Search Results (23)

Large-scale, high-density flatness measurement is critical for manufacturing reference surfaces in ultra-precision machine tools. Traditional methods exhibit degradation in both accuracy and efficiency as measurement points and area size increase. In order to overcome these limitations to meet the requirements for integrated in-process measurement and machining of structural components in ultra-precision machine tools, this paper proposes a novel nine-probe third-order matrix system that integrates the Fine Sequential Three-Point (FSTRP) method with automated scanning path planning. The system utilizes a multi-probe error separation algorithm based on the FSTRP principle, combined with real-time adaptive sampling, to decouple machine tool motion errors from intrinsic workpiece flatness deviations. This system breaks through traditional multi-probe 1D straightness measurement limitations, enabling direct 2D flatness measurement (with X/Y error decoupling), higher sampling density, and a repeatability standard deviation of 0.32 μm for large precision machine tool components. This high-efficiency, high-precision solution is particularly suitable for automated flatness inspection of large-scale components, providing a reliable metrology solution for integrated measurement-machining of flatness on precision machine tool critical components. Full article

The aim of almost every production firm is to gain maximum profit along with customer satisfaction. The formation of imperfect products is an obvious process in a production system, which is not a good thing from a business point of view. This paper considers an inventory model for an imperfect production system. All the imperfect products are assumed to be reworkable. An investment occurs for in-process inspection to reduce the rate of formation of imperfect items. A comparison is performed with a production system without in-process inspection to demonstrate the effectiveness of the model. The study shows that the implementation of in-process inspection significantly reduces the total cost of the system as compared to a production system without in-process inspection. The results obtained show that the use of in-process inspection can reduce the total cost by up to 9.3%. Moreover, reducing the formation of defective items saves energy as well as resources. In addition, to reduce carbon emissions, a penalty is implemented on carbon emissions caused by manufacturing, reworking, disposal, and indirect emissions caused by the transportation of disposed items to the treatment facility. As everyone should now be concerned about the environment, green technology is implemented to reduce the amount of carbon emissions to some extent. A classical optimization technique is used to achieve decision variables, i.e., optimal production quantity (Q), fraction of profit invested in in-process inspection ($P_f$), and green technology investment (G), such that the total cost of the system is minimized. A sensitivity analysis is performed to determine the effects of various parameters on the decision variables and total cost. Maple 18 and Mathematica 11 software are used for mathematical work and graphical representation. Full article

This study aims to provide an overview of Zero Defect, Zero Waste, and non-destructive inspection technologies (NDITs), which play a crucial role in the early detection of defects and material consumption in industrial processes. Integrating Zero Defect and Zero Waste strategies with non-destructive inspection technologies supports Industry 4.0 by using advanced sensors, robotics, and AI to create smart manufacturing systems that optimise resources and improve quality. The analysis covers the main functionalities, applications and technical specifications of several NDITs to automate the inspection of industrial processes. It also discusses both the benefits and limitations of these techniques through benchmarking. Deploying inspection as a service solution based on NDITs with data-driven decision-making Artificial Intelligence for in-process or in-line inspection policies increases production control by reducing material waste and energy use, and by optimising the final factory cost. After a comprehensive assessment, this paper aims to examine and review recent developments in the Zero Defects and Zero Waste field due to emerging non-destructive inspection systems, and their combination with other technologies, such as augmented reality. Advances in sensors, robotics, and decision-making processes through Artificial Intelligence can increase Human–Robot Collaboration in the inspection process by enhancing quality assurance during production. Full article

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed. Full article

One of the key aspects of the automation of machining processes is the elimination of manual measurements. This is crucial in the production of precision parts, where the absence of in-process control can lead to an increased number of non-compliant parts, resulting in financial losses for the company. In addition to economic considerations, environmental care is a fundamental requirement for manufacturing companies. While many efforts focus on finding environmentally friendly coolants or reducing machining time, researchers often overlook the impact of the measurement method on the balanced development of machining. The conditions inside CNC machines are quite demanding in terms of maintaining measurement stability. For this reason, this paper presents a comparative study of two types of machine inspection probes. The influence of the measurement axis and the effect of returning the probe to the magazine on the accuracy of the measurement were examined. This study revealed that the probe with a kinematic resistive design has a higher measurement uncertainty (2.7 µm) than a probe based on strain gauges (0.6 µm). This paper emphasizes the positive impact of the conducted activity on the sustainability of machining, highlighting benefits such as resource savings, energy savings, and positive effects on the health and safety of operators. Full article

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s. Full article

Although robotic vision systems offer a promising technology solution for rapid and reconfigurable in-process 3D inspection of complex and large parts in contemporary manufacturing, measurement accuracy poses a challenge for its wide deployment. One of the key issues in adopting a robotic vision system is to understand the extent of its measurement errors which are directly correlated with the calibration process. In this paper, a possible source of practical and inherent measurement uncertainties involved in the calibration process of a robotic vision system are discussed. The system considered in this work consists of an image sensor mounted on an industrial robot manipulator with six degrees of freedom. Based on a series of experimental tests and computer simulations, the paper gives a comprehensive performance comparison of different calibration approaches and shows the impact of measurement uncertainties on the calibration process. It has been found from the error sensitivity analysis that minor uncertainties in the calibration process can significantly affect the accuracy of the robotic vision system. Further investigations suggest that inducing errors in image calibration patterns can have an adverse effect on the hand–eye calibration process compared to the angular errors in the robot joints. Full article

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair. Full article

The Barkhausen noise (BN) analysis is a method increasingly used for the post-process assessment of thermo-mechanical surface damages from grinding and has several advantages compared with the established nital etching method. In-process measurement of the BN has not been used industrially yet, but the basics have already been developed and promise time savings by avoiding time spent on inspections after grinding. Furthermore, it bears potential for the optimization of grinding processes and, in perspective, a process control. In the present work, the suitability of in-process BN analysis for the detection of thermo-mechanically influenced near-surface regions was assessed. Case-hardened workpieces were ground, and BN signals were related to the properties of the surface and subsurface area, in particular residual stresses, microstructure and surface hardness after grinding. The results show a clear dependency of BN on surface layer properties that allows for an in-process detection of detrimental changes in the surface state. Special attention was paid to the differences between in-process and post-process measured signals, and the suitability of the different measurement parameters for in-process detection was investigated. Full article

Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile robot capable of autonomously inspecting aircraft systems and providing feedback to workers. The mobile robot can follow operators and localise the position of the inspection using a thermal camera and 2D lidars. While moving, a depth camera collects 3D data about the system being installed. The in-process monitoring algorithm uses this information to check if the system has been correctly installed. Finally, based on these measurements, indications are shown on a screen to provide feedback to the workers. The performance of this solution has been validated in a laboratory environment, replicating a trailing edge equipping task. During testing, the tracking and localisation systems have proven to be reliable. The in-process monitoring system was also found to provide accurate feedback to the operators. Overall, the results show that the solution is promising for industrial applications. Full article

The in-process inspection procedure is one of the critical operations in the active pharmaceutical ingredients manufacturing process. This study aims to improve the performance of the IPI service system in terms of three main criteria, namely service level, cycle time, and maximum tardy time. In solving this multiple-criteria decision-making problem, the proposed study seeks to redesign three process control factors, namely the service configuration, the dispatching rule, and the scheduling rule. The problem is solved using the Taguchi robust design methodology. Since the Taguchi method handles parameter design problems with only one criterion, Technique for Order Preference by Similarity to an Ideal Solution, a multiple-criteria decision-making method, is used to provide a surrogate response to the Taguchi method. The numerical results show that the redesigned IPI system improves the service level by 28.75%, the cycle time by 18.32%, and the maximum tardy time by 22.22%. Full article

The technology of hairpin welding, which is frequently used in the automotive industry, entails high-quality requirements in the welding process. It can be difficult to trace the defect back to the affected weld if a non-functioning stator is detected during the final inspection. Often, a visual assessment of a cooled weld seam does not provide any information about its strength. However, based on the behavior during welding, especially about spattering, conclusions can be made about the quality of the weld. In addition, spatter on the component can have serious consequences. In this paper, we present in-process monitoring of laser-based hairpin welding. Using an in-process image analyzed by a neural network, we present a spatter detection method that allows conclusions to be drawn about the quality of the weld. In this way, faults caused by spattering can be detected at an early stage and the affected components sorted out. The implementation is based on a small data set and under consideration of a fast process time on hardware with limited computing power. With a network architecture that uses dilated convolutions, we obtain a large receptive field and can therefore consider feature interrelation in the image. As a result, we obtain a pixel-wise classifier, which allows us to infer the spatter areas directly on the production lines. Full article

Directed energy deposition (DED) is a family of additive manufacturing technologies. With these processes, metal parts are built layer by layer, introducing dynamics that propagate in time and layer-domains, which implies additional complexity and consequently, the resulting part quality is hard to predict. Control of the deposit layer thickness and height is a critical issue since it impacts on geometrical accuracy, process stability, and the overall quality of the product. Therefore, online feedback height control for DED processes with proper sensor strategies is required. This work presents a novel vision-based triangulation technique through an off-axis located CCD camera synchronized with a 640 nm wavelength pulsed illumination laser. Image processing and machine vision techniques allow in-line height measurement right after metal solidification. The linearity and the precision of the proposed setup are validated through off-and in-process trials in the laser metal deposition (LMD) process. Besides, the performance of the developed in-line inspection system has also been tested for the Arc based DED process and compared against experimental weld bead characterization data. In this last case, the system additionally allowed for the measurement of weld bead width and contact angles, which are critical in first runs of multilayer buildups. Full article

The growth of the automated welding sector and emerging technological requirements of Industry 4.0 have driven demand and research into intelligent sensor-enabled robotic systems. The higher production rates of automated welding have increased the need for fast, robotically deployed Non-Destructive Evaluation (NDE), replacing current time-consuming manually deployed inspection. This paper presents the development and deployment of a novel multi-robot system for automated welding and in-process NDE. Full external positional control is achieved in real time allowing for on-the-fly motion correction, based on multi-sensory input. The inspection capabilities of the system are demonstrated at three different stages of the manufacturing process: after all welding passes are complete; between individual welding passes; and during live-arc welding deposition. The specific advantages and challenges of each approach are outlined, and the defect detection capability is demonstrated through inspection of artificially induced defects. The developed system offers an early defect detection opportunity compared to current inspection methods, drastically reducing the delay between defect formation and discovery. This approach would enable in-process weld repair, leading to higher production efficiency, reduced rework rates and lower production costs. Full article

The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves $99.74\%$ classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard. Full article