Smart Manufacturing Research Articles

Extended reality implementation possibilities in direct energy deposition-arc

The state-of-the-art cleaner smart manufacturing process in the metal industry is the direct energy deposition-arc (DED-arc) process, which has emerged as an energy-efficient method for producing complex geometry metallic constructions. Process flexibility, material-consumption efficiency and high performance have drawn attention amongst both academics and industry, as DED-arc presents an ecologically viable alternative to traditional manufacturing techniques. Concurrently, the parallel emergence of extended reality (XR) technology has unveiled multiple novel possibilities for enhancing the sustainable development of DED-arc processing toward cleaner manufacturing. However, an evident knowledge gap exists concerning the integration of XR into the DED-arc process chain. This research aims to solve this problem by systematically exploring the potential of implementing XR technology within the DED-arc framework. Therefore, this study identifies through a literature review the technological difficulties and prospects associated with merging XR and DED-arc. Subsequently, a series of practical experiments are executed, presenting various applications of XR within the DED-arc process chain. The current research makes several noteworthy contributions to the practical understanding of how XR can be integrated into the DED-arc manufacturing process. Technological challenges are discussed, while the potential benefits of XR adoption in the DED-arc process chain are illuminated in practical applications.

Der Mensch in der intelligenten Fabrik

Abstract The seamless merging of the physical and digital worlds, has led to an unprecedented increase in the speed at which automation can be introduced into production. can be introduced. Smart manufacturing systems will, at a fundamental level, enable the use of artificial intelligence (AI) through machine learning (ML). This involves the alignment of information flows through suitable interfaces to humans is essential. is indispensable. This human-centered approach is referred to as Industry 5.0 (I5.0) or the human-centered approach (HCA) [1, 2]. The prioritization of people can be achieved prioritization can be achieved by placing the process-related interests of people at the at the center of production monitoring and relying on technologies that help employees by developing knowledge and skills, initiate optimizations.

IMPROVING A CIRCULAR REVERSE LOGISTICS TO HANDLE PRODUCT RETURN: INSIGHTS FROM A GLOBAL ELECTRONICS SERVICES PROVIDER

In the retail industry, consumers often return goods for various reasons, including damage, flaws, or plain dissatisfaction. The storing of returned goods can result in insufficient arrangement of the existing storage space and capacity. Such issue can cause problems like longer processing times, higher operating costs, and reduced customer satisfaction, hence necessitating prompt response. Therefore, this case study was conducted in a global electronics services provider to examine how circular reverse logistics can enhance the handling of product returns as well as developing a smart product return strategy. It is believed that creating a plan for product returns and integrating internal process improvements with external collaboration are highly important. The results showed that the organisation can establish a circular reverse logistic process to lower the cost and amount of product returns, enhance consumer service, and boost profitability. Such findings can benefit organisations wishing to establish circular reverse logistics procedures to handle product returns.

Evaluation and Selection of Manufacturing Suppliers under the Perspective of Smart Manufacturing Combined with Bilevel Programming

To address the issue of supplier evaluation and selection in the current intelligent manufacturing industry, a supplier intelligent evaluation and selection model combining bilevel programming is proposed. Firstly, based on existing research results and the current situation of the manufacturing market, a manufacturing supplier evaluation index system is constructed, and the principal component analysis method is used to simplify it; Then, the simplified indicator system is input into the back propagation neural network to build a supplier intelligent evaluation model and achieve supplier intelligent evaluation; A mathematical model for supplier selection problem is constructed based on bilevel programming, and an optimized sparrow search algorithm is put forward to solve it. A supplier intelligent selection model is constructed to achieve intelligent supplier selection. The lab findings denote that the F1 value of the supplier intelligent evaluation model is 0.953, the accuracy is 0.985, the area under the curve is 0.962, and the error of the output comprehensive evaluation value is 0.003; The output accuracy of the supplier intelligent selection model is 0.8. The above results indicate that the model raised in the article has good application effects in supplier evaluation and selection, and has a certain promoting effect on the development of the intelligent manufacturing industry.

Investigating smart manufacturing process implementation in the Indian manufacturing industries using tecnomatix and response surface methodology

Investigating smart manufacturing process implementation in the Indian manufacturing industries using tecnomatix and response surface methodology

Enhancing Cybersecurity in Smart Manufacturing: A Comprehensive Approach

The industry 4.0 defines a new era of much efficient and intelligent manufacturing processes that works in integration with other advanced technologies like AI, Big data, Block chain, etc. The smart manufacturing industry has remarkably evolved due to integration of advanced technologies like cyber-physical systems (CPS), Internet of Things (IoT), digital twins that makes up a highly connected infrastructure where data flows in real time. However, the involvement of these technologies has also significantly increased the exposure of manufacturing industry to the cyber threats. This article investigates several security challenges and major threats that are associated with the manufacturing industry. It evaluates various security implementations including digital twins, machine learning, and block chain based on their effectiveness, scalability, and feasibility in terms of security. Through a comprehensive literature review, case study analysis of notable cyberattacks and qualitative exploratory methods, this study explores the best line of defence against the cyber threats. Our findings highlight the critical role of robust and adaptive multiple line of defense mechanisms in mitigating cyber risks. The research concludes by emphasizing the need for an effective security strategy that combines advanced technological solutions with continuous employee education and proactive threat management to safeguard smart manufacturing processes against evolving cyber threats.

О законодательном регулировании продуцирования, применения и пользования смарт-систем

The emergence of “generative artificial intelligence” (GII), led to the adoption in China of the first regulatory legal act regulating the provision of services with its help. In order to regulate social relations, it is necessary to know what these relations arise about, i.e. what is called GII. Therefore, such a product of human activity should be accurately and fully formalized and correctly named. In addition, it is necessary to determine not only the form and/or content of the carrier, called artifact (artificial) intelligence, but also its structure, including its functions, which may be negative. Given the emergence of the trend for smartization, which is replacing digitalization, it is logical to use the terms “smart product” and “smart system”. The main properties of a smart product in the form of smart systems and smart media products are determined, and the features of the use of smart agents or smart actors are considered. It is argued that the use of smart systems by some legal entities against others, the voluntary use of smart tools and/or the use of smart objects by people without realizing their true properties, creates risks of negative impact on them in the form of violations of constitutional freedoms, rights and legitimate interests.

A Cyber-Physical Production System for the Integrated Operation and Monitoring of a Continuous Manufacturing Train for the Production of Monoclonal Antibodies.

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber-physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

Effect of TiC addition on microstructure and properties of network structured TiC–ZrO2 composite conductive ceramics

Zirconia (ZrO2) ceramics have excellent mechanical properties and high-temperature conductivities. However, ZrO2 ceramics do not possess room-temperature conductivity, which significantly limits their application as functional components in smart communication devices and wearable smart products. To achieve room-temperature conductivity in ZrO2 ceramics, this study prepared a network-structured TiC–ZrO2 composite conductive ceramic with a low titanium carbide (TiC) addition using pressureless sintering. The influence of the TiC addition on the microstructure, mechanical properties, and electrical properties of the TiC–ZrO2 composite material was investigated. The results showed that the composite material consists of cubic zirconia (c-ZrO2), tetragonal zirconia (t-ZrO2), and titanium carbide (TiC) phases. When the TiC content was 11 vol %, the relative density, open porosity, flexural strength, and fracture toughness of the sample were 98.6 ± 0.1 %, 0.16 ± 0.06 %, 383 ± 38 MPa, and 4.66 ± 0.26 MPa m1/2, respectively. The electrical percolation threshold of the TiC–ZrO2 composite was between 8.0 and 11.0 vol %. When the TiC content is 18.8 vol %, the sample exhibited the lowest room-temperature resistivity of 3.00 × 10−5 Ω m. The electrical resistance of the TiC–ZrO2 composite material is mainly attributed to the grain boundary effect. With increasing TiC content, the resistivity of the composite material decreased, exhibiting linear conductivity and achieving conductive percolation characteristics.

Modeling coupling impacts of self-healing mechanisms and dynamic environments on systems subject to dependent failure processes

Modern smart products are endowed with the capability to autonomously repair damage from random shocks, thereby highlighting the critical necessity to incorporate this self-healing attribute in reliability assessments. This paper investigates periodically inspected self-healing systems that are exposed to dependent competing failure processes within dynamically evolving environments. The impact of environmental changes is manifested in that both the natural degradation process and the self-healing behavior are governed by distinct laws in varying environments. Explicit formulas for the system reliability and availability are analytically derived using the stochastic process theory, and their correctness is verified by proposed simulation algorithms respectively. Meanwhile, the inspection period is optimized by minimizing the long-run average cost rate. Lastly, a practical example of semiconductor lasers is applied to showcase the application of the presented methods. The sensitivity analysis reveals that increasing the self-healing time threshold or the shock arrival rate can improve the system performance while lowering the long-run average cost rate.

How can firms implement smart manufacturing through supply chain collaboration? A multiple-case study from China

The complex system characteristics of Smart manufacturing (SM) require firms collaborating with supply chain members to implement SM transformation. However, the motives, methods, and influencing factors of such collaboration remain unclear. Addressing this gap, we conduct a multi-case study of four manufacturing firms and obtain three findings based on complex adaptive system theory. First, the motives of supply chain collaboration include the adaptation to the environment of high customisation, technical complexity, policy, and horizontal competition. Second, firms collaborate with supply chain members in terms of equipment customisation and information interconnection, to establish the data and equipment foundation of SM. This process is affected by the scale and volume of invested resources. Based on these, firms further build collaborative platforms to realise the integrated management of all supply chain links and improve the SM capability through the iteration of solutions, which are affected by the degree of security in data sharing. Third, supply chain collaboration improves the quality and satisfaction, reduces the cost, and shortens the cycle of SM implementation. This study not only addresses whether and how supply chain collaboration influences SM implementation, but also provides guidance to help companies implement SM through a gradual multi-stage collaboration in supply chain.

An investigation of worker decision-making optimization for smart manufacturing

The transition to smart factories, characterized by computer-integrated facilities and advanced technologies, has heightened the demand for specific and advanced skills in workers. This multidisciplinary project brings together insights from cognitive psychology research and qualitative field research to present a framework that identifies factors at the individual and organizational system levels for success in smart manufacturing. The cognitive psychology research indicated that augmenting human cognition, particularly memory, significantly improved performance in training scenarios. The field research resulted in a multi-factor framework to address individual and organizational decision-making factors. A proposal is presented to apply the research findings to designing a training paradigm for smart manufacturing workers and facilities to be adaptable for a variety of situations.

STRATEGIC IMPERATIVES FOR DIGITISATION OF THE UKRAINIAN PHARMACEUTICAL INDUSTRY

The purpose of the paper is to identify the strategic imperatives of the pharmaceutical industry in the context of the development of the digital economy. Inclusion of the pharmaceutical industry in the list of priority sectors of the economy will help attract state investment support for production and increase the number of jobs. This will help to prevent a collapse in the supply of medicines and medical devices to the healthcare system and ensure their uninterrupted production and supply to the end consumer. The population of Ukraine will have access to quality medicines and medical products. Methodology. The article presents an analysis of the indicators of the pharmaceutical industry for the period 2019-2023. These include the volume of import and export of pharmaceutical products, the structure of the main marketing organisations in the pharmaceutical market, leading drugs in terms of sales volume on the Ukrainian market, the structure of sales of "pharmacy basket" products, and the volume of hospital supplies in terms of funding sources. This paper analyses the main trends of digitisation in the pharmaceutical industry at the global level and in Ukraine in particular. A SWOT analysis was conducted to assess the development of the pharmaceutical industry in Ukraine. The analysis focused on identifying the key factors that contribute to the successful development of e-commerce in the pharmaceutical industry. Results. The publication identifies the main positive and negative manifestations of digitalisation of the pharmaceutical industry in Ukraine and the factors that hinder its development. Practical implications. Thus, the introduction of digitalisation tools in the Ukrainian pharmaceutical industry is an important step towards improving its competitiveness. The deployment of smart manufacturing systems makes it possible to produce medicines faster and more accurately. Such technological solutions can help to optimise the supply chain, which is important for the pharmaceutical industry, where the accuracy and speed of supply of medicines is critical. The use of digital technologies improves control over the safety of pharmaceutical products and increases production efficiency. Automation of business processes through the use of AI, VR, AR, cloud computing, big data, etc. can help reduce production costs and improve product quality. However, the implementation of digitalisation technologies in the pharmaceutical industry also requires significant investments and qualified personnel, which may temporarily reduce competitiveness due to higher prices for finished products.

A novel fuzzy algorithm for assembly precision management

The assembly precision of a specified product is subject to epistemic uncertainty due to manufacturing and measurement errors. The efficient and accurate assembly precision management is essential for realizing smart production lines, which rely on the robust assembly precision analysis model and calculation method. Parametric models are extensively used for assembly precision analysis in industrial products, typically integrating worst-case and statistical methods for calculations. Nevertheless, the combined application of these methods presents inherent limitations. Therefore, a novel fuzzy algorithm for assembly precision management is proposed in this paper by using fuzzy sets to quantify epistemic uncertainty in assembly precision, which integrates the proposed fuzzy-based assembly precision analysis model and fuzzy-based calculation method. The proposed fuzzy small displacement torsor model is a fuzzy-based model for deviation representation and tolerance classification using decomposed fuzzy numbers, where an assembly reliability index is always established for the hierarchical evaluation and management of assembly precision. Subsequently, a comprehensive assembly precision analysis model is developed for precision prediction and contribution quantification by integrating the Jacobian model for deviation propagation. A new constrained transformation method is proposed as a fuzzy-based calculation method, offering efficient and highly accurate assembly precision computation. It accounts for torsor parameter constraints to ensure greater prediction accuracy than the worst-case method and improves computational efficiency compared to statistical methods. An assembly case of the centering pin mechanism is applied to verify the superiority of the proposed novel fuzzy algorithm compared with the Jacobian-Torsor model with worst-case or statistical methods.

Analytical-bibliometric structure of the international production of smart cities management and planning

analytical-bibliometric structure of the international production of smart cities management and planning

Analysis of financing strategies for digital technology investment under privacy concerns and competition

Digital technology improves consumption experiences and facilitates manufacturers' production of smart products. However, customer privacy concerns and market competition may negatively affect smart product sales and reduce manufacturers' investment in digital technology, particularly for innovation startups with limited funding. This raises an important question about how financing strategies affect a manufacturer's investment in digital technology under privacy concerns and competition. To solve this problem, this paper considers a supply chain in which two manufacturers sell products through a platform, one of which (M1) produces smart products by committing to investing in standard or premium digital technology and adopting platform financing or equity financing to maintain operations, and the other (M2) produces regular products. Intuition may suggest that decreasing product sales prices can stimulate consumption and increase sales. Surprisingly, we find that the sales prices and quantity of smart products decrease simultaneously with an increase in privacy concerns, in sharp contrast to regular products. Additionally, although low privacy concerns can encourage M1 to invest in premium digital technology to attract consumption and adopt equity financing to save investment costs, we find that, interestingly, the manufacturer may invest in standard digital technology through platform financing, particularly when the marginal technology utility is moderate. Finally, we find that for either standard or premium digital technology investments, platform financing can help participants achieve a triple-win result, whereas equity financing cannot. Our research provides insights into the interactions between financing strategies and digital technology investments in the context of customer privacy concerns and market competition.

A green strategic approach towards a smart production system with promotional and environment sensitive demand

A green strategic approach towards a smart production system with promotional and environment sensitive demand

Fuzzy Testing Model Built on Confidence Interval of Process Capability Index CPMK

A variety of process capability indices are applied to the quantitative measurement of the potential and performance of processes in manufacturing. As it is easy to understand the formulae of these indices, this method is easy to apply. Furthermore, a process capability index is frequently utilized by a manufacturer to gauge the quality of a process. This index can be utilized by not only an internal process engineer to assess the quality of the process but also as a communication tool for an external sales department. When the manufacturing process deviates from the target value T, the process capability index CPMK can be quickly detected, which is conducive to the promotion of smart manufacturing. Therefore, this study applied the index CPMK as an evaluation tool for process quality. As noted by some studies, process capability indices have unknown parameters and therefore must be estimated from sample data. Additionally, numerous studies have addressed that it is essential for companies to establish a rapid response mechanism, as they wish to make decisions quickly when using a small sample size. Considering the small sample size, this study proposed a 100 (1 − α)% confidence interval for the process capability index CPMK based on suggestions from previous studies. Subsequently, this study built a fuzzy testing model on the 100 (1 − α)% confidence interval for the process capability index CPMK. This fuzzy testing model can help enterprises make decisions rapidly with a small sample size, meeting their expectation of having a rapid response mechanism.

Harnessing Hybridized Machine Learning Algorithms for Sustainable Smart Production: A Case Study of Solar PV Energy in China

Harnessing Hybridized Machine Learning Algorithms for Sustainable Smart Production: A Case Study of Solar PV Energy in China

Unveiling the impact of the SMARTCLAP project on habilitation

This report summarises the SMARTCLAP research project, which employs a user-centred design approach to develop a revolutionary smart product service system. The system offers personalised motivation to encourage children with cerebral palsy to actively participate more during their occupational therapy sessions, while providing paediatric occupational therapists with an optimal tool to monitor children’s progress from one session to another. The product service system developed includes of a smart wearable device called DigiClap used to interact with a serious game in an Augmented Reality environment. The report highlights the research methodology used to advance the technology readiness level from 4 to 6, acknowledging the contribution of the consortium team and funding source. As part of the technology’s maturity process, DigiClap and the respective serious game were evaluated with target users, to identify the system’s impact in supporting the children’s overall participation and hand function, and to gather feedback from occupational therapists and caregivers on this novel technology. The outcomes of this study are discussed, highlighting limitations and lessons learned. The report also outlines future work and further funding for the sustainability of the project and to reach other individuals who have upper limb limitations. Ultimately, the potential of DigiClap and the overall achievements of this project are discussed.