Five-axis flank milling is prevalent in complex surfaces manufacturing, and it typically consumes high electricity energy. To save energy and improve energy efficiency, this paper proposes a tool path optimisation of five-axis flank milling by meta-reinforcement learning. Firstly, considering flank milling features, a feed angle is defined that guides tool spatial motion and identifies an ideal principal path. Then, machining energy consumption and time are modelled by tool path variables, i.e., feed angle, cutting strip width and path length. Secondly, an energy-efficient tool path dynamic optimisation model is constructed, which is then described by multiple Markov Decision Processes (MDPs). Thirdly, meta-learning integrating with the Soft Actor-Critic (MSAC) framework is utilised to address the MDPs. In an MDP with one principal path randomly generated by a feed angle, cutting strip width is dynamically optimised under a maximum scallop height limit to realise energy-efficient multi-expansions. By quick traversal of MDPs with various feed angles, MSAC enables an energy-efficient path generation and expansion integrated scheme. Experiments show that, regarding machining energy consumption and time, the proposed method achieves a reduction of 69.96% and 68.44% over the end milling with an iso-scallop height, and of 41.50% and 39.80% over the flank milling with an iso-scallop height, with a minimum amount of machining carbon emission, which highlights its contribution to the arena of energy-oriented and sustainable intelligent manufacturing.

The data reported in this article presents actual installation costs and performance data for a selection of residential Ground Source Heat Pump (GSHP) systems in Melbourne, Australia. The installation cost data includes five main cost components: ground loop installation, head pipe installation, heat pump, mechanical room installation, and fittings. The performance data presented here includes timestamp, air temperature and thermal loading. A more comprehensive analysis of this data may be obtained from the article entitled "Economic analysis of vertical ground source heat pump systems in Melbourne" (Q. Lu, G.A. Narsilio, G.R. Aditya, I.W. Johnston, 2017) [1].

Because of the increasing concern regarding environmental sustainability, manufacturing equipment faces a challenge to optimize carbon emissions from a life-cycle perspective. Current approaches lack the overall low-carbon optimization flow guidance needed in order to take effective measures for manufacturing equipment. Consequently, this paper proposes a framework of low-carbon optimization for manufacturing equipment. Firstly, a four-layer framework for low-carbon optimization processes is established, consisting of the optimization operation layer, life-cycle layer, optimization method layer, and tools and data layer. Then, the characteristics, functions, and technologies involved in the four layers are elaborated on in detail. Finally, the honing machine considering the reduction of carbon emissions is given as an example. The results indicate that by using the proposed methodology effectively, the carbon emissions of the lower column, a key part of the honing machine, can be reduced by 4.38% without sacrificing structure performance. The framework can provide specific guidance on achieving a low-carbon optimization process and contribute to the sustainable development of energy-intensive manufacturing industries.

To achieve a fundamental reduction in the carbon emissions associated with grinding machines, it is imperative to systematically explore low-carbon considerations in the design phase. The spindle is a significant contributor to carbon emissions in grinding machines, and an effective approach for reducing carbon emissions is the structural optimization of the spindle. Most of the current optimization methods aim at improving processability without considering the reduction of carbon emissions. In this context, the present study addresses the issue of carbon emissions within the spindle design phase. Initially, the determination of the spindle’s carbon emissions function and the selection of the optimization objective were undertaken. The structural factors that have a significant influence on the optimization objective were identified as optimization variables. Subsequently, the optimization objective function was established through the application of the fitting method. Finally, the proposed model was refined through the utilization of an enhanced whale algorithm. The findings indicate an 8.22% reduction in carbon emissions associated with the spindle, accompanied by marginal enhancements in both static and dynamic spindle performance. The concluding section of this paper deliberates on the impact of structural parameters on the specified objectives, thereby providing insights for the optimal design of the spindle.

After a disaster, the recovery sequence of damaged bridges in a road–bridge transportation system greatly influences system restoration time and total economic loss. In this paper, the skew of recovery trajectory is introduced to evaluate the average restoration time, and the total economic loss is extended to consider the indirect loss, such as the energy consumption of detours or the emergency service fee. So, the post-disaster resilience optimization model is constructed by minimizing the total economic loss. The improved genetic algorithm is developed to obtain the optimal recovery scheme for damaged bridges by considering the recovery sequence and repair modes. The composition and influence factors of total economic loss are analyzed through three experiments. The experimental results show that the indirect loss accounts for approximately half of the economic loss, while the higher price of emergency service promotes the reduction of indirect loss using the expedited modes to repair damaged bridges. Moreover, to minimize the total economic loss, it is essential to design the optimal recovery scheme (repair sequence and repair mode) wisely to balance the conflicts between indirect loss and direct loss.