Search

We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances [ABSTRACT FROM AUTHOR]

In most European member states, employees stay with a company for over ten years. During this time, people gain valuable experience and build up expertise that is lost to production companies when employees leave. Despite increasing digitalisation, there will still be processes, especially in individual production, that require experts. Ontologies are an established method for formalising knowledge. Standards are needed to ensure interoperability. However, not all reference models are based on the standardised basic formal ontology. To the best of our knowledge, there are also no references for modelling human-bound production steps. We have developed the freely accessible ROXANA ontology based on actual use cases from special-purpose machines. This ontology is based on the basic formal ontology and uses references from the common core ontologies and the industrial ontology foundry. The difficulty with expert-bound process steps in production is individuality and complexity. The design of special-purpose machines is individual, as are the machine-specific documents and the associated workflows. This paper presents the ontology, explains it using practical examples, and shows its implementation in a real software-based expert system. [ABSTRACT FROM AUTHOR]

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at any time; therefore, shop-floor managers need to decide on the most efficient assignments for machines and workers. However, the processing time is variable and uncertain due to the fluctuating production environment caused by unsteady operating conditions of machines and learning effect of workers. Meanwhile, they also need to balance the worker workload while meeting production efficiency. Thus a dual resource-constrained FJSP with worker's learning effect and fuzzy processing time (F-DRCFJSP-WL) is investigated to simultaneously minimise makespan, total machine workloads and maximum worker workload. Subsequently, the reinforcement learning enhanced multi-objective memetic algorithm based on decomposition (RL-MOMA/D) is proposed for solving F-DRCFJSP-WL. For RL-MOMA/D, the Q-learning is incorporated into memetic algorithm to perform variable neighbourhood search and further strengthen the exploitation capability for the algorithm. Finally, comprehensive experiments on extensive test instances and a case study of aircraft overhaul shop-floor are conducted to demonstrate effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This study addresses a system-level configuration selection problem for reconfigurable single part flow lines (R-SPFLs) with parallel identical flexible machines at each stage. The problem is to determine the number of stages, the number of machines of a certain type at each stage and the assignment of operations to each stage in order to satisfy the demand of a part type. Precedence relations among the operations and space limitations are also considered. For the objective of minimising the sum of machine purchase and operation processing costs, a nonlinear integer programming model is developed that gives the optimal solutions. Then, due to the problem's complexity, a basic variable neighbourhood search algorithm is proposed that constructs an initial solution using a greedy-type heuristic and improves it using a shaking and a local search improvement methods. Moreover, the basic algorithm is extended to a general one that uses a variable neighbourhood descent method. Computational results show that the two algorithms outperform the previous one significantly in both solution quality and computation times, and the general algorithm improves the basic one significantly. Finally, the applicability of the variable neighbourhood search algorithms is shown by reporting a brief case study. [ABSTRACT FROM AUTHOR]

Despite the widespread applications of high-pressure plasma in semiconductor and display industry, such as deposition and ashing process, the use of cutoff probes for diagnosing high-pressure plasma was rarely studied. In this study, we investigated a method of measuring electron density in a cutoff probe using the resonance peak in a high-pressure plasma environment. This method is validated through both electromagnetic wave simulations and experimental methodologies. Our findings reveal that the proposed method demonstrates discrepancies of less than 1.47% compared to the input plasma frequency in the results of electromagnetic wave simulations at a gas pressure of 10 mTorr, while at 2.5 Torr, it exhibited a maximum discrepancy of 13.3% when selecting resonance frequencies lower than the electron–neutral collision frequency. This discrepancy at high pressure is reduced to within 1.92% by selecting a resonance frequency higher than the electron–neutral collision frequency. Also, the feasibility of these electron density measurements has been confirmed under conditions of high gas pressure where the cutoff frequency is not measurable, as evidenced by both simulation and experimental results. Our research on the diagnostic methods in high-pressure plasmas could significantly enhance the measurement and interpretation of plasma parameters in various industrial processes. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution has necessitated the integration of sustainability and customer centricity into modern manufacturing systems, thereby emphasising their significance. However, the production process itself is characterised by inherent uncertainties. Hence, this paper primarily focuses on the multi-objective flexible job shop rescheduling problem with uncertain processing time and new job insertions. Two fuzzy constraints are incorporated: fuzzy processing time and fuzzy due date, which aim to model the uncertainty of processing time and ensure flexibility in meeting deadlines. The objective function aims to optimise both customer satisfaction and energy consumption. To address two types of rescheduling problems: time delay caused by fuzzy features and new job insertions, this paper is dedicated to providing a hybrid adaptative rescheduling strategy that combines a satisfaction-driven policy with two specific rescheduling methods. Moreover, an enhanced version of the non-dominated sorting genetic algorithm II (NSGA-II) is developed, incorporating population initialisation rules and multiple genetic operators to obtain solutions of superior quality. Experimental studies have demonstrated the impact of time delay caused by fuzzy features on customer satisfaction. The proposed hybrid adaptative rescheduling strategy and methods exhibit significant improvements in rescheduling plan performance under uncertain environments. [ABSTRACT FROM AUTHOR]

In many industrial processes, it may not be practical to obtain sample sizes larger than one due to various reasons. As a result, practitioners must rely on control charts based on individual observations for statistical process monitoring and control. The sequential probability ratio test (SPRT) chart is an appropriate solution for such situations. Existing literature predominantly delves into the statistical aspects of designing or optimising an SPRT chart, neglecting the economic and associated design aspects. However, past experience suggests that statistical design may not always be optimal from an economic perspective. This paper aims to bridge this research gap by proposing a two-stage Markov chain approach to develop an economic model for the optimal design of the SPRT chart with individual observations. Following the principles of economic-statistical designs, optimal parameters are determined by solving a mixed-integer non-linear programming problem that minimises the long-run cost per item while ensuring compliance with practical constraints. Through an extensive study, we find that the economic performance of the SPRT chart surpasses that of competing individual control charts in many cases. This indicates that the economic-statistical design of the SPRT chart can yield significant economic benefits while maintaining desirable statistical properties. Moreover, enhancements have been introduced to refine the optimal design of the SPRT chart, retaining excellent overall performance across a wide range of shifts. Finally, we provide two industrial examples to illustrate the effectiveness of the proposed SPRT chart. [ABSTRACT FROM AUTHOR]

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model's practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost. [ABSTRACT FROM AUTHOR]

Liquified Petroleum Gas (LPG) is an oil refinery product that must adhere to quality specifications with respect to certain impurities. Refineries apply an LPG purification process that consists of a flow network with several process units (PUs). Current methods focus on optimising the performance of each PU separately; there exists no known approach for identifying the whole process optimum. In this paper, we present an approach for optimising the LPG purification process as a whole. We utilise operational scenarios to model the non-linear transformations of each PU. These scenarios enable us to devise a Mixed Integer Linear Program (MILP) that minimises energy consumption. The obtained solution is an approximation of the optimum but offers actionable support to refinery engineers. To enable the applicability of our approach at large scale, we propose two filters based on Pareto dominance and Data Envelopment Analysis (DEA) to identify the Pareto optimal and the Best Practice set of scenarios, respectively. By filtering out the rest, we reduce the solution space of the corresponding MILP. Further, we provide computational evidence that the application of these filters vastly improves solution time and enables applicability under real production conditions. [ABSTRACT FROM AUTHOR]

Over the last decades, scheduling theory has been put into practice so as to tackle production process as a combinatorial optimisation problem and specifically as a PFSP (Permutation Flowshop Scheduling Problem). Often, NP-completeness of PFSP leads numerous research towards suggesting heuristic algorithms. The objective is to propose an optimal or near-to-optimal order of n jobs processing on m machines with a minimum completion time of all jobs. In this paper, a two-phase heuristic algorithm will be presented, named IFRS (Improved FRS). FRS, within the first phase, is going to be used to find a superior order of jobs out of Taillard's instances; then the order of jobs will be improved again in phase 2. While running IFRS, a completely new idea, named Pixelation of time matrices, will be used for the very first time and provide a final pattern, which can be used in any heuristic algorithm with makespan criterion. After using the hard benchmark instances of Taillard, the superiority of IFRS over 20 algorithms is going to be shown by tables and statistical graphics. Due to its low makespan values, IFRS benefits flow production, as does NEH. [ABSTRACT FROM AUTHOR]

The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring. [ABSTRACT FROM AUTHOR]

Monitoring production processes to provide potential customers with quality products while utilising fewer resources and reducing the cost of inspection is a challenge to quality and industrial engineers. Aiming at developing an improved design using reduced sample sizes, this study proposes a triple sampling (TS) $ \bar{X} $ X ¯ chart to monitor shifts in the process mean when the quality characteristic of interest follows the gamma distribution. Optimal parameters are determined by employing three optimisation procedures. The detection ability of the TS $ \bar{X} $ X ¯ chart is evaluated and contrasted with competing charts via the average number of observations to signal (ANOS), average run length (ARL) and expected ANOS (EANOS) criteria. When the TS $ \bar{X} $ X ¯ chart is designed for detecting small and large shifts, it is noticed that the sample sizes at the first, second and third sampling stages are larger for detecting small shifts. Compared to its competitors, the TS $ \bar{X} $ X ¯ chart requires fewer observations on the average to signal a shift; hence, it uses resources more efficiently and incurs lower inspection costs. To aid in the implementation of the TS $ \bar{X} $ X ¯ chart, an illustrative example of monitoring the wafer process is provided. Overwhelming evidence based on reported results supports the TS $ \bar{X} $ X ¯ chart. [ABSTRACT FROM AUTHOR]

Time-resolved photoemission electron microscopy (PEEM) has established itself as a versatile experimental technique to unravel the ultrafast electron dynamics of materials with nanometer-scale resolution. However, the approach of performing PEEM-based, pixel-by-pixel lifetime mapping has not been reported thus far. Herein, we describe in detail the data pre-processing procedure and an algorithm to perform time-trace fittings of each pixel. We impose an energy cutoff for each pixel prior to spectral integration to enhance the robustness of our approach. With the energy cutoff, the energy-integrated time traces show improved statistics and lower fitting errors, thus resulting in a more accurate determination of the fit parameters, e.g., decay time constants. Our work allows us to reliably construct PEEM-based lifetime maps, which potentially shed light on the effects of local microenvironment on the ultrafast processes of the material and allow spatial distributions of lifetimes to be correlated with observables obtained from complementary microscopic techniques, hence enabling a more comprehensive characterization of the material. [ABSTRACT FROM AUTHOR]

One of the main challenges in operating multistate manufacturing systems (MMSs) is maintaining stable and robust production against various disruptions. Therefore, an urgent need exists for an operation and maintenance (O&M) method that optimises MMS resilience, i.e. the capability of withstanding or recover from disruptions of various sources. Consequently, this study proposes an integrated resilience-oriented production and maintenance scheduling approach for MMSs. This approach enhances MMS resilience by reinforcing its adaptivity to the variation in production requirements. Based on a conceptual investigation of operational uncertainty and its mechanism of disruption, this study devotes to (i) formulating a performance loss-based resilience measurement with consideration of operational uncertainties and (ii) proposing a reinforcement learning-based approach to schedule MMS operation and maintenance activities for MMS components of different performance states. An industrial case study of a ferrite phase shifter manufacturing system is subsequently conducted to validate the proposed approach. Results demonstrate the effectiveness of the proposed approach in the resilience optimisation of MMSs. [ABSTRACT FROM AUTHOR]

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory's scheduling performance by sharing general scheduling knowledge among heterogeneous factories under the consideration of data privacy protection remains challenging. To this end, this paper proposes to solve the dynamic job scheduling in the context of CCMS with a novel federated deep reinforcement learning (FDRL) approach. Within each factory, the scheduling objective involves minimising the makespan and energy consumption, accounting for machine warm-up procedures and real-time dynamics. To handle heterogeneous policy structures, we aggregate their hidden parameters through FDRL, with states, actions, and rewards designed to facilitate the aggregation. The two-phase algorithm, comprising iterative local training and global aggregation, trains the scheduling policies. Constraint items are introduced to the loss functions to smooth local training, and the global aggregation considers production scales and obtained objectives. The proposed approach enhances the solution quality and generalisation of each factory's scheduling policy without exposing original production data. Numerical experiments conducted on sixty scheduling instances validate the superiority of the proposed approach compared to twelve dynamic scheduling methods. Compared to independently trained DRL-based approaches, the proposed FDRL-based approach achieves up to an 8.9% reduction in makespan and a 22.3% decrease in energy consumption through knowledge sharing. [ABSTRACT FROM AUTHOR]

Fabric dyeing is an important production process in clothing industry and consists of some batch processing machines (BPM). In this study, parallel BPM scheduling problem with machine eligibility in fabric dyeing process is considered and an adaptive shuffled frog-leaping algorithm (ASFLA) is presented to minimise makespan and total tardiness simultaneously. A heuristic is used to produce initial population. Evolution quality of population on the last generation is used to implement adaptive population division and adaptive search process in each memeplex on the current generation. Search process of each memeplex is divided into some segments and search operators are dynamically adjusted after each segment is done. Adaptive elimination is performed on the worst memeplex. Extensive experiments are conducted to test the performance of ASFLA and the computational results demonstrate that new strategies of ASFLA are effective and ASFLA has promising advantages on the considered problem. [ABSTRACT FROM AUTHOR]

With the escalating integration of renewable energy sources (RESs) within the manufacturing sector, the synergy between energy and production management has gained paramount importance. To harmonise energy efficiency with production effectiveness, the concept of energy-aware scheduling, comprising off-line, on-line, and hybrid scheduling, has been introduced. While each approach has merits and drawbacks in handling unexpected disturbances, recovering optimal performance, and managing computational costs, their designs often tend to be ad hoc. Currently, there is a lack of a systematic design that effectively balances computational costs, robustness to unexpected disturbances, and optimal performance. Moreover, existing results do not fully leverage the flexibility in factories integrated with Renewable Energy Sources (RESs). In response, this study introduces the paradigm of event-triggered hybrid scheduling (ETHS) to achieve a balance among optimality, robustness, and computational cost by fully exploring the system's flexibility. The efficacy of ETHS is empirically demonstrated through a comprehensive case study involving a real-world manufacturing facility. Diverse scenarios are scrutinised, accompanied by sensitivity analyses pertaining to parameter choices. The outcomes conspicuously underscore the potency of ETHS in facilitating the harmonious alignment of energy and production management within the ambit of manufacturing industries. [ABSTRACT FROM AUTHOR]

Product co-design which connects the firm and consumers to achieve value co-creation is widely applied in the service-oriented manufacturing system (SOMS) to improve service quality. One of the key challenges in practice is to understand how the product co-design affects the performance of SOMS under different business models. This study considers a SOMS in which the firm optimises its product co-design effort and consumers decide whether to participate in product co-design under the selling and leasing models. The results show that the firm can always benefit from implementing the product co-design, even though consumers do not participate in product co-design under some specific circumstances. However, it does not always hold for consumers. Interestingly, we find that the free rider effect occurs when the participation cost is high. We further elaborate on the firm's business model selection strategy, introducing a threshold-based policy designed to assist the firm in making decisions when confronted with varying residual values and participation costs. Lastly, our findings indicate a lack of clear-cut pros and cons between the two business models in terms of the firm's product co-design efforts and consumer surplus. [ABSTRACT FROM AUTHOR]

Service-oriented manufacturing (SOM) is a new industrial model that combines manufacturing with service. To enhance the competitiveness of manufacturers in SOM systems, we investigate a coordinated production and delivery scheduling problem considering a third-party logistics (3PL) provider. The considered problem can be divided into two phases, including production phase and delivery phase. In the production phase, jobs are processed by multiple manufacturers located in different geographical zones. Jobs are with deteriorating processing time and set-up time. Several structural properties of the problem in the production phase are proposed, based on which a heuristic algorithm is designed to make decisions on job batching and batch sequencing. After being processed, the finished jobs are immediately delivered to a customer by manufacturers' own vehicles or the 3PL provider. A dynamic programming algorithm is developed to solve the problem in the delivery phase. The integrated scheduling problem is proved to be NP-hard. Hence, we develop a general variable neighbourhood search (GVNS) to solve the problem in a reasonable time. The basic variable neighbourhood descent (BVND) is used as a local search method in the GVNS. Finally, the computational experiments are conducted and the results verify the effectiveness and stability of the proposed approach. [ABSTRACT FROM AUTHOR]

Service-oriented manufacturing has been a prevalent trend in integrating product collaborative development with maintenance service, whereas the final products are frequently trapped into severe quality defects lacking appropriate contracting incentives. This paper establishes a tractable framework investigating the alignment of inspection and warranty strategies to manage product quality throughout the R&D, manufacturing and usage processes. Specifically, the manufacturing effort is motivated by a cost-sharing contract provided by the researcher, who performs quality inspection upon receiving the final product. Subsequent maintenance service is predesignated for warranty period coverage that the cost is partially claimed for compensation from the manufacturer. Under both breakdown and preventive maintenance strategies, we elaborate and compare the optimal incentive intensities solving the equilibrium effort decisions and inspection accuracy. The results suggest that both R&D and manufacturing effort levels under preventive maintenance are slightly higher than that under breakdown maintenance scenario, while inspection accuracy contradicts conversely. The breakdown maintenance strategy is associated with a lower proportion of warranty costs borne by the manufacturer. For a sufficient long warranty period, the researcher is better off to conduct preventive maintenance periodically. Our study contributes to providing explicit implications to manage product development quality with adequate inspection and warranty strategies. [ABSTRACT FROM AUTHOR]

To overcome product homogenisation issues, an increasing number of supply chain members have been binding their competitive advantages to services which extend supply chain competition to a new dimension. This paper proposes a product service supply chain (PSSC) network competition model with multiple competing manufacturers and retailers offering demand-enhancing and costly services. In order to obtain the decision-making results in a non-cooperative game context, equilibrium conditions are established using variational inequality, and the qualitative properties are discussed. We compare the two competition scenarios, price competition only and simultaneous price and service competition, to discover the impact of service-offering on supply chain members. After introducing the service competition, both manufacturers and retailers realise higher profits and marginal profits with the same service costs. In contrast, the manufacturers' marginal profits achieve higher growth rates. We also find that (a) the increase of the demand sensitivities to the service levels of retailers would contribute more to the revenue growth of the whole PSSC network and (b) when service costs are different for manufacturers and retailers, the optimal decision-making on service levels are not only affected by the service costs of the members in the same tier but also correlated with those in other tiers. [ABSTRACT FROM AUTHOR]

Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, many production scenarios utilise sensors to monitor manufacturing processes, resulting in massive data that can be leveraged to build machine-learning models to improve production efficiency and quality. In semiconductor manufacturing, where many sensors are employed to monitor the production process, the resulting multi-sensor data poses challenges for constructing a virtual metrology (VM) model to assess wafer quality. Furthermore, building separate prediction models for each sensor can result in a loss of redundancy present in the multi-sensor data. Therefore, this paper proposes a multi-source AdaBoost with a cross-weight sampling method for predicting the critical dimension required in VM. Compared with the existing AdaBoost algorithm in regression, the proposed method adapts well to multi-source data by incorporating a cross-weight sampling distribution. Thus, when updating the sampling weight distribution, the impact of both individual and multi-source data is considered to re-sample high-quality observations. Based on the numerical results from the synthetic datasets and case study in semiconductor manufacturing, the proposed method outperforms benchmark methods. Additionally, owing to the redundancy of multi-sensor data, the proposed method demonstrates robust performance regardless of the noise level in the semiconductor VM data. [ABSTRACT FROM AUTHOR]

Nowadays industrial robots have been increasingly widely deployed across various sectors and assembly tasks are seen as one of the dominant application fields. The assembly tasks, as the critical process in manufacturing, are still challenging for the robot because of the complex contact state between the robot and the environment (i.e. assembly components). In the assembly task, the robot should switch its controller from a non-contact mode to a contact-rich mode when the contact condition changes, where compliant behaviour is necessary for robustness to uncertain contact and safety of physical interaction. This paper proposes a deep reinforcement learning (DRL) method to achieve such compliance using variable stiffness compliant control. Concretely, a Cartesian compliant controller is built on a virtual dynamics model with a variable non-diagonal stiffness matrix to derive desired motion that reacts with the external force/torque. Upon that, a deep deterministic policy gradient (DDPG)-based agent is deployed to fine-tune such a non-diagonal stiffness matrix. After error-and-trial learning, robots can handle changes in contact state in assembly tasks without any pause and switching its controller mode, thus increasing task efficiency and compliance. The simulation and experiments show that our method allows robots to complete assembly tasks safely and efficiently under noisy observation. [ABSTRACT FROM AUTHOR]

We consider a stochastic capacitated lot sizing problem with in-line rework. Both defective and defect-free products can be the result of an imperfect production process in real-world production systems. However, the proportion of defective items in a production lot is subject to uncertainty. For economic and environmental reasons, the possibility to rework defective products appears reasonable since these products usually have considerable value. In this paper, a nonlinear model formulation for integrated lot sizing and rework planning is proposed. We use a sample average approach to approximate the generic nonlinear model formulation. To cope with the randomness of the proportion of defective products, we apply a flexible planning approach that allows the adjustment of production and rework quantities. We conduct extensive numerical investigations to evaluate the performance of the proposed planning approach. [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]

The microstructure plays a crucial role in the manufacturing and application of polyacrylonitrile fibers, which serve as precursors for carbon fibers. Synchrotron radiation small angle x-ray scattering (SR-SAXS) is a non-destructive and precise technique for analyzing fiber structures. This study employed one-dimensional SR-SAXS mapping to extract key structural parameters such as periodicity, lamellae thickness, and the extent of amorphous regions, as well as the directional orientation in γ-irradiated, pre-oxidized polyacrylonitrile fibers. The analysis revealed a three-layered structure comprising a surface skin, a transitional layer, and a central core. Notably, the lamellar thickness exhibits a "U"-shaped distribution, while the long-period structures, amorphous regions, and orientational properties demonstrate a "wave-like" pattern. Within this structure, the skin exhibits a higher level of orientation, with the orientation decreasing progressively from the skin toward the core layer. The structure of the layered crystal was further corroborated by the morphological analysis. In addition, molecular simulations were performed to propose the mechanisms underlying the formation of this layered structure. This comprehensive investigation using SR-SAXS and one-dimensional mapping provides detailed insights into the microstructural and morphological characteristics of polyacrylonitrile fibers, which can inform future advancements in material processing and refinement techniques for the production of advanced fibers. [ABSTRACT FROM AUTHOR]

We present a novel method for generating optical vortex needle beams (focused optical vortices with extended depth-of-focus) using a compact flat multilevel diffractive lens (MDL). Our experiments demonstrate that the MDL can produce focused optical vortices (FOVs) with topological charges l = 1 − 4 (extendable to other l values), maintaining focus over distances significantly longer than conventional optical vortices. Specifically, FOVs exhibit non-diffracting behavior with a depth-of-focus (DOF) extended beyond 5 cm, compared to conventional optical vortices, which show continuous size increase due to diffraction. When the MDL is illuminated by an optical vortex of 3 mm diameter, it achieves a transmission efficiency of approximately 90 % and extends the DOF several times beyond that of traditional lenses. Increasing the size of the input optical vortex further extends the DOF but introduces additional rings, with their number increasing proportionally to the value of l. Our approach, validated by both experimental results and numerical simulations, proves effective for beams such as optical vortex and Hermite-Gaussian modes and holds potential applications in high-resolution imaging, material processing, optical coherence tomography, and three-dimensional optical tweezers, offering a simple and efficient solution for generating non-diffracting beams. [ABSTRACT FROM AUTHOR]

In approaches to analyze material composition or in processing tasks using ultra-short laser ablation, it is of particular interest how ablated materials are distributed across the solid angle in front of the interaction region. We found that with our sub-10-fs laser in the regime from 10 14 W / c m 2 to 10 17 W / c m 2 , the solid angle of the ablation cone decreases significantly along with the laser intensity in a vacuum environment. For this observation, we used ion-beam analysis to investigate the distribution of tungsten collected on silicon catcher plates arranged across the solid angle of the laser-ablation cone. Moreover, we used other post-mortem tools, such as scanning electron microscopy and confocal laser scanning microscopy, to determine the ablation threshold of F t h = 468 mJ / c m 2 of our tungsten samples. Here, clearly, two laser intensity-dependent ablation regimes can be observed in the detection of a crater depth and a ablation cone angle. [ABSTRACT FROM AUTHOR]

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]

We study the impedance behavior of two ferrofluids, of a similar magnetic material, one constituted by spherical nanoparticles and the other constituted by cubes, both suspended in kerosene. The ferrofluid constituted by cubic nanoparticles has 10% doping of a rare earth ion. The samples were inserted between two parallel disk-like electrodes of area S = 2.3 c m 2 made of surgical steel, separated by d = 127 μ m. The impedance was measured by applying a sinusoidal voltage of amplitude V 0 = 30 mV, from 1 mHz to 100 kHz. To analyze the experimental data, we use a model based on the Poisson–Nernst–Planck equations, with Ohmic boundary conditions. In the analysis, we assume that the ferrofluids contain free ions, originated from the manufacturing process, released by the stabilization layer around the magnetic nanoparticles dispersed in kerosene. The corresponding nanoparticles are charged of opposite signs with respect to these free ions. In the high frequency region, the effective diffusion coefficient coincides with that from the free diffusion coefficients, defined as the mathematical average between the diffusion coefficients of the nanoparticles and the free ions. In the low frequency region, we found the ambipolar diffusion coefficient, defined as their harmonic average. The effect of the electrodes is taken into account by means of surface conductivity to describe the conduction current across the electrode, assumed to be proportional to the surface electric field. In this model, the role of the electrodes is important just in the low frequency region. On the contrary, in the high frequency region, where the electric current is dominated by the displacement current, the role of the electrodes is negligible. The results show that the nanoparticles of the magnetic material have no effects on the higher-frequency range of the impedance spectra. In the low frequency region, our results indicate a difference in the electric response of the two ferrofluids. Due to their similar dimensions and, hence, similar ambipolar diffusion coefficients, we impute the observed different behavior to the charge transfer from the bulk to the external circuit included in the surface conductivity. [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]

The freezing/melting transition is at the heart of many natural and industrial processes. In the classical picture, the transition proceeds via the nucleation of the new phase, which has to overcome a barrier associated with the free energy cost of the growing nucleus. The total nucleation rate is also influenced by a kinetic factor, which somehow depends on the number of attempts to create a nucleus, that translates into a significant density of proto-nuclei in the system. These transient tiny nuclei are not accessible to experiments, but they can be observed in molecular simulations, and their number and size distributions can be acquired and analyzed. The number distributions are carefully characterized as a function of the system size, showing the expected behavior, with limited spurious effects due to the finite simulation box. It is also shown that the proto-nuclei do exist even in the stable phase, in agreement with the fact that the (unfavorable) volume contribution to their free energy is negligible in the first stages of nucleation. Moreover, the number and size distributions evolve continuously between the stable and the metastable phases, in particular when crossing the coexistence temperature. The size distributions associated with any nucleus and with the largest one have also been calculated, and their relationship recently established for bubbles in a liquid [Puibasset, J. Chem. Phys. 157, 191102 (2022)] has been shown to apply here. This is an important relation for free energy barrier calculations with biased molecular simulations. [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]

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]

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]

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]

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]

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]

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]