Significance of sensors for industry 4.0: Roles, capabilities, and applications

Sensitive and selective detection of heavy metal ions and organic pollutants with graphene-integrated sensing platforms

Graphene-based sensors have emerged as promising tools for environmental monitoring due to their exceptional properties such as high surface area, excellent electrical conductivity, and sensitivity to various analytes. This paper presents a review of recent advancements in the development and application of graphene-based sensors for the detection of heavy metal ions and organic pollutants. These sensors employ either graphene or its derivatives, often in combination with graphene hybrid nanocomposites, as the primary sensing material. The synthesis methods of graphene and sensing mechanisms of graphene-based sensors are discussed. Furthermore, performance metrics including the determination range and detection limits of these sensors are itemized. The potential challenges and future directions in the field of graphene-based sensors for environmental monitoring are also highlighted. Overall, this review provides valuable insights into the current state-of-the-art technologies and paves the way for the development of highly efficient and reliable sensors for environmental monitoring purposes.

Nanomaterial-Based Sensors for Coumarin Detection

Sensors are widely used owing to their advantages including excellent sensing performance, user-friendliness, portability, rapid response, high sensitivity, and specificity. Sensor technologies have been expanded rapidly in recent years to offer many applications in medicine, pharmaceuticals, the environment, food safety, and national security. Various nanomaterial-based sensors have been developed for their exciting features, such as a powerful absorption band in the visible region, excellent electrical conductivity, and good mechanical properties. Natural and synthetic coumarin derivatives are attracting attention in the development of functional polymers and polymeric networks for their unique biological, optical, and photochemical properties. They are the most abundant organic molecules in medicine because of their biological and pharmacological impacts. Furthermore, coumarin derivatives can modulate signaling pathways that affect various cellular processes. This review covers the discovery of coumarins and their derivatives, the integration of nanomaterial-based sensors, and recent advances in nanomaterial-based sensing for coumarins. This review also explains how sensors work, their types, their pros and cons, and sensor studies for coumarin detection in recent years.

Macromolecule-Nanoparticle-Based Hybrid Materials for Biosensor Applications

Biosensors function as sophisticated devices, converting biochemical reactions into electrical signals. Contemporary emphasis on developing biosensor devices with refined sensitivity and selectivity is critical due to their extensive functional capabilities. However, a significant challenge lies in the binding affinity of biosensors to biomolecules, requiring adept conversion and amplification of interactions into various signal modalities like electrical, optical, gravimetric, and electrochemical outputs. Overcoming challenges associated with sensitivity, detection limits, response time, reproducibility, and stability is essential for efficient biosensor creation. The central aspect of the fabrication of any biosensor is focused towards forming an effective interface between the analyte electrode which significantly influences the overall biosensor quality. Polymers and macromolecular systems are favored for their distinct properties and versatile applications. Enhancing the properties and conductivity of these systems can be achieved through incorporating nanoparticles or carbonaceous moieties. Hybrid composite materials, possessing a unique combination of attributes like advanced sensitivity, selectivity, thermal stability, mechanical flexibility, biocompatibility, and tunable electrical properties, emerge as promising candidates for biosensor applications. In addition, this approach enhances the electrochemical response, signal amplification, and stability of fabricated biosensors, contributing to their effectiveness. This review predominantly explores recent advancements in utilizing macrocyclic and macromolecular conjugated systems, such as phthalocyanines, porphyrins, polymers, etc. and their hybrids, with a specific focus on signal amplification in biosensors. It comprehensively covers synthetic strategies, properties, working mechanisms, and the potential of these systems for detecting biomolecules like glucose, hydrogen peroxide, uric acid, ascorbic acid, dopamine, cholesterol, amino acids, and cancer cells. Furthermore, this review delves into the progress made, elucidating the mechanisms responsible for signal amplification. The Conclusion addresses the challenges and future directions of macromolecule-based hybrids in biosensor applications, providing a concise overview of this evolving field. The narrative emphasizes the importance of biosensor technology advancement, illustrating the role of smart design and material enhancement in improving performance across various domains.

Advances in flame synthesis of nano-scale architectures for chemical, biomolecular, plasmonic, and light sensing

Flame spray pyrolysis (FSP), a key technique under the broader category of flame aerosol synthesis, is being increasingly explored for the design of advanced miniaturized sensor architectures with applications including chemical, biomolecular, plasmonic, and light sensing. This review provides an overview of the advantages of FSP for the fabrication of nanostructured materials for sensing, delving into synthesis strategies and material structures that meet the increasing demands for miniaturized sensor devices. We focus on the fundamentals of FSP, discussing reactor configurations and how process parameters such as precursor compositions, flow rates, and temperature influence nanoparticle characteristics and their sensing performance. A detailed analysis of nanostructures, compositions, and morphologies made by FSP and their applications in chemical, chemiresistive, plasmonic, biosensing, and light sensing is presented. This review identifies the challenges and opportunities of FSP, exploring current limitations and potential improvements for industrial translation. We conclude by highlighting future research directions aiming to establish guidelines for the flame-based design of nano-scale sensing architectures.

Coaxial Flexible Fiber-Shaped Triboelectric Nanogenerator Assisted by Deep Learning for Self-Powered Vibration Monitoring

Self-powered vibration sensor is highly desired for distributed and continuous monitoring requirements of Industry 4.0. Herein, a flexible fiber-shaped triboelectric nanogenerator (F-TENG) with a coaxial core-shell structure is proposed for the vibration monitoring. The F-TENG exhibits higher adaptability to the complex surfaces, which has an outstanding application prospect due to vital compensation for the existing rigid sensors. Initially, the contact characteristics between the dielectric layers, that related to the perceiving performance of the TENG, are theoretically analyzed. Such a TENG with 1D structure endows high sensitivity, allowing for accurately responding to a wide range of vibration frequencies (0.1 to 100 Hz). Even applying to the real diesel engine, the error in detecting the vibration frequencies is only 0.32% compared with the commercial vibration sensor, highlighting its potential in practical application. Further, assisted by deep learning, the recognition accuracy in monitoring nine operating conditions of the system achieves 97.87%. Overall, the newly designed F-TENG with the merits of high-adaptability, cost-efficiency, and self-powered, has offered a promising solution to fulfill an extensive range of vibration sensing applications in the future.

A Wireless Passive Pressure-Sensing Method for Cryogenic Applications Using Magnetoresistors

In this study, we developed a novel wireless, passive pressure-sensing method functional at cryogenic temperatures (-196 °C). The currently used pressure sensors are inconvenient and complicated in cryogenic environments for their weak low-temperature tolerances and long wires for power supply and data transmission. We propose a novel pressure-sensing method for cryogenic applications by only using low-temperature-tolerant passive devices. By innovatively integrating a magnetoresistor (MR) on a backscattering antenna, the pressure inside a cryogenic environment is transferred to a wirelessly obtainable return loss. Wireless passive measurement is thus achieved using a backscattering method. In the measurement, the pressure causes a relative displacement between the MR and a magnet. The MR's resistance changes with the varied magnetic field, thus modulating the antenna's return loss. The experimental results indicate that our fabricated sensor successfully identified different pressures, with high sensitivities of 4.3 dB/MPa at room temperature (24 °C) and 1.3 dB/MPa at cryogenic temperature (-196 °C). Additionally, our method allows for simultaneous wireless readings of multi sensors via a single reading device by separating the frequency band of each sensor. Our method performs low-cost, simple, robust, passive, and wireless pressure measurement at -196 °C; thus, it is desirable for cryogenic applications.

Blockchain Protocols and Edge Computing Targeting Industry 5.0 Needs

"Industry 5.0" is the latest industrial revolution. A variety of cutting-edge technologies, including artificial intelligence, the Internet of Things (IoT), and others, come together to form it. Billions of devices are connected for high-speed data transfer, especially in a 5G-enabled industrial environment for information collection and processing. Most of the issues, such as access control mechanism, time to fetch the data from different devices, and protocols used, may not be applicable in the future as these protocols are based upon a centralized mechanism. This centralized mechanism may have a single point of failure along with the computational overhead. Thus, there is a need for an efficient decentralized access control mechanism for device-to-device (D2D) communication in various industrial sectors, for example, sensors in different regions may collect and process the data for making intelligent decisions. In such an environment, reliability, security, and privacy are major concerns as most of the solutions are based upon a centralized control mechanism. To mitigate the aforementioned issues, this paper provides the opportunities for and highlights some of the most impressive initiatives that help to curve the future. This new era will bring about significant changes in the way businesses operate, allowing them to become more cost-effective, more efficient, and produce higher-quality goods and services. As sensors are getting more accurate, cheaper, and have lower time responses, 5G networks are being integrated, and more industrial equipment and machinery are becoming available; hence, various sectors, including the manufacturing sector, are going through a significant period of transition right now. Additionally, the emergence of the cloud enables modern production models that use the cloud (both internal and external services), networks, and systems to leverage the cloud's low cost, scalability, increased computational power, real-time communication, and data transfer capabilities to create much smarter and more autonomous systems. We discuss the ways in which decentralized networks that make use of protocols help to achieve decentralization and how network meshes can grow to make things more secure, reliable, and cohere with these technologies, which are not going away anytime soon. We emphasize the significance of new design in regard to cybersecurity, data integrity, and storage by using straightforward examples that have the potential to lead to the excellence of distributed systems. This groundbreaking paper delves deep into the world of industrial automation and explores the possibilities to adopt blockchain for developing solutions for smart cities, smart homes, healthcare, smart agriculture, autonomous vehicles, and supply chain management within Industry 5.0. With an in-depth examination of various consensus mechanisms, readers gain a comprehensive understanding of the latest developments in this field. The paper also explores the current issues and challenges associated with blockchain adaptation for industrial automation and provides a thorough comparison of the available consensus, enabling end customers to select the most suitable one based on its unique advantages. Case studies highlight how to enable the adoption of blockchain in Industry 5.0 solutions effectively and efficiently, offering valuable insights into the potential challenges that lie ahead, particularly for smart industrial applications.

Discussion on Secure Standard Network of Sensors Powered by Microbial Fuel Cells

Everyday tasks use sensors to monitor and provide information about processes in different scenarios, such as monitoring devices in manufacturing or homes. Sensors need to communicate, with or without wires, while providing secure information. Power can be derived from various energy sources, such as batteries, electrical power grids, and energy harvesting. Energy harvesting is a promising way to provide a sustainable and renewable source to power sensors by scavenging and converting energy from ambient energy sources. However, low energy is harvested through these methods. Therefore, it is becoming a challenge to design and deploy wireless sensor networks while ensuring the sensors have enough power to perform their tasks and communicate with each other through careful management and optimization, matching energy supply with demand. For this reason, data cryptography and authentication are needed to protect sensor communication. This paper studies how energy harvested with microbial fuel cells can be employed in algorithms used in data protection during sensor communication.

Large-scale cascading of first-order FBG array in a highly multimode coreless fiber using femtosecond laser for distributed thermal sensing

This research focuses on the performance analysis and characterization of a fiber Bragg gratings (FBGs) array, consisting of 10 first-order FBGs inscribed by a femtosecond (FS) laser in a highly multimode coreless fiber. The study evaluates the FBG array's ability to function as a distributed thermal sensing (DTS) platform, with the coreless fiber chosen as the sensing element due to its immunity to dopant migration at high temperatures. The design of a large cascaded first-order FBG array effectively eliminates unwanted harmonic peaks across a wide spectrum range. In contrast, higher-order FBGs introduce limitations due to the overlapping of Bragg peaks with harmonics. The FBG array's performance is evaluated by measuring the reflection spectrum of each grating at different temperatures, showing a high temperature sensitivity of 15.05 pm/°C at a Bragg wavelength of 1606.3 nm, with a linear response in the temperature range of 24 - 1100 °C. The FBG array was designed for a spatial resolution of 5 mm. A mode scrambler in the sensing network is employed, which suppresses multimodal interference, characterizes FBG peak visibility, and stabilizes the interference spectrum. The stability of the FBG array is also assessed over 24 hrs at 1100 °C, and it is observed to be stable during thermal treatment. Heat treatment at 1100°C improves the signal to noise ratio of the FBG array, demonstrating the robustness and suitability of the proposed FBG array on highly multimode coreless fiber as a potential sensing platform for DTS applications in harsh environmental conditions, overcoming the issues of dopant migration presented by dopes silica optical fibers at high temperatures.

A Circuit-Level Solution for Secure Temperature Sensor

Temperature sensors play an important role in modern monitoring and control applications. When more and more sensors are integrated into internet-connected systems, the integrity and security of sensors become a concern and cannot be ignored anymore. As sensors are typically low-end devices, there is no built-in defense mechanism in sensors. It is common that system-level defense provides protection against security threats on sensors. Unfortunately, high-level countermeasures do not differentiate the root of cause and treat all anomalies with system-level recovery processes, resulting in high-cost overhead on delay and power consumption. In this work, we propose a secure architecture for temperature sensors with a transducer and a signal conditioning unit. The proposed architecture estimates the sensor data with statistical analysis and generates a residual signal for anomaly detection at the signal conditioning unit. Moreover, complementary current-temperature characteristics are exploited to generate a constant current reference for attack detection at the transducer level. Anomaly detection at the signal conditioning unit and attack detection at the transducer unit make the temperature sensor attack resilient to intentional and unintentional attacks. Simulation results show that our sensor is capable of detecting an under-powering attack and analog Trojan from a significant signal vibration in the constant current reference. Furthermore, the anomaly detection unit detects anomalies at the signal conditioning level from the generated residual signal. The proposed detection system is resilient against any intentional and unintentional attacks, with a detection rate of 97.73%.

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process's performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line.

Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm

Thermostatic bath calibration performance is usually checked for uniformity and stability to serve a wide range of industrial applications. Particularly challenging is the assessment at the limiting specification ends where the sensor system may be less effective in achieving consistency. An ensemble of eight sensors is used to test temperature measurement stability at various topological locations in a thermostatic bath (antifreeze) fluid at -20 °C. Eight streaks of temperature data were collected, and the resulting time-series were processed for normality, stationarity, and independence and identical distribution by employing regular statistical inference methods. Moreover, they were evaluated for autoregressive patterns and other underlying trends using classical Auto-Regressive Integrated Moving Average (ARIMA) modeling. In contrast, a continuous-time quantum walker algorithm was implemented, using an available R-package, in order to test the behavior of the fitted coefficients on the probabilistic node transitions of the temperature time series dataset. Tracking the network sequence for persistence and hierarchical mode strength was the objective. The quantum walker approach favoring a network probabilistic framework was posited as a faster way to arrive at simultaneous instability quantifications for all the examined time-series. The quantum walker algorithm may furnish expedient modal information in comparison to the classical ARIMA modeling and in conjunction with several popular stochastic analyzers of time-series stationarity, normality, and data sequence independence of temperature end-of-scale calibration datasets, which are investigated for temporal consistency.

How Industry 4.0 and Sensors Can Leverage Product Design: Opportunities and Challenges

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. Thus, it becomes imperative to understand how these innovations can leverage product design to maximize benefits and opportunities. This research paper employs a Systematic Literature Review with Bibliometric Analysis (SLBA) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.

A framework to implement augmented reality based on BIM to improve operation and maintenance of mechanical facilities of commercial complexes

Purpose

The mechanical facilities of large-scale buildings have many complexities, so facility management (FM) encounters a massive amount of information during the operation, and inspection is not implemented efficiently. This study aims to integrate building information modeling (BIM), augmented reality (AR) and sensors to mitigate the operation and maintenance (O&M) problems of mechanical facilities by establishing intelligent management.

Design/methodology/approach

The component-level data of an under-construction commercial complex were implemented in a 3D model in Autodesk Revit and Navisworks. Then, sensors were installed in mechanical facilities to provide vital online information for the model. Moreover, Unity was used on Vuforia for the AR visualization of the devices.

Findings

It was found that FM not only could obtain building information but can also visualize the functioning of mechanical facilities online through a 3D model in BIM. Thus, in the case of a failure, the AR platform is used on tablets, smartphones and smart glasses to show the technician how mechanical facilities can be repaired and maintained.

Originality/value

Previous studies focused on some factors and processes to improve mechanical FM and did not cover the entire aspect. However, this method reduces the average maintenance time, extends the lives of mechanical devices and prevents unpredicted failures.

Recent Development in Metasurfaces: A Focus on Sensing Applications

One of the fastest-expanding study areas in optics over the past decade has been metasurfaces (MSs). These subwavelength meta-atom-based ultrathin arrays have been developed for a broad range of functions, including lenses, polarization control, holography, coloring, spectroscopy, sensors, and many more. They allow exact control of the many properties of electromagnetic waves. The performance of MSs has dramatically improved because of recent developments in nanofabrication methods, and this concept has developed to the point that it may be used in commercial applications. In this review, a vital topic of sensing has been considered and an up-to-date study has been carried out. Three different kinds of MS absorber sensor formations, all-dielectric, all-metallic, and hybrid configurations, are presented for biochemical sensing applications. We believe that this review paper will provide current knowledge on state-of-the-art sensing devices based on MSs.

High Temperature Measurement with Low Cost, VCSEL-Based, Interrogation System Using Femtosecond Bragg Gratings

In this article, a cost-effective and fast interrogating system for wide temperature measurement with Fiber Bragg Gratings is presented. The system consists of a Vertical Cavity Surface Emitting Laser (VCSEL) with a High Contrast Grating (HCG)-based cavity that allows for the fast tuning of the output wavelength. The work focuses on methods of bypassing the limitations of the used VCSEL laser, especially its relatively narrow tuning range. Moreover, an error analysis is provided by means of the VCSEL temperature instability and its influence on the system performance. A simple proof of concept of the measurement system is shown, where two femtosecond Bragg gratings were used to measure temperature in the range of 25 to 800 °C. In addition, an exemplary simulation of a system with sapphire Bragg gratings is provided, where we propose multiplexation in the wavelength and reflectance domains. The presented concept can be further used to measure a wide range of temperatures with scanning frequencies up to hundreds of kHz.

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

Plasmonic Refractive Index and Temperature Sensor Based on Graphene and LiNbO3

A high-efficiency dual-purpose plasmonic perfect absorber sensor based on LiNbO₃ and graphene layers was investigated in this paper for the refractive index and thermal sensing. The sensor design was kept simple for easy fabrication, comprising a LiNbO₃ substrate with a quartz layer, thin layer of graphene, four gold nanorods, and a nanocavity in each unit cell. The nanocavity is located in the middle of the cell to facilitate the penetration of EM energy to the subsurface layers. The proposed sensor design achieved an output response of 99.9% reflection, which was easy to detect without having any specialized conditions for operability. The performance of the device was numerically investigated for the biomedical refractive index range of 1.33 to 1.40, yielding a sensitivity value of 981 nm/RIU with a figure-of-merit of 61.31 RIU^-1. By including an additional polydimethylsiloxane polymer functional layer on the top, the device was also tested as a thermal sensor, which yielded a sensitivity level of -0.23 nm/°C.

A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0

The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain and enhance the quality of their products, manufacturers invest a lot of resources in quality control and quality assurance. During the assembly line, parts will arrive at a constant interval for assembly. The quality criteria must first be met before the parts are sent to the assembly line where the parts and subparts are assembled to get the final product. Once the product has been assembled, it is again inspected and tested before it is delivered to the customer. Because manufacturers are mostly focused on visual quality inspection, there can be bottlenecks before and after assembly. The manufacturer may suffer a loss if the assembly line is slowed down by this bottleneck. To improve quality, state-of-the-art sensors are being used to replace visual inspections and machine learning is used to help determine which part will fail. Using machine learning techniques, a review of quality assessment in various production processes is presented, along with a summary of the four industrial revolutions that have occurred in manufacturing, highlighting the need to detect anomalies in assembly lines, the need to detect the features of the assembly line, the use of machine learning algorithms in manufacturing, the research challenges, the computing paradigms, and the use of state-of-the-art sensors in Industry 4.0.

Implementation of DDS Cloud Platform for Real-Time Data Acquisition of Sensors for a Legacy Machine

Industry 4.0 (I4.0) is a multidisciplinary engineering principle combing the IoT (Internet of things), big data, and cloud computing to cope with the dynamic changing industry. In this paper, the DDS (data distribution service) communication protocol was employed to implement a cloud platform for data acquisition from various sensors on a precision legacy machine tool including an accelerometer and sound, temperature, brightness, and humidity sensors. The sensor signals were acquired using Raspberry Pi as the edge device, then published to the cloud using the DDS application, and stored in the MySQL database. Using the Django web server, the acquired sensor signals could be shown in real time on the webpage via a combination of MQTT and Node-RED. In addition, the motion displacement of the machine tool detected by the encoder could be recorded through the edge device for further performance examination. With the proposed DDS cloud platform, it is demonstrated that a legacy machine can enable sensing and communication abilities such that the development of a smart machine is achievable for future I4.0 application.

Measurement of the Machined Surface Diameter by a Laser Triangulation Sensor and Optimalization of Turning Conditions Based on the Diameter Deviation and Tool Wear by GRA and ANOVA

One of the most important operations in the technological production process is the inspection of the manufactured product. The gradual wear of the tool affects the achievement of the required quality of the functional surfaces. In this research, we present the results of measuring the diameter deviation with a new generation laser triangulation sensor (LTS). At the same time, we have performed parametric optimization of several multi-responses, such as insert wear on the VBB flank side of cutting edge and diameter deviation Δd for a C45 steel sample during dry turning and using a sintered carbide insert, using the method of grey relational analysis (GRA) in combination with the Taguchi L16 orthogonal array. The optimal setting of input factors for multi-response parameters is ap 4-f 4-vc 1 i.e., depth of cut 0.5 mm, feed 0.4 mm per revolution, and a cutting speed of 70 m/min. At the same time, we present an evaluation of the significance of input factors using the method ANOVA.

A Second Harmonic Wave Angle Sensor with a Collimated Beam of Femtosecond Laser

An optical angle sensor based on the collimated mode-locked femtosecond laser (Fs) beam, in which the angle measurement is carried out by observing the second harmonic wave generated by irradiating the Fs beam towards MgO:LiNbO3 crystal, is proposed. The angle detection is demonstrated by identifying the second harmonic generation (SHG) spectrum peaks against the angular position of the rotating stage-mounted MgO:LiNbO3 crystal that represents the measurement object. The MgO:LiNbO3 with a length of 2 mm, a cut-off angle of 47°, a diameter of 5 mm, and 5% of MgO:LN, is rotated within the phase-matching angle over a wavelength range from 1480 nm to 1640 nm. Angle measurement is also carried out by placing the crystal at different positions along the laser beam propagation direction to confirm the feasibility of the angle sensor where the measurement range, measurement sensitivity, and resolution of the proposed method are also evaluated. The result shows that the proposed angle sensor can overcome the working distance limitation of conventional angle sensors and has no specific material target requirement. Since the configuration of the proposed angle sensor is not limited by the specification of the measurement target, it is expected to have wide potential applications, especially in the manufacturing process and inspection process.