Hasil untuk "Manufactures"

K. Boulding, J. Bain

J. Heskett, W. Sasser

R. Schonberger

R. Grant, Azar P. Jammine, H. Thomas

This study investigated the causal relationships between diversity, diversification, and profitability among 304 large British manufacturing companies that differed in both product and multinationa...

Ioannis Balatsos, Athanasios Liakos, Panagiotis Karakostas et al.

This paper develops a data-driven, constraint-based optimization framework for a complex industrial job shop scheduling problem variant in pharmaceutical manufacturing. The formulation captures fixed routings and designated machines, explicit resource calendars with weekends and planned maintenance, and campaign sequencing through sequence-dependent cleaning times derived from site tables. The model is implemented with an open source constraint solver and evaluated on deterministic snapshots from a solid oral dosage facility under three objective formulations: makespan, makespan plus total tardiness, and makespan plus average tardiness. On three industrial instances of increasing size (10, 30, and 84 jobs) the proposed schedules dominate reference plans that solve a simplified variant without the added site rules. Makespan reductions reach \(88.1\%\), \(77.6\%\), and \(54.9\%\) and total tardiness reductions reach \(72.1\%\), \(58.7\%\), and \(18.2\%\), respectively. The composite objectives further decrease late job counts with negligible makespan change on the smaller instances and a modest increase on the largest instance. Optimality is proven on the small case, with relative gaps of \(0.77\%\) and \(14.92\%\) on the medium and large cases under a fixed time limit. The results show that a compact constraint programming formulation can deliver feasible, transparent schedules that respect site rules while improving adherence to due dates on real industrial data.

Zhenxiang Huang, Lior Skoury, Tim Stark et al.

Automating large-scale manufacturing in domains like timber construction requires multi-robot systems to manage tightly coupled spatiotemporal constraints, such as collision avoidance and process-driven deadlines. This paper introduces LASER (Level-based Asynchronous Scheduling and Execution Regime), a complete framework for scheduling and executing complex assembly tasks, demonstrated on a screw-press gluing application for timber slab manufacturing. Our central contribution is to integrate a barrier-based mechanism into a constraint programming (CP) scheduling formulation that partitions tasks into spatiotemporally disjoint sets, which we define as levels. This structure enables robots to execute tasks in parallel and asynchronously within a level, synchronizing only at level barriers, which guarantees collision-free operation by construction and provides robustness to timing uncertainties. To solve this formulation for large problems, we propose two specialized algorithms: an iterative temporal-relaxation approach for heterogeneous task sequences and a bi-level decomposition for homogeneous tasks that balances workload. We validate the LASER framework by fabricating a full-scale 2.4m x 6m timber slab with a two-robot system mounted on parallel linear tracks, successfully coordinating 108 subroutines and 352 screws under tight adhesive time windows. Computational studies show our method scales steadily with size compared to a monolithic approach.

P. Kidd

P. Adler

A. Gunasekaran

Lori L. Koste, M. Malhotra

Leoncio Jimenez

To conceptualize living systems based on the processes that create them, rather than their interactions with the environment, as in systems theory. Maturana and Varela (1969) at the University of Chile introduced the term autopoiesis (from Greek self and production). This concept emphasizes autonomy as the defining feature of living systems. It describes them as self-sustaining entities that preserve their identity through continuous self-renewal to preserve their unity. Furthermore, these systems can only be understood in reference to themselves, as all internal activities are inherently self-determined by self-production and self-referentiality. This thesis introduces the Fuzzy Autopoietic Knowledge Management (FAKM) model, which integrates the system theory of living systems, the cybernetic theory of viable systems, and the autopoiesis theory of autopoietic systems. The goal is to move beyond traditional knowledge management models that rely on Cartesian dualism (cognition/action) where knowledge is treated as symbolic information processing. Instead, the FAKM model adopts a dualism of organization/structure to define an autopoietic system within a sociotechnical approach. The model is experimentally applied to a manufacturing company in the Maule Region, south of Santiago, Chile.

Jonghan Lim, Ilya Kovalenko

Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they demonstrate limitations in responsiveness within dynamic industrial settings. Multi-agent systems address these challenges through decentralization of decision-making, enabling systems to respond dynamically to operational changes. However, current multi-agent systems encounter challenges related to real-time adaptation, context-aware decision-making, and the dynamic exploration of resource capabilities. Large language models provide the possibility to overcome these limitations through context-aware decision-making capabilities. This paper introduces a large language model-enabled control architecture for multi-agent manufacturing systems to dynamically explore resource capabilities in response to real-time disruptions. A simulation-based case study demonstrates that the proposed architecture improves system resilience and flexibility. The case study findings show improved throughput and efficient resource utilization compared to existing approaches.

Nicholas J. Sullivan, Julio J. Valdés, Kirk H. Bevan et al.

Scanning probe microscopy (SPM) is a valuable technique by which one can investigate the physical characteristics of the surfaces of materials. However, its widespread use is hampered by the time-consuming nature of running an experiment and the significant domain knowledge required. Recent studies have shown the value of multiple forms of automation in improving this, but their use is limited due to the difficulty of integrating them with SPMs other than the one it was developed for. With this in mind, we propose an automation framework for SPMs aimed toward facilitating code sharing and reusability of developed components. Our framework defines generic control and data structure schemas which are passed among independent software processes (components), with the final SPM commands sent after passing through an SPM-specific translator. This approach permits multi-language support and allows for experimental components to be decoupled among multiple computers. Our mediation logic limits access to the SPM to a single component at a time, with a simple override mechanism in order to correct detected experiment problems. To validate our proposal, we integrated and tested it with two SPMs from separate manufacturers, and ran an experiment involving a thermal drift correction component.

Jiachen Li, Shihao Li, Christopher Martin et al.

Roll-to-roll (R2R) manufacturing requires precise tension and velocity control under operational constraints. Model predictive control demands gradient computation, while sampling-based methods like MPPI struggle with hard constraint satisfaction. This paper presents an adaptive trajectory bundle method that achieves rigorous constraint handling through derivative-free sequential convex programming. The approach approximates nonlinear dynamics and costs via interpolated sample bundles, replacing Taylor-series linearization with function-value interpolation. Adaptive trust region and penalty mechanisms automatically adjust based on constraint violation metrics, eliminating manual tuning. We establish convergence guarantees proving finite-time feasibility and convergence to stationary points of the constrained problem. Simulations on a six-zone R2R system demonstrate that the adaptive method achieves 4.3\% lower tension RMSE than gradient-based MPC and 11.1\% improvement over baseline TBM in velocity transients, with superior constraint satisfaction compared to MPPI variants. Experimental validation on an R2R dry transfer system confirms faster settling and reduced overshoot relative to LQR and non-adaptive TBM.

Chuirong Chi, Qichao Hou, Guangyuan Zhao et al.

Overcoming the trade-off between wide field of view (FOV) and compactness remains a central challenge for integrating near-infrared (NIR) imaging into smartphones and AR glasses. Existing refractive NIR optics cannot simultaneously achieve ultra-wide angles above 100° and ultrathin total track length (TTL) below 5 mm, limiting their use in portable devices. Here, we present a wafer-level-manufactured meta-aspheric lens (MAL) that achieves a 101.5° FOV, 3.39 mm TTL, and F/1.64 aperture within a compact volume of 0.02 cubic centimeters. Unlike previous hybrid lenses with separate refractive and diffractive components, our MAL features a fully integrated structure, which enables a compact form factor. This integration also simplifies fabrication, allowing high-throughput production via micrometer-level precision alignment and bonding on a single wafer, with only one dicing step and no need for additional mechanical fixtures. Furthermore, the design process explicitly considers manufacturability and accurately models metalens dispersion, ensuring that experimental performance matches simulated results. We validate our MAL through both direct and computational imaging experiments. Despite its small form factor, our scalable MAL demonstrates strong NIR imaging performance in blood vessel imaging, eye tracking, and computational pixel super-resolution tasks. This scalable MAL technology establishes a new benchmark for high-performance, miniaturized NIR imaging and opens the door to next-generation smartphone and AR optical systems.

Chenglong Duan, Dazhong Wu

Part qualification in additive manufacturing (AM) ensures that additively manufactured parts can be consistently produced and reliably used in critical applications. One crucial aspect of part qualification is to determine the complex stress-strain behavior of additively manufactured parts. However, conventional part qualification techniques such as the destructive testing and non-destructive testing are costly and time consuming, especially for metal AM. To address this challenge, we develop a dynamic time warping (DTW)-transfer learning (TL) framework for AM part qualification by transferring knowledge gained from the stress-strain behaviors of additively manufactured low-cost polymers to high-performance, expensive metals. Specifically, the framework selects one single optimal polymer dataset that is the most similar to the metal dataset in the target domain using DTW among multiple polymer datasets, including Nylon, PLA, CF-ABS, and Resin. A long short-term memory (LSTM) model is then trained on one single optimal polymer dataset and tested on one of three target metal datasets, including AlSi10Mg, Ti6Al4V, and carbon steel datasets. Experimental results show that the Resin dataset is selected as the optimal polymer dataset in the source domain for the AlSi10Mg and Ti6Al4V datasets, while the Nylon dataset is selected as the optimal polymer dataset in the source domain for the carbon steel dataset. The DTWTL model trained on one single optimal polymer dataset as the source domain achieves the best predictive performance, including an average mean absolute percentage error of 12.41%, an average root mean squared error of 63.75, and an average coefficient of determination of 0.96 when three metals are used as the target domain, outperforming the vanilla LSTM model without TL as well as the TL model trained on all four polymer datasets as the source domain.

M. Vázquez-Rey, D. Lang

R. Fullerton, William F. Wempe

Zhuangzhuang Li, Minxun Lu, Haoyuan Lei et al.

SLM fabrication of NiTi alloy is receiving increasing attention. However, the relatively high austenite transformation finish (Af) temperature is the main problem for biomedical applications. In this study, it was found that phase transformation temperature (PTT) increased with increasing laser power, decreasing scanning speed, and decreasing hatch spacing. The combination of PPs with an energy density of less than 75 J/mm³ is expected to yield the Af temperature below 37 °C. P = 80 W, v = 600 mm/s, and h = 70 μm were identified as the optimal combination because of a good tensile strength of 612 MPa and a peak shape recovery rate of 96.94%. Biocompatibility assessment showed that SLM-NiTi porous scaffolds supported pre-osteoblast survival and proliferation. A patient-specific mesh-like supporting prosthesis was designed to show the prospect of 4D-printed orthopedics implant. In clinical application, SLM-NiTi prosthesis could reduce bony window and facilitate easier insertion through compressive deformation.

Alves Wellington, Orfão Ana, Silva Ângela

This research aimed to use a sustainable approach based on the internalisation of external cost analysis of intermodal transportation of freight to assess the impacts of these activities on the environment. This research used two approaches to develop a model that illustrates the internalisation of the external cost of freight transport. The first approach was used to calculate the cost of emissions for each route considering the transportation and its’ cost in the country of destination. The second approach calculated the external cost considering only the distance travelled by the vehicle. The results showed that the companies operating in the selected scenarios would have to pay an additional cost for the transportation of goods. The scenarios had different pollutants emitted during the transportation, which means that the negative impact on human health and the environment is evident. The urgency to limit carbon dioxide and other greenhouse gases in the atmosphere has increased concerns for all activity sectors. Climate change has drawn the attention of governments, companies, and academics, promoting initiatives that mitigate the impact of their activities. The model for measuring emissions was used due to the need for a comprehensive cost analysis to further assess the impact on the environment. Regarding the internalisation of the external cost emissions, the findings showed that different scenarios had a different pollutant emitted during the transportation, which means that the negative impact for human health and the environment is evident. Findings also indicate that to minimise the impact during the transportation, considering the “user-pays principle”, these impacts should be discussed in more detail between stakeholders.