Advanced Manufacturing Systems Research Articles

Induced alterations driven compromised structural properties in additively manufactured products

PurposeAdditive manufacturing also known as 3D printing is an evolving advanced manufacturing technology critical for the new era of complex machinery and operating systems. Manufacturing systems are increasingly faced with risk of attacks not only by traditional malicious actors such as hackers and cyber-criminals but also by some competitors and organizations engaged in corporate espionage. This paper aims to elaborate a plausible risk practice of designing and demonstrate a case study for the compromised-based malicious for polymer 3D printing system.Design/methodology/approachThis study assumes conditions when a machine was compromised and evaluates the effect of post compromised attack by studying its effects on tensile dog bone specimens as the printed object. The designed algorithm removed predetermined specific number of layers from the tensile samples. The samples were visually identical in terms of external physical dimensions even after removal of the layers. Samples were examined nondestructively for density. Additionally, destructive uniaxial tensile tests were carried out on the modified samples and compared to the unmodified sample as a control for various mechanical properties. It is worth noting that the current approach was adapted for illustrating the impact of cyber altercations on properties of additively produced parts in a quantitative manner. It concurrently pointed towards the vulnerabilities of advanced manufacturing systems and a need for designing robust mitigation/defense mechanism against the cyber altercations.FindingsDensity, Young’s modulus and maximum strength steadily decreased with an increase in the number of missing layers, whereas a no clear trend was observed in the case of % elongation. Post tensile test observations of the sample cross-sections confirmed the successful removal of the layers from the samples by the designed method. As a result, the current work presented a cyber-attack model and its quantitative implications on the mechanical properties of 3D printed objects.Originality/valueTo the best of the authors’ knowledge, this is the original work from the team. It is currently not under consideration for publication in any other avenue. The paper provides quantitative approach of realizing impact of cyber intrusions on deteriorated performance of additively manufactured products. It also enlists important intrusion mechanisms relevant to additive manufacturing.

Engineered substrates for metasurface antennas

This letter explores the advantages of additively manufactured substrates with spatially varying electromagnetic properties. These engineered substrates will be constructed using space filling curves (SFC) of various orders. New advanced manufacturing systems such as the nScrypt 3Dn-300, have enabled the rapid fabrication of these SFC substrates. This letter will apply the engineered SFC substrate to the design and fabrication of metasurface antennas. By utilising a SFC to vary the local substrate permittivity, along with the printed conductive patch dimensions, the range of achievable surface impedances can be greatly expanded. This enlarged design space will be leveraged to yield increased gain for a given metasurface antenna size. Methods to characterise the substrate permittivity and conductive ink are discussed along with a complete description of the metasurface antenna design, fabrication and validation process.

On the feasibility and the impact resistance of a 3D cross-based fractal produced by powder bed fusion additive manufacturing

Designers have been fascinated by exploring new geometries made by high-performance structures. In more specific terms, biological systems have always been proven to be characterised by sophisticated structures with adapting properties to nature challenges. Insightful analyses have shown how these natural structures are dominated by characteristics such as high energy absorption and elevated strength-weight proportion. Fractal geometries are examples of bio-inspired mathematical objects whose complex 3D structures can be obtained only by advanced manufacturing systems, such as additive manufacturing (AM). This study investigates the feasibility and energy absorption properties of a novel fractal structure based on a 3D Greek cross (3D-CFS). The structure was designed with different volume fractions and produced by powder bed fusion (PBF) AM processes in polyamide (PA12) and thermoplastic polyurethane (TPU). The 3D-CFS properties are investigated under quasi-static and dynamic compression tests. The analysis revealed that for certain geometrical parameters, the manufacturing of the structures is constrained by the sintered powder entrapped in the structure. However, in the case of powder-free structures, the results showed a high impact resistance and cushioning capability. Overall, in terms of specific energy absorption (SEA), the TPU structures showed values between 2.5 and 3.5 kJ/kg, while PA12 ones are between 7.5 and 17.4 kJ/kg, making the 3D-CFS structure compatible with personal protective equipment (PPE) applications. Compared to the literature data on cellular structures made by AM, 3D-CFS performs considerably better. Also, PA12 3D-CFS is better, with a SEA value up to 170% higher than that of a typical material employed for head PPE (e.g. EPS-60 SEA equal to 2.76 kJ/kg). In contrast, TPU 3D-CFS looks more promising in the case of multiple impact conditions.

Research on tool wear state identification method driven by multi-source information fusion and multi-dimension attention mechanism

Cutting tool wear condition monitoring technology is the key technology of advanced manufacturing system and a crucial component of machining. The stage of the tool wear has a direct impact on performance of the workpiece and efficacy of machine tool. However, the duplication and lack of wear information and the fuzzy area of the tool wear transition stage are the key factors contributing to the incorrect estimation of the tool wear state when extracting cutting tool wear feature information. As a consequence, this study presents a data fusion from several sources-based intelligent tool wear state detection technique. The fusion of data from several sources can effectively realize the complementarity of machining information. This provides the model with more comprehensive identification data. The mapping between wear state and wear characteristics is precisely established. To address these issues, the attention mechanism of channel and spatial latitude is integrated into the feature extraction. The model that was constructed in the present investigation has a comprehensive identification accuracy of 0.982. The F1 score of initial, normal and severe wear stage of tool wear are 0.977, 0.968 and 0.993, which are better than other models. Experiments show that the identification method proposed in this study may provide accurate tool wear condition identification based on machining process data, allowing for more flexible and precise cutting tool change decisions in machining.

Digital transformation and innovation ambidexterity: perspectives on accumulation and resilience effects

PurposeDigital transformation (DT) is among the vital factors contributing to innovation ambidexterity, especially for advanced manufacturing firms (AMFs). However, the empirical studies on the relationship between DT and innovation ambidexterity in AMFs from the perspective of knowledge management are inadequate. Therefore, this study aims to systematically analyze the impact of DT on innovation ambidexterity and its mechanism of action.Design/methodology/approachThis study selects 254 listed firms within the ten key areas of “Made in China 2025,” as they occupy a key position in China’s advanced manufacturing system. Based on the knowledge-based view (KBV) and contingency theory, it constructs a model of the influence mechanism of DT on innovation ambidexterity.FindingsThe results show that the DT of AMFs positively influence innovation ambidexterity. External pressure from environmental turbulence enhances the positive relationship between DT and innovation ambidexterity, demonstrating the “resilience effect,” external knowledge search (EKS) and broadening the knowledge base mediating roles between them, highlighting the “accumulation effect.”Originality/valueBy identifying this mediation mechanism of DT and innovation ambidexterity, this study provides new ideas for path research on the KBV. Moreover, this study explores the triggering effect of market environmental turbulence on the DT of firms. It reveals the boundary conditions of DT acting on innovation ambidexterity, expands the research perspective on organizational resilience and enriches the theory of power change.

Data-driven decision making in advanced manufacturing Systems: modeling and analysis of critical success factors

ABSTRACT Data-driven decision making (DDDM) in advanced manufacturing systems (AMS) is the use of data to make smart decisions that improve manufacturing operations. Companies can make themselves more competitive, cut costs, and improve their production by using data analytics. The investigation of critical success factors aids companies in identifying vital areas that demand attention for the implementation of DDDM in AMS. This comprehension enables companies to devise effective strategies for the successful adoption of DDDM within AMS. In this research, twelve critical success factors that affect the use of DDDM in AMS were discovered and statistically analysed using an integrated methodology of ISM, MICMAC, and DEMATEL to create a hierarchical model. This research paper suggests that companies should focus on developing a skilled workforce and creating a data-driven culture to successfully adopt DDDM in AMS. Additionally, the findings highlight the importance of top management support and government initiatives in promoting the adoption of DDDM in manufacturing.

Developing a Modular Continuous Drug Product Manufacturing System with Real Time Quality Assurance for Producing Pharmaceutical Mini-Tablets

The pharmaceutical industry has shown keen interest in developing small-scale modular manufacturing systems for producing medicinal products. These systems offer agile and flexible manufacturing, and are well-suited for use in situations requiring rapid production of drugs such as pandemics and humanitarian disasters. The creation of such systems requires the development of modular facilities for making solid oral drug products. In recent years, however, the development of such facilities has seen limited progress. This study presents a development of a prototype modular system that uses drop on demand (DoD) printing to produce personalized solid oral drug products. The system's operation is demonstrated for manufacturing mini-tablets, a category of pediatric drug products, in continuous and semi-batch modes. In this process, the DoD printer is used to generate molten formulation drops that are solidified into mini-tablets. These dosages are then extracted, washed and dried in a continuous filtration and drying unit which is integrated with the printer. Process monitoring tools are also incorporated in the system to track the critical quality attributes of the product and the critical process parameters of the manufacturing operation in real time. Future areas of innovation are also proposed to improve this prototype unit and to enable the development of advanced drug manufacturing systems based on this platform.

Designing a closed-loop green outsourced maintenance supply chain network for advanced manufacturing systems with redundancy strategy and eco-friendly parts

Designing a closed-loop green outsourced maintenance supply chain network for advanced manufacturing systems with redundancy strategy and eco-friendly parts

Mobile robotics platform for strawberry sensing and harvesting within precision indoor farming systems

AbstractIndoor farming is expected to have an increasing impact on future food and agricultural systems. It can produce crops year‐round without relying on weather patterns, bringing crop production systems closer to advanced manufacturing systems for industrial and consumer goods. Indoor farming also provides an ideal platform for implementing intelligence‐driven and empowered agricultural systems that can autonomously gather/process data, build a knowledge base, provide management decision support, and execute crop cultural tasks. Towards that goal, a mobile robotics platform (MRP) has been under development. This platform will provide new capabilities for implementing indoor farming cyber–physical systems. The ultimate purpose is to equip the MRP with systematically integrated capabilities of perception, reasoning, learning, communication, and task planning/execution for use in precision indoor farming systems. This presentation reports the current state of MRP's development. The MRP can monitor the growing status of individual strawberry plants and fruit with a perception system and nondestructively harvest individual ripe strawberries with an onboard robot. The robotic system has a stereo camera, a six‐degrees of freedom robotic manipulator, and a pneumatically actuated soft gripper (i.e., end‐effector) mounted on an autonomous mobile base. These subsystems were specifically chosen from commercially available products. An open‐loop fruit positioning and handling algorithm, in a software form, capable of performing the tasks of fruit detection, localization, grasping, detachment, and placement, has been developed. The location and ripeness of each fruit can be simultaneously obtained through deep learning techniques. We found that a mean average precision value of 96.4% for ripe strawberries could be obtained in a custom indoor strawberry data set. Three‐dimensional (3D) locations of individual strawberries are used in the motion planner to guide the movements of the mobile platform, manipulator, and end‐effector. The robot can autonomously classify each strawberry fruit's growth scene through the fusion of the detection results and the corresponding depth information, to determine whether to perform the harvesting action. The accuracy of the proposed strawberry growth scene categorization approach is 89.1%, and the weighted‐averaged F1 score is 0.7. The soft gripper can approach and gently hold a target strawberry fruit. A novel approach for fruit detachment has been developed to separate the fruit's sepal and the stalk by a drag‐and‐rotate motion. The experiments conducted in a commercial indoor strawberry farm showed an overall success rate of 78% in handling harvestable strawberries with a damage rate of 23%. The average harvesting cycle time was found to be 10.5 s. We found that poor fruit visibility mainly reduced the success rate of the target fruit's robotic detection and grasping action. The grasping and detachment tasks were affected by fruit size and stalk length. These factors together determined the overall harvesting performance of the robotic system. The robotic system showed better performance when harvesting fruit of small size (width is less than 31 mm and height is less than 33 mm) with a medium stalk (between 81 and 101 mm). The MRP's functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks.

Evaluation research on the independent controllability of high-end equipment manufacturing industry based on dual incentive control line: an empirical analysis from Jiangsu Province

PurposeIn order to analyze the level of independent controllability and its evolution of high-end equipment manufacturing industry from Jiangsu Province, this article introduces the dual-excitation control line method to construct a comprehensive evaluation model for independent controllability.Design/methodology/approachThrough the collection of information of high-end equipment manufacturing industry's independent and controllable capabilities on different indicators, the three aspects of advancement, autonomy and controllability, an empirical evaluation of 10 enterprises in the high-end equipment cluster in Jiangsu Province was conducted in terms of advancement, autonomy and controllability.FindingsIt effectively reveals the area and evolution characteristics of the “reward” and “punishment” of different indicators of each representative enterprise and reflects the development status and different characteristics of each representative enterprise on the three indicators. The research results provide decision-making guidance for enterprises in the management and control of advanced manufacturing systems with independent and controllable capabilities.Originality/valueExisting research focuses on the evaluation of enterprises' independent controllability only on a single angle or index. This paper maps the dynamic evaluation problem of multiple time-point data to the evaluation problem of single time-point multi-index data and investigates the fluctuation of the performance of the same enterprise under different indexes, so as to comprehensively evaluate the independent controllable level of high-end equipment manufacturing industry and analyze the reasons. Further, this paper first establishes an evaluation index system of independent controllable level of high-end equipment manufacturing industry and quantitatively measures the advanced, independent, controllable and other aspects of typical enterprises in this industry by constructing a double incentive control line evaluation model.

How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review

The Fourth Industrial Revolution has introduced innovative technologies to manufacturing, resulting in digital production systems with consequences on workers’ roles and well-being. From the literature emerges the necessity to delve into the work-related stress phenomenon since it affects workers’ health status and performance and companies’ productivity. This review summarises the stress indicators and other influential factors in order to contribute to a stress assessment of human workers in smart and intelligent manufacturing systems. The PRISMA methodology is adopted to select studies consistent with the aim of the study. The analysis reviews objective measurements, such as physical, physiological, and subjective measurements, usually driven by a psychological perspective. In addition, experimental protocols and environmental and demographic variables that influence stress are illustrated. However, the investigation of stress indicators combined with other factors leads to more reliable and effective results. Finally, it is discovered that standards regarding stress indicators and research variables investigated by experimental studies are lacking. In addition, it is revealed that environmental and demographic variables, which may reveal significant suggestions for stress investigation, are rather neglected. This review provides a theorical summary of stress indicators for advanced manufacturing systems and highlights gaps to inspire future studies. Moreover, it provides practical guidelines to analyse other factors that may influence stress evaluation.

Abnormal Event Detection of Workshop Logistics Production Management Based on RFID Technology

RFID, as one of the most important key technologies in the next generation of advanced manufacturing system, has increasingly significant advantages in the application of data collection in the manufacturing workshop, real-time tracking of the manufacturing process and product quality backtracking, etc., and is considered to be the most potential and play a huge role in the technological innovation of manufacturing informatization. In order to better study the abnormal event detection technology of workshop logistics production management based on RFID technology, this paper firstly studies the abnormal event detection method of workshop production based on RFID technology based on the analysis of RFID technology principle, data characteristics and workshop production process. Then, a formalized complex event representation method is constructed to meet the complex and changeable business logic requirements in the actual workshop production application. Then, the solution idea of extracting complex RFID events from massive RFID data using Rete algorithm based on MapReduce framework is discussed. The results show that the accuracy of detecting abnormal events increases by 18.21%, which proves the validity of the model.

Preface

The 2022 8th International Forum on Manufacturing Technology and Engineering Materials (IFEMMT 2022) was successfully held on October 14th-16th, 2022 in Chongqing, China (virtual conference). IFEMMT 2022 brought together academics and experts in the field of Manufacturing Technology and Engineering Materials to a common forum, making it a great platform for people to share views and experiences in related areas.We were honored to have Prof. Xiansheng Ning from Shenyang Ligong University, China to serve as our General Conference Chair. The conference covered keynote speeches, oral presentations, and online Q&A discussion, attracting 150 individuals. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers selected were presented by their authors in sequence.During the conference, five sophisticated professors were invited to make keynote speeches. Among them, Prof. Vijay Kumar Thakur from Scotland’s Rural College delivered a keynote speech on Polymer Nanocomposites as Promising Materials for the Future. Polymer nanocomposites are rapidly emerging as novel materials for a number of advanced engineering applications. The combination of both the polymers and fillers provides enhanced properties depending on the type and nature of polymer matrices as well as fillers. Therefore, this presentation mainly discussed about the work on the synthesis and processing of polymer-based materials for advanced application. Additionally, Assoc. Prof. Xiao Lu from Shenyang Ligong University made a report on Structure Design of Defective Electrocatalysts and Effect of High Magnetic Field on Electrocatalysis Process. The report proposed a method to achieve high efficiency oxygen reduction reaction by regulating the defect structure of carbon materials and metal catalyst materials and put forward the preparation of catalyst materials under strong magnetic field and the influence of strong magnetic field on oxygen evolution reaction and oxygen reduction reaction process. Their excellent presentations had sparked heated discussion in the conference. Moreover, every participant praised this conference for disseminating insightful knowledge.After months of well preparation and hard work, the proceedings of IFEMMT 2022 covering a bunch of papers are smoothly published. These papers feature but are not limited to the following topics: Advanced Design Technology, Manufacturing Systems and Automation, New Materials and Advanced Materials, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.We would like to express our heartfelt appreciation to all the keynote speakers, peer reviewers, and all the participants. Particularly, we would like to acknowledge the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We are sure that IFEMMT 2022 will contribute to not only scientific and engineering progress but also other new products and processes.The Committee of IFEMMT 2022List of Committee member is available in this Pdf.

Firm's Intelligent Manufacturing Strategy and R&D Expenditures

Intelligent manufacturing strategy becomes important and influential as the development trend of advanced manufacturing systems. This study examines the impact of firm's intelligent manufacturing on managers' R&D investment decisions from the perspective of strategic transformation. Moreover, we assess whether this impact is moderated by CEO tenure and government subsidies. Based on a sample of 7,522 firm-year observations in Chinese listed manufacturing firms from 2015-2019, we construct a more detailed measurement of intelligent manufacturing strategy by conducting textual analysis of Management Discussion and Analysis (MD&A) report to obtain the information of firms' implementing intelligent manufacturing. Our empirical findings show that firms' intelligent manufacturing strategies are positively related with R&D expenditures after controlling for other firm characteristics that would affect R&D expenditures, consistent with the notion that managers increase R&D investment for the realization of intelligent manufacturing strategy to achieve corporate profitability and their self-worth enhancement. Furthermore, we find that this positive relationship is more pronounced in firms with longer CEO tenure, suggesting that CEOs have a higher investment success rate and solid organizational identity with the increase of their tenure. We also find that government subsidies positively moderate the relationship between intelligent manufacturing strategy and R&D expenditures, which reveals the positive role government subsidies play in alleviating firms' financing constraint and thus helping management have enough funds to invest. The results are robust to a battery of sensitivity tests, including a propensity score matching method, alternative R&D investment measures as well as controlling for other innovation impact. In additional analyses, we find that intelligent manufacturing strategy enhances the level of innovation output by promoting firms' R&D investment, confirming the mediating effect of R&D investment in the relationship between intelligent manufacturing strategy and innovation performance. Overall, our findings are consistent with the view that intelligent manufacturing strategy improves manager's incentives and resulting more investment in innovation. Given that R&D is an activity under the purview of management subject to incentives and is of great interest to firms' long-term benefit and the overall development of society, our findings should be of great interest to management, investors, and regulators.

Artificial Intelligence-Based Smart Quality Inspection for Manufacturing.

In today's era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.

Towards advanced manufacturing systems for large parts: a review

Towards advanced manufacturing systems for large parts: a review

Data-Driven Approaches for Decision-Making in Advanced Manufacturing Systems: A Systematic Literature Review

Data-Driven Approaches for Decision-Making in Advanced Manufacturing Systems: A Systematic Literature Review

A review of the applications of multi-agent reinforcement learning in smart factories.

The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories.

Total Productive Maintenance, Affective Commitment and Employee Retention in Apparel Production

The success of the implementation of advanced manufacturing systems, such as total productive maintenance (TPM), depends on employee commitment and retention. This study investigated total productive maintenance (TPM) practices implemented by apparel production firms, and the effect of these practices on affective commitment (AC), and employee retention (ER) of operators on the production floor. The study adopted the survey research methodology. In total, 411 operators from apparel production firms responded to the study. The responses were analyzed using statistical methods. Four hundred and eleven responses were received for the survey and data were analyzed using statistical methods. The findings suggest that TPM practices significantly positively affect AC. Affective commitment mediates between TPM and ER. The success and sustainability of TPM implementations depend on the firm’s ability to retain trained operators and keep their AC high. The study has several implications for theory and practice.

Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.

Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.