Manufacturing Automation Research Articles

Microbial Dynamics and Quality Monitoring in Biopharmaceutical Production

AbstractProkaryotic cells are pivotal in meeting the global demand for biopharmaceuticals. However, challenges such as the absence of advanced technology for real‐time monitoring, standardized testing methodologies, and quality risk assessment of microbial activity have led to increased production costs, delays, and shortages of biopharmaceutical products. A thorough understanding of how biomolecule production interacts with microbial population structure and function is vital for improving continuous manufacturing and process automation. In this review, we discuss the current microbiological techniques that meet good manufacturing practice requirements in industrial settings, explore the advantages of monitoring and measuring biomass growth efficiency and turnover rates beyond regulatory criteria for product release, and provide a critical assessment of the current state of knowledge on bioassays and engineering tools for biomolecule yield measurement and monitoring. Furthermore, we identify areas for future development, potential applications, and the need for interdisciplinary innovation to drive future research, including advancing bioassays for biopharmaceutical wastewater risk.

Towards a Unified Framework for Software-Hardware Integration in Evolutionary Robotics

The discrepancy between simulated and hardware experiments, the reality gap, is a challenge in evolutionary robotics. While strategies have been proposed to address this gap in fixed-body robots, they are not viable when dealing with populations and generations where the body is in constant change. The continual evolution of body designs necessitates the manufacturing of new robotic structures, a process that can be time-consuming if carried out manually. Moreover, the increased manufacturing time not only prolongs hardware experimental durations but also disrupts the synergy between hardware and simulated experiments. Failure to effectively manage these challenges could impede the implementation of evolutionary robotics in real-life environments. The Autonomous Robot Evolution project presents a framework to tackle these challenges through a case study. This paper describes the main three contributions of this work: Firstly, it analyses the different reality gap experienced by each different robot or the heterogenous reality gap. Secondly, it emphasizes the importance of automation in robot manufacturing. And thirdly, it highlights the necessity of a framework to orchestrate the synergy between simulated and hardware experiments. In the long term, integrating these contributions into evolutionary robotics is envisioned to enable the continuous production of robots in real-world environments.

Biomass Energy from Agricultural Waste: Evaluating Groundnut Vines and Pumpkin Straws for Combustion Performance

Energy demand has risen significantly in developing countries due to rapid population growth and increase in economic activities such as industrialization, automation manufacturing and digitalization. Dependency on conventional sources of energy in both rural and peri-urban is high. Traditional energy resources lead to environmental degradation due to carbon oxide release leading global warming and deforestation which results to hydrological decline; therefore, need to come up with renewable resources from other sources such as agricultural wastes to meet the demand and solve environmental pollution problems. This study carried out an investigation on combustion and performance analysis of pumpkin straws and groundnut vines to establish their energy potential. ASTM standards were used to determine the proximate and ultimate properties. This analysis revealed moisture content (8.7-12.4%), volatile matter (63-69%), fixed carbon (11.5-18.5%), ash content (6.3-8.2%), Sulphur (0.09-0.13%), nitrogen (1.02-1.03%), oxygen (5.01-55.88%), carbon (41.8-43%) and hydrogen (5.96-6.3%). Calorific values for the briquettes were above 4389 kCal, which is above most biomass. The density and compressive strength were above 0.63 g/cm3 and 17N/mm2 respectively from compaction pressure of 150, 250 and 350 kg/cm2. The burning time was between 4 and 5.3 min, showing an increase with compaction pressure, burning rate ranged between 4.3 and 2.9 g/min, a decrease with compaction pressure increase. The specific fuel consumption portrayed an inverse relationship with calorific value and a direct relationship with compaction pressure, ranging between 166 to 176 grams per litre. Temperature-weight analysis revealed thermo stability of this feedstock, portraying resemblance to proximate analysis. Having exhibited close relationship to other agricultural wastes used for fuel, groundnut vines and pumpkin straws are a solution to environmental degradation by reducing deforestation and emissions. It is recommended that further research on mixing this feedstock with other agricultural wastes as well as making and testing of pellets be carried out to maximize energy output.

Intelligent mobile robot navigation in unknown and complex environment using reinforcement learning technique

The usage of mobile robots (MRs) has expanded dramatically in the last several years across a wide range of industries, including manufacturing, surveillance, healthcare, and warehouse automation. To ensure the efficient and safe operation of these MRs, it is crucial to design effective control strategies that can adapt to changing environments. In this paper, we propose a new technique for controlling MRs using reinforcement learning (RL). Our approach involves mathematical model generation and later training a neural network (NN) to learn a policy for robot control using RL. The policy is learned through trial and error, where MR explores the environment and receives rewards based on its actions. The rewards are designed to encourage the robot to move towards its goal while avoiding obstacles. In this work, a deep Q-learning (QL) agent is used to enable robots to autonomously learn to avoid collisions with obstacles and enhance navigation abilities in an unknown environment. When operating MR independently within an unfamiliar area, a RL model is used to identify the targeted location, and the Deep Q-Network (DQN) is used to navigate to the goal location. We evaluate our approach using a simulation using the Epsilon-Greedy algorithm. The results show that our approach outperforms traditional MR control strategies in terms of both efficiency and safety.

Enhancing manufacturing efficiency and quality through automation and deep learning: addressing redundancy, defects, vibration analysis, and material strength optimization

This article explores the integration of automation and deep learning in modern manufacturing to address critical challenges such as redundancy, defects, vibration analysis, and material strength. As manufacturing processes evolve, the need for more sophisticated methods to optimize production efficiency and product quality becomes paramount. Automation, coupled with deep learning techniques, offers powerful tools for enhancing manufacturing processes. These technologies enable predictive maintenance, reducing downtime by identifying potential equipment failures before they occur. Furthermore, deep learning algorithms can analyse complex data sets to detect defects in products with greater accuracy and speed than traditional methods. Vibration analysis, a key aspect of predictive maintenance, benefits from automated systems that monitor and diagnose issues in real-time, preventing costly disruptions. Additionally, deep learning models can assess material strength and predict potential failures, ensuring that products meet rigorous safety and quality standards. The synergy between automation and deep learning not only streamlines manufacturing processes but also enhances the ability to adapt to changing conditions, thereby minimizing operational inefficiencies. This article highlights the transformative impact of these technologies on the manufacturing industry, illustrating their potential through case studies and practical examples. By addressing key challenges such as redundancy and defects, automation and deep learning contribute to the creation of more reliable, efficient, and resilient manufacturing systems. The insights provided in this study underscore the importance of continued innovation in integrating these technologies to maintain a competitive edge in the rapidly evolving manufacturing landscape.

30th international conference on flexible automation and intelligent manufacturing (FAIM2021) special issue: modern manufacturing systems management

30th international conference on flexible automation and intelligent manufacturing (FAIM2021) special issue: modern manufacturing systems management

Monitoring process stability in robotic wire-laser directed energy deposition based on multi-modal deep learning

Robotized wire-laser directed energy deposition (DED) is a highly anticipated technology with excellent material utilization and deposition efficiency for fabricating complex-structure metal parts. Nevertheless, the immaturity of the monitoring and control techniques for the deposition process stability are the main challenges in achieving automatic and repeatable manufacturing of metal parts. This study proposes a novel approach to monitor the process stability, i.e., the intersection point between the laser beam and the wire to the top layer distance (IPTD), in robotic wire-laser DED based on a designed multi-modal IPTD estimation model and coaxial visual sensing. The novelty is that directly extracting deposition height features from coaxial molten pool images is attempted based on deep learning. In particular, the designed multi-modal IPTD estimation model, consisting of a convolutional neural network (CNN) part and a fully connected network, combines the features of molten pool images and process parameters to estimate the IPTD state. Six model architectures and three image pixel sizes are used in the IPTD classification tasks to determine the CNN part architecture and the input image pixel size of the multi-modal deep learning model based on classification results. The regression performances of established single-modal and multi-modal IPTD estimation models are studied and discussed. Compared to other model architectures, the ResNet-18 model possesses the highest convergence rate during training and the best classification accuracy of 0.9975 on the testing dataset with the image pixel size of 180 × 105. The fitting accuracy and generalization performance of the multi-modal IPTD estimation model are marked superior to the single-modal IPTD estimation model. Validation experiments reveal the effectiveness of the proposed monitoring approach of process stability. This study will lay a solid foundation for the future control of process stability in robotic wire-laser DED.

AI-Driven Automation in Custom Manufacturing: Enhancing Precision and Efficiency in Automotive Components Production

AI-Driven Automation in Custom Manufacturing: Enhancing Precision and Efficiency in Automotive Components Production

EA-CTFVS: An Environment-Agnostic Coarse-to-Fine Visual Servoing Method for Sub-Millimeter-Accurate Assembly

Peg-in-hole assembly, a crucial component of robotic automation in manufacturing, continues to pose challenges due to its strict tolerance requirements. To date, most conventional peg-in-hole assembly algorithms have been validated only within simulated environments or under limited observational scenarios. In this paper, an environment-agnostic coarse-to-fine visual servoing (EA-CTFVS) assembly algorithm is proposed. Firstly, to solve the frequent issue of visual blindness during visual servoing, a bottleneck pose is proposed to be used as the desired pose for the visual servoing. Secondly, to achieve accurate assembly, a coarse-to-fine framework is constructed, in which a rough pose is given by the coarse controller to remove large initial alignment errors. For the fine controller, a twin network-based fine controller is provided to improve assembly accuracy. Furthermore, EA-CTFVS utilizes the Oriented Bounding Box (OBB) of objects as the input for visual servoing, which guarantees the system’s ability to operate effectively in diverse and complex scenes. The proposed EA-CTFVS achieves a successful assembly rate of 0.92/0.89 for initial alignment errors of 15/30 cm and 0.6 mm tolerance in real-world D-sub plug assembly tasks under complex scenarios.

Preface

With the continuous progress of science and technology, the innovation and development in the fields of advanced manufacturing and materials engineering has become an important force to promote global industrial upgrading. It is against this backdrop that the 2024 10th International Conference on Applied Materials and Manufacturing Technology (ICAMMT 2024) gathered scholars, researchers, and industry experts in Guangzhou, China from May 22nd to 23rd, 2024 via hybrid form to share their insights and advancements in the domains of applied materials and manufacturing technology. ICAMMT is a premier interdisciplinary platform for the presentation of new advances and research results in applied materials and manufacturing technology. ICAMMT 2024 brought together leading scientists, researchers, practitioners, and other personnel to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the domains of interest from around the world to share their experiences and research results in advanced manufacturing and materials engineering, and exchange views. Themed around advanced manufacturing and materials engineering, the Conference set up a number of topics, including but not limited to: Research and Development of New Materials, Mechanical Behavior & Fracture, Advanced Forming Manufacturing and Equipment, Manufacturing Systems and Automation, Measure Control Technologies and Intelligent Systems, etc. Through in-depth discussion of these topics, the Conference gathered global wisdom and strength, and jointly promoted the innovative development of applied materials and manufacturing technology. The Conference agenda was designed to cater to a wide range of interests and expertise levels, including one main forum and two sub-forums. One of the highlights of the event was keynote speeches delivered by eight renowned experts in their respective fields at home and abroad. They discussed about the latest research results and experiences of advanced manufacturing technology and materials engineering, and shared their unique views on the development trend and research and development direction of the industry. Topics cover Discussion on Low-Carbon Development Approach of China’s Iron and Steel Industry (Prof. Liejun Li, South China University of Technology, China), High Performance Organic-Inorganichybrid Cement-Based Materials (Prof. Jiangxiong Wei, South China University of Technology, China), Applications of Light Alloys in Battery-Powered Electric Vehicles (Prof. Henry Hu, University of Windsor, Canada), Machine Vision and Human-Computer Interaction (Prof. Wei Xie, South China University of Technology, China), etc. These speeches not only provided a comprehensive overview of the latest trends and developments but also offered insights into the potential challenges and opportunities that lie ahead. Last but not the least is our gratitude. We would like to express our sincere thanks to all the authors, speakers, committee members, supporters, and all those involved for the complete success of the Conference, which has then given rise to this Proceedings of selected papers. Special thanks to the members of Journal of Physics: Conference Series for their efforts in making this volume published. The Committee of ICAMMT 2024 List of Committee Member is available in this Pdf.

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

Analysis on the Development Trend of Mechanical Design and Manufacturing and its Automation Technology under the Background of Intelligent Manufacturing

With the rapid development of intelligent manufacturing, mechanical design and manufacturing and its automation technology are facing new opportunities and challenges. This paper starts from the advantages of mechanical design and manufacturing and automation technology, on this basis, discusses the development under the background of intelligent manufacturing machinery design and automation technology strategy and trend, including improve the independent innovation ability, improve the management and supervision system, strengthen the talent training and introduction and promote the sustainable development, etc. Research shows that the development of mechanical design and manufacturing and its automation technology will provide strong support for the promotion of intelligent manufacturing, and promote the development of manufacturing to a higher level and higher quality.

A Survey on AI Integration into Industry 5.0

Industry 5.0 (IR 5.0) is a modern production model focused on human-machine collaboration. The goal is to maintain a balance between machine and human interaction, with an emphasis on creative production and customization. Artificial intelligence (AI) will play a key role in IR 5.0, enabling intelligent manufacturing and transforming many aspects of society. Technologies such as AI, Internet of Things (IoT), Blockchain, Virtual Reality (VR)/Augmented Reality (AR), Big Data Analytics and Cyber-Physical Systems (CPS) are essential to achieve the goals of an intelligent society. This article explores the integration of AI in IR 5.0. However, there are some challenges to overcome such as data security, ethical concerns, employee training, black box AI, etc. Despite its challenges, AI integration to IR 5.0 promises to drive manufacturing automation, efficiency, and customization. To ensure inclusive and sustainable development, the social implications and impacts of IR 5.0 must be carefully considered.

Reshoring manufacturing: the influence of industry 4.0, Covid-19, and made-in effects

AbstractEmpirical investigations of how the reshoring of manufacturing is affected by Industry 4.0 technologies, supply chain disruptions, and made-in effects are rare in the extant academic literature. This paper contains an empirical analysis of how these variables affect reshoring and reshoring intentions. Results from a 2022 questionnaire survey including 152 offshoring manufacturing firms show that reshoring and reshoring intentions are associated positively with investments in automation in manufacturing, and with employee made-in. Results also showed that while Covid-19 associated disruptions increased firms’ reshoring intentions equally across firm sizes, smaller and larger firms reacted quite differently towards more well-known disruption types: larger firms decreasing reshoring intentions with higher levels of uncertainty and smaller firms increasing reshoring intentions with higher levels of uncertainty. These results point to the importance of creating consciousness about the dynamics of production localization and how firm-level and situation-specific contingencies may interfere with Industry 4.0 technology-, supply chain disruption-, and made-in effects on strategic reshoring decisions.

Synthetic biology for Monascus: From strain breeding to industrial production.

Traditional Chinese food therapies often motivate the development of modern medicines, and learning from them will bring bright prospects. Monascus, a conventional Chinese fungus with centuries of use in the food industry, produces various metabolites, including natural pigments, lipid-lowering substances, and other bioactive ingredients. Recent Monascus studies focused on the metabolite biosynthesis mechanisms, strain modifications, and fermentation process optimizations, significantly advancing Monascus development on a lab scale. However, the advanced manufacture for Monascus is lacking, restricting its scale production. Here, the synthetic biology techniques and their challenges for engineering filamentous fungi were summarized, especially for Monascus. With further in-depth discussions of automatic solid-state fermentation manufacturing and prospects for combining synthetic biology and process intensification, the industrial scale production of Monascus will succeed with the help of Monascus improvement and intelligent fermentation control, promoting Monascus applications in food, cosmetic, agriculture, medicine, and environmental protection industries.

Integrated Subsystems of Materials and Information Flow for Continuous Manufacturing of Coal and Steel

Abstract With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technologies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Collaborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous production is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel production are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manufacturing for continuous production.

Keycap character detection model based on target classification

Abstract The production process of keycaps may result in surface character defects. The existing keycap character defect detection technology has low efficiency and accuracy, hindering the automation of keycap manufacturing. Addressing the challenge of extracting the entire keyboard from illuminated regions, this paper proposes a method using image feature points for template matching to extract the region of interest. Subsequently, leveraging prior knowledge, the paper addresses character segmentation and extraction on the keyboard. For character detection, the paper preprocesses images and classifies them by using an S-VGG network, yielding corresponding labels.

LDCNet: Limb Direction Cues-Aware Network for Flexible HPE in Industrial Behavioral Biometrics Systems

2D Human pose estimation (HPE) has been widely used in the many fields such as behavioral understanding, identity authentication, and industrial automatic manufacturing. Most of the previous studies have encountered many constraints, such as restricted scenarios and strict inputs. To solve this problem, we present a simple yet effective HPE network called limb direction cues-aware network (LDCNet) with limb direction cues and differentiated Cauchy labels, which can efficiently suppress uncertainties and prevent deep networks from over-fitting uncertain keypoint positions. In particular, LDCNet suppresses the uncertainties from two aspects. (1) A differentiated Cauchy coordinate encoding method is designed to reveal the limb direction information among adjacent keypoints. (2) Jeffreys divergence is introduced as loss function to measure the prediction heatmap and ground-truth one. Positions of keypoints are perceived at the limb direction based deep network in an end-to-end manner. An extensive study on two benchmark data sets (i.e., MS COCO and MPII) illustrates the superiority of the proposed LDCNet model over state-of-the-art approaches.

Semi Automated Welding Machine Using PLC

Abstract: In this project, we will be doing Design,Analysis & Manufacturing automation for circular parts welding with uniform weld structure usingPLC. We will be designing & manufacturing the turntable, which will be rotating at specific requiredspeed depending upon the requirement of fillet material to be added. Further, the electrode nozzle iskept stationary, which will be in contact with the surface of components to be welded. Therefore, thisproject proposed a detailed design for the conversionof the conventional MIG welding machine (ARC) into an automated machine for welding round components. Together with this main modification, the existing MIG welding machine – (a stationary, downward-facing ARG – HEAD, which has provisions for horizontal and upward movements) isto be modified into portable robotic weldingmachine.

Explore the Application of Machine Vision Technology in the Automation of Machinery Manufacturing

The continuous acceleration of China’s industrialization process has made automation and intelligent technologies increasingly dominant. Implementing these technologies can enhance machining and production efficiency, free up manpower, automate machinery manufacturing processes, and yield greater economic benefits. Thus, this paper outlines the key applications of machine vision technology in modern machinery manufacturing automation. It discusses how machine vision can contribute to core functions such as workpiece measurement, detection, and robot welding, facilitating automation, intelligence, and sustainable development. The aim is for the findings of this paper to offer valuable insights into relevant professions and production processes, thereby catalyzing the pace of mechanical manufacturing automation.