Machine Status Research Articles

Feasibility of a clouds and edge computing scheme for internal due date assignment in advanced manufacturing systems

Most of the existing edge computing applications in manufacturing systems are used for machine status detection, material transportation tracking, resource management, process control, and production planning, but are rarely used to support management activities. To fill this gap, this study designs a cloud and edge computing scheme for internal due date assignment, in which a sales representative visits a customer and negotiates with him/her the due date of a possible order. In the past, such tasks were accomplished by querying a deep learning system on the backend or cloud server, which was affected by difficulties such as cloud computing application licensing, network speed, and bandwidth. To overcome these difficulties, in the cloud and edge computing scheme, a possible order is broken down into lots that are released based on daily quotas. A deep neural network (DNN) is then constructed on the cloud server to estimate the output time of each lot, thereby obtaining the completion time of the possible order for internal due date assignment. A random forest (RF) is also built to approximate the trained DNN for internal due date assignment using edge computing. The cloud and edge computing scheme for internal due date assignment has been experimentally applied to a wafer fabrication case. According to the experimental results, the cloud and edge computing scheme provided a portable, self-contained, and instant solution for internal due date assignment that the common practice and cloud computing counterparts could not achieve. The cloud and edge computing scheme also avoided the potential leakage of confidential production condition data.

Optical vibration sensor enabled by coupling mechanoluminescence with photostimulable phosphor.

In the working environment, machines without vibrations are non-existent. The abnormal operating conditions of machines can be discerned through characteristic patterns within vibration signals. Therefore, real-time, low-cost vibration sensing is essential for industrial applications to track the status of machines. Herein, we propose an optical vibration sensor that is self-powered, supporting on-demand visual readouts. Without external power, this prototype device can cover a broad frequency range from 50 to 800 Hz, fitting into most industrial machinery scenarios. Through finite element analysis and experimental validation, the device exhibits exceptional performance, with a predicted minimum detectable deformation as low as 0.19 µm. Notably, the device possesses vibration signal storage functionality and adopts near-infrared light to achieve on-demand readout, bringing a novel visual perspective to the fields of vibration sensing and equipment health diagnostics.

A SAW Wireless Passive Sensing System for Rotating Metal Parts

Passive wireless surface acoustic wave (SAW) sensors are very useful for on-site monitoring of the working status of machines in complex environments, such as high-temperature rotating objects. For rotating parts, it is difficult to realize real-time and continuous monitoring because of the unstable sensing signal caused by the continuous change of the relative position of the rotating part to the sensor and shielding of the signal. In our SAW sensing system, we propose a loop antenna integrated with the rotating part to obtain a stable sensing signal owing to its omnidirectional radiation pattern. Methodologies for determining the antenna dimension, system operating frequency, and procedures for designing a SAW sensor tag are discussed in this paper. By fully utilizing the influence of metal rotor on antenna performance, the antenna needs no impedance matching elements while it provides sufficient gain, which equips the antenna with nearly zero temperature drift at a wide temperature-sensing range. Experimental verification results show that this sensing system can greatly improve the stability of the sensing signal significantly and can achieve a temperature sensing accuracy of ~1 °C at different rotational speeds, demonstrated by the feasibility of the loop antenna for monitoring the working status of rotating metal parts.

Introduction. Our relationships with GenAI and the media: Testing the limits of transparency, trust and moral agency

We present the last part of this special issue on Use of Artificial Intelligence in Communication: Ethical Implications for Media. This editorial examines the role that humans should play in the responsible use of generative artificial intelligence (GenAI) in the media. It provides an overview of transparency as an ethical obligation in relation to trust and truthfulness when using GenAI to create different kinds of content. Finally, it enters into the debate about the moral status of machines as we anticipate ethical questions in the study of ethics and GenAI in the media. Six articles complete this special issue with studies examining implications of GenAI for semiotics research, public relations, advertising, fact checking, information literacy and journalism education.

The simulation and analysis of the spraying status of large sprinkler machines loaded with atomizing micro-sprinklers

Due to the typically high flow rate of nozzles on large sprinkler machines, direct use for pesticide application can lead to pesticide waste and non-point source pollution. To fulfill the multifunctional needs of large sprinkler machines for irrigation, fertilization, and pesticide application, this study installed atomizing micro-sprinklers on the machines to enable their pesticide application capabilities. Based on the spraying patterns of the atomizing micro-sprinklers, three types were selected for this study: hollow cone, solid cone, and fan-shaped micro-sprinklers. Three working pressure levels were set at 0.2 MPa, 0.3 MPa, and 0.5 MPa, with the height of the micro-sprinklers above the ground set at three levels: 0.8 m, 1.2 m, and 1.5 m. A completely randomized design was used for the experiment, which included hydraulic performance tests and software simulation. The results indicated that the spray volume of the hollow cone, solid cone, and fan-shaped atomizing micro-sprinklers approximately presented a hollow conical, solid conical, and normal distribution, respectively. The spray intensity (average and maximum values) of the atomizing micro-sprinklers increased with the increase in pressure, while the median showed an initial increase followed by a decrease as pressure increased. Under the same pressure conditions, the spray intensity of the different atomizing micro-sprinklers decreased with the increase in height above the ground, while the spray width increased with height. By comparing the spraying characteristics of micro-sprinklers with various patterns, large sprinkler machines can select atomizing micro-sprinklers in different spraying statuses according to actual conditions and determine the optimal height of the atomizing micro-sprinklers above the ground to achieve efficient and uniform pesticide application effects. This reduces the evaporation and drift loss of liquid medicine. The research findings provide a scientific basis for achieving the pesticide application function of large sprinkler machines and expanding their usage capabilities.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

An Analysis of Radiotherapy Machine Requirements in India: Impact of the Pandemic and Regional Disparities.

This article examines India's present radiotherapy (RT) machine status and requirements, geographical distribution, and infrastructure need in six regional areas, which include 31 member states and union territories (UTs). It also considers the influence of the COVID-19 pandemic on India's teletherapy sector. Data from reliable resources, including Atomic Energy Regulatory Board, Global Cancer Observatory, and Directory of Radiotherapy Centres databases, were used to analyze the current status of RT machine (RTM) density, regional disparity, and COVID-19 impact on infrastructure growth-rate. In India, the number of functioning RTM and facilities are 823 and 554, respectively, with an average of 1.5 RTM per institute, of which 69.4% have only one RTM. Over the past 22 years, there has been a paradigm shift towards medical linear accelerator (linac) installation instead of telecobalt machines. Presently, there is a teletherapy density of 0.6 RTM per million population, and there is a shortfall of 1209 RTMs. There is a considerable regional disparity in the distribution of RTMs, ranging from (0.08 RTM/million-2.94 RTM/million) across different regions. There is a significant demand for RTMs in the Northern region (480) and the state of Uttar Pradesh (279). The COVID-19 pandemic temporarily impacted India's RT growth rate, reducing it from 5% to 1.9% in 2020-2021. New policies must be established to accelerate the rate of RT installation growth. To better serve local populations and save patient costs, this article proposes that RT facilities be dispersed equitably across states.

Virtual instrumentation-based data collection and analysis for CNC machining process

Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

Analysis of the development of friction and wear testing machine based on complex working conditions

Friction is one of the three main factors leading to the failure of metal materials. In order to improve the service life of machinery and reduce unnecessary losses in industrial production, it is necessary to study the friction and wear properties of industrial materials. Simultaneously in order to explore the impact of various environmental conditions on material properties, friction and wear testing machine war born as the times require. This paper mainly introduces the experimental principles and application scenarios of several universal friction and wear testing machines. At the same time, this paper also explores the situation of several special friction and wear testing machines that adapt to complex environments by organizing the industrial needs generated by the rapid development of technology. This paper collects and organizes literature on the current status and development of friction and wear testing machine in recent years. The paper has found that adding or improving special equipment to the special type of friction and wear testing machine on the basis of the universal model can better meet the needs of actual working conditions.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

Effects of feedback in manufacturing: A field experiment using smartwatch technology

AbstractWe investigate the impact of performance feedback delivered to front‐line workers through new digital technologies in the manufacturing sector. Our study takes place in a globally operating manufacturing company that employs smartwatches for real‐time control on the shop floor. In a large‐scale and multi‐site field experiment, we examine 29,669 machine status reports to assess the productivity effects of providing near real‐time feedback to workers via smartwatches. We develop our hypotheses drawing on construal‐level theory and its central idea of psychological distance. Initially, we observe production without feedback to establish comparability between treatment groups. Subsequently, we allocate various combinations of feedback to workers in four separate areas in two manufacturing plants. We manipulate whether the feedback is targeted at individuals with psychologically close feedback (“you did…”) or targeted at the team with relatively psychologically distant feedback (“your team did…”). Furthermore, we vary the message framing: positive framing emphasizes completed tasks, while negative framing emphasizes unfinished tasks. Our findings indicate that positively framed feedback, targeting individuals directly, enhances productivity more than other feedback combinations. Our study contributes new theoretical insights into the interplay between feedback framing and targets in fast‐paced and highly automated batch production environments.

Vibration displacement measurement method based on vision Gaussian fitting and edge optimisation for rotating shafts

The vibrations of rotating machinery contain crucial information for monitoring the operational status of machines and diagnosing potential faults. Measuring vibration displacement using machine vision is currently a promising approach for machine monitoring in industries. However, the existing vision-based vibration displacement measurement methods cannot satisfy the high-accuracy requirements for detecting micrometre-level displacements generated by low-amplitude vibrations of rotating machinery. Therefore, this study proposes an improved noncontact-type vibration displacement measurement method based on Gaussian fitting and edge optimisation for rotating shafts. The real number-type edge positions of the rotating shafts within the selected region of interest (ROI) are accurately extracted via Gaussian edge fitting. Subsequently, edge-linking schemes, double-threshold detection and edge fitting are preformed to optimise the real number-type position of the edges. Finally, continuous displacement extraction of rotating shafts is achieved by monitoring the edge positions in the ROI. The coefficient of determination is used to evaluate a series of simulated and actual experiments quantitatively. The experimental results verify the feasibility, effectiveness, and robustness of the proposed method. An eddy current sensor is used in the actual experiments to verify the accuracy of the proposed method. The result shows that the coefficient of determination reaches 0.9792 when the amplitude of the measured displacement signals is only 22.7 μm.

Synthetic data generation for digital twins: enabling production systems analysis in the absence of data

ABSTRACT Industry increasingly focuses on data-driven digital twins of production lines, especially for planning, controlling and optimising applications. However, the lack of open data on manufacturing systems presents a challenge to the development of new data-driven strategies. To fill this gap, the paper aim to introduce a strategy for generating random production lines and simulating their behaviour, thus enabling the generation of synthetic data. So far, such data can be recorded in event logs or machine status format, with the latter adopted for the use cases. To do so, the production lines are modelled using complex network concepts, with the system’s behaviour simulated via an algorithm in Python. Three use cases were assessed, in order to present possible applications. Firstly, the stabilisation of working, starved and blocked machines was investigated until a steady state was reached. The system behaviour was then investigated for different model parameters and simulation intervals. Finally, the production bottleneck behaviour (a phenomenon that can harm the production capacity of manufacturing systems) was statistically studied and described. The authors anticipate that this artificial and parametric data benchmark will enable the development of data-driven techniques without prior need for a real dataset.

Criminal Responsibility for Errors Committed by Medical Robots: Legal and Ethical Challenges

Objective: This study aims to know Criminal Responsibility for Errors Committed by Medical Robots, where the use of robots in healthcare and medicine has been steadily growing in recent years. Robotic surgical systems, robotic prosthetics, and other assistive robots are being into patient care. However, these autonomous systems also carry risks of errors and adverse events resulting from mechanical failures, software bugs, or other technical issues. When such errors occur and lead to patient harm, it raises complex questions around legal and ethical responsibility Char. Method: A descriptive analytical method was followed. Results: Traditional principles of criminal law have not been designed to address the issue of liability for actions committed by artificial intelligence systems and robots. There are open questions around whether autonomous medical robots can or should be held criminally responsible for errors that result in patient injury or death. If criminal charges cannot be brought against the robot itself, legal responsibility could potentially be attributed to manufacturers, operators, hospitals, or software programmers connected to the robot. However, proving causation and intent in such cases can be very difficult. Conclusions: The prospect of bringing criminal charges against a non-human triggers ethical dilemma. Should autonomous machines have legal personhood? How to weigh patient safety versus promoting innovation in medical technology? This research will analyze the legal and ethical challenges associated with determining criminal responsibility when medical robots cause unintended harm. It has important implications for patient rights, healthcare regulation, technological ethics and the legal status of intelligent machines.

Bio-Intelligent Machine Tool: Vision and Steps Towards Realisation

Artificial intelligence enables machines to increase their autonomy. A concept of a bio-intelligent manufacturing machine has been developed. This is characterised by a self-learning expert system and a sensor strategy which are intensively integrated with each other. The machine tool control is at any time completely aware of the process performance, the environmental conditions and the machine status. It exploits further information streams from human operators and other machines in a federated learning approach and is connected to the MES system of the company. The expert system learns from human operators by mere commanding the operation. It is capable to generate new process parameters and deal with incomplete process data by adding the missing information.

Current Status and Prospects of Mineral Sorting Technology Research at Home and Abroad

With the continuous exploitation of mining resources, the grade of selected ores gradually decreases, and the cost of crushing, grinding, and beneficiation gradually increases. The pre selection and waste disposal of crushed products can improve the utilization rate of resources and the grade of ore entering the mill, reduce the amount of ore entering the mill, improve the economic benefits of enterprises, improve environmental conditions, and extend the service life of tailings ponds in mining enterprises. This article focuses on the development and application research status of color sorting machines, X-ray sorting machines, and artificial intelligence sorting machines, and looks forward to the development direction of intelligent sorting machines.

Implementation and performance estimation of new archive system for the TLS control system

The TLS (Taiwan Light Source) is a third generation of synchrotron light source that has been operated for more than 25 years, and its control system is a proprietary designed system. The TLS control system maintenance is difficult because of component outage problems. Some parts of the control system are being rejuvenated with the help of the EPICS framework used in the TPS (Taiwan Photon Source) control system to ensure that TLS continues to operate normally, saving both manpower and money. A new EPICS archive system was needed to efficiently record various machine parameters and status information during routine operations. As a result, the EPICS Archiver Appliance has been evaluated as suitable for deploying to archive TLS machine data which encapsulated the PV (Process Variable) channel access. Specific graphical user interfaces and API packages have been supported for quickly retrieving archived data, as well as a plotting function for easy diagnosis. Furthermore, the performance of this new TLS archive system has been estimated, and related system resources will be manually adjusted for better service. The efforts will be summarized in this paper.

Using KMeans Clustering to Evaluate and Alert for Deviations of Linac Photon Beam Parameters.

To analyse the daily measured Dosimetric Quality Assurance (QA) parameters of linear accelerator (linac) using Unsupervised Machine Learning (ML) Algorithm thereby evaluating the current machine status and to highlight the probable cause of the 'out-of-range' measured parameter. Five Parameters measured using PTW QuickCheckwebline device in a linac is subjected to KMeans clustering technique. The measured parameters comprise of Central Axis Dose (CAX), Beam Flatness, SymmetryLR, SymmetryGT and Beam Quality (BQF). Data from Varian with 55- and 107-day's measurements and from Elekta with 75 days measurements from 2 beam matched linacs were used in this clustering technique. This evaluation is used to review the current linac status and obtain 1) Upper and lower limits of each parameter (CAX, Flatness, Symmetry, Beam Quality), 2) Frequency of the days when the linac parameters are closer to the target value and when they deviate from the target value. 3) The date when these parameters deviate from the estimated limits. 4) The probable reason for the deviation and 5) Finally if the machine requires maintenance. This methodology ensures that the machine is always closest to the target value, thus providing quality radiation treatment for the cancer patients. Moreover, the performance of the linac is studied meticulously and the need for maintenance is alerted before the linac beam shows marked deviation from the base value. KMeans clustering is a very simple and easy to use ML tool. With quick computation time and with lesser data it can arrive at the actual limits of the linac parameters and help to determine if the linac needs maintenance well in advance.

Future Wars and Their Morale in Post- and Transhumanist Discourse

Post- and transhumanism postulate the end of anthropocene, and advocate the necessity of reflecting on the role of technology, status of non-human animals or machines in human life. In the forecasts of post- and transhumanists, one can identify possible future participants, the character, and the way of conducting modern wars. It was hypothesized that there is probably no consensus in the minds of post- and transhumanists about the definitional considerations of the concepts of "posthumanism" and "transhumanism," as is the vast majority about the nature of future wars and the "morale" of their participants.

Energy-efficient resource allocation in cloud infrastructure using L3F-MGA and E-ANFIS

In industry along with academia, Task Scheduling (TS) along with Resources Allocation (RA) remains a challenging issue even though lots of work was carried out in the Cloud Computing (CC) field. CC uses massive virtualized Data Centers (DC). Still, it took an enormous quantity of energy on account of the constant demand by users for resources in addition to inefficient RA techniques. The higher Energy Consumption (EC) because of improper RA inside DC can be overcome using energy-aware RA techniques. Therefore, this paper proposes a Levy flight Firefly Modified Genetic Algorithm (L3F-MGA) as well as an Entropy-based Adaptive Neuros-Fuzzy Inferences System (E-ANFIS) for energy-efficient Resources Allocation (EE-RA) in cloud structure that manages the user demands and also lessens the EC of Cloud Data Center (CDC). In the proposed work, centred on task features together with cloud resource features, the input tasks are arranged using L3F-MGA. Conversely, the Virtual Machine (VM) status is identified. After that, centred on the task feature and VM status details, the E-ANFIS allows the active VM to complete the tasks effectively. The experiments compare the proposed method's performance for effective RA with other associated works. The proposed RA technique shows EC by 11 % lesser than the existing CM-GA. The Processing Time of E-ANFIS is 8 % lesser than CM-GA, 14 % less than ANFIS, 20 % less than GA, and 16 % less than PSO.