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The cordial existence of robots and humans in the same workstation is an important aspect of the deployment of autonomous systems. Usually, robots are integrated with auxiliary sensors for monitoring the surroundings and tackling unexpected actions. The incorporation of LiDAR (light detection and ranging) and SLAM (simultaneous localization and mapping) has gained the researcher's attention these days. The current study aims at developing a collision-free motion for an autonomous robot to ensure the least possible travel period at internodes. First, a SLAM-based algorithm for a static environment is proposed. A low-cost microcontroller Arduino UNO is used here along with LiDAR VL53L0X and is integrated with MATLAB to store and display the acquired data. The A* algorithm is used to navigate between the shortest paths on the obtained map. The robot is also integrated with radio frequency identification (RFID) sensors to automate the process. The proposed system would be useful for material handling, assistance, and cleaning purpose in large warehouses or households. [ABSTRACT FROM AUTHOR]

In-process damage to a cutting tool degrades the surface finish of the job shaped by machining and causes a significant financial loss. This stimulates the need for Tool Condition Monitoring (TCM) to assist detection of failure before it extends to the worse phase. Machine Learning (ML) based TCM has been extensively explored in the last decade. However, most of the research is now directed toward Deep Learning (DL). The "Deep" formulation, hierarchical compositionality, distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform efficiently in a high-noise environment of cross-domain machining. With this motivation, the design of different CNN (Convolutional Neural Network) architectures such as AlexNet, ResNet-50, LeNet-5, and VGG-16 is presented in this paper. Real-time spindle vibrations corresponding to healthy and various faulty configurations of milling cutter were acquired. This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form, i.e., spectrogram. The model is trained, tested, and validated considering different datasets and showcased promising results. [ABSTRACT FROM AUTHOR]

In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system. [ABSTRACT FROM AUTHOR]

The intelligent factory defined by Industry 4.0 is established on intelligent machines, services, and production. To cope up with these requirements, topics such as Artificial Intelligence (AI), Evolutionary Computation (EC), Internet of Things (IoT), and Big Data are gaining a lot of attention in the manufacturing sector. In spite of the use of optimized input parameters, owing to some unknown moments, a machining activity tends to produce tool wear, break, and chatter that affect tool life, resulting in poorer surface roughness. Thus, there is a need to adopt self-monitoring of tools so that the diagnosis can be done without any human intervention. As the 4th industrial revolution pervades production sector, the volume of manufacturing data created has approached big data proportions, and is very dynamic. In an attempt to compute such vast data, Deep Learning (DL) approach is being considered to be a potential tool. In this paper, investigation of unknown vibration moments leading to cutting tool faults is ventured upon through spectrogram and deep learning ensemble i.e. LSTM (Long-Short-Term Memory)-Auto encoder. [ABSTRACT FROM AUTHOR]

The regulation of tyre pressure is treated as a significant aspect of 'tyre maintenance' in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the same in near future. The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle. In order to examine the vibration response of a wheel hub, the in-house design & development of DAQ (Data Acquisition System) is described. Micro Electro-Mechanical Scheme (MEMS) built accelerometer is incorporated with open source hardware and software to collect and store the data. This framework is easy to develop, monitor and can be retrofitted in two wheeled vehicle. For various pressure conditions, the change in response of wheel hub vibration with respect to time is collected. The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters. The classification of different conditions of tyre pressure is carried out using ML classifiers. [ABSTRACT FROM AUTHOR]

A cutting tool is a significant constituent in the manufacturing process and a framework assisting its self-monitoring is one of the requirements of Industry 4.0. The Deep Learning (DL) approach is suitable for modeling such a framework and the application of multilayer fully connected neural nets makes the model robust. This article presents the design of an Artificial Neural Network (ANN) classifier based on statistical learning of machining vibrations. Six distinct tool faults have been analyzed considering turning operations incorporating feature computation, choice, and classification. The output of the trained ANN is utilized for the classification of the fault and fault-free condition in the cutting tool and exhibited an accuracy of 93.33%. Later, the performance of this model has been compared with Machine Learning (ML) classifiers. Considering the comparative study, it is understood that the Deep Learning-based ANN model shows higher accuracy and can therefore be suggested for condition monitoring of a single-point cutting tool. [ABSTRACT FROM AUTHOR]