A digital twin framework for Industry 4.0 enabling next-gen manufacturing

Digital twins offer a framework to support the ever-rising demands in the fast-paced industrial evolution. This technology not only adds to the reliability of industrial processes but also offers an insight in to long-term behaviors and pattern during the aging of the industrial equipment. In this paper, a digital twin framework is presented to replicate the processes of a real production line for product assembly. The proposed work implements a digital/graphical replica of Festo Cyber Physical Factory (CPF) for Industry 4.0 (I4.0). The implemented system allows to schedule orders and specify product configuration which embodies the actions of CPF in digital world. In addition, the paper also presents a viable framework to interlink the physical system with the digital instance to offer extended services and a pathway towards realization of fully functional digital twins

Hybrid ARQ in wireless packetized predictive control

Motivated by new wireless applications that rely on ultra-reliable low latency communications, while supporting the transmission of short packets, we introduce a method that reduces the wireless resources consumption for real-time control of physical systems. Leveraging the tight interaction between control and communication systems, we make use of packetized predictive control along with incremental redundancy hybrid automatic repeat request, aiming at minimizing the transmission energy consumption of a packet by optimizing the transmit power and prediction length of the controller. Our results show that the proposed strategy can save up to 45% of wireless resources when compared to a state-of-the-art method

toward a real time reconfiguration of self adaptive smart assembly systems

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PENGARUH PENERAPAN SMART FACTORY DI ERA VUCA PADA UMKM DI KOTA KEDIRI

Pada era globalisasi saat ini kita diperkenalkan dengan sebuah konsep bernama VUCA (Volatility, Uncertainty, Complexity, Ambiguity) yang menjelaskan mengenai sifat tantangan masa depan dan perubahan yang akan dihadapi. Era VUCA yang mengarah ketidakpastian dan mudah berubah pada sebuah situasi bisnis menimbulkan kecemasan bagi para pemimpin bisnis. Banyak profesi lama bertumbangan di era VUCA, namun ada pula yang muncul sebagai profesi baru. Mampu menjalankan bisnis dalam jangka pendek di era VUCA tidaklah cukup, setiap perusahaan tentu menginginkan agar proses bisnis yang mereka jalankan memiliki kelanjutan. Penelitian ini fokus untuk mengetahui pengaruh penerapan smart factory di era VUCA terutama saat pelaku usaha UMKM menghadapi revolusi industri 4.0. Metode penelitian yang digunakan adalah penelitian kuantitatif deskriptif. Metode ini digunakan untuk mengetahui pengaruh penerapan smart factory di era VUCA terhadap UMKM di Kota Kediri. Hasil dari penelitian ini adalah pengembangan dan pembuatan produk, penerapan transformasi digital dengan melakukan pengumpulan data diinternet, dan penerapan additive manufacturing, prototype, / pencetakan 3D mempunyai nilai signifikansi < 0,05 sehingga berpengaruh terhadap kinerja UMKM. Penerapan penggunaan layanan cloud yang terkait produk dan penerapan penggabungan layanan digital ke dalam produk mempunyai nilai signifikans

Multi-agent Manufacturing Execution System (MES):Concept, architecture & ML algorithm for a smart factory case

Smart factory of the future is expected to support interoperability on the shop floor, where information systems are pivotal in enabling interconnectivity between its physical assets. In this era of digital transformation, manufacturing execution system (MES) is emerging as a critical software tool to support production planning and control while accessing the shop floor data. However, application of MES as an enterprise information system still lacks the decision support capabilities on the shop floor. As an attempt to design intelligent MES, this paper demonstrates one of the artificial intelligence (AI) applications in the manufacturing domain by presenting a decision support mechanism for MES aimed at production coordination. Machine learning (ML) was used to develop an anomaly detection algorithm for multi-agent based MES to facilitate autonomous production execution and process optimization (in this paper switching the machine off after anomaly detection on the production line). Thus, MES executes the ‘turning off’ of the machine without human intervention. The contribution of the paper includes a concept of next-generation MES that has embedded AI, i.e., a MES system architecture combined with machine learning (ML) technique for multi-agent MES. Future research directions are also put forward in this position paper

Bluetooth Mesh Networking: an Enabler of Smart Factory Connectivity and Management

Smart factory is an environment where machinery and equipment are able to work together to improve processes through automation and self-optimisation. Connectivity in smart factory is the key enabler to optimise operations through collection of data to accelerate automation in a factory setting. This paper proposes the use of Bluetooth wireless mesh networking to realise the vision of smart factory, providing efficient connectivity to collect data from the shop-floor in real-time. Downstream communication to the sensor devices can also be performed, thus creating a digital twin of the shop-floor and its process. A web-based visualisation dashboard is implemented to monitor the status of sensors and machinery in real-time. The developed system is also integrated with an indoor localisation mechanism to provision new sensors into the mesh network. An augmented reality dashboard enables a user who is physically patrolling the smart factory to view sensor status in real-time

An Overview of the Rising Challenges in Implementing Industry 4.0

Industry 4.0 is the fourth industrial revolution that was first introduced in Germany which then becomes a trend of future manufacturing industries. The Industry 4.0 also referred as the umbrella concept for new industrial paradigm which consists of a number of future industry characteristics, were related to cyber-physical systems (CPS), internet of things (IoT), internet of services (IoS), robotics, big data, cloud manufacturing and augmented reality. By adopting these technologies as the key development in more intelligent manufacturing processes including devices, machines, modules, and products, the process of information exchange, action and control will stimulate each other, subsequently to an intelligent manufacturing environment. However, in order to fully utilize the advantages of industry 4.0, there are some challenges that need to be overcome. This paper reviews the challenges in implementing Industry 4.0. The literatures found in this paper mainly from Google Scholar, Science Direct and Emerald. In short, the challenges can be imparted into seven major categories. There are data management and Integration, knowledge-driven, process, security, capital, workforce, and education