Implementation Of Smart Factories Research Articles

Hyper Progressive Single Shot Detector; HPSSD

Studies into intelligent production and the implementation of smart factories have been promising in recent years, first generation artificial intelligence is out and all major companies are trying to integrate some form of machine learning into their systems. In the field of Machine vision, promising architectures have been built to strengthen the literature, this paper proses the development of a Neural Network called HPSSD (Hyper Progressive Single Shot Detector) to serve as the computational backbone for Machine vision applications specifically for Inspection. HPSSD is a Single Shot Detector that was built on the basis of a RCBAM, an MDCM, and a PAM module. The improvements shown after the development of HPSSD shows that there is no extent that machine Learning cannot take us through the Lens of Machine Vision. The tests demonstrates encouraging outcomes and enhancing the inspection accuracy in intelligent workshops.

Towards scalable and reusable predictive models for cyber twins in manufacturing systems

Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories.

Real-World Failure Prevention Framework for Manufacturing Facilities Using Text Data

In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor.

Research on The Implementation of Smart Factories through Bottleneck improvement on extrusion production sites using NFC

Research on The Implementation of Smart Factories through Bottleneck improvement on extrusion production sites using NFC

Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0.

The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories.

4차산업혁명 시대에 중소기업의 스마트 팩토리의 성공적 구현을 위한 준비도(Readiness)에 관한 연구

본 연구는 정부가 갖추어야 할 가이드라인으로 선지원 후 관리가 아니라 선 점검 및 관리를 위한 프레임워크인 중소기업의 스마트 팩토리 준비도 모형을 개발하는 것을 목표로 하였다. 본 연구가 갖는 실무적 의의는 스마트 팩토리를 받아들일 준비가 되지 않는 상황에서 혹은 무엇을 준비해야 할지 모르고 있는 상황에서 많은 중소기업들이 경험하지 못한 생소한 기술들의 집합체인 ICBMA(IoT, Cloud, Big Data, Mobile, AI) 등으로 구성된 스마트 팩토리를 구축하라고 일방적으로 독려하기보다는 안정적으로 성공적인 스마트 팩토리 구축을 위해 무엇을 미리 준비해야 하는지 그리고 스마트 팩토리의 성공적 구현을 위해 중소기업이 갖추어야 할 준비물이 무엇인지 미리 알려 줄 수 있다는 점이다. 또한, 중소기업들이 스마트 팩토리 도입에 앞서 현재 자사의 상태를 스스로 진단해 볼 수 있다는 점에서도 실무적 의의가 있을 것으로 판단된다. 특히 그동안 별개의 개념으로만 인식되어왔던 개인의 준비도, 조직의 준비도, 기술 준비도를 포괄적 개념으로 접근하여 학문적 영역을 확장할 수 있다는 점에서도 의의가 있다고 사료된다. 그뿐만 아니라 아직 국내는 물론이고 해외에서도 개발되지 않았던 스마트 팩토리 준비도 지표를 개발함으로 인해 해당 분야에 있어서 학문적 선두로 나아갈 수 있다는 점도 의의가 될 수 있을 것이다.

Smart Factory Implementation in Morrocan Phosphate Industry

Nowadays, the phosphate industry in Morocco is expecting a crucial round of its development cycle, which is mainly dominated by the digitization challenge. The concept of smart factory is reminiscent of a new way of organizing the production of goods, taking smarter decisions based on environmental variables and optimizing available resources. As for the phosphate industry in Morocco, building smart factories to upgrade the manufacturing processes constitutes a key element in its drive for innovation, growth and sustainable profitability. To successfully build smart factories, phosphate-manufacturing entities should consider some issues with care. This includes fostering IT and data-driven culture among leadership and employees, ensuring security, reliability and stability of IT infrastructure; maintaining the integrity of production processes and IT-controlled processes as well as enhancing IT and data skills and know how. However, there is still a journey to carry out the implementation of smart factories; given the large-scale, systemic transformation, the move requires alongside the lack of Enterprise Reference Model confirmed to fit with the issues of Moroccan phosphate industry. In this paper, we propose a reference model built around three overarching principles: (1) Culture of smart people 4.0, (2) introducing agile organizations, and (3) upgrading IT infrastructure. The paper then makes a case of the transition to smart factory using the proposed model in a pilot entity located at Safi. The results showed that the smart factory implemented yield a range of benefits, such as increased process efficiency, product quality, sustainability, and safety and decreased costs.

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.