Due to the inability to restore the original performance, a significant portion of retired mechanical products is often replaced with new ones and discarded or recycled as low-value materials. This practice leads to energy waste and a decline in their residual value. The generalized growth remanufacturing model (GGRM) presents opportunities to enhance the residual value of retired products and parts. It achieves this by incorporating a broader range of growth modes compared to traditional restorative remanufacturing approaches. The selection of the growth mode is a crucial step to achieve GGRM. However, there is a limited number of growth mode selection methods that are specifically suitable for GGRM. The capacity and efficiency of the method are also significant factors to consider. Therefore, we propose a growth mode selection method based on association rule mining. This method consists of three main steps: Firstly, the ReliefF method is used to select the core failure characteristics of retired parts. Secondly, a genetic algorithm (GA) is employed to identify the association between core failure characteristics, repair technology, and maximum recoverability. Finally, based on the maximum recoverability, the appropriate growth mode is selected for each retired part. We conduct a case study on retired automobile universal transmission, and the results demonstrate the feasibility, efficiency, and accuracy of the proposed method.

Introduction: Despite the rapid expansion of China's Professional Master of Engineering (PME) programs, empirical research specifically investigating industrial placements within this context remains scarce. This study aims to address this significant gap in the literature.Methods: A mixed-methods approach was employed, focusing on Mechanical Engineering PME students. Data were collected through surveys from 133 participants and analyzed using descriptive statistics and thematic analysis.Results: Analyses revealed moderate to high levels of satisfaction. Communication and time management were identified as key skill gains. Students expressed strong demand for expanded internship opportunities, clearer university support, and stronger company commitment regarding mentorship, compensation, and working conditions. Hypothesis testing showed no significant differences in satisfaction across gender, year of study, program type, or career plans (all p > 0.05), though interpretation is cautioned due to limited statistical power from unbalanced sample sizes.Discussion: These findings provide new insights into the understudied context of PME industrial placements in China and highlight practical directions for enhancing university-industry collaboration. The absence of demographic differences contrasts with international evidence and suggests preliminary signs of equitable benefits. Further research with larger, multi-institutional, and longitudinal samples is needed to validate and extend these conclusions.

The widespread scrapping of retired mechanical parts has led to severe waste of resources and environmental burdens, posing a significant challenge to sustainable industrial development. To enable efficient recycling of retired mechanical parts and enhance the sustainability of their remanufacturing processes, the concept of biological genes is adopted to characterize the changes in the information of retired mechanical parts during the remanufacturing process as gene mutations of parts, aiming to maximize remanufacturing potential and devise an optimal generalized remanufacturing strategy for extending part life cycles. However, gene mutation of retired mechanical parts is not an isolated event. The modification of local genes may disrupt the original equilibrium of the part’s state, leading to conflicts such as material–performance, structure–function/performance, and function–performance. These conflicts constitute a major challenge and bottleneck in designing generalized remanufacturing schemes. Therefore, we propose a conflict identification and resolution method for gene mutation of retired mechanical parts. First, gene mutation graph of retired mechanical parts is established to express its all-potential remanufacturing pathways. Using discrimination rules and the element representation method from extenics, mutation conflicts are identified, and a conflict problem model is constructed. Then, the theory of inventive problem solving (TRIZ) engineering parameters are reconstructed and mapped to the mutation conflict parameters. The semantic mapping between the inventive principles and the transforming bridges is established by the Word2Vec algorithm, thereby improving the transforming bridge method to generate conflict resolution solutions. A coexistence degree function of transforming bridges is proposed to verify the feasibility of the resolution solutions. Finally, taking the generalized remanufacturing of a retired gear shaft as an example, we analyze and discuss the process and outcome of resolving gene mutation conflicts, thereby verifying the feasibility and effectiveness of the proposed concepts and methodology.

The local regions of complex gyration class mechanical parts are largely different, which results in the problem that their surface images are partially clear. This problem directly affects the effectiveness of vision methods on detecting surface defects. To tackle this problem, a large depth-of-field (DoF) surface image fusion method is proposed to obtain a full-focus image of complex mechanical parts, such as gyration class mechanical parts. We design an image acquisition platform based on the characteristics of gyration class parts to acquire their surface images accurately and clearly. We then propose a feature detection-based image registration method, by which the registered image can represent the surface information of the part completely and accurately. Additionally, we adopt a convolutional neural network-based image fusion method to achieve a fused large DoF surface image. Experimental studies were conducted to evaluate the performance of the deep-learning-based methods. The experimental results show that the proposed method can completely and clearly fuse the large DoF surface images of complex gyration class mechanical parts. The quality of the fused images has a significant improvement, and the proposed method is significantly more efficient than traditional fusion methods and other deep-learning methods.

In the leveling process of ultra-high-strength steel plates, sample scarcity—driven by high prototyping costs and small-batch production—coupled with a narrow and unevenly distributed feasible region due to high yield-to-tensile ratios and limited ductility, impedes the balanced optimization of plate shape quality and energy consumption. To address this issue, this paper develops an optimization framework for the balanced trade-off between these two objectives. First, a high-precision response surface model based on Box–Behnken experimental design and finite element simulation was constructed using initial wave height, entry roll reduction, exit roll reduction, and leveling speed as key process parameters; peak residual stress difference (characterizing potential sheet quality) and leveling energy consumption as co-optimization objectives; and post-leveling flatness as a constraint. Next, by introducing the NSGA-II multi-objective genetic algorithm, the Pareto optimal solution set for the quality and energy efficiency objectives was obtained, clearly revealing the trade-off relationship between the two; furthermore, the TOPSIS decision-making method was employed to select the comprehensive optimal process scheme that achieves a balance between quality and energy efficiency from the Pareto solution set. An adaptive recommendation curve for the leveling process parameters of MS1500 ultra-high-strength steel plates was established, covering an initial wave height range of 10.5–14.6 mm, thereby enabling intelligent parameter matching based on different incoming material conditions. Finally, industrial validation demonstrated that this optimized scheme significantly reduced leveling energy consumption while ensuring that post-leveling flatness meets the high-quality requirement of less than 3.5 mm·m⁻¹. This achieves a balanced optimization of quality and energy efficiency. This study provides a reliable theoretical basis and practical engineering solution for the efficient and environmentally friendly leveling production of ultra-high-strength steel.