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At present, increased attention has been given to energy efficiency promotion and energy saving of manufacturing equipment and systems. Hydraulic system is widely used in engineering machinery industries; however, the high energy consumption and low energy efficiency of which limit its development and application. On the basis of previous research on hydraulic system, an energy-efficient hydraulic system with double actuator is proposed in this study. This hydraulic system can reduce the energy consumption by reusing the potential energy loss of the system. In this novel hydraulic system, the chambers of two actuators are connected with rod by pipes and valves. During the operating processes, the falling operation of one cylinder and the returning operation of the other are synchronized. That is, one actuator remains at the top point performing the demanded operation, whereas the other one remains at the bottom performing the corresponding operation. System models are developed to address the energy consumption and dynamic performance of the proposed hydraulic system. This hydraulic system is applied into a press to study its energy-saving mechanism and dynamic performance during transient under various working conditions. On the basis of the simulation and experimental results, energy and production efficiency are compared and discussed. Results show that in comparison with the energy and time consumption of the original 630 kN hydraulic press, the energy-saving potential has reached 17.34%, and the production efficiency can be improved by 18.85%.

Selective laser melting (SLM) exhibits excellent manufacturing accuracy and forming ability. However, the laser beam layering process is characterized by high specific energy consumption, long manufacturing cycle, and low energy efficiency. The use of supports increases the SLM building quality and eliminates defects caused by thermal and residual stresses; however, an improper support structural design increases the process energy consumption for manufactured parts. To control energy consumption and building quality during SLM, this study first discusses the main challenges related to energy saving and improving the building quality by performing an energy consumption analysis, process energy consumption optimization, and supporting structure optimization. The obtained results reveal that it is difficult to achieve high building quality only by controlling the process parameters and energy consumption by the SLM equipment. Next, the effect of supporting structures on the process energy consumption is examined to enable the construction of an SLM energy consumption model that considers the presence of supports. Finally, the effect of supports on the building quality is elucidated by studying the influence of supporting structures on thermal and residual stresses. By identifying the most energy-efficient support, the process energy efficiency and building quality may be simultaneously optimized. The proposed method represents a new approach to reducing energy consumption and improving the building quality during SLM. This study establishes a theoretical foundation for the subsequent industrial applications, providing a thorough literature review and describing the existing challenges in the SLM manufacturing field.

Selective laser melting (SLM) can form complex and precise metal parts simultaneously and is widely used in medical and aerospace fields. The support structure plays an important role in SLM process, including supporting the overhanging structure, dissipating heat, and minimizing geometric deformation caused by internal stress. However, a non-optimal support structure causes increased energy and material consumption during processing and must be removed afterward to allow for utilization of the parts. Existing support structure design methods only consider reducing the support of a single part, and research on the support and energy consumption of simultaneous multi-part printing is lacking. Therefore, to reduce the energy and material consumption of simultaneous multi-part printing by SLM and improve processing efficiency, an energy consumption analysis and optimization method is proposed in this study from the perspective of the support structure. Based on previous studies on energy consumption distribution of the additive manufacturing process, a multi-component SLM energy consumption and material consumption model was established. Furthermore, a shared-support optimization strategy for simultaneous multi-part processing is proposed. For optimization, the method selects the appropriate printing direction of one part, and then combines multiple parts to form a shared support structure to minimize energy consumption. Finally, under the constraint of minimizing the mass, an optimization strategy of the SLM multi-part shared support combination is established, and the purpose of reducing the energy consumption and material consumption of the SLM is achieved under the premise of ensuring the geometric accuracy of the parts. The method was applied to the manufacturing process of a group of parts with a beam structure. Compared with the printing method using independent support, the shared support structure method reduced energy consumption more than 5.5%, material consumption for the support structure more than 17.2%, and printing time to a certain extent. This method effectively improves SLM production efficiency and sustainability and provides strategic support for additive manufacturing designers and producers.

Regional innovation capability is considered an important driving force for the sustainable and high-quality development of regional economy. Therefore, data-driven evaluation method of regional innovation capability is proposed. First, the article collects regional innovation development statistics; second, min-max standardization method is used for dimensionless data processing, and anti-entropy method is used to calculate index weight; third, a composite system synergy degree model for objective and quantitative evaluation is built; finally, using Anhui Province as an example, the feasibility of the method was verified . The results indicate that the order and synergy degree of the innovation input and innovation output subsystems of Anhui Province have shown an upward trend from 2010 to 2019. However, innovation output has not been synchronized with innovation input. Therefore, suggestions for improving Anhui's regional innovation capability are proposed. This study provides theoretical and methodological support for evaluating and optimizing regional innovation capability.

Ensuring high product quality is of paramount importance in pharmaceutical drug manufacturing, as it is subject to rigorous regulatory practices. This study presents a research focused on the development of an on-line detection method and system for identifying surface defects in pharmaceutical products packaged in aluminum-plastic blisters. Firstly, the aluminum-plastic blister packages exhibit multi-scale features and inter-class indistinction. To address this, the deep semantic network with boundary refinement (DSN-BR) model is proposed, which leverages semantic segmentation domain knowledge, to accurately segment the defects in pixel level. Additionally, a specialized image acquisition module that minimizes the impact of ambient light is established, ensuring high-quality image capture. Finally, the image acquisition module, image detection module, and data management module are designed to construct a comprehensive online surface defect detection system. To validate the effectiveness of our approach, we employ a real dataset for instance verification on the implemented system. The experimental results substantiate the outstanding performance of the DSN-BR, achieving the mean intersection over union (MIoU) of 90.5%. Furthermore, the proposed system achieves an inference speed of up to 14.12 f/s, while attaining an F1-Score of 98.25%. These results demonstrate that the system meets the actual needs of the enterprise and provides theoretical and methodological support for intelligent inspection of product surface quality. By standardizing the control process of pharmaceutical manufacturing and improving the management capability of the manufacturing process, our approach holds significant market application prospects.

The energy benchmark has been recognised as an effective analytical methodology and management tool that help to improve the efficiency and performance of energy utilisation. With a wide distribution and large amount of energy consumption at a low efficiency, machining systems have considerable energy-saving potential. This paper proposes a task-oriented energy benchmark in machining systems, and illustrates the concept of the task-oriented energy benchmark and indicators. A method for developing the task-oriented energy benchmark considering the certainty production task and the uncertainty production task is proposed, which lays a solid foundation for studying the energy benchmark, benchmark rating system and energy certification. Furthermore, a case study of the task-oriented energy benchmark not only verifies the reliability but the effectiveness for energy-efficient production.

The uncertainty of remanufactured parts is a key factor in the stability of remanufacturing systems. Therefore, the purpose of this paper is to identify these uncertainties and measure them to improve the optimisation management level of remanufacturing production process. Contrasting the ideal dimensional accuracy, manufactured dimensional accuracy and remanufactured dimensional accuracy, we analyse the connotation of uncertainty for reassembly dimensions. We construct the uncertainty measurement model for reassembly dimensions to realise quantitative measurement by entropy. So the coupling mechanism of uncertainty for reassembly dimensions is studied and the corollary is in conformity with the reality. It can use data mining technology to optimise remanufacturing process management. Finally, the feasibility and effectiveness of the model are verified in grading selection of remanufacturing enterprise parts. This research provides support for the uncertain optimisation decision for lean remanufacturing from both theoretical and practical aspects by uncertain data mining techniques.