Automatic defect detection on strip steel surfaces is a challenging task in computer vision, owing to miscellaneous patterns of defects, disturbance of pseudo defects, and random arrangement of gray-level in background. In this paper, a novel template establishment is presented. Further, a simple guidance template-based algorithm for strip steel surface defect detection is proposed. Firstly, a large number of defect-free images are collected to obtain the statistical characteristic of normal textures. Secondly, for each given test image, the initial template is built according to the statistical characteristic and the size of test image. Then, a sorting operation is applied to the given test image. Further, by updating the initial template, a unique guidance template is generated based on specific intensity distribution of the sorted test image. So far, the background of each test image is approximately reconstructed in the guidance template. Finally, based on pixel-wise detection, the defects can be located accurately by subtraction operation between the guidance template and sorted test image, reverse sorting operation, and adaptive threshold determination. Experimental results show that the proposed method is both efficient and effective. It achieves a better average detection rate of 96.2% on a data set including 1500 test images.

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy.

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects.

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions.

Bevel gear transmission is characterized by strong load capacity, stable transmission, and compact structure. It is a key component in power transmission systems for vehicles, ships, machine tools, oil drilling equipment, aerospace, and other industries. Amid the rapid development of electrification, gears remain indispensable in mechanical systems. Bevel gears are typically machined using gear shaping or dual-cutter disk milling methods, which are efficient but require specialized machines, resulting in high procurement costs. These methods are also unsuitable for machining large gears or small-batch production. Inspired by the linear mechanism of the internal pendulum, this paper proposes a CNC machining method for straight bevel gears based on hypocycloid mechanisms. By leveraging the straight-line properties of hypocycloid motion, straight bevel gears are machined via milling or grinding on a five-axis machining center using the end-face hobbing method. This method transforms the traditional non-continuous machining into continuous indexing machining and enables CNC machining on a general five-axis machining center. This approach reduces the cost of specialized equipment and improves the efficiency of straight bevel gear manufacturing.