadays the flexibility and specific cost of manufacturing have a relevant role in the competitiveness of the companies. In our opinion the most important objective of Industry 4.0 is the realization of intermittent manufacturing at mass production’s productivity and specific cost. This aim can be only reached by creating more complex manufacturing systems. The increase in manufacturing complexity results in new challenges in the quality assurance of manufacturing processes. We can collect new types of data that enable the improvement of product and process service quality. This paper introduces the essence of Industry 4.0, as well as the new challenges for the quality assurance of manufacturing processes. Possible research directions for overcoming challenges are also presented.

2016 ◽  
Vol 686 ◽  
pp. 168-173 ◽  
Author(s):  
Péter Tamás

Nowadays the lean philosophy is very important in the improvement of the manufacturing processes. The value stream mapping is a fundamental lean device which can decrease the material-and information flow wastes. This paper introduces in details the static and dynamic value stream mapping’s method and it summarizes the application possibilities of these devices. There are numerous research directions in this topic which will be outlined in this paper.


Author(s):  
Ercan Oztemel

Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the introduction of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. This explicitly implies that the manufacturing systems will entirely be integrated and all manufacturing functions including quality control and management will have to be made as much intelligent as possible in operating with minimum human intervention. This Chapter will present a brief overview of some implications about intelligent quality systems. It intends to provide the readers of the book to understand how the concept of artificial intelligence is to be embedded into quality functions. It is known that the interoperability is the rapid transformation requirement of industry specific operations. This requires the integration of quality functions to other manufacturing functions for sharing the quality related knowledge with other manufacturing functions in order to sustain total intelligent collaboration. Achieving this, on the other hand, ensures the improvement of manufacturing processes for better performance in an integrated manner. Note that, although some general information about intelligent manufacturing systems are given, this chapter is particularly focused on discussing intelligent quality related issues.


2019 ◽  
Vol 2 (1) ◽  
pp. 283-295
Author(s):  
Bożena Gajdzik

Abstract This paper presents the importance of the prediction of steel production in industry 4.0 along with forecasts for steel production in the world until 2022. In the last two decades, the virtual world has been increasingly entering production. Today’s manufacturing systems are becoming faster and more flexible – easily adaptable to new products. Steel is the basic structural material (base material) for many industrial sectors. Industries such as automotive, mechanical engineering, construction and transport use steel in their production processes. Prediction methods in cyber-physical production systems are gaining in importance. The task of prediction is to reduce risk in the decision-making process. In autonomous manufacturing systems in industry 4.0 the role of prediction is more active than passive. Forecasts have the following functions: warning, reaction, prevention, normative, etc. The growing number of customized solutions in industry 4.0 translates into new challenges in the production process. Manufacturers must respond to individual customer needs more quickly, be able to personalize products while reducing energy and resource costs (saving energy and resources can increase the product competitiveness). The modern market becomes increasingly unpredictable. Production prediction under such conditions should be carried out continuously, which is possible because there is more empirical data and access to data. Information from the ongoing monitoring of the company’s production is directly transferred to the prospective evaluation. In view of the contemporary reciprocal use of automation, data processing, data exchange and manufacturing techniques, there is greater access to external data, e.g. on production in different target markets and with global, international, national, regional coverage. Companies can forecast in real time, and the forecasts obtained give the possibility to quickly change their production. Industry 4.0 (from the business objective point of view) aims to provide companies with concrete economic benefits – primarily by reducing manufacturing costs, standardizing and stabilizing quality, increasing productivity. Industry 4.0 aims to create a given autonomous smart factory system in which machines, factory components and services communicate and cooperate with each other, producing a personalized product. The aim of this paper is to present new challenges in the production processes in relation to steel production, as well as to prepare and present forecasts of (quantitative) steel production of territorial, global and temporary range until 2022, taking into account the applied production technologies (BOF and EAF). For forecasting purposes, classic trend models and adaptive trend models were used. This methodology was used to build separate forecasts for: total steel production, BOF steel and EAF steel. Empirical data is world steel production in 2000-2017 (annual production volume in Mt).


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Usharani Hareesh Govindarajan ◽  
Amy J. C. Trappey ◽  
Charles V. Trappey

Immersive technology for human-centric cyberphysical systems includes broad concepts that enable users in the physical world to connect with the cyberworld with a sense of immersion. Complex systems such as virtual reality, augmented reality, brain-computer interfaces, and brain-machine interfaces are emerging as immersive technologies that have the potential for improving manufacturing systems. Industry 4.0 includes all technologies, standards, and frameworks for the fourth industrial revolution to facilitate intelligent manufacturing. Industrial immersive technologies will be used for smart manufacturing innovation in the context of Industry 4.0’s human machine interfaces. This research provides a thorough review of the literature, construction of a domain ontology, presentation of patent metatrend statistical analysis, and data mining analysis using a technology function matrix and highlights technical and functional development trends using latent Dirichlet allocation (LDA) models. A total of 179 references from the IEEE and IET databases and 2,672 patents are systematically analyzed to identify current trends. The paper establishes an essential foundation for the development of advanced human-centric cyberphysical systems in complex manufacturing processes.


Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4626
Author(s):  
Aitor Toichoa Eyam ◽  
Wael M. Mohammed ◽  
Jose L. Martinez Lastra

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.


Logistics ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 49
Author(s):  
Guilherme F. Frederico

The main purpose of this paper is to present what the Industry 5.0 phenomenon means in the supply chain context. A systematic literature review method was used to get evidence from the current knowledge linked to this theme. The results have evidenced a strong gap related to Industry 5.0 approaches for the supply chain field. Forty-one (41) publications, including conference and journal papers, have been found in the literature. Nineteen (19) words, which were grouped in four (4) clusters, have been identified in the data analysis. This was the basis to form the four (4) constructs of Industry 5.0: Industry Strategy, Innovation and Technologies, Society and Sustainability, and Transition Issues. Then, an alignment with the supply chain context was proposed, being the basis for the incipient Supply Chain 5.0 framework and its research agenda. Industry 5.0 is still in an embryonic and ideal stage. The literature is scarce and many other concepts and discoveries are going to emerge. Although this literature review is based on few available sources, it provides insightful and novel concepts related to Industry 5.0 in the supply chain context. Moreover, it presents a clear set of constructs and a structured research agenda to encourage researchers in deploying further conceptual and empirical works linked to the subject herein explored. Organizations’ leadership, policymakers, and other practitioners involved in supply chains, and mainly those currently working with Industry 4.0 initiatives, can benefit from this research by having clear guidance regarding the dimensions needed to structurally design and implement an Industry 5.0 strategy. This article adds valuable insights to researchers and practitioners, by approaching the newest and revolutionary concept of the Industry 5.0 phenomenon in the supply chain context, which is still an unexplored theme.


Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 869
Author(s):  
Pablo F. S. Melo ◽  
Eduardo P. Godoy ◽  
Paolo Ferrari ◽  
Emiliano Sisinni

The technical innovation of the fourth industrial revolution (Industry 4.0—I4.0) is based on the following respective conditions: horizontal and vertical integration of manufacturing systems, decentralization of computing resources and continuous digital engineering throughout the product life cycle. The reference architecture model for Industry 4.0 (RAMI 4.0) is a common model for systematizing, structuring and mapping the complex relationships and functionalities required in I4.0 applications. Despite its adoption in I4.0 projects, RAMI 4.0 is an abstract model, not an implementation guide, which hinders its current adoption and full deployment. As a result, many papers have recently studied the interactions required among the elements distributed along the three axes of RAMI 4.0 to develop a solution compatible with the model. This paper investigates RAMI 4.0 and describes our proposal for the development of an open-source control device for I4.0 applications. The control device is one of the elements in the hierarchy-level axis of RAMI 4.0. Its main contribution is the integration of open-source solutions of hardware, software, communication and programming, covering the relationships among three layers of RAMI 4.0 (assets, integration and communication). The implementation of a proof of concept of the control device is discussed. Experiments in an I4.0 scenario were used to validate the operation of the control device and demonstrated its effectiveness and robustness without interruption, failure or communication problems during the experiments.


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