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1. High Performance Turning Assisted by Chip-Pulling

   Sencer, Burak ; Maulimov, Mukhtar ( October 2018 , American Society for Precision Engineering (ASPE))

   Friction is one of the key factors limiting the achievable productivity and efficiency in most machining processes. Typically, adverse effects of friction in machining has been addressed through better tool material design and use of coolants. This paper presents an innovative technique to significantly increase the efficiency of turning processes by alleviating friction forces using an assistive device. As opposed to breaking the cut chip using chip breakers, in the proposed technique, the chip is not broken but pulled using a system to realize a new turning process so-called the “chip-pulling turning”. By pulling the cut chip externally, the friction force acting along tool’s rake face could be reduced and even cancelled. This, in return, increases the shear angle and leads to efficient material removal with significantly lower process forces and energy. An electro-mechanical chip-pulling device is designed that can pull the guided chip continuously during the turning operation. Design of the chip-pulling system, proposed pulling device and its automatic control are presented. The effect of chip-pulling is validated experimentally through various cutting experiments. Furthermore, orthogonal cutting force models are used to model the effect of chip-pulling on the process. 

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2. Generalized mechanics and dynamics of modulated turning

   https://doi.org/10.1016/j.jmatprotec.2022.117708  

   Eren, Bora ; Nam, Soohyun ; Sencer, Burak ( October 2022 , Journal of materials processing technology)

   Budak, Erhan (Ed.)

   This paper presents a generalized cutting force and regenerative chatter stability prediction for the modulated turning (MT) process. Uncut chip thickness is modeled by considering current tool kinematics and undulated (previously generated) surface topography for any given modulation condition in the feed direction. It is found that chip formation is governed by the undulated surface generated in multiple past spindle rotations. Uncut chip thickness is computed analytically in the form of trigonometric functions, and cutting forces are predicted by making use of orthogonal cutting mechanics. Regenerative chatter stability of the process is also modelled. Analytical semi-discretization-based solution is developed to accurately predict the stability lobe diagrams (SLDs) of the MT process. Predicted stability lobes are validated through numerical time-domain simulations and experimentally via orthogonal (plunge) turning tests. It is found that as compared to conventional single-point continuous turning, regenerative stability of MT exhibits multiple (3) regenerative delay loops and long out-of-cut duration in-between tool engagement stabilizes the process to reach up to 2x higher stable widths/depths as compared to the conventional continuous turning. 

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3. Wear Performance Evaluation of Minimum Quantity Lubrication with Exfoliated Graphite Nanoplatelets in Turning Titanium Alloy

   Nguyen, D. ( May 2019 , Journal of manufacturing science and engineering)

   This paper evaluates the performances of dry, minimum quantity lubrication (MQL), and MQL with nanofluid conditions in turning of the most common titanium (Ti) alloy, Ti-6Al-4 V, in a solution treated and aged (STA) microstructure. In particular, the nanofluid evaluated here is vegetable (rapeseed) oil mixed with small concentrations of exfoliated graphite nanoplatelets (xGnPs). This paper focuses on turning process that imposes a challenging condition to apply the oil or nanofluid droplets directly onto the tribological surfaces of a cutting tool due to the uninterrupted engagement between tool and work material during cutting. A series of turning experiments was conducted with uncoated carbide inserts, while measuring the cutting forces with a dynamometer under the dry, MQL and MQL with nanofluid conditions supplying oil droplets externally from our MQL device. The inserts are retrieved intermittently to measure the progress of flank and crater wear using a confocal microscopy. This preliminary experimental result shows that MQL and in particular MQL with the nanofluid significantly improve the machinability of Ti alloys even in turning process. However, to attain the best performance, the MQL conditions such as nozzle orientation and the concentration of xGnP must be optimized. 

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4. Rapid 1024-pixel Electrochemical Imaging at 10,000 Frames per Second using Monolithic CMOS Sensor and Multifunctional Data Acquisition System

   https://doi.org/10.1109/JSEN.2018.2835829  

   White, Kevin A. ; Mulberry, Geoffrey ; Kim, Brian N. ( May 2018 , IEEE Sensors Journal)

   Fast electrochemical imaging enables the dynamic study of electroactive molecule diffusion in neurotransmitter release from single cells and dopamine mapping in brain slices. In this paper, we discuss the design of an electrochemical imaging sensor using a monolithic CMOS sensor array and a multifunctional data acquisition system. Using post-CMOS fabrication, the CMOS sensor integrates 1024 on-chip electrodes on the surface and contains 1024 low-noise amplifiers to simultaneous process parallel electrochemical recordings. Each electrochemical electrode and amplifier are optimized to operate at 10.38 kHz sampling rate. To support the operation of the high-throughput CMOS device, a multifunctional data acquisition device is developed to provide the required speed and accuracy. The high analog data rate of 10.63 MHz from all 1024 amplifiers is redundantly sampled by the custom-designed data acquisition system which can process up to 73.6 MHz with up to ~400 Mbytes/s data rate to a computer using USB 3.0 interface. To contain the liquid above the electrochemical sensors and prevent electronic and wire damage, we packaged the monolithic sensor using a 3D-printed well. Using the presented device, 32 pixel × 32 pixel electrochemical imaging of dopamine diffusion is successfully demonstrated at over 10,000 frames per second, the fastest reported to date. 

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5. Wake-up radio-based data forwarding for green wireless networks

   https://doi.org/10.1016/j.comcom.2020.05.046  

   Koutsandria, G. ; DiValerio, V. ; Spenza, D. ; Basagni, S. ; Petrioli, C. ( April 2020 , Computer communications)

   Green wireless networks Wake-up radio Energy harvesting Routing Markov decision process Reinforcement learning 1. Introduction With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite. Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption ∗ Corresponding author. E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria). https://doi.org/10.1016/j.comcom.2020.05.046 (sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12]. This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a ABSTRACT This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively. 

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