Industrial scheduling problems require simultaneous optimization of conflicting objectives such as makespan and tardiness. This paper presents a gate-based quantum computing approach for multi-objective job shop scheduling using permutation-based encoding and Filtering Variational Quantum Eigensolver. Systematic weight parameter variation generates solutions that represent a trade-off between makespan and tardiness. Computational studies demonstrate successful multi-objective optimization on both quantum simulators and IBM11Naming of specific companies is done solely for the sake of completeness and does not necessarily imply an endorsement of the named companies nor that the products are necessarily the best for the purpose. Quantum hardware. Both platforms successfully generate Pareto solutions in certain problem sizes, with quantum hardware validation confirming practical feasibility of the approach. The investigation provides a foundation for quantum–classical hybrid scheduling as quantum hardware capabilities advance toward industrial applications.
Digital Twin (DT) technology is rapidly evolving to aid in part development and manufacturing process quality improvement, particularly for additive manufacturing (AM). Although several AM DTs have been developed, they have been designed for a single purpose. Due to the complexity of developing a DT, the cost to develop these single-purpose DTs out weighs the potential value it provides. DT services have been identified as a core component in the design of fit-for-purpose DTs for manufacturing. By utilizing service-oriented design principles, stateful components within the DT Core can access stateless DT services to expand a DT’s technical capabilities. Despite the existing wealth of research on traditional service-oriented architecture, the transfer to DT has posed as a challenge due to the unique connection between the DT and the physical twin. Thus, new techniques are needed to define and design DT service architectures for dynamically composed DT services. This work seeks to address this need by proposing a two-step service discovery methodology providing the DT user with a context-dependent service solution knowledge graph. Step 1 defines the underlying schema used to represent all services and related AM domain-specific context (e.g., AM events, AM data items, AM computation models), which is then used to create a customized service solution in Step 2. Validation of this method is demonstrated through the discovery of potential DT services for AM process parameter optimization. This work aims to support users in selecting the appropriate DT services for their purpose.
To achieve multifunctional integration of sound absorption, radiation shielding, and energy dissipation, this study fabricates high-quality B4Cp/316 L lattice composites via Laser Powder Bed Fusion (LPBF). The results demonstrate that the incorporation of B4C particles substantially enhances both tensile and compressive yield strengths by over 85%. However, localized stress concentrations induced by micro-scale B4C reinforcements significantly increase material brittleness, this embrittlement effect is particularly pronounced within the lattice structures. While the B4Cp/316 L composites exhibit superior yield stress and energy absorption capacity, the complex internal stress states and particle-induced failures lead to reduced deformation stability, with macroscopic cracks initiating and propagating at geometric transitions and particle interfaces. Notably, the interpenetrating Body-Centered Cubic and Simple Cubic (SBCC) structure yields a more stable stress distribution and enhanced energy absorption. These findings have completed the preliminary exploration of LPBF preparation technology for metal based composite materials, providing critical insights for the additive manufacturing of multifunctional structural composite materials.
The analysis of tool wear through cutting forces in milling processes is commonly based on the intensity of the forces observed throughout machining. However, this approach is reliable only when the force-wear relationship of the tool has been previously characterized. For tools with unknown behavior, the increase in force intensity often leads to uncertainty in determining the wear limit state. This limitation becomes even more critical in micromilling, where wear characterization is so small that it can compromise visual inspection. In this article, a model based on the cutting force profile is proposed to determine the condition of the tool cutting edge, with potential application in tool wear identification and monitoring. This novel proposal enables tracking the evolution of wear without the need for pre-tests or comparative references, and is applicable to both milling and micromilling processes. The model is demonstrated for microtools applied to micromilling, whose edge radii were measured using a confocal microscope. These measurements were employed to establish the relationship with the cutting force profile. Confocal microscopy indicated edge radii ranging from 2.4 µm to 3.3 µm. From the analysis of the force profile, the first tool was identified with an edge radius between 2.74 µm and 3.10 µm, and the second between 2.90 µm and 3.70 µm. The results demonstrate that the proposed model is a promising method for determining the condition of tool cutting edges, offering a viable alternative for tool wear monitoring through the analysis of cutting force signals.
The fabrication of metallic components from ER2319 aluminum alloy using wire arc additive manufacturing (WAAM) faces challenges primarily due to solidification cracks and porosity. Such limitations result in reduced mechanical properties, prompting numerous studies by researchers to pursue improvements. This study investigates the feasibility of fabricating a radial thin-wall composite from ER2319 aluminum wire using the WAAM process, incorporating ER316L cold wire (CW) reinforcement as a novel approach to enhance mechanical properties. Optical microscope (OM), scanning electron microscope (SEM), and electron probe gun microanalyzer (EPMA) images during microstructural studies revealed an appropriate interface between the matrix and the reinforcement. Vickers hardness testing confirmed a significant rise in the hardness of the radial thin-wall composite.
High-speed laser welding is essential for increasing the production rate of fuel cell fabrication. However, when the welding speed exceeds a critical limit, humping occurs and reduces the weld quality. In this study, two tailored beam configurations, including an adjustable ring mode and a dual-beam configuration, were employed to suppress humping. Computational fluid dynamics simulations were performed to elucidate the underlying suppression mechanisms. The results show that, in the adjustable ring mode, humping mitigation arises from a reduced backward cross-sectional melt flow rate and a more stable molten pool. In the dual-beam configuration, humping suppression is attributed to the deceleration of melt flow, the conduction-mode behavior of the trailing beam, and the widening of the molten pool induced by the trailing laser. Furthermore, because the dual-beam configuration directly modifies the trailing molten pool dynamics, it achieves more effective humping suppression, extending the welding speed limit to 1.50 m/s, compared with 1.00 m/s for the adjustable ring mode. (c) 2026 Published by Elsevier Ltd on behalf of Society of Manufacturing Engineers (SME).
Manufacturing systems are one of the most unique systems when it comes to data collection and storage, with each component often created by different companies with their own communication structures. However, researchers want to record as many data types from these systems as possible- in the case of additive manufacturing (AM), this includes the machines themselves (ie. tool wear, position) and sensor data (ie. thermal, audio, visual). This data is key to understanding, predicting, and avoiding part defects, whether the information is used in situ or by machine learning algorithms. In the current literature, many attempts have been made to create architectures to connect specific components, but there is a lack of generalized communication that can be applied to "any" AM system. This work proposes an alternative digital architecture that allows each connection to be a modular addition to a centralized structure. From each module added, one streamlined data set is generated. By assigning timestamps to each data point, data with different communication protocols (ie. TCP, UDP) and different frequencies of collection can be aligned to provide a clearer picture of the AM process. (c) 2026 The Author(s). Published by Elsevier Ltd on behalf of Society of Manufacturing Engineers (SME). This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
This paper describes the effects of mechanistic force model edge and process damping coefficients on milling surface location error and stability. To quantify the surface location error (SLE) and stability changes, stability maps with SLE contours are predicted using time domain simulation and the number of stable points and maximum and minimum SLE values in the maps are recorded and compared. It is shown that there is little change in the number of stable points with small variation in edge coefficients, while the number of stable points is strongly dependent on the process damping coefficient. The minimum SLE is strongly dependent on both the edge coefficients and the process damping coefficient. It becomes more negative (i.e., less accurate parts) for larger edge coefficients, while it becomes less negative (i.e., more accurate parts) fora larger process damping coefficient. The maximum SLE was less sensitive to the edge coefficients and becomes less positive (i.e., more accurate parts) fora larger process damping coefficient. Overall, SLE exhibited reduced sensitivity to spindle speed at lower spindle speeds and higher axial depths for a larger process damping coefficient. Implications for tool designs are discussed, including stability effects for large edge coefficients. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PEDOT: PSS is a widely used conductive polymer whose electrical performance depends strongly on doping level, microstructure, and domain connectivity. Although its bulk conductivity is well characterized, it is challenging to achieve controlled nanoscale modulation of conductivity on a continuous film. In this work, we demonstrate that electric-field-assisted atomic force microscopy (E-AFM) enables selective, localized modification of the electrical properties of PEDOT: PSS, allowing the formation of nanoscale regions of suppressed conductivity with and without measurable morphological changes. Direct-write patterning at 17.5 V, a scan speed of 0.5 lm/s, and a contact force of 2 nN produces protruding features with average heights of similar to 8 nm and widths of similar to 280 nm, while patterning at a lower voltage of 12.25 V results in only minor morphological changes (similar to 2-3 nm). Despite these differences in surface morphology, conductive atomic force microscope (C-AFM) current mapping reveals a pronounced reduction in local conductivity within the patterned regions relative to the surrounding film. Furthermore, repeated CAFM scanning at 3 V leads to conductivity reduction without measurable morphological modification, indicating that cumulative electrical stress alone can modify transport pathways. These observations are consistent with electrically driven processes such as localized Joule heating and partial de-doping that may alter percolative transport within the film. Together, the results demonstrate a resist-free, AFMbased direct-write approach for spatially controlling conductivity in PEDOT: PSS thin films, providing a foundation for nanoscale electrical patterning in conductive polymer systems. Published by Elsevier Ltd on behalf of Society of Manufacturing Engineers (SME).
Gravity-driven sedimentation during curing can produce pronounced through-thickness copper gradients in copper-epoxy composite plates. We quantify the through-thickness copper distribution via cross-sectional image analysis and model it with a one-dimensional stratified profile. By averaging the calibrated profile over a near-surface window, we map the remaining thickness after facing, hp, to the copper fraction at the working surface. Brass slurry-lapping tests show an approximately 13% increase in MRR as hp increases from 0.4 to 6.7 mm, consistent with the microscopy-calibrated profile. Therefore, bulk recipe control alone does not uniquely determine the working surface; documenting hp after facing is expected to improve reproducibility of surface-layer composition and reduce variability upon re-facing
Machine-readable semantic information, including simulation settings, inspection configurations, machine parameters, process outcomes, and logs, is central to digital manufacturing because it enables automation, interoperability, data traceability, and adaptive reconfiguration across the product lifecycle. When anchored to specific geometric entities, such semantics transform product models into authoritative information carriers in which geometry, design intent, process knowledge, and lifecycle feedback remain persistently linked within a machine-readable dataset. This principle underlies Model-Based Definition (MBD), in which the CAD model serves as the single source of truth for realizing the digital thread. However, most engineering file formats either lack expressive machine-readable semantics or encode them in native, format-specific structures that are difficult to adapt, exchange, and reuse. This letter presents a conceptual framework for the eXtended Formats (X-Formats), a lightweight stand-off markup approach that links structured annotations to engineering carriers without modifying the original data. Semantics can be defined independently of the underlying carrier syntax, including STL, STEP, and DXF for CAD; PNG, JPEG, and DICOM for images; MP4 and MOV for videos; and GeoTIFF for geospatial data. These semantics are anchored to carrier-specific entities that can be detected through external entity-extraction mechanisms. X-Formats organize semantics into a three-level hierarchy of properties, layers, and schemas, which supports modular semantic dataset design and rapid customization across domains by adapting only carrier-specific entity types, descriptors, and anchoring rules. This letter introduces the core architectural principles, anchoring strategy, supporting tools, and broader implications of this novel product-centric approach to engineering data management.
Traditional manufacturing supply chains face growing disruption risks due to limited visibility into supplier manufacturing capabilities, insufficient transparency in capability evaluation, and limited adaptability to dynamic production changes. This paper proposes a Cyber Manufacturing Mesh Network (CMMN), a manufacturing capability-aware supplier decision support framework designed to address these challenges. In the proposed framework, each supplier is characterized by a quantified manufacturing capability profile derived from historical production data. Based on these profiles, the framework identifies technically feasible suppliers, supports multi-supplier task allocation, and enables reassignment under disruptions. Unlike conventional supply chain approaches that rely primarily on cost- and time-based criteria, the proposed framework explicitly represents fine-grained manufacturing capability compatibility using multi-modal production data, including geometry, material, and tolerance information. An illustrative operational scenario is presented to demonstrate how the framework supports supplier identification, allocation, and disruption recovery in manufacturing supply chains. (c) 2026 The Authors. Published by Elsevier Ltd on behalf of Society of Manufacturing Engineers (SME). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
High-quality microgrooves are essential for wafer dicing of low-k dielectric stacks, where back-end-of-line layers are prone to thermal and mechanical damage. This work introduces a dual-step laser approach that combines UV nanosecond ablation with a low-fluence polishing pass to improve groove integrity. The ablation step defines the microgroove geometry, while the polishing step induces localized remelting to remove recast and microcracks and reduce roughness. Experiments on silicon wafers show that a polishing fluence of 10.6 J/cm² significantly lowers surface roughness by up to 70% without additional ablation. Profilometry and SEM confirm smoother groove bottoms and cleaner sidewalls. The method offers a scalable solution for producing high-quality microgrooves in low-k wafer dicing.
Ultrasonic-assisted friction stir channeling for ZL114 cast aluminum alloys was proposed to achieve highrectangularity channels optimized by neural network. The machine learning model achieved prediction accuracies of 100% for surface index, 92% for width, 82% for height, and 85% for rectangularity, respectively, with rectangularity enhanced the range from 0.85 to 0.95. Pareto frontier analysis demonstrated that ultrasonic assistance increased channel height by 26% while significantly improving width and rectangularity. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Technology adoption, when aligned with business imperatives, ensures that new tools, such as Digital Twins (DTs), support strategic goals, driving efficiency and innovation. This paper adapts Coleman & Papp's (2006) Business-Technology Strategic Alignment Model to the DT domain to evaluate and determine the extent of alignment of a DT corporate strategy, where the DT Business and Technological Strategies must align, and the DT Application requirements with its DT architecture capabilities must achieve functional integration to effectively and efficiently create value fora manufacturing company. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
This study presents a novel stereolithography-inspired method for fabricating high-strength continuous carbon fiber reinforced polymer (CFRP) composites. A high-modulus, dual-curable photopolymer resin (ultraviolet and thermal curing) was pre-impregnated into continuous carbon fiber bundles immediately prior to fiber placement using a customized 3D printing system. Resin infiltration between deposited bundles reduced void formation, while consolidation using a glass plate lined with a fluorinated ethylene propylene release film minimized macro-porosity commonly observed in 3D-printed CFRPs. Ultraviolet laser curing through the glass plate enabled high-resolution fa brication of semi-cured structures, followed by thermal post-curing to ensure complete polymerization. The 3D-printed unidirectional composites exhibited an average flexural strength and modulus of 1050 MPa and 61 GPa, respectively. Microstructural characterization confirmed elimination of interlaminar voids and enhanced interfacial bonding, while consolidation reduced surface roughness from 87.8 lm to 10.8 lm. The results demonstrate that integrating fiber placement with laser-based stereolithography provides a practical route for manufacturing high-performance continuous CFRPs. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Machining swarf constitutes a major source of material loss in metal processing, yet its recyclability is constrained by oxidation and contamination. Near-solidus forging (NSF) provides a solid-state consolidation route operating close to the alloy solidus temperature, where it is suggested that (although not verified) localised liquid formation enables densification under comparatively low loads. This study examines the applicability of NSF to EN24 steel swarf in its industrially generated, uncleaned condition. Swarf was pre-compacted into briquettes, de-oiled thermally, and consolidated before being forged under NSF conditions at 1350 degrees C. The forged billet exhibited a predominantly martensitic microstructure with a measured porosity of 1.5 %. Hardness mapping yielded bulk values of 400 HV, while tensile testing demonstrated yield and ultimate strengths of 1000 MPa and 1080 MPa respectively, exceeding the corresponding EN24T-V literature data. Reduced ductility, however, was attributed to incomplete tempering, residual porosity, and/or localised soft regions associated with feedstock contamination. These findings demonstrate that whilst currently not optimised, NSF can convert heterogeneous steel swarf into mechanically robust, consolidated billets, indicating its potential as an energy-efficient, closed-loop recycling pathway for metallic alloys. (c) 2026 The Author(s). Published by Elsevier Ltd on behalf of Society of Manufacturing Engineers (SME). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Industrial activities represent about one-third of the world's annual energy consumption. A humancentered, smart factory environment can help to reduce energy demand and promote sustainable operations. While various cyber-physical systems have enabled the creation of new data value chains in manufacturing, most data flows are now automated; human cognitive skills still need to be better integrated into production processes. Despite existing digital support, there is still a lack of ways to engage workers that capture and enhance their cognitive abilities within socio-technical systems, which might also stem from incomplete data collection. This paper highlights the use of unconventional sensor data, including wrist acceleration, video data of operations, and electroencephalogram (EEG) measurements, to extract relevant information about human expertise in manual manufacturing operations. Our results demonstrate how unconventional human-sourced data can enrich cyber-physical systems and pave the way for more sustainable, human-centered manufacturing. The paper presents a feasibility study with illustrative examples rather than quantitatively proven sustainability improvements, explicitly framing the contribution as a proof-of-concept for human-in-the-loop integration. The aim is to inspire future research in this area. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
This is the account of a nine-month industry redesign effort in a clean-room air-handling unit (AHU) cabinet production line. The initiative was focused on chronic production inefficiencies, now in early-stage design complexity and variant proliferation, such as extensive material scrappage, reworking, assembly mistakes, and setup time. Five concurrent design measures were taken: (1) remove mirrored variants by placing holes centrally and use redundant-hole strategies to eliminate left/right part dichotomy; (2) standardize chassis cross-section profiles; (3) standardize tolerances to allow cross compatibility; (4) insert parametrized CAD-based automation; and (5) substitute punching for laser cutting for additional flexibility. The result has been dramatic improvement in nesting efficiency, reduction in waste streams, reduction of set-up time, improved assembly accuracy and greater part reuse. One revelation is that decisions made during upstream design have disproportionate influence on manufacturing yield and circularity. Since it incorporates error-proofing, variant minimization and flexible manufacturing into the design stage, the methodology represents a shift from reactive lean waste minimization to proactive design-for-waste prevention. The research also considers the major challenges such as the capital expenditures, the technology integration barriers, the residual human error in the parameter input, and the domain-specific limitations. In the future, efforts need to be turned to creating standardized hole-pattern templates for chassis recycling, embedding sensor-based feedback, and leveraging predictive analytics to inform design decisions. This case delivers an accelerated, high-leverage lesson in waste reduction by design, and can be duplicated in sheet-metal manufacturing and other high-variant production systems. It demonstrates that strategic design innovation can enhance sustainability, flexibility and operational effectiveness concurrently. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
