Tool condition monitoring (TCM) during mechanical cutting is critical for maximising the utilisation of cutting tools and minimising the risk of equipment damage and personnel injury. The demand for highly efficient and sustainable machining in modern industries has led to the development of new processes operating under specific conditions. Real-world datasets obtained under harsh cutting conditions often suffer from intense interference, making the anti-interference capabilities of TCM methods crucial for effective industrial applications. Previous literature reviews on TCM have focused on theoretical methods for monitoring tool wear and breakage. However, reviews of the scientific methodologies and technologies employed in TCM for industrial production are limited. The lack of scientific understanding relevant to the monitoring of cutting tools in industrial production should be addressed urgently. The current data processing, feature dimensionality reduction, and decision-making methods utilised in TCM may not adequately fulfil the real-time and anti-interference demands. The TCM methods also face the challenges of small sample sizes and imbalanced data during real-world dataset processing. Therefore, this study conducts a systematic review of TCM methods to overcome these limitations. First, the theoretical guidelines for the application of TCM methods in industrial production are provided. The sensing system, signal processing, feature dimensionality reduction, and decision-making methods for TCM methods are comprehensively discussed in terms of both their advantages and limitations for applications in industrial production. Considering the effects of real-world datasets with small samples and imbalanced data caused by the harsh environment of a real factory, a systematic presentation is proposed at the data, feature, and decision levels. Finally, the challenges and potential research directions of TCM methods for industrial applications are discussed. A research route for smart factory-oriented machining system management is proposed based on published literature. This review bridges the gap between theoretical research and the industrial application of TCM techniques in industrial production. Prospective research and further development of TCM systems will provide the groundwork for establishing smart factories.
In high speed milling, interrupted cutting conditions can lead to period doubling chatter vibrations. While many studies have already confirmed that the use of helical tools can effectively shrink or remove these regions of unstable cutting, none of them has provided clear guidance to select the minimum helix that completely cancels the period doubling lobes. This study addresses this gap by introducing a novel analytical formula for a critical tool helix pitch: if the helix pitch is below the critical flip depth of cut of the straight helix cutter multiplied by , the flip lobes will totally vanish. This rule is not only valuable for chatter-free process planning purposes, but it also establishes exact limit below which the fast and simple zeroth order stability algorithm can provide exact stability boundaries for helical tools. The effectiveness of the formula is numerically corroborated over three different milling scenarios: thin wall milling, slender tool and machine tool structure chatter cases. Finally, the findings are validated through experimental cutting tests.
Laser powder bed fusion (LPBF) technology enables the development of NiTi alloys with complex geometries and tunable phase-transformation temperatures (PTTs). This technology is increasingly acknowledged as promising in the field of elastocaloric (eC) refrigeration. However, the mechanisms governing the manner in which this technology tunes the eC performance remain ambiguous. This study evaluated the fine-tuning of the eC properties by regulating Ni evaporation through laser manipulation. Our results demonstrate that although adjusting Ni loss via laser heat input can effectively control the PTTs, inappropriate combinations of laser parameters may result in lower than anticipated cooling capacity (ΔTad) and coefficient of performance (COPmat) of produced samples. An excessive heat input results in Ni evaporation and in grain coarsening through the remelting and combination of fine grains owing to overlapping molten pools. Lower Ni enhances the phase-transformation enthalpy (ΔHtr). However, larger grains increase the energy dissipation and thereby, counteracting ΔTad improvements. Theoretical analysis and experiments revealed that finer grains increase the misorientation angles. This hinders the dislocation motion and thereby, enhances the mechanical properties. Meanwhile, coarser grains can more conveniently promote PT and thereby, increase ΔHtr. Thus, based on the naturally controllable grain size heterogeneity in LPBF-manufactured NiTi alloys, we propose optimizing the eC properties by controlling the morphology of the molten pool. Thermal-history simulations could balance this relationship. Ultimately, we developed two NiTi alloys for both high-temperature (70 °C) and room-temperature (25 °C) refrigeration. This study has provided effective insights for customizing high-performance eC components such as multistage caloric cascade regenerators, using additive manufacturing.
Despite the remarkable achievements in single-energy field-assisted diamond cutting technology, its performance remains unsatisfactory for processing high-entropy alloys (HEAs), targeted for next-generation large-scale industrial applications due to their exceptional properties. The challenge lies in overcoming the limitations of current single-energy field-assisted processing to achieve ultra-precision manufacturing of these advanced materials. This study proposes a multi-energy field-assisted ultra-precision machining technology, the magnetic and ultrasonic vibration dual-field assisted diamond cutting (MUVFDC), to address the current challenges. The phenomenological aspects of the dual-field coupling effect on HEAs are explored and investigated through comprehensive characterization of the workpiece material, ranging from macroscopic surface morphology to microscopic structural features. These analyses are performed based on experimental results from four different processing technologies: non-energy field, magnetic field, ultrasonic vibration field, and dual-field assisted machining. Research results demonstrate that MUVFDC technology effectively combines the advantages of a vibration field, which enhances cutting stability, and a magnetic field, which improves the machinability of materials. Additionally, this coupling technology addresses the challenges associated with single-energy field machining: it mitigates the difficulty of controlling surface scratches caused by tiny hard particles in a vibration field and suppresses the rapid tool wear encountered in a magnetic field. Furthermore, the gradient evolution of the subsurface microstructure reveals that the vibration field suppresses the severe matrix deformation induced by magnetic excitation. Simultaneously, the magnetic field reduces the size inhomogeneity of recrystallized grains caused by intermittent cutting. Overall, MUVFDC technology enhances surface quality, suppresses tool wear, smooths chip morphology, and reduces subsurface damage compared to single-energy field or non-energy-assisted machining. This work breaks through the performance limitations of traditional single-energy field-assisted processing and advances the understanding of the dual-field coupling effects in HEAs machining. It also presents a promising processing technology for the future ultra-precision manufacturing of advanced materials.