Industrial Machine Research Articles

Voice User Interface based control for Industrial machine tools

To meet productivity goals, machining operations require high levels of efficiency to achieve the desired manufacturing quality in the available time. Additionally, machine tools can lead to safety-critical scenarios due to the high process dynamics and hazardous forces and torques. Therefore, workers need to exhibit special care while interacting with certain kinds of machines. Operational safety can be increased by eradicating the dependence on tangible control of machine tools. Current developments in Artificial Intelligence, especially in natural language processing, lead to increased utilization of speech-based interaction between humans and machines. However, these developments are mainly limited to areas of consumer electronics. Nevertheless, this type of human-machine interaction offers great potential for machining operations. Artificial neural network architectures like Transformer have rapidly emerged as the dominant architecture for natural language processing, overtaking alternative neural architectures such as Convolutional and Recurrent Neural Networks in performance for natural language understanding and natural language generation tasks. The architecture scales with training data and model size, enables efficient parallel training, and captures wide-ranging sequence features. These advancements can be used for enhancing the ease of use and safety of machine tools, which are often safety critical. In this article, a Voice-User-Interface based control of machine tools is conceptually outlined. Such a control can increase the efficiency of the workers, as it eradicates the need of hand usage. It further increases the safety of workers operating the machines and can detect anomalous sounds corresponding to machine failures during the machining process. The voice is first passed through a speech-to-text pipeline for converting the speech signals to relevant text, the text output is mapped to machine commands and passed to the physical machine. The implementation of the setup guarantees the speech-based control – even for safety-critical tasks -of machine tools. Moreover, this paper outlines the benefits and challenges of the developed VUI architecture.

Welding of 20 mm thick EH40 steel by means of a single-pass hybrid laser-arc welding technique

In the naval applications, as well as for infrastructure and construction sectors, welding of thick steel plates (>15 mm) has become of crucial importance. Nowadays, the common techniques involve several passes of traditional Gas Metal Arc Welding (GMAW) to fill a gap with a precise geometry. These techniques are, inevitably, time consuming due to the necessity of multiple passes, material consuming because of the volume of the gap which needs to be filled, and complicated due to the precise preparation of the joints. To overcome these drawbacks, we propose here a Hybrid Laser-Arc Welding (HLAW) technique in a configuration which allowed us to achieve a sound weld of two plates, 20 mm thick, of naval steel EH40 in a single pass. The goal has been obtained with a 15 kW fiber laser and a welding speed of 1500 mm/min. The most important result of this research is that the welds have been performed in a simple butt configuration, with straight edges of the plates and without any chamfer or additional preparation. Moreover, the length of the weld seam, 1 meter, guarantees the stability and the solidity of the process, its scalability over longer plates, and thus, an easy implementation on an industrial machine for massive production.

Dictionary Learning Augmented Beamforming for Industrial Machine Inspection With Microphone Array

Dictionary Learning Augmented Beamforming for Industrial Machine Inspection With Microphone Array

Measurability of quality characteristics identified in latent spaces of Generative AI Models

Deep Learning can learn complex properties from image datasets, which are difficult to model with traditional machine vision algorithms, inherently in the form of disentangled latent spaces. With latent spaces of Generative AI models, a feature extraction method to access these properties can be implemented. This work evaluates whether the learned properties can be measured in the latent space. Quantity and quantity-value scale properties and the measurability of the dimensional quality characteristic ‘filling degree’ using a linear calibration function are demonstrated for an industrial machine vision application. An uncertainty indicator between 0.4–0.9 mm is estimated for the latent space measurements.

Jugoslovenska ekonomska pomoć Albaniji 1945–1948: motivi, opseg i ishod

The study focuses on the phenomenon of Yugoslav economic aid to Albania following WWII. The Yugoslav communist leadership’s idea to unite Yugoslavia and Albania into a single state began to be implemented in the field of economy, with the transfer of industrial machines and products from Yugoslavia to Albania and the establishment of a common economic and customs space between the two countries. Around 700 Yugoslav experts and workers were sent to Albania to help implement the country’s first five-year economic plan, which had previously been agreed upon with the Yugoslav five-year plan. Over 1000 Albanian students and youth received professional training at Yugoslav faculties, secondary vocational schools, and industries. In 1948, the ambitious project came to an end with the abolition of diplomatic relations between the two countries and the expulsion of Yugoslav citizens from Albania.

Research on optimal path planning technology for industrial robots based on improved IDE algorithm

Abstract Realizing efficient and accurate positional movement of industrial robots is one of the key technologies to accelerate industrial development. In this paper, a four-wheel-drive McNamee mobile robot is selected as the research object, and the improved IPSO-IDE algorithm is proposed, which is combined with a PID controller to realize the accurate positioning and optimal path planning of industrial machines. The experimental results of robot kinematic parameter calibration show that the distance error of the robot calibrated with the algorithm is greatly reduced, and not only that, the IPSO-IDE algorithm reduces the average distance error of the robot to only 0.124 mm, which is much smaller than that of the PSO algorithm and the DE algorithm. The IPSO-IDE algorithm is capable of planning the optimal motion path in a short time, as demonstrated by the results of robot map environment simulation experiments. In the simulated map environment of 10 m × 10 m, the convergence speed of the algorithm’s planning path is 12.69 seconds, and the generated optimal path length is 15.0221 m, which is better than other algorithms. In conclusion, the proposed IPSO-IDE algorithm has an excellent performance in terms of path-planning capabilities for industrial robots.

Development of an AR-based application for assembly assistance and servicing

Recent advancements in the field of digital manufacturing, especially the adoption of fast connectivity through 5G, Digital Twins and Extended Reality (XR) in manufacturing, offer new possibilities for innovative and effective design of production workflows. Augmented Reality (AR) can assist in speeding up the assembly and maintenance processes by facilitating the operators to perform these processes without dealing with detailed paper manuals. AR-based interactive user interfaces can support operators to be more productive by visualizing certain product and component 3D models dynamically, in addition to assembly steps along the corresponding manufacturing process. Likewise, AR-based applications can facilitate the setup and maintenance service of a machine by providing advanced machine visualization and digitalized information. This paper presents a conceptual model and case-based demo applications adopting AR technology for the maintenance of an industrial machine and supporting operators during the assembly process of a specific product. The goal is to improve productivity by reducing the processing time and minimize the operator training time. Moreover, the proposed AR application is integrated with the non-conformance reporting feature which helps to address the quality related issues quickly and efficiently, leading to a reduction in the number of nonconformities. Two case studies demonstrate the relevance of the proposed conceptual model and testing of the applications.

Influence of illumination on the image-based classification accuracy of wear on milling tools

For reasons of cost savings and resource conservation, it is of great interest to the machining industry to utilise the entire service life of milling tools. Image-based machine learning methods are suitable for classifying tool wear. In this context, the influence of two different illumination sources during image data collection on the performance of a CNN for the classification of wear on milling tools was investigated. The series of tests shows that both the use of diffuse illumination and direct illumination are suitable for collecting image data on the basis of which a high-performance CNN can be trained.

Performance evaluation of hybrid biodegradable oils as a lubricant during cylindrical turning of AISI 304 austenitic stainless steel

<abstract> <p>In the engineering sector, machining plays a significant role. The ability of the cutting zone to dissipate heat has grown in importance for the machining industry’s sustainability. Government legislation is pushing the industry to use fewer conventional lubricants as concerns about the effects on the environment and human health grow. This shifts attention to Minimum Quantity Lubrication (MQL) and biodegradable oils. The purpose of this study is to show how well two vegetable oils, in their raw forms, perform as cutting fluids during the MQL turning process of AISI 304 stainless steel. Each vegetable oil’s physiochemical and lubricating qualities were examined separately. After that, the two oils that comprised the hybrid vegetable oil were blended at a ratio of 0.5:0.5. During machining with an external threading tool, the hybrid vegetable oil was compared to its equivalent mineral-based oil in terms of cutting temperature and surface roughness. The Taguchi L9 orthogonal array was used in the study. According to the data, the cutting temperature was lowest when cutting with mineral oil, and highest when cutting with a hybrid mixture. In general, the mineral oil produced a reduced surface roughness compared to the vegetable oil mixture by about 68.6%. The combo of palm kernel and yellow orleander oil marginally outperformed mineral by about 2.3% when it came to cutting temperature. The significance of this study is to develop a more sustainable and environmentally friendly lubricants for industrial applications.</p> </abstract>

Evaluation and cultivation method of high-tech value patents for mechanical products.

Evaluation of high value patents is essential for the enterprise's technical layout and innovative product design. The existing research on the patent value needs the support of a large number of professional statistical information and is difficult to directly reflect the technical value. Since technological innovation is the fundamental means to enhance the sustainable competitiveness of enterprises. Therefore, a high-tech value patent evaluation and cultivation method for engineering designers need to be proposed. Firstly, the patent samples based on design methodology are retrieved and the indicators for evaluating technical value are summarized and the rationality of the evaluation indicators is verifier through empirical study based on improved evidence theory. Secondly, based on principal component analysis and factor analysis, a high-tech value patent evaluation and cultivation method is proposed. Finally, the proposed method is applied to identify the high-tech value patents in the cutting machine industry, and structural improvement is made based on this patent to demonstrate the cultivation process of high-tech value patents. The proposed method provides a clear guiding direction for the cultivation of high novelty patents and enterprise innovative product design. The method can effectively assist the product R&D activities of engineering designers and enhance the sustainable competitiveness of enterprises from a technological perspective.

Tool Condition Monitoring for milling process using Convolutional Neural Networks

Tool condition monitoring has emerged as an essential approach in the machining industry to drive cost reductions and enhance productivity. In the era of Industry 4.0, tool wear monitoring can be done using various Internet of Things sensors, such as those recording acoustic emission, vibration, temperature, and pressure. Signals acquired can be processed by extracting numeric features or converting them into images.In this study, experimental milling campaigns with different input machine parameters were conducted, leading to the creation of a dataset based on tool wear measurements and acoustic emission registrations. Then we investigated a deep convolutional neural networks approach to classify image representations of the registered acoustic signals, aiming to predict the status of tool wear. Since the dataset was imbalanced, a class weights approach was used to improve model performance. An accuracy exceeding 90% was reached, demonstrating a great potential of the model in predicting tool wear state.

Territorial concentration of the production of electrical machines in the branch policy of the Soviet government in the first half of the 1920s and Ukrainian electrical machine building industry: historiography and reality

In the Soviet territories of the former Russian Empire in the early 1920s, an attempt was made to carry out global concentration of production - the geographical concentration of production not of any range of a particular type of product, but of its entire volume. That is, the concentration of the type of industrial activity as such was assumed, the possibility of it in socialist society was justified by K. Marx. Due to objective and subjective factors, the only industry where such an attempt became possible was electrical machine-building, and during 1920�1923. the production of electric machines began to concentrate in Moscow and Petrograd, which undermined the potential of Ukrainian electric machine branch. Considering the political importance for the Soviet government of neutralizing this negative, the concentration of electrical machine-building activities in Soviet capitals from 1925 began to be presented as a result of the industrial policy of Tsarist Russia, what moved into historical works. However, the information available in the documents completely refutes this view, while simultaneously confirming the purposeful actions of the Soviet government to curtail the electrical machine industry in Ukraine.

Effect of formulations over a Periodic Capacitated Vehicle Routing Problem with multiple depots, heterogeneous fleet, and hard time-windows.

This article presents a novel contribution to the Periodic Vehicle Routing Problem (PVRP) by introducing two new problem formulations which differ in the usage of the crucial flow variable. The formulations are tailored to meet the specific demands of the vending machine industry in Medellín, Colombia, and require considering a PVRP with time windows, a heterogeneous fleet, and multiple depots. This scenario, tailored to address real-world complexity and computational challenges, brings to light an exponential surge in integer variables as customer numbers increase. The research presents an analysis of PVRPs that include the four mentioned attributes, compares their similarities, and delves into their nuances. From the analysis it is derived that the variant of the PVRP presented has not been considered previously, taking into account not only these attributes, but also the restrictions involved. Empirical experiments are conducted to examine the intricate interplay between the two proposed formulations, highlighting their impact on the performance of the GUROBI solver. The study provides valuable insights into problem-specific adaptations and algorithmic approaches, emphasizing the significance of the proposed formulations in addressing multifaceted PVRPs. In essence, this research positions the introduction of these two formulations as a pioneering step, offering a new paradigm for approaching the PVRP.

Environmental Effects of Alkaline Degreasing for Automotive, Boat and Machine Industry Purposes

Environmental Effects of Alkaline Degreasing for Automotive, Boat and Machine Industry Purposes

The use of AI algorithms in architecture, engineering and construction: A tool for crisis prevention? The uncertainty perspective

Within the Architecture, engineering and construction (AEC) sector we see movements toward greater use of industrial robots, machine learning, algorithms, and other artificial intelligence (AI) tools. Yet, the AEC industry, despite being one of the largest fields on a global scale, is known for being the slowest to digitalize and innovate. Factors such as unrecognizing the value of digitalization by the decision-makers and making safety-related decisions under high levels of uncertainty, appear to be critical in preventing successful large-scale digitalization. This situation raises multiple questions from a risk science perspective. How, among other things, might the expansion of AI and more specifically AI algorithms usage in the AEC field affect uncertainties, and could AI be considered a tool for preventing crises? To obtain responses to these questions, we conducted 21 semi-structured, in-depth interviews with AEC employees who are currently using AI and AI algorithms or will soon be doing so in their everyday work. Our findings show potential for wider use within the AEC field, subject to overcoming knowledge gaps. Moreover, although having the potential to reduce some uncertainties, the increased use of AI and AI algorithms appears to be introducing an entirely new set of uncertainties. As a result, although AI may effectively prevent certain crises and be regarded as crisis prevention tool, its inadequate implementation could potentially create new risks.

Smart manufacturing system based on industrial internet of things

The primary challenge in the manufacturing industry today is the subpar productivity and efficiency in production processes. This predicament can be attributed to the widespread utilization of suboptimal production systems and the underutilization of advanced technology in manufacturing. Furthermore, the lack of accurate production data, limited employee involvement in production processes, and the high costs associated with production and machine maintenance have emerged as significant concerns in the contemporary manufacturing landscape. In response to these challenges, the adoption of Smart Manufacturing Systems (SMS) based on the Industrial Internet of Things (IIoT) has become crucial to address these issues andenhance overall production quality and efficiency. The SMS IIoT system empowers companies to gather real-time data from various industrial devices and machines. This data is subsequently analyzed to optimize production processes, resulting in increased efficiency and productivity. With this system in place, companies can closely monitor production processes in real-time and promptly detect and address defective or non-compliant products at the onset of production. The research results show an availability value of 88%, indicating that the machine operates 88% of the scheduled time. A Performance value of 100% indicates that the engine is operating at the desired maximum speed. OEE is calculated as the product of Availability, Performance, and Quality of Rate. With a value of 88%, OEE reflects the overall level of effectiveness of the machine in the production process. This paper highlights the significance of IIoT-based Smart Manufacturing Systems in addressing the prevailing challenges encountered by the manufacturing industry.

Education and Industrial Revolution 4.0: Prospects and Challenges to ASEAN Education in the Case of Philippine Education

ASEAN Education is confronted with the emerging paradigms of twenty-first century education brought about by the rapid globalization process and the dawning of the new industrial revolution popularly dubbed as “IR 4.0” (Industrial Revolution 4.0). IR 4.0 demands a new framework of education – one that readies individuals with appropriate, competent, and highly innovative skills as man-machine collaboration intensifies. Hence, there is now a shift of focus from improving industrial machines to investing more in human capital. This is the pathway ASEAN education is traversing right now. ASEAN Education aims primarily at producing highly skilled service providers who are twenty-first century ready and who will work effectively and efficiently for industries in the IR 4.0. Nevertheless, education is more than being able to stabilize and improve one’s economic standing in life. There is a more noble purpose towards which one undertakes the tedious process of learning. That is: to be fully human and transform the world. Using Paolo Freire’s Critical Pedagogy, the modalities and paradigms of twenty-first century education were critiqued in order to align with this vision. Thus, with the ongoing innovative progress in ASEAN Education, serious considerations arise: “Will the alignment of man and machine to enable new possibilities cause further alienation for humanity?”; “When real classrooms are substituted by virtual classrooms, will healthy social interaction be maintained?”; “Are the trends of the twenty-first century education truly set towards the furtherance of human depth and breadth? Keywords: criticality, education, fourth industrial revolution, globalization

Flexural Torsional Buckling of a Beam from the Dee Bridge of 1846 - Analytical and Finite Element Analyses

The Industrial Revolution in Victorian England was a period marked by rapid development of the machine industry, accompanied by new technologies in metallurgy and a need for transport between economic areas that was fast, cheap and convenient. This is a period when the railway transport arose and in a short time acquired an image familiar to us to this day. Passage of train sets across rivers and hard-to-reach areas requires transport facilities. In this period, an extremely large number of bridges and aqueducts were built, which have different constructive solutions, different technologies are applied and they are made of different materials, depending on the preference of their creator. This paper examines the collapse of one of the beams of the Dee Bridge on 24 May 1847. The analysis carried out shows the irrationality of the cross-sectional dimensions and types of girder supports witch generate large compressive forces in the beam, leading to flexural torsional buckling.

Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology

In the process of industrial production, whether the machine can work stably is an important issue related to production efficiency and production safety. The cleaning of mechanical parts is an important factor affecting the stable operation of mechanical equipment, so the cleaning of mechanical parts is one of the important manufacturing links before assembly and manufacturing. The important value of the application of image recognition technology in the quality inspection of mechanical parts is briefly analyzed, and then the common problems in the quality inspection of mechanical parts are studied, the relevant measures of the application of image recognition technology in the quality inspection of mechanical parts are discussed, and finally the case analysis of the quality inspection of mechanical parts is carried out. The main purpose is to rationally use image recognition technology to complete the quality inspection of mechanical parts, so as to improve the overall level of production automation and give full play to the maximum role of this technology. At present, a variety of mechanical parts classification methods based on deep learning are studied. According to the demand of industrial machine parts, the corresponding machine parts data set is created. According to the production requirements, the SPP structure in the traditional YOLOV5 network is improved, and a new SPC structure is proposed: The YOLOv5 network with SPC structure is characterized by no pooling layer, which improves the accuracy and recall rate compared with the traditional YOLOv5 network. Secondly, the control method of robot grasping mechanical parts is studied. Using the camera to capture the image and the upper computer to calculate the mechanical parts under the camera coordinate system; Through camera calibration and hand-eye calibration, the transformation relationship between the grasping coordinates of mechanical parts in the camera coordinate system and the grasping coordinates of mechanical parts in the robot base coordinate system is obtained. The grasping coordinates and part type information of the mechanical parts in the robot base coordinate system are transmitted from the host computer to the robot through Modbus/TCP communication protocol.

Methods for Electrolyte Treatment and Hexavalent Chromium Reduction in Industrial Electrochemical Machining (ECM)

Electrochemical Machining (ECM) is a metal removal process that operates on the principle of anodic dissolution. ECM has the ability to produce complex shapes in difficult-to-machine alloys with precision and excellent surface finish. However, since most of these alloys contain chromium, one of the major challenges to ECM’s wider utilisation in industry is how to eliminate the resulting hexavalent chromium (Cr+6) from the spent electrolyte. This paper presents methods for controlling the Cr+6 level to within the permitted guidelines for an industry application of ECM, and reducing it to trivalent chromium (Cr+3), to then precipitating it from the electrolyte solution,The workpiece material – Triboloy (Co; Mo; Cr alloy) has hitherto not been machined by ECM and hence no literature exists on how to treat the by-products generated. This research involves experimental analysis of various Cr+6 to Cr+3 chemical reduction methods for ECM of Tribaloy in sodium nitrate electrolyte, using spent electrolyte samples. The reduction in Cr+6 is assessed over time. It was observed that the concentration of Cr+6 decreased within a span of two minutes. Subsequently, the solution pH level was increased by the addition of an alkali to precipitate the Cr+3 ions, which then can be filtered out from the solution. The optimum solution for hexavalent chromium is thus implemented in the industrial electrolyte system for successfully machining the Tribaloy component.