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Challenges in blockchain implementation in manufacturing systems

Challenges in blockchain implementation in manufacturing systems

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Cyber-Physical Production Systems (CPPSs) are complex manufacturing systems which aim to integrate and synchronize machine world and manufacturing facility to the cyber computational space. However, having intensive interconnectivity and a computational platform is crucial for real-world implementation of CPPSs. In this paper, the potential impacts...

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The design of production plants in the form of a Cyber Physical Production System (CPPS) promises rapid adaptability to changing market requirements, high flexibility during production, robust behavior in the event of failure and offers the possibility of integrating customers into the production process. In contradiction to these advantages there...

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... Other applications are of cyber physical systems are as follows: Smart workshop (Leng et al., 2019), cyber security (Lee et al., 2019), safe human robot collaboration (Nikolakis et al., 2019), manufacturing technology (Leng et al., 2019), production planning (Ma et al.,2019), Health security( (S. Das et al., 2022;, adaptive shop floor (Mourtzis & Vlachou, 2018;, industry 4.0 (O'Donovan et al.,2018), cost efficient resource management (Gu et al., 2017), optimisation (Liang et al., 2018), anomaly detection1 (Schneider et al., 2018). ...
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Digital protection is the act and protocol to protect personal computers, mobile, electronic devices, and data from malicious attacks. Artificial intelligence (AI) and machine learning (ML) are presently essential for our daily existence, and this incorporates network protection. AI/ML can recognize vulnerabilities and lessen occurrence reaction time. Due to this unprecedented test, AI based instruments for network safety have arisen to help data security groups, decrease break chance and further develop their security act proficiently and successfully. Simulated intelligence and AI developed basic advances in information security, as they can fast examine a huge quantity of datasets and recognize various kinds of network stranded and breaches – from malware, one can pursue advantage to distinguishing unsafe comportment which might prompt a phishing attack or downloading malicious program. The advancements learned over the system, fascinate the previous for identifying new types of attacks in future.
... The fuel tube incorporates most components of the ancient design into a single model but weighs 25% lower. It is also more efficient as it uses an additive manufacturing process (Krugh & Mears, 2018;Lee et al., 2019). ...
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... Bitcoin [1] and Ethereum [2] achieve consensus among participants who do not trust each other, and blockchain has attracted the attention of the public [3][4][5]. Inspired by Bitcoin and cryptography, blockchain has emerged, evolved, and spread in several fields [6][7][8][9][10], such as finance [11], health [12], administration [13], industry [14], agriculture [15], smart cities [16,17], and Internet-of-ings networks [18,19]. Blockchain is a new type of technology that is integrated with a variety of computer technologies, such as distributed storage, peer-to-peer (P2P) networking, consistency verification, consensus algorithms, and cryptography. ...
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... In the course of increasing production variety and decreasing lot size, cyber-physical production systems (CPPS) have emerged that enable flexible, automated and self-configuring production [95]. Even though such systems have gained a lot of recent attention [95][96][97][98], several challenges such as trustworthy cross-company interactions, data security, robustness against failures or transparent and reliably documented processes remain unsolved [99]. Blockchain technology as a distributed ledger presents a potential solution to these issues, due to its irreversible, redundant and distributed data storage [100]. ...
... A physical prototype of M2M communication or a blockchain-based CPPS is not implemented. Lee et al. propose a three-layered blockchain architecture for manufacturing systems and carry out a case study in manufacturing machines [99]. Additionally, they present several challenges in blockchain implementation such as realtime implementation, storage capacity and lack of knowledge and infrastructure. ...
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... However, CPS depends on third-party trust operations and centralized industrial networks due to which manufacturing processes suffer problems such as security, transparency, privacy, trustworthiness, and efficiency. Lee et al. (2019) proposes a three-layer blockchain enabled cyber physical system (BCPS) for to address these issues. ...
... The amalgamation of AI and blockchain technologies has also introduced decentralized autonomous business models that brings greater flexibility, agility, and cost-effectiveness to business. For example, Lee et al. (2019) introduce cyber physical system (CPS) for manufacturing industry, which facilitates self-optimizing, self-adjusting, and self-configuring production systems and solves the inadequacies of existing manufacturing processes. CPS has laid the foundation to build advanced production systems in which every functional element of the production chain such as design, manufacturing, supply chains, customer service, and support can be influenced (Lu, 2017). ...
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... This makes the system more fault-tolerant by avoiding a single point of failure [34]. In a recent study, the authors [27] also advocated that some sort of distribution is required to manage the functionality and security at physical and cyber level of CPSs. They stated that in [26], "a 5 level architecture, namely 5C-CPS, has been proposed for developing CPSs. ...
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... A three-layer architecture (physical, middleware and computation) is then presented and verified on a flexible automated production line [30]. Since blockchain technology has received significant focus in financial field, a conceptual three-level (Connection Net, Cyber Net and Management Net) blockchain framework is provided to resolve the inherent real-time implementation constraints of CPS in the application of manufacturing domains [31]. Bringing the human factor inside the cybernetic control loop presents a significant challenge to CPS, and the strengths and weaknesses of both robots and operators have been explored to compensate for the limitations of one another [32]. ...
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... The IoT is used to integrate the smart manufacturing value chain actors, which leads to seamless connectivity and communication (Yang et al. 2019). It improves communication and transparency; the need for proper cybersecurity measures is adopted to prevent espionage and leakage of classified data (Lee, Azamfar, and Singh 2019;Pilloni 2018). The cybersecurity measures prevent any potential cyber threat over the smart manufacturing system, making it safe, secure, and reliable (Nimawat and Gidwani 2021;Sharpe et al. 2019). ...
... Data security ensures the system to be secure and facilitates the interoperability of the systems. R t C can be defined as the lapse time between the breach due to a cyber-attack and the response time to recover from the attack (Lee, Azamfar, and Singh 2019;Abraham and Nair 2014). The less is the R t C, the more reactive the system is towards cyber-attacks, hence better security infrastructure. ...
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The deployment of smart manufacturing technologies like communication, computing, sensors, cyber-physical systems, simulation, and data sciences has enabled the operational transformations of the factory ecosystem. Therefore, it is essential to monitor the alterations commenced by the business due to smart manufacturing implementation. Smart Manufacturing Performance Measures (SMPMs) are used to evaluate operational transformations realized through digitalization of systems. However, there is no reported work focusing on the quantification of SMPMs. This study addresses the research gap by defining potential indicators to quantify SMPMs referred to as smart manufacturing performance indicators (SMPIs) identified through literature review methodology. The SMPIs are discussed in detail besides their mathematical expression advocated through in-depth literature study. Further, a conceptual framework for decision-making in smart manufacturing environment based on SMPIs is proposed. The conceptual framework provides guidelines to plan and select the preferred focused manufacturing output and the relevant set of SMPIs contributing to the outputs for expediting effective smart manufacturing implementation. The research findings are beneficial for the managers and consultants to gauge the potential structure of measurement that can be used to evaluate smart manufacturing systems' performance. ARTICLE HISTORY