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Composite materials use recently increased, although treatments at their End of Life are inexistent or highly inefficient (from the environmental perspective). Thus, openness to cooperation is needed, supported by methodologies for design for de-manufacturing. The approach proposed in the present work aims at transforming industrial processes' scraps and off-specification pieces in primary materials, through re-design, without the risk of cannibalisation. It is mainly intended for industrial processes of composite materials; its objective is to find alternative applications to their invaluable final disposal and supports the merging of existing supply chains (Industrial Symbiosis). Nevertheless, it can be easily extended to non-composite and/or non-scraps. Re-design enables the establishment of waste-to-treasure composite scraps' life cycles and is evaluated through Life Cycle Assessment. The application of the approach involved four Italian companies and results reveal that industrial symbiosis can reduce emissions (from −45% to more than −90%). Guidelines were outlined: involve End of Life operators to know how waste treatments, share information, favour networking and proximity, apply design for disassembly principles, consider simple shapes and modularity during (re)design. Future works will focus on off-the-shelf components and the economic evaluation of the proposed de-manufacturing actions and supply strategies. [ABSTRACT FROM AUTHOR]

To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. [ABSTRACT FROM AUTHOR]

The Industry 5.0 concept has placed human needs at the heart of industrial processes. This raises the question of how new technologies can enhance employee decision-making processes and influence the evolution of team autonomy. Recent studies have shown that the best way to measure these impacts is to conduct experiments in complex and realistic environmental settings. However, the main methods cannot satisfy this requirement while controlling the events and associated variables, whereas a set of use cases can. Therefore, a model should be defined to generate and structure these use cases while validating their relevance. Following the decomposition of the global research objective and case-definition recommendations, this study proposes a framework for designing complementary use cases to evaluate the impact of new technologies on emerging autonomy models in a structured, realistic, and global manner. Based on widely recognised related work, the 6-step framework helps define a coherent context specifying the business process model, agent, autonomy, technologies to be implemented, their fields of action, detailed variable collection protocol, and experimental setup. A cross-analysis of existing cases from the literature and empirical use of the framework validated the relevance of the model in designing experimental environments that are close to real-world settings. [ABSTRACT FROM AUTHOR]

In additive manufacturing processes such as laser powder bed fusion, the build orientation and packing of components affect the required support structures, the number of parts in each build, and the surface roughness of the printed parts, among other factors. Maximising the packing density while minimising the build height can increase effective machine utilisation and decrease per-part cost. Yet, the build layout optimisation problem is highly nonlinear and difficult to solve using human intuition, so a systematic algorithm approach is required. Here, we present and demonstrate a voxel-based analysis method with Bayesian optimisation for determining component build orientation in additive manufacturing. We introduce selected case studies incorporating exemplary process attributes of laser powder bed fusion, including the determination of orientation and packing configurations based on support removal and tool-accessibility constraints. [ABSTRACT FROM AUTHOR]

The use of wearables in the workplace allows for close monitoring of work processes and might also have consequences for work content and skill requirements. Past research has emphasized the detrimental effects of wearables, particularly those caused by the standardization of work and monitoring of workers. By contrast, this study asks under what conditions the implementation of wearables as part of digital assistance systems is beneficial for workers. Based on recent contributions in the field of labor process theory, this study analyzes the implementation of new technologies using the concepts of the regulatory regime, organizational first-order factors, and workplace second-order choices. The analysis is based on findings from 48 interviews with 83 interviewees in 16 German manufacturing workplaces along with making site visits. It examines the implementation of wearables and the impacts on work content, skills, working conditions, and employment. Besides showing how labor agency affects the implementation of new technologies, the particular contribution of this study lies in analyzing the differences in the implementation of wearables in capital- and labor-intensive organizations. While standardization of work and reduction of work content prevailed in labor-intensive processes, capital-intensive processes were most often characterized by the extension of skill requirements and the risk of work intensification. [ABSTRACT FROM AUTHOR]

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are promising candidate materials for photo-driven processes. Their crystalline and tunable structure makes them well-suited for placing photoactive molecules at controlled distances and orientations that support processes such as light harvesting and photocatalysis. In order to optimize their performance, it is important to understand how these molecules evolve shortly after photoexcitation. Here, we use resonance Raman intensity analysis (RRIA) to quantify the excited state nuclear distortions of four modified UiO-68 MOFs. We find that stretching vibrations localized on the central ring within the terphenyl linker are most distorted upon interaction with light. We use a combined computational and experimental approach to create a picture of the early excited state structure of the MOFs upon photoactivation. Overall, we show that RRIA is an effective method to probe the excited state structure of photoactive MOFs and can guide the synthesis and optimization of photoactive designs. [ABSTRACT FROM AUTHOR]

Manganese–rhodium (Mn–Rh) nanoparticles have emerged as a promising candidate for catalytic applications in the production of syngas, a critical precursor for a wide range of industrial processes. This study employs a comprehensive, theoretical, and computational approach to investigate the structural and electronic properties of Mn–Rh nanoparticles, with a specific focus on their interaction with titanium oxide (TiO2) surfaces and their potential as catalysts for syngas reactions. The density functional theory calculations are employed to explore the adsorption behavior of Mn–Rh nanoparticles on TiO2 surfaces. By analyzing the adsorption energies, geometries, and electronic structure at the nanoscale interface, we provide valuable insights into the stability and reactivity of Mn–Rh nanoparticles when immobilized on TiO2 supports. Furthermore, the catalytic performance of Mn–Rh nanoparticles in syngas production is thoroughly examined. Through detailed reaction mechanism studies and kinetic analysis, we elucidate the role of Mn and Rh in promoting syngas generation via carbon dioxide reforming and partial oxidation reactions. The findings demonstrate the potential of Mn–Rh nanoparticles as efficient catalysts for these crucial syngas reactions. This research work not only enhances our understanding of the fundamental properties of Mn–Rh nanoparticles but also highlights their application as catalysts for sustainable and industrially significant syngas production. [ABSTRACT FROM AUTHOR]

Due to the growing demand for customised products, companies have faced increasing product and process complexity levels. To address this issue, manufacturing processes should become more flexible. One of the most promising technologies to achieve this goal is collaborative robotics (or 'cobots'). In collaborative assembly processes, human and robot combine their skills. However, the co-existence of humans and cobots in the same workspace may influence the operators' perception of assembly complexity. The analysis and control of assembly complexity are crucial to achieving better performances in terms of process quality and operators' well-being. Many qualitative methods have been proposed in the literature to provide a holistic assessment of assembly complexity. This paper proposes a novel method to define a quantitative scale of perceived assembly complexity, based on Thurstone Law of Comparative Judgements. This method was applied to an experimental case-study concerning the assembly of three different products in two modalities (i.e. manual and collaborative). Regression analysis showed that the perceived complexity may be related to the occurrence of process failures and to the perceived workload. The method also proved capable of identifying assembly processes where cobot assistance was helpful, providing process designers with a supporting tool to minimise perceived complexity. [ABSTRACT FROM AUTHOR]

Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator [ABSTRACT FROM AUTHOR]

This paper investigates the game-changing paradigm in modern manufacturing, known as the "smart factory," from an organizational perspective. Factories get smarter—or smartized—by becoming more data-intensive and more tightly integrating operations with management. We design a unique survey to measure this intangible organizational capital accumulation process for 939 Korean manufacturing plants. Observing heterogeneous levels of smartization across factories, we explore their relationship with three performance dimensions: productivity, cost efficiency, and product variety. The drivers of smartization are also identified. Our analysis uncovers a strong association between smartization and heightened productivity, with varying interactions with other performance metrics based on the specific manufacturing process (e.g. lower defect rate only in assembly line process). Importantly, we find that investments in digital technologies drive smartization only when complemented by structured incentive management and proper chief executive officer (CEO) leadership. [ABSTRACT FROM AUTHOR]

Disabled people can be successfully employed in most production processes, provided that one knows how to exploit their abilities and take into account their limitations in order to give them an appropriate job. However, because the level and type of production must be constantly adapted to the needs of the market, the involvement of disabled people in the production process may also change. Additionally, people with disabilities have limitations as well as additional rights that must be considered. As a result, the organisation and planning of their work, side by side with other employees, becomes more complex. Computer simulations can be a support for organising and planning the involvement of employees with disabilities in production processes. The aim of the article is to show how simulations can facilitate the organisation of work of employees with disabilities, with the changing demand for manufactured products. The paper identifies the factors that should be considered, and then presents how the employment of disabled people can affect the operation of the production line and the commercial image of the company. The study uses a combination of System Dynamics and Discrete Event Simulations. The relevant data for the simulation were derived from a production company. [ABSTRACT FROM AUTHOR]

This paper deals with an overview of flowshop group scheduling problems in the manufacturing environment. The aim of this paper is twofold: (i) making a comprehensive survey of research on flowshop group scheduling problems in manufacturing systems, and (ii) presenting a bibliometric analysis. We address the general definition of flowshop group scheduling problems and provide a taxonomy of methodologies used in previous literature. The papers are presented from several perspectives, including the utilised objective functions, a transformation of problem structure, benchmarks in existing literature, and solution approaches. Additionally, bibliometric analysis, including keyword and journal analyses, is conducted for articles published between 1986 and 2022. Finally, suggestions for future developments are listed to further consolidate this area. [ABSTRACT FROM AUTHOR]

The vast majority of control schemes related to the sequential probability ratio test (SPRT) are designed for the purpose of monitoring only the process mean. Nonetheless, most manufacturing processes are vulnerable to external factors that cause the process mean and variability to change simultaneously. It is, therefore, crucial to consider a joint scheme for monitoring both the location and scale parameters of a production process. In this article, we develop a scheme that combines both mean and variance information in a single SPRT, known as the omnibus SPRT (OSPRT) chart. Expressions for the run-length properties of the OSPRT chart are derived by means of the Markov chain approach. We also propose optimal designs for the OSPRT chart based on two different metrics, i.e. by minimising the average time to signal and the average extra quadratic loss. Through a comprehensive analysis, this article reveals that the optimal OSPRT chart outperforms the classical $ \bar{X} $ X ¯ -S, weighted-loss cumulative sum, absolute-value SPRT, and two maximum weighted-moving-average-type charts. The optimal OSPRT chart also has the advantage of collecting a small number of samples on average before producing a decision. Finally, the implementation of the OSPRT chart is presented with a wire bonding industrial dataset. [ABSTRACT FROM AUTHOR]

Machine learning is a promising method to model production processes and predict product quality. It is challenging to accurately model complex systems due to data scarcity, as mass customisation leads to various high-variety low-volume products. This study conceptualised knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge embedded in similar entities to address data scarcity. A sequential cross-product KAET (SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing, feature selection (FS), feature learning (FL), and transfer learning (TL). The FS and FL modules conduct knowledge extraction and help address various practical challenges such as changing operating conditions and unbalanced datasets. In this paper, sequential TL is introduced to production modelling to conduct knowledge transfer among multiple entities. The first case study of auxetic material performance prediction demonstrates the effectiveness of sequential TL. Compared with conventional TL, sequential TL can achieve the same test mean square errors with 300 fewer training examples when facing data scarcity. In the second case study, balancing anomaly detection models were constructed for two gas turbines in the same series using real-world production data. With SeqTrans, the F1-score of the anomaly detection model of the data-poor engine was improved from 0.769 to 0.909. [ABSTRACT FROM AUTHOR]

Nowadays, achieving higher levels of flexibility in manufacturing systems is necessary to maintain and enhance competitiveness. Accordingly, a new generation of production machine, namely Reconfigurable Machine Tool (RMT), has recently been introduced that can be effectively adapted to changes. Nevertheless, such systems are more worker-reliant, and neglecting workforce aspects results in suboptimal or even infeasible production schedules. In this regard, this study investigates an integrated lot-sizing and scheduling problem benefiting from RMTs under workforce constraints. First, a novel Mixed-Integer Linear Programming (MILP) model is provided to formulate the problem. Afterward, to confront the high complexity of the problem, an efficient Decomposition Heuristic (DH) empowered by a tailored feasibility cut is devised. A combination of MILP and Constraint Programming (CP) is employed in the DH to model the relevant master and sub-problems, respectively. Finally, the performance of the DH compared to the MILP model and an extended lower bound is evaluated. Moreover, the advantages of utilizing RMTs in the system are explored based on four defined key performance indicators. [ABSTRACT FROM AUTHOR]

Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [ABSTRACT FROM AUTHOR]

The enterprises of the make-to-order production mode are required to make accurate judgments and reasonable decisions when dealing with the disturbance factors in the manufacturing process to ensure the timely completion of orders. Order remaining completion time (ORCT) prediction can quantify the production process and is an important basis to ensure the delivery date of orders. However, an accurate prediction model of the ORCT is challenging because of the spatial and temporal (ST) characteristics of the manufacturing process. An ST features-based prediction method is proposed to solve this problem. Firstly, the ST data sets are established on the basis of analysing ST characteristics of the manufacturing process. Secondly, the spatial feature extraction network and temporal feature extraction network are built to make a more comprehensive analysis. Finally, the prediction model of the ORCT is proposed by fusing the spatial and temporal features and validated in a practical workshop. Experimental results show that considering the spatial and temporal characteristics of the manufacturing process can significantly improve the prediction model's performance, and the proposed prediction model is superior to graph convolutional neural network, gated recurrent unit network, deep auto-encoder, deep neural network, and back propagation neural network. [ABSTRACT FROM AUTHOR]

Laser forming is a highly flexible and iterative contactless thermomechanical forming process that utilises a defocused laser beam to induce material shortening and bending. There is a high potential for the use of laser forming in rapid prototyping, small and unique batch production; however, to achieve that potential a certain level of automation is required. The presented work seeks to address this need of automation by proposing an end-to-end sheet-metal manufacturing system utilising laser forming and cutting, where the input for the system is a 3D CAD model and the output is a manufactured product. The contribution of the presented work is interconnected laser forming and cutting framework. The presented framework consists of subsystems for automatic extraction of geometrical information from a CAD model, process planning and generation of manufacturing information, scheduling of tasks and a feedback control loop for laser forming. The framework is experimentally validated and has shown consistent forming and cutting behaviour on two types of parts with variation in cutting features, bend angle and lengths of the bend's free edge. [ABSTRACT FROM AUTHOR]

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy. [ABSTRACT FROM AUTHOR]

Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework. [ABSTRACT FROM AUTHOR]

A novel bilateral impact-ionization-avalanche-transit-time (BIMPATT) diode based on AlGaN/GaN two-dimensional electron gas is proposed in this article. The BIMPATT is compatible with the available GaN high electron mobility transistor (HEMT) manufacturing process and has a shorter actual electron transit distance than existing HEMT-like IMPATT (HIMPATT) diodes. Compared with the same-sized HIMPATT, the optimum frequency of BIMPATT rises from 320 to 420 GHz and possesses a far wider operating frequency band, especially in the near 0.9 THz range. The maximum DC-RF conversion efficiency rises from 12.9% to 17.6%. The maximum RF power of BIMPATT is 3.18 W/mm, which is similar to 3.12 W/mm of the HIMPATT. Furthermore, our simulation demonstrated that the characteristics of BIMPATT are significantly affected by the length of anode and the thickness of the AlGaN barrier layer. The effects of ohmic contact resistance and background impurities on BIMPATT are also taken into account. This paper provides a reference for the design and characteristics enhancement of the lateral IMPATT devices. [ABSTRACT FROM AUTHOR]

There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions' quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations. [ABSTRACT FROM AUTHOR]

Order release optimisation is essential in production planning, especially in discrete manufacturing. Order release planning models with load-dependent lead times must anticipate the time-dependent work-in-process and output for any given release schedule and thus require an anticipation model that approximates the time-dependent behaviour of queueing systems. We present a generic optimisation model for order release planning in stochastic, non-stationary manufacturing systems that includes a well-defined interface for the anticipation model. We develop two stationary backlog carryover (SBC) approaches to approximate time-dependent queueing behaviour and prove their consistency with the order release model. The resulting nonlinear programming model is shown to be a special case of the well-known clearing function models. A numerical study demonstrates that the optimised order releases for different demand patterns are close to the optimum that results from simulation-based optimisation even for extreme demand and release patterns. The resulting output closely matches the simulated output with some deviations. [ABSTRACT FROM AUTHOR]

This paper studies a multi-period dynamic production planning problem in a hybrid manufacturing and remanufacturing system (HMRS), where new and remanufactured products are perfect substitutes. The HMRS encounters uncertain return amounts due to products' random usage durations. As returns are from previous sales, production planning in each period impacts both current sales and future returns. The random return amount and the correlation among different periods make it a complex system. To solve the problem, we first utilise the hazard rate function and in-use products' information to derive an estimator of the return amount. Then, we formulate a dynamic programming model and prove a threshold policy is optimal under uniformly distributed demand. We employ marginal analysis to derive an approximation of the optimal threshold value. Through simulating all alternatives, the derived threshold is verified to be a good approximation as it achieves more than 99% of the optimal revenue in most scenarios. In addition, the calculated return amount based on the hazard rate function is almost identical to the return amount obtained via simulation. Compared to other return measurements, our method achieves the highest revenue in all considered scenarios, including heterogeneous usage durations and general demand distributions. [ABSTRACT FROM AUTHOR]

The nucleation of ice from aqueous solutions is a process essential to myriad environmental and industrial processes, but the physical factors affecting the capacity of different solutes to depress the homogeneous nucleation temperature of ice are yet poorly understood. In this work, we demonstrate that for many binary aqueous solutions of non-ionic solutes, this depression is dominated by the entropy of the liquid phase. Employing the classic Turnbull interpretation of the interfacial free energy γ ∼ T S l i q u i d − S s o l i d and estimating solution entropies with a Flory-style modification of the ideal entropy of mixing that accounts for solute size effects, we demonstrate that mixing entropy alone predicts experimental homogeneous nucleation temperatures across a wide variety of non-ionic solutions. We anticipate that this physical insight will not only enhance a fundamental understanding of homogeneous nucleation processes across fields but also open new avenues to the rational design of aqueous solutions for desired nucleation behaviors. [ABSTRACT FROM AUTHOR]

Reconfigurable Manufacturing Systems (RMS) have gained attention in the aerospace industry in the past years, as post-pandemic context shows drastic production capacity changes and new environmental regulations to which it must adapt quicker than before to maintain competitiveness. Nevertheless, current RMS design methods have not thoroughly considered this industry specificities, nor industrial resources requirements in the current concurrent design practice. These limitations have been identified in several recent research works. Ontology-based Engineering (OBE) systems can stand overly complex collaborative design processes involving multidisciplinary stakeholders and various digital tools, integrating different levels of decision. Models for Manufacturing (MfM) is an OBE methodology aiming to enable industrial design and decision-making in manufacturing by preserving the company knowledge in ontology models, usable as knowledge base to generate and integrate the aircraft and manufacturing systems design. This paper presents an MfM application for RMS design in aerospace, introducing innovative design concepts that allow implementing RMS in a collaborative engineering process of an aerospace product. An implementation is shown designing the RMS of a model aircraft family to illustrate the concepts introduced and considerations are given to transfer this knowledge base into an OBE system to support complex real-life applications. [ABSTRACT FROM AUTHOR]

The technological landscape for industrial processes handling asphaltene is evolving at a rapid pace due to the increase in the extraction of heavy crude oil. The main underlying challenges in this regard are the flow assurance, the recovery of the spent solvent, and the sophisticated extractor setup required to develop the process to an industrial scale. The number of studies focused on the handling of the asphaltene at the atomic and molecular scales is growing enormously in order to identify new sustainable solvents for the effective extraction of asphaltene from heavy crude oil or oil-bearing sands. This Perspective focuses on the importance of density functional theory and molecular dynamics simulations to explore the broader range of asphaltene inhibitors, e.g., nanoparticles, ionic liquids, and deep eutectic solvents, to prevent asphaltene precipitation. We provide a concise overview of the major accomplishments, analyze the aspects that require attention, and highlight the path-breaking studies having a significant impact on the process of chemical enhanced oil recovery from heavy crude oil reservoirs primarily based on atomistic and molecular simulations. [ABSTRACT FROM AUTHOR]

To support adequate strategic decisions, every digital transformation begins with an analysis of the current state of the company. These analyses often include the use of digital maturity models (DMMs). There are numerous DMMs in a wide variety of industries and application areas. While most of these models primarily address the digital maturity of manufacturing companies as a whole, only a few DMMs focus on particular departments or processes. Thus, there is a research gap in this field of study. This paper contributes to the identified gap by developing a DMM for the sales process in B2B project business. Building on a literature review that identified and examined specific DMMs which have been developed in the B2B context, we conducted a case study using expert interviews at a large German industrial company to develop a DMM of the different phases of the sales process. This DMM shall contribute to the research of maturity models and guide companies of different industries in their digital transformation activities focusing on the digitalization of their sales process. [ABSTRACT FROM AUTHOR]

Laser-based powder bed fusion (L-PBF) has become the de facto choice for metal additive manufacturing (AM) processes. Even after considerable research investments, components manufactured using L-PBF lack consistency in their quality. Realizing the crucial role of the melt pool in controlling the final build quality, we predict the morphology of the melt pool directly from the build commands in an L-PBF process. We leverage machine learning techniques to predict quantitative attributes like the size as well as qualitative attributes like the shape of the melt pool. The area of the melt pool is predicted using an LSTM network. The outlined LSTM-based approach estimates the area with $ 90.7\% $ 90.7 % accuracy. The shape is inferred by synthesising the images of the melt pool by using a Melt Pool Generative Adversarial Network (MP-GAN). The synthetic images attain a structural similarity score of 0.91. The precision and accuracy of the results showcase the efficacy of the outlined approach and pave the way for real-time monitoring and control of the melt pool to build products with consistently better quality. [ABSTRACT FROM AUTHOR]

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput. [ABSTRACT FROM AUTHOR]

Human–robot collaboration has been incorporated into production and assembly processes to promote system flexibility, changeability and adaptability. However, it poses new challenges to resource allocation and production scheduling due to its intrinsic uncertainty and also the increasing complexity of resources. This paper investigates a stochastic flow shop scheduling problem in the context of human–robot collaboration. The goal is to achieve efficient utilisation of flexible resources including human workers and cobots and take full advantage of human–robot collaboration in production scheduling. A stochastic Cobb–Douglas production function is utilised to evaluate the production efficiency of human–robot collaboration considering instabilities of human performance. A joint chance-constrained programming model is formulated to ensure that the required system performance can be achieved. A CVaR approximation-based approach is proposed to solve the formulated model with mixed-integer variables and a nonconvex constraint. The effectiveness of the formulated model and the efficiency of the proposed solution approach are evaluated via numerical experiments. Computational results show the superiority of our solution approach over three other approaches including Bonferroni approximation, scenario approach and individual chance-constrained programming. [ABSTRACT FROM AUTHOR]

In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data; however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a Variational Autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based $ {T^2} $ T 2 chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study. [ABSTRACT FROM AUTHOR]

Process automation and mass customisation requirements of modern manufacturing systems are driven by artificial intelligence (AI). As AI derives decisions from data, securing the data against tampering is crucial to prevent ensuing operational risks. Additionally, manufacturing systems necessitate collaboration, transparency, and trust among participants while preserving a competitive advantage. Thus, we position blockchain, an enabler of transparent and secure operations, as a security solution for AI-assisted manufacturing systems. In this conceptual viewpoint paper, we present a framework to integrate blockchain in AI-assisted manufacturing systems. We highlight the special needs of manufacturing BCs over generic BCs. We delineate the ways in which manufacturing can be a beneficiary of the synergy between AI and BC. We discuss how BC and AI can accelerate early-phase product design, collaboration, and manufacturing processes and secure supply chains against counterfeit products and for ethical consumerism. Lastly, we identify the needs of modern manufacturing systems and cite a few examples of organisational failures to underscore the importance of security while delineating the significant challenges in adopting blockchain-based solutions in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear Programming (MILP) model and an efficient Constraint Programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time. [ABSTRACT FROM AUTHOR]

Recent advancement of sensor technology has made it possible to monitor high-dimensional data streams in various manufacturing systems for quality improvement. However, existing monitoring schemes commonly assume that all data streams have the same sampling interval. This assumption does not always hold in practice, which poses new and unique challenges for multivariate statistical process control. In this paper, we propose a generic nonparametric monitoring framework to online monitor high-dimensional asynchronous and heterogeneous data streams, where sampling intervals of data streams are different from each other, and measurements of each data stream follow arbitrary distributions. In particular, we first propose a quantile-based nonparametric framework to monitor each data stream locally for possible shifts in both location and scale. Then, for unsampled measurements due to different sampling intervals, a compensation strategy based on the Bayesian approach is introduced. Furthermore, we develop a global monitoring scheme using the sum of top- $ r $ r local statistics, which can quickly detect a wide range of possible shifts in all directions. Simulations and case studies are conducted to evaluate the performance and demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Simultaneous allocation of service times and buffer capacities in manufacturing systems in a random environment is a NP-hard combinatorial optimisation problem. This paper presents a sophisticated simulation-based optimisation approach for the design of unreliable production lines to maximise the production rate. The proposed method allows for a global search using a Genetic Algorithm (GA), which is coupled with Finite Perturbation Analysis (FPA) as a local search technique. Traditional techniques based on perturbation analysis optimise decision variables of the same nature (e.g. service time only, buffer capacity only), whereas the proposed technique simultaneously provides an allocation of service times and buffer capacities. One of the main focuses of this paper is the investigation of the persistence or absence of the buffer and service rate allocation patterns which are among the most essential insights that come from designing production lines. The results show the superiority of the combined GA-FPA approach regarding GA and FPA in terms of solution quality and convergence behaviour. Moreover, considering instances ranging from 3 to 100 machines, our numerical experiments are in line with the literature for small instances (as similar allocation patterns are identified in our work), but important differences are highlighted for medium/large instances. [ABSTRACT FROM AUTHOR]

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method. [ABSTRACT FROM AUTHOR]

Polymer blends are critical in many commercial products and industrial processes and their phase behavior is therefore of paramount importance. In most circumstances, such blends are formulated with samples of high dispersity, which have generally only been studied at the mean-field level. Here, we extend the renormalized one-loop theory of concentration fluctuations to account for blends of disperse polymers. Analyzing the short and long length-scale fluctuations in a consistent manner, various measures of polymer molecular weight and dispersity arise naturally in the free energy. Thermodynamic analysis in terms of moments of the molecular weight distribution(s) provides exact results for the inverse susceptibility and demonstrates that the theory is not formally renormalizable. However, physically motivated approximations allow for an "effective" renormalization, yielding (1) an effective interaction parameter, χe, which depends directly on the sample dispersities (i.e., Mw/Mn) and leaves the form of the mean-field spinodal unchanged, and (2) an apparent interaction parameter χa that depends on higher-order dispersity indices, for instance Mz/Mw, and characterizes the true limits of blend stability accounting for long-range off-critical fluctuations. We demonstrate the importance of dispersity on several example systems, including both "toy" models that may be realized in computer simulation and more realistic industrially relevant blends. We find that the effects of long-range fluctuations are particularly prominent in blends where the component dispersities are mismatched, especially when there is a small quantity of the high-dispersity species. This can be understood as a consequence of the shift in the critical concentration(s) from the monodisperse value(s). [ABSTRACT FROM AUTHOR]

SnO2 is a prototypical transparent conducting oxide that finds widespread applications as transparent electrodes, gas sensors, and transparent thin-film devices. Hydrogen impurities in SnO2 give rise to unintentional n-type behavior and unexpected changes to conductivity. Interstitial H (Hi) and H at an oxygen vacancy (HO) are both shallow donors in SnO2. An O–H vibrational line at 3155 cm−1, that can be produced by a thermal anneal at 500 °C followed by a rapid quench, has been assigned to the Hi center and is unstable at room temperature on a timescale of weeks. An IR absorption study of the decay kinetics of the 3155 cm−1 O–H line has been performed. The disappearance of Hi upon annealing has been found to follow second-order kinetics. Measurements of the decay rate for a range of temperatures have determined an activation energy for the diffusion of interstitial H in SnO2. These results provide fundamental information about how unintentional hydrogen impurities and their reactions can change the conductivity of SnO2 device materials in processes as simple as thermal annealing in an inert ambient. [ABSTRACT FROM AUTHOR]

SnO2 is a prototypical transparent conducting oxide that finds widespread applications as transparent electrodes, gas sensors, and transparent thin-film devices. Hydrogen impurities in SnO2 give rise to unintentional n-type behavior and unexpected changes to conductivity. Interstitial H (Hi) and H at an oxygen vacancy (HO) are both shallow donors in SnO2. An O–H vibrational line at 3155 cm−1, that can be produced by a thermal anneal at 500 °C followed by a rapid quench, has been assigned to the Hi center and is unstable at room temperature on a timescale of weeks. An IR absorption study of the decay kinetics of the 3155 cm−1 O–H line has been performed. The disappearance of Hi upon annealing has been found to follow second-order kinetics. Measurements of the decay rate for a range of temperatures have determined an activation energy for the diffusion of interstitial H in SnO2. These results provide fundamental information about how unintentional hydrogen impurities and their reactions can change the conductivity of SnO2 device materials in processes as simple as thermal annealing in an inert ambient. [ABSTRACT FROM AUTHOR]

Manufacturing companies strive to identify and manage the effects of unexpected disruptions (risks) on their production processes, which affect their performance and resilience. In this study, we propose a decision framework to capture the impact of interconnected risk sources, on the efficiency of manufacturing companies. The proposed framework utilises a novel mixed-integer linear programming (MILP) model to minimize the time of satisfying the orders while it considers the risk associated with suppliers and manufacturers. The MILP model considers the relationships among (i) material and suppliers and (ii) work centers to measure the propagation of risks throughout the production system. The proposed framework also utilises the Monte Carlo simulation to calculate the associated likelihood of delay and the distribution of the delivery time of orders. To show the complication of the propagation of risk, two distinct scenarios are compared. The first scenario considers zero risks, while the second one assigns probabilistic risk to the suppliers and work centers. The results highlight the magnitude and the complexity of the risk propagation from various interconnected sources through the production system. It also identifies the most vulnerable components of the production system affected more by various types of risk. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) introduces a set of technology-specific problems, such as the proper orientation of parts or the placement of several heterogeneous parts in the same build cycle, which are not addressed by traditional approaches to production planning and scheduling. Although these new production subproblems have been implicitly addressed by several works according to generic nesting and scheduling concepts, a literature review revealed that there is no uniformity in identifying and, thus, solving all these subproblems. For this reason, and as a result of an in-depth analysis of the existent literature on AM production planning and an analogy with classic cutting and packing typologies, the present paper offers a framework to formalise the production planning problem in AM at the operational level. This framework can be used as a reference to focus on and address these AM-related problems for efficient production planning. It is designed at the subproblem level and centres on production order processing in AM. A coding strategy is specifically developed for the framework, which is applied to a review of relevant works that propose models for the production planning of AM systems. Finally, the review results are discussed and possible extensions of the framework are proposed. [ABSTRACT FROM AUTHOR]