Interest in natural fiber–reinforced composites (NFRCs) is increasing rapidly thanks to their numerous advantages such as low cost, biodegradability, eco-friendly nature, relatively good mechanical properties, and a growing emphasis on the environmental and sustainability aspects of engineering materials. However, large-scale use of NFRCs is still considered as challenging due to the difficulties in manufacturing, limited knowledge of its machinability and appropriate parameter settings, and being prone to machining-induced defects. This article presents a comprehensive review on various aspects of NFRCs, with a focus on the manufacturing and machinability. It covers some recent works related to NFRCs, including the manufacturing processes and parameters, characterization of mechanical properties, applications, and machinability and machining process monitoring, many for the first time. The main challenges associated with machining of NFRCs and the induced damages are outlined, with special attention paid to the effect of physical properties of the fibers and manufacturing process on the machinability, along with the essential machining parameters that affect the quality of the machined surface. The research perspectives and the current application status are also discussed. The article is intended to help readers attain a fundamental understanding of key technologies and the state of the arts in this research area.

Precision positioning machines are required to run with higher speed and higher accuracy. The high running speed can cause strong excitations on the machine structure, which results in severe structure vibration and long settling time. This paper presents a low-vibration motion profile generation method to reduce the residual vibration. The acceleration profile is designed by using a level-shifted sinusoidal waveform to have an s-shape in order to control its change rate. Simulation and experimental studies showed that in comparison with conventional trapezoidal profile and s-curve profile, the residual vibration by using the proposed motion profile can be reduced significantly.

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

The selection of the optimal demodulation frequency band is a significant step in bearing fault diagnosis because it determines whether the fault information can be extracted from the demodulated signal via envelope analysis. Two well-known methods for selecting the demodulation band are the Fast Kurtogram, based on the kurtosis of the filtered time signal, and the Protrugram, which uses the kurtosis of the envelope (amplitude) spectrum. Although these two methods have been successfully applied in many cases, the authors have observed that they may fail in specific environments, such as in the presence of electromagnetic interference (EMI) or other impulsive masking signals. In this paper, a simple spectral kurtosis-based approach is proposed for selecting the best demodulation band to extract bearing fault-related impulsive content from vibration signals contaminated with strong EMI. The method is applied to vibration signals obtained from a planetary gearbox test rig with planet bearings seeded with inner and outer race faults. Results from the Fast Kurtogram and Protrugram methods are also included for comparison. The proposed approach is found to exhibit superior diagnostic performance in the presence of intense EMI. Another contribution of the paper is to introduce and explain the issue of EMI to the condition monitoring community. The paper outlines the characteristics of EMI arising from widely-used variable frequency drives, and these characteristics are used to simulate an EMI-contaminated vibration signal to further test the performance of the proposed approach. Although EMI has been acknowledged as a serious problem in many industrial cases, there have been very few studies showing its adverse effects on machine diagnostics. It is important for analysts to be able to identify EMI in measured vibration signals, lest it interfere with the analysis undertaken