The micro-defects on KH₂PO₄ (KDP) optic surfaces are mainly repaired by the micro-milling technique, while it is very easy to introduce brittle cracks on repaired surfaces, as KDP is soft and brittle. To estimate machined surface morphologies, the conventional method is surface roughness, but it fails to distinguish ductile-regime machining from brittle-regime machining directly. To achieve this objective, it is of great significance to explore new evaluation methods to further characterize machined surface morphologies. In this study, the fractal dimension (F_D) was introduced to characterize the surface morphologies of soft-brittle KDP crystals machined by micro bell-end milling. The 3D and 2D fractal dimensions of the machined surfaces and their typical cross-sectional contours have been calculated, respectively, based on Box-counting methods, and were further discussed comprehensively by combining the analysis of surface quality and textures. The 3D F_D is identified to have a negative correlation with surface roughness (S_a and S_q), meaning the worse the surface quality the smaller the F_D. The circumferential 2D F_D could quantitively characterize the anisotropy of micro-milled surfaces, which could not be analyzed by surface roughness. Normally, there is obvious symmetry of 2D F_D and anisotropy on the micro ball-end milled surfaces generated by ductile-regime machining. However, once the 2D F_D is distributed asymmetrically and the anisotropy becomes weaker, the assessed surface contours would be occupied by brittle cracks and fractures, and corresponding machining processes will be in a brittle regime. This fractal analysis would facilitate the accurate and efficient evaluation of the repaired KDP optics by micro-milling.

Dynamic demand response (DDR) is the process of shifting power consumption towards periods of lower demand based on real-time energy pricing data. It is a flexibility measure utilised in the decarbonisation of the UK’s power system to reduce peak demand. Dynamic time-of-use (dTOU) tariffs, such as Agile Octopus, incentivise DDR by providing half-hourly electricity prices for each day. Through this incentive, households are offered the opportunity to reduce their energy costs by applying DDR to energy-intensive, deferrable loads. This paper presents an open-source, Internet of Things (IoT)-based system designed to automate DDR and streamline its implementation. The system identifies the period of lowest electricity prices and activates a relay during this period each day. For validation, the system was tested over a one-month experiment, which showed that, in a favourable scenario, it could reduce an appliance’s electricity costs by up to 44%. These results highlighted the system’s potential to deliver substantial energy cost savings, while also encouraging households to participate in flexibility measures that alleviate pressure on the National Grid.

Metrology is the key to ensuring the quality of manufactured parts. With the fast development in the manufacturing sector, particularly with the sustainable manufacturing requirement, there is a significant demand for manufacturing metrology to ensure not only the accuracy of the final parts but also the quality of the whole manufacturing process. As such, defects in the manufacturing process can be identified as early as possible and can potentially lead to saving of materials, energy, and cost and contribute to sustainability. In this paper, we present SurfMon, an in-process optical surface monitoring system that is portable and powered by laser speckle pattern measurements and a machine learning algorithm, making it highly flexible and able to learn from normal surfaces and detect anomalies in manufacturing processes. SurfMon essentially consists of a laser, a camera, a microcontroller with wireless connection functionality, and some optics. It is battery-powered and rechargeable, making it hassle-free to integrate into manufacturing facilities. Reflective laser speckle patterns from manufactured surfaces are captured, and a principal component analysis model is trained and used to detect defects. The system can be mounted on a machine tool and in-process surface measurements can be performed following the cutting tool. Anomalies are detected whenever they affect the surface topographies. The effectiveness of SurfMon was demonstrated on a CNC machine, and the results showed that SurfMon was promising for in-process surface monitoring in manufacturing processes.

Metrology is the key to ensuring the quality of manufactured parts. With the fast development in the manufacturing sector, particularly with the sustainable manufacturing requirement, there is a significant demand for manufacturing metrology to ensure not only the accuracy of the final parts but also the quality of the whole manufacturing process. As such, defects in the manufacturing process can be identified as early as possible and can potentially lead to saving of materials, energy, and cost and contribute to sustainability. In this paper, we present SurfMon, an in-process optical surface monitoring system that is portable and powered by laser speckle pattern measurements and a machine learning algorithm, making it highly flexible and able to learn from normal surfaces and detect anomalies in manufacturing processes. SurfMon essentially consists of a laser, a camera, a microcontroller with wireless connection functionality, and some optics. It is battery-powered and rechargeable, making it hassle-free to integrate into manufacturing facilities. Reflective laser speckle patterns from manufactured surfaces are captured, and a principal component analysis model is trained and used to detect defects. The system can be mounted on a machine tool and in-process surface measurements can be performed following the cutting tool. Anomalies are detected whenever they affect the surface topographies. The effectiveness of SurfMon was demonstrated on a CNC machine, and the results showed that SurfMon was promising for in-process surface monitoring in manufacturing processes.

Dynamic demand response (DDR) is the process of shifting power consumption towards periods of lower demand based on real-time energy pricing data. It is a flexibility measure utilised in the decarbonisation of the UK’s power system to reduce peak demand. Dynamic time-of-use (dTOU) tariffs, such as Agile Octopus, incentivise DDR by providing half-hourly electricity prices for each day. Through this incentive, households are offered the opportunity to reduce their energy costs by applying DDR to energy-intensive, deferrable loads. This paper presents an open-source, Internet of Things (IoT)-based system designed to automate DDR and streamline its implementation. The system identifies the period of lowest electricity prices and activates a relay during this period each day. For validation, the system was tested over a one-month experiment, which showed that, in a favourable scenario, it could reduce an appliance’s electricity costs by up to 44%. These results highlight the system’s potential to deliver substantial energy cost savings, while also encouraging households to participate in flexibility measures that alleviate pressure on the National Grid.

Metrology is the key to ensuring the quality of manufactured parts. With the fast development in the manufacturing sector, particularly with the sustainable manufacturing requirement, there is a significant demand for manufacturing metrology to ensure not only the accuracy of the final parts but also the quality of the whole manufacturing process. As such, defects in the manufacturing process can be identified as early as possible and can potentially lead to saving of materials, energy, and cost and contribute to sustainability. In this paper, we present SurfMon, an in-process optical surface monitoring system that is portable and powered by laser speckle pattern measurements and a machine learning algorithm, making it highly flexible and able to learn from normal surfaces and detect anomalies in manufacturing processes. SurfMon essentially consists of a laser, a camera, a microcontroller with wireless connection functionality, and some optics. It is battery-powered and rechargeable, making it hassle-free to integrate into manufacturing facilities. Reflective laser speckle patterns from manufactured surfaces are captured, and a principal component analysis model is trained and used to detect defects. The system can be mounted on a machine tool and in-process surface measurements can be performed following the cutting tool. Anomalies are detected whenever they affect the surface topographies. The effectiveness of SurfMon was demonstrated on a CNC machine, and the results showed that SurfMon was promising for in-process surface monitoring in manufacturing processes.