The development of advanced manufacturing paradigms is significantly influenced by the ongoing digital transformation and green transformation (GX). In this context, key challenges across various domains, including design, processing, fabrication, inspection, maintenance, and recycling, must be addressed to satisfy the requirements for labor reduction, skill-free operation, and efficiency enhancement throughout the entire production process. Social prosperity must be sustained while concurrently managing resource consumption by linking these challenges. Consequently, the fundamental performance of production tools, such as machine tools, assembly machinery, industrial robots, and measuring instruments, is paramount. Additionally, the circulation of design, evaluation, and operational information within the value cycle should be actively promoted.

This special issue explores diverse topics related to GX and its role in promoting cleaner production in manufacturing industries and related sectors. It comprises papers on sustainable manufacturing processes, the circular economy, green supply chains, as well as algorithms and models that address their theoretical foundations.

The editor extends their sincere gratitude to all authors for their dedication and high-quality submissions. We also acknowledge the reviewers for their thorough evaluations that have contributed significantly to the excellence of this special issue. Finally, we hope that the publications in this issue will support the advancement of cutting-edge technologies and next-generation systems dedicated to sustainable manufacturing and cleaner production.

This study introduces Breakdown T&BRL, an advanced evaluation method that extends traditional technology readiness levels (TRL) by incorporating four additional axes: business readiness level (BRL), social readiness level (SRL), governance readiness level (GRL), and human resource readiness level (HRL). This approach subdivides TRL/BRL 5 to 7 demonstration stages to precisely identify barriers to real-world application. In addition, this approach integrates evaluations from both developers and independent third parties to highlight critical discrepancies and inform tailored roadmaps. This study applied Breakdown T&BRL to a case study on next-generation mobility robot device development within the Minami-Kurihashi bridge life platform (BLP) concept. The results of the case study demonstrated its effectiveness in pinpointing specific issues for social implementation, including the need for production-ready products and sustainable business models, while also proposing concrete countermeasures. The comparative analysis with previous Breakdown T&BRL case studies revealed unique trends for this mobility project. The TRL/BRL evaluation gap between developers and third parties was small, which can be attributed to consistent information sharing from extensive BLP events and demonstrations. Third-party SRL and GRL evaluations were notably high, which suggests that proactive stakeholder engagement through consortium operations and discussions with regulators significantly enhanced social acceptance and regulatory alignment for these mobility devices. These findings highlight how comprehensive engagement can notably influence technology’s successful path toward social implementation.

Reducing the energy needed for the machining processes used in manufacturing is a concrete step toward achieving a green transformation (GX). In this study, we investigated the use of a three-axis vertical machining center to drill and end mill a workpiece and evaluated the effects of different process designs and machining conditions on the energy efficiency. Evaluations of the energy consumption, which included the energy consumed during machining as well as that consumed for material mounting and tool changes, showed that the optimal process differed depending on the production volume. Furthermore, the evaluation results mostly agreed with the measurements. The proposed method can be implemented using numerical-control-program simulation data based on computer-aided manufacturing software. Thus, it is a practical energy-saving technology that does not require additional equipment. The proposed method is expected to contribute to the realization of sustainable production activities that promote a GX at manufacturing sites.

In Japan’s key yellowtail (buri) aquaculture industry, the increasing exports are contrasted with the production instability caused by declining producers and a reliance on wild juveniles with long, unpredictable lead times. This study addresses these challenges by proposing a production management approach that utilizes hatchery-produced juveniles with controllable, twice-yearly spawning schedules (spring and autumn) to improve the efficiency and predictability. A Monte Carlo simulation using probability distributions determined by the Anderson–Darling test was employed to analyze the shipment patterns and revenue under three pricing scenarios: high-seasonal, moderate, and size-differentiated. The results demonstrate that an increase in the number of spawning seasons distributes shipments more uniformly. This results in a stable year-round supply. The approach can significantly increase the annual revenue, particularly in the high-seasonal price scenario. Conversely, although the differentiated pricing model marginally reduces the total revenue, it significantly mitigates seasonal sales fluctuations. Thus, it provides enhanced stability for producers. To conclude, this study shows that utilizing hatchery-produced juveniles enhances the economic resilience and operational sustainability. This shift toward a more controlled and efficient system aligns with the core sustainability principles. The methodology also provides an adaptable model for other aquaculture sectors. Future work should incorporate the market demand and environmental variables to develop more comprehensive decision-making tools.

Electric arc furnace slag is a byproduct of the steel scrap refining process. It must be effectively used as a recycled resource because of its enormous production volume. Cement usage is a promising application; however, there is a risk that chromium (Cr) in the slag will be oxidized to hexavalent chromium during the cement manufacturing process and subsequently leach into the environment. Because chemical treatments, such as alkali roasting, are required to significantly reduce Cr in electric arc furnace slag, it is important to establish a chemical-free physical pretreatment to reduce the overall energy demand. In this study, we investigated the presence of Cr compounds in oxidizing and reducing slag and comparatively evaluated the effectiveness of physical pretreatment using air classification. The results showed that Cr mainly existed as a spinel phase bonded to other metals, such as Fe and Mg, and this feature was common in both slags. In air classification experiments using an elbow-jet air classifier, the Cr concentrations were similar for all classifications of the oxidizing slag. However, the Cr concentrations in the reducing slag were higher in the coarse powder fraction, whereas Ca tended to be concentrated in the fine powder fraction. Air classification using an elbow-jet air classifier was effective in reducing the Cr content in the coarse fraction of reducing slag, suggesting that it could be used as a pretreatment prior to chemical processing to reduce the overall energy demand. The contrasting classification behavior of oxidizing and reducing slags highlights the importance of slag-type-specific separation strategies.

Grinding is a process that still today largely depends on the skill and experience of the operator. For grinding, no generalized computer-aided manufacturing tool exists as for milling, and the grinding machine manufacturers have their proprietary process planning tools for the path planning. The success of a grinding process furthermore depends on a suitable conditioning of the grinding wheel and not only on the appropriate selection of the process parameters. Skilled operators are, on the one hand, capable of setting up the process in shorter time than their less skilled colleagues and with immediate success. Artificial intelligence, driven by sufficiently increased computational performance, is increasingly capable of handling manufacturing processes, particularly as these processes become more complicated and experience-based. Therefore, extending the machine’s ability towards what today is the task of the operator, namely process planning feeding the vision of “operator integrated” is a breakthrough in zero defect manufacturing and first part right for grinding processes. The paper conceptualizes an intelligent grinding machine that uses ontologies, applies rule-based planning tools, makes use of physical as well as autonomous modeling, and is capable of learning. Moreover, the processes of grinding and dressing are fully monitored so that a self-learning ability is provided. Learning is fed from different sources, from monitoring, from other machines requiring filters built on physical models, and from the operator with the ability to deal with incomplete, unstructured, and unreliable data. From this the way, how such a machine communicates with operators must be completely different than today. Research results and literature are provided to discuss the different aspects like machine state monitoring, process monitoring, and parameter selection for an optimized grinding process.

In response to the increasing demand for high-mix, low-volume production and the simultaneous shortage of experienced workers, the use of 5-axis controlled machine tools has attracted growing attention. Although complex solid shapes that include free-form surfaces are typically machined using simultaneous 5-axis machining, generation of corresponding numerical control programs continues to depend heavily on the expertise and judgment of experienced operators, rendering parts of the process highly reliant on individual proficiency. In contrast, 5-axis indexing machining involves fewer degrees of freedom compared to simultaneous 5-axis machining. Therefore, by fixing the tool orientation, it achieves easier control and higher machining stability, rendering it suitable for automation. This study developed an automated system that determines both the indexing orientations and tool paths for 5-axis indexing machining, using standard triangulated language format computer-aided design models of complex shapes as input. The system includes a shape removal simulation based on the dexel model, which automatically identifies air-cut regions for each indexing orientation and subsequently generates efficient, waste-free tool paths. Additionally, by leveraging parallel processing on a graphics processing unit, the system accelerates critical operations including tool orientation determination, tool path generation, and air-cut detection, thereby achieving practical computation times for automated process planning.

Injection molding is the most common method for manufacturing plastic products. However, products made by injection molding often exhibit various molding defects. One major contributing factor to these defects is mold temperature, especially at the cavity section where the resin enters. In our laboratory’s research, the feasibility of measuring the cavity temperature using non-contact thermometers by making simple modifications around the mold cavity area was investigated. It was also examined whether it is possible to distinguish between normal and defective molded products—specifically those with short shots—based on this temperature data. Analyzing data from predefined temperature measurement points raises concerns about significantly increased computation times. For remote monitoring applications, it is necessary to reduce the number of measurement points to decrease data volume. In this paper, an attempt is made to reduce the number of measurement points used in analysis by selecting items with an orthogonal array. As a result, the number of measurement points for analysis was reduced by 55%, thereby decreasing computation time and achieving favorable results for implementing a remote monitoring system equipped with defect detection functionality, which is reported here.

Steel manufacturing involves complex scheduling because of the interdependence between the steelmaking and rolling processes. This study proposes an integrated scheduling model for long-term planning of these processes under uncertainty. The proposed model abstracts an existing detailed scheduling model using two key techniques: charge integration and rolling process simplification. Through numerical experiments, we evaluated the characteristics and effectiveness of the model under different objective functions and time and resource buffer allocation strategies. The results demonstrate that the model achieves computational efficiency while maintaining accuracy.

This study investigated the resistance forces acting during the feed deceleration of an interrupted cutting process, aiming to develop a methodology for characterizing the friction among the cutting tool, cutting fluid, and different workpiece materials. The cutting forces were analyzed to calculate the normal and tangential forces under three conditions: constant feed cutting, deceleration, and zero-cutting state. Resistance forces persisted despite the absence of active cutting. The behavior of zero-cutting forces depended on the workpiece material. A method was proposed to estimate ploughing forces, assuming a direct relationship with the frictional properties of the tool flank face. In addition, a procedure was developed to characterize the friction on the tool rake face by analyzing the relationship between normal and tangential forces obtained experimentally. By identifying inflection points in the –Fn relationship, the frictional components acting on the tool rake face could be decomposed. Furthermore, the influence of the uncut chip thickness on the stability of the cutting resistance was evaluated. The results revealed a direct correlation between normal forces and frictional characteristics under the zero-cutting conditions. Finally, the study assessed the impact of different cutting fluids (dry, water-soluble oil, and vegetable oil) on frictional behavior, exhibiting significant changes in the interaction between the workpiece, cutting fluid, and tool.