ABSTRACT Integrating digital representations into industrial automation offers substantial benefits, particularly in enhancing the understanding and optimization of manufacturing processes during runtime, as exemplified by the KI.Fabrik (translated as AI.Factory) at the Deutsches Museum in Munich (https://kifabrik.mirmi.tum.de/). Two use cases illustrate these advantages: dynamic fault handling, where strategies are identified and evaluated within the digital twin (DT) to maintain operational continuity, and dynamic task scheduling optimization based on the real-time status of the field-level system. Beyond enabling new use cases by integrating a live DT into industrial automation, strict hard real-time and reliability requirements should be met, posing significant challenges because of various factors. Among the challenges, the stringent requirements for real-time performance and reliability create a bottleneck in the volume of data that can be utilized. Consequently, an exact integration between data exploration processes within the DT and its response is essential to meet these demands. Addressing these challenges cannot be achieved through a single technology alone but requires a collective approach involving multiple domains, operational phases, and a holistic perspective on signal flow and processing. This paper presents a DT architecture that integrates concepts from skill-based systems, multi-agent systems (MAS), and DT technologies across the entire lifecycle of an automated production plant. It is demonstrated through two use cases, showing how DTs can be used at runtime. The use cases are embedded within a field-level control architecture based on programmable logic controllers, utilizing open platform communications unified architecture and message queuing telemetry transport (MQTT) as representative industry-standard communication protocols.
ABSTRACT Conventional pretreatment methods used in metallic textile printing are associated with high water consumption, intensive chemical use, and significant wastewater generation. This study investigates low-pressure air plasma treatment as an environmentally sustainable pretreatment strategy for metallic pigment printing on cotton fabrics. Gray and bleached woven cotton fabrics were exposed to air plasma at 200 W for 3 and 5 min before screen printing with gold- and silver-based metallic pigments. The effects of plasma treatment on fabric surface chemistry and morphology were evaluated using contact angle measurements, attenuated total reflectance-Fourier transform infrared spectroscopy, scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). Plasma treatment significantly increased the hydrophilicity of both cotton substrates, reducing liquid penetration time from 61 s to less than 10 s for bleached cotton and from more than 20 min to 6 s for gray cotton. ATR-FTIR and XPS analyses confirmed the formation of oxygen-containing functional groups, whereas SEM images showed surface etching and increased roughness that enhanced pigment anchorage. The improved surface functionality translated into superior metallic print performance. Plasma-treated fabrics exhibited enhanced color fastness to washing, rubbing, perspiration, and light compared with untreated fabrics. The greatest improvement was observed after 5 min of plasma exposure, particularly for gold pigment prints. However, SEM analysis revealed that prolonged exposure (5 min) may lead to increased surface erosion, highlighting the need for careful optimization to balance surface activation with the preservation of fiber integrity. Ultraviolet-visible spectroscopy of wash effluents demonstrated lower pigment release from plasma-treated samples, confirming improved pigment retention and reduced wastewater contamination. The results demonstrate that low-pressure air plasma treatment can replace conventional wet chemical pretreatments for metallic textile printing, offering substantial reductions in water use, chemical consumption, and pigment discharge. The approach provides a practical route toward cleaner and more sustainable textile production, particularly for emerging textile industries.
ABSTRACT Digital twin (DT) technology has demonstrated advantages in various industries, including manufacturing, service, and healthcare. In particular, the significance of this technology has grown substantially in the manufacturing sector, particularly in light of Industry 4.0 developments. In the past ten years, much research has been conducted on the framework and methods for DT implementation in a manufacturing system. However, the current status of DT implementation remains unclear, particularly about the level of integration, which refers to the extent and direction of data exchange between the physical system and its digital counterpart, and the extent of coverage across manufacturing system components such as machines, tools, workers, and control systems. This study presents a systematic literature review to examine the current status of DT implementation about key manufacturing system components and provides directions for future research. It first explores the purpose and historical background of DTs, as well as the key challenges associated with their implementation. Based on these insights, the study then proposes a four-step planning framework for implementing an intelligent DT in manufacturing systems.
ABSTRACT Momentum is building to transition from a linear (take-make-use-discard) economy into a more circular one in which materials are kept in the economy through value-retention and -recovery processes. This momentum is taking the form of shifts in consumer demand, policies, and business models. As the producer of products and materials, the manufacturing sector is integral to this transition. Voluntary, consensus-based standards are also key to this shift, as they establish foundational terminology; technical guidance in the form of best practices for production; consistent, measurable, and comparable responses to changing consumer demand and regulatory environments; and transparency for consumers and society on product quality and embedded impacts. Despite ongoing momentum, a 2022 survey and workshop led by ASTM Committee E60 on Sustainability found that there was no cohesive, systematic plan for creating the circular economy standards necessary for the manufacturing sector to remain competitive. Results from the survey and workshop were used to develop a roadmap for manufacturing standards across the product life cycle (i.e., from design through production, use, and end of use). Some of these standards are in development now at organizations like ASTM International and the International Organization for Standardization using a consensus-based process; however, many others require new research and development to reach their full potential. This paper identifies three areas, where additional pre-standardization research is needed—design for circularity, system-level modeling and tools, and digital threads—and presents open research questions for future work in each area. Although the research areas are distinct, they collectively require advances in measurement science to enable a systems approach to the circular economy transition; these advances include the development of comparable metrics, standard test methods, and interoperability standards. Advancing research in these areas will also help manufacturers assess progress toward circularity goals. This paper aims to inspire researchers to answer these research questions and help create voluntary, consensus-based standards to support circular manufacturing.
ABSTRACT Material selection in a digitally connected factory is not a single screening event: Evidence changes as supplier certificates, pilot runs, qualification tests, and first-article records become available. This paper develops a reliability- and data-maturity-aware multicriteria decision model for early-stage industrial material selection. The model combines fixed engineering normalization anchors, subjective priorities, entropy information, criterion-specific evidence maturity, service reliability, TOPSIS, VIKOR, Monte Carlo rank probabilities, and a confidence-based retain-or-switch rule. A literature-calibrated synthetic case evaluates six materials for a removable guarding panel used on an automated manufacturing cell. The decision is updated through five evidence gates, from literature prior to first-article production. Early evidence can favor glass-fiber polypropylene, but pilot and qualification data shift the recommendation toward recycled AA5052-H32. At the final gate, recycled AA5052-H32 has a 0.828 probability of ranking first and a 0.995 probability of ranking in the top two; the model then recommends switching from the galvanized-steel incumbent. Scenario tests show that galvanized high-strength low-alloy (HSLA) steel remains preferred for high-throughput, corrosion-critical, and cost-constrained settings. The contribution is a traceable smart-manufacturing decision architecture that links material ranking to evidence quality and production learning rather than treating literature values as equally credible and permanently fixed.
