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The use of lightweight material such as CFRP/Ti6Al4V in stacked structures in the aerospace industry is associated with improved physical and mechanical characteristics. The drilling process of nonuniform structures plays a significant role prior to the assembly operation. However, this drilling process is typically associated with unacceptable CFRP delamination, hole accuracy, and high tool wear. These machining difficulties are attributed to high thermal load and poor chip evacuation mechanism. Low-frequency vibration-assisted drilling (LF-VAD) is an advanced manufacturing technique where the dynamic change of the uncut chip thickness is used to manipulate the cutting energy. An efficient chip evacuation mechanism was achieved through axial tool oscillation. This study investigates the effect of vibration-assisted drilling machining parameters on tool wear mechanisms. The paper also presents the effect of tool wear progression on drilled hole quality. Hole quality is described by CFRP entry and exit delamination and hole accuracy. The results showed a significant reduction in the thrust force, cutting torque, cutting temperature, and flank wear-land.This wear mechanism was attributed to hard and soft abrasion modes [14]. The hard mode is located at WC grains due to the generated dynamic stresses from the broken fiber, material reinforcement, and powder like chips [15]. Consequently, the WC grains suffer crack initiation and propagation fracture [16]. On the other hand, the relatively low hardness Co binder is more easily damaged by carbon fibers (CF) in a process known as soft abrasion mode [14,17,18]. Hence, WC grain spalling is more rapid under the cyclic machining load.High cutting temperature, poor chip evacuation mechanism, and burr formation are of the common issue during the drilling process of Ti6Al4V [19][20][21]. These problems resulted in a higher probability of flute-chip accumulation, progressive tool wear, and poor surface integrity [19,20,22]. Low thermal conductivity and high chemical affinity to the majority of tool materials are due to the adverse titanium characteristics, which resulted in a significant reduction in the tool life with a high probability of catastrophic failure [20,23,24]. This effect contributed to the cutting stress concentration on the tool cutting edge where the maximum cutting temperature is located. Therefore, the machining process of Ti6Al4V is typically associated with the formation of built-up-edge (BUE). The formation of BUE has a significant drawback on the machined surface integrity and the tool life [25,26]. Thus, adding more limitations in the aerospace production procedure [27].Despite, the extensive to optimize separate machining process of both materials, the machining process suffers when it comes to production time, cost, and assembly positional error. Single process drilling of the CFRP and Ti6Al4V materials in situ, also known as stacked drilling shows considerable benefits to overcome separate drilling issues [1][2][3]28,29]. However, the stack drilling o...

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The TULIPP project aims to simplify development of embedded vision applications with low-power and real-time requirements by providing a complete image processing system package called the TULIPP Starter Kit. To achieve this, the chosen high-performance embedded vision platform needs to be extended with performance analysis and power measurement features. The lack of such features plagues most embedded vision platforms in general and practitioners have adopted adhoc methods to circumvent the problem. In this paper, we describe four generic utilities that complement and refine the capabilities of existing platforms for embedded vision applications. Concretely, we describe a novel power measurement and analysis utility, a platform-optimized image processing library, a dynamic partial reconfiguration utility, and an utility providing support for using the real-time OS HIPPEROS within Xilinx SDSoC. Collectively, these utilities enable efficient development of image processing applications on the TULIPP hardware platform. In future work, we will evaluate the relative benefit of these utilities on key embedded image processing metrics such as frame rate and power consumption.

In high-speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial demands. Several studies for TCM were carried out to capture tool failure using complex conventional and artificial intelligence (AI) techniques. However, these studies suffer from the absence of standardization and generalization. Hence, this paper presents a robust and reliable processing technique for the cutting process signals to extract generalized features in time and frequency domains. The proposed technique masks the effects of the cutting conditions on the extracted features and accentuates the tool condition effect. Characterization and statistical analysis of the processed features were performed to examine their sensitivity to the tool condition. The results revealed the processing technique capability to separate the features extracted from the spindle motor current signals into two mutually exclusive clusters according to their tool condition. The statistical analysis results were employed to optimize the tool condition detection approach using linear discrimination analysis (LDA) model. The results indicate the capability of the processing technique to minimize the system learning effort and to detect tool wear above the threshold level with accuracy above 90%.