Automated Manufacturing Systems Research Articles

Modeling and optimizing an AMS with DV policy, waiting servers, impatient customers, and failures: A queueing analysis

In this paper, we analyze an automated manufacturing system modeled as a queueing system with multiple servers, differentiated vacation (DV) policy, customer impatience, and server failures. We consider the waiting servers policy, where the servers wait for a random duration before leaving for a vacation once the system gets empty. Upon their return, if no customers are waiting, the servers may leave for a second type-2 vacation of shorter duration. The system may experience breakdowns and require immediate repair. During the repair period, customers are served at a lower rate, and they become impatient during busy, vacation, and repair periods, abandoning the queue if their timer expires. Further, based on the queue length, arriving customers may either join the system or balk with some probability. We use the matrix analytic method to derive the steady-state probability of the system and several performance measures. Moreover, we illustrate the effect of different system parameters on various performance measures. Additionally, we develop a cost model and optimize the system capacity, number of servers, and service rates such that cost per unit time is minimized. Finally, we analyze the impact of system parameters on the cost through numerical examples.

The State of The Knowledge of Cloud Computing Technology for Manufacturing Automation Systems

The manufacturing sector is grappling with the need to adapt to rapid technological changes, and leveraging cloud computing is becoming crucial for staying competitive and resilient. The presentation of knowledge in this work focuses on highlighting the specific challenges and opportunities in integrating cloud technologies into manufacturing systems. It aims to answer critical questions such as how cloud computing can enhance manufacturing automation, solve problems, and benefit to the industry. The methodology employed in this research takes a comprehensive and top-down approach, aligning and exploring the practical aspects of implementing these X-as-a-Service (XaaS) model in manufacturing setups. The research also acknowledges the shift from legacy Distributed Numerical Control (DNC) systems to modern solutions like MTConnect and Open Platform Communication (OPC) for data exchange in automated manufacturing systems. Emphasizing the important of data collection and real-time monitoring, the study highlights the role of Industrial Internet of Things (IoT) sensors deployed at various points of manufacturing system components (machine tools, spindles, cutting tools, production units, etc.). These sensors capture real-time production and condition data, enabling informed decision-making in manufacturing systems. This research not only presents the latest knowledge but also offers insights into the challenges, strategies, and methodologies involved in the successful integration of cloud-based technology into manufacturing automation systems. It also aims to serve as a valuable resource for manufacturers, researchers, and industry professionals navigating the transformative journey toward cloud-powered manufacturing.

83 Automated PDMS Engraving and Assembly of a Prototype Microfluidic Artificial Lung

OBJECTIVES/GOALS: We report an automated manufacturing system, and a series of cylindrical multi-layer microfluidic artificial lungs manufactured with the system and tested for fluidic fidelity and function. METHODS/STUDY POPULATION: A Roll-to-Roll (R2R) system to engrave multiple-layer devices was assembled. A 100 um-thick silicone sheet passes through an embedded CO2 laser engraver, which creates patterns of any geometry on the surface. The sheet is plasma-activated to create an irreversible bond, and rerolled into a processed device. Unlike typical applications of R2R, this process is synchronized to achieve consistent radial positioning. This allows the fluidics in the device to be accessed without being unwrapped. The result is a cylindrical core surrounded by many layers of microfluidic channels that can be accessed through the side of the device or through fluidic vias. This core is cut to expose the microfluidic layers, and then installed into a housing which routes the fluids into their respective microfluidic flow paths. RESULTS/ANTICIPATED RESULTS: To demonstrate the capabilities of the R2R manufacturing system, this method was used to manufacture multi-layer microfluidic artificial lungs (µALs). Gas and blood flow channels are engraved in alternating layers and routed orthogonally. The close proximity of gas and blood separated by gas-permeable PDMS permits CO2 and O2 exchange. Three µALs were successfully manufactured. Their flow paths were visualized using dyed water and checked for leaks. Then they were evaluated using water for pressure drop and CO2 gas-exchange. The top performing device had 15 alternating blood and gas layers. Test with whole blood demonstrated oxygenation from venous (70%) saturation levels to arterial (95%) saturation levels at a flow rate of 3 ml/min. DISCUSSION/SIGNIFICANCE: The ability to cost-effectively produce high surface area microfluidic devices would bring many small-scale technologies from the realm of research to clinical and commercial applications. In particular, most microfluidic artificial lungs only have small rated flows due to a lack of manufacturing processes able to create high surface area devices.

Adaptive supervisory control for automated manufacturing systems using borrowed-buffer slots

Robust deadlock supervisory control techniques for automated manufacturing systems under resource failures that need additional central buffers may lead to supererogatory cost in control implementation. To mitigate this issue, this paper reports a Petri net-based low-cost adaptive supervisory control policy that does not require extra buffers. If an unreliable resource fails, three classes of buffer spaces can be temporarily used to store parts that require the failed resource in their impending processing routes. The proposed adaptive supervisory control comprises of control places and switch controllers. If an unreliable resource fails, the switch controllers are activated to move the part types that require the failed resource in their subsequent processing stages into borrowed buffer spaces. After the failed resource is recovered, the system returns to its normal operating mode. The part types in the borrowed buffer spaces will be returned to their last processing stages before the occurrence of the failure and continued along their processing route. We propose criteria for selecting buffer space borrowers and buffer space lenders. Furthermore, redundant control places are removed by a new technique proposed in the paper in order to reduce the complexity of the supervisory control structure. We demonstrate the proposed method using examples.

Improving Equipment Effectiveness through Visual Stream Mapping: Some Exploratory Research Findings in the Ready-Made Garment (RMG) Sector

Improving equipment effectiveness is crucial for flexible manufacturing, ensuring that machines and tools perform their functions efficiently and consistently. Our study aimed to enhance the Overall Equipment Effectiveness (OEE) in Bangladesh’s Ready-Made Garments (RMG) manufacturing system. We used the DMADV methodology incorporating Visual Stream Mapping (VSM) and OEE. By utilising VSM, we identified issues, eliminated them in the design phase, and evaluated performance in the verification phase. We automated the material handling system to reduce handling time, and the result was a significantly improved OEE in the automated manufacturing system compared to the manual one. This study has numerous benefits in flexible manufacturing and operations management, from immediate efficiency improvements to long-lasting organisational cultural transformations. Thus, it’s a noteworthy topic for practical applications and research. Enhancing Equipment Effectiveness through Visual Stream Mapping 4.0 has broad-reaching implications, including improved productivity, reduced waste, increased efficiency, better resource utilisation, and a more agile and responsive manufacturing environment. Although OEE and VSM are frequently used separately in different manufacturing systems, this study’s novelty lies in their combined application within garment manufacturing.

Deep-learning-based design of active fault-tolerant control for automated manufacturing systems subjected to faulty sensors

This research paper proposes a new implementation of a long short-term memory network for active fault-tolerant control subjected to single and multiple undiagnosable sensor faults. There are two networks used: the first performs as a diagnosis for automated manufacturing systems and can identify faulty sensors, while the second acts as an inverse model of these systems and is used to determine the reconfigured control action to take when sensors are not functioning as expected. The Factory I/O simulator is interfaced with MATLAB to simulate and verify the proposed approach for automated material handling case study with faultless and multiple fault sensors. Four sensors of the case study out of six are tolerated.

Robust supervisory control for automated manufacturing systems with unreliable resources by analyzing reachable state space

Robust supervisory control is an important issue for automated manufacturing systems (AMSs) with multiple unreliable resources. This study uses Petri nets to model failure-prone AMSs with multi-unit and multi-type resource acquisitions and releases. First, we propose the necessary and sufficient condition for the robustness of a supervisor. Then, two kinds of states, namely blocking-markings (BMs) and result-in-BMs (RBMs), are found to cause blocking during system operation, and further they are defined precisely based on the structure perfect activity circuit (PAC) in Petri net model. An algorithm of polynomial complexity is presented to determine whether a BM or RBM is co-reachable from a non-BM or non-RBM. By the aid of this algorithm, we compute the reduced state space that does not contain any BM or RBM without enumerating each reachable state. Based on the reduced state space, an optimal robust supervisor is established, not only guaranteeing the continuous production of all job types even if some resources fail, but also having the most reachable states. Some experiments are used to validate the effectiveness and efficiency of the proposed method.

Maximally Permissive Robustness Discovery in Automated Manufacturing Systems With an Unreliable Resource

Maximally Permissive Robustness Discovery in Automated Manufacturing Systems With an Unreliable Resource

《智能自动化制造系统》课程教学改革与思考

《智能自动化制造系统》课程教学改革与思考

ТЕХНОЛОГИЧЕСКИЕ ИННОВАЦИИ В СФЕРЕ АВТОМАТИЗАЦИИ ПРОИЗВОДСТВА

The study discusses the significance of innovations in automated manufacturing systems for modern industry. Describes the importance of using advanced technologies such as artificial intelligence, machine learning, robotics, Internet of Things and augmented/virtual reality in manufacturing processes. The advantages and problems associated with the implementation of these innovations are analyzed, and possible solutions to them are proposed. In conclusion, the importance of priority implementation of innovations to ensure the competitiveness of enterprises in modern industry is emphasized.

Adaptive Supervisory Control of Automated Manufacturing Systems With Unreliable Resources Based on Smart Switch Controllers

Adaptive Supervisory Control of Automated Manufacturing Systems With Unreliable Resources Based on Smart Switch Controllers

Application of computer vision techniques to estimate surface roughness on wood-based sanded workpieces

In the wood industry, surface inspection post-sanding is critical during the final production stages. This inspection relies on subjective assessments made by operators due to the challenges of adapting traditional measuring techniques to industrial environments. This study introduces an innovative artificial vision approach for the in-line inspection of surface roughness applied to wood and wood-based products in highly automated manufacturing systems. The procedure is based on extracting features from the grey-level co-occurrence matrix in images processed with edge-detection algorithms. Images are derived from photographs of sanded medium-density fibreboard, and models predicting surface roughness were formulated and compared with direct measurements using a stylus profilometer and a confocal microscope. Results showed that the most effective algorithm-feature combination was Roberts and Uniformity of Energy, with consistent performance for different roughness parameters. This approach can improve quality control and extend to a broader range of materials and applications.

In-process non-destructive evaluation of metal additive manufactured components at build using ultrasound and eddy-current approaches

Metal additive manufacturing is rapidly gaining popularity and interest from sectors aiming to produce larger-scale high-value components cost-effectively. To ensure each component is leaving the fabrication cell defect-free, it is highly desirable to inspect each layer or selected volume of the build. This is a significant challenge, given that conventional non-destructive evaluation (NDE) is a post-manufacturing operation. The opportunity exists in the development of novel flexible automated manufacturing systems aiming to merge deposition and inspection. Hence, enabling defect detection at the point of the creation allows subsequent rapid repair or reduction in scrappage. In this work, the authors present research from one such multi-robot cell, where a directed energy deposition process called wire + arc additive manufacture is used to build components while novel in-process ultrasound and eddy-current approaches are deployed to inspect a component with artificially embedded reflectors. The outcome of this work demonstrates a promising ability to merge manufacturing and NDE into a single process and hence, strengthen the overall benefits of metal additive manufacturing fields.

Supervisory Controller Design to Enforce Boundedness, Reversibility and Liveness in Systems Modeled by Timed Petri Nets

Supervisory controller design to enforce boundedness, reversibility, and liveness in discrete-event systems modeled by timed Petri nets where both transitions and places are timed is considered. A controller design approach which uses the approach of stretching is proposed. The approach produces a supervisory controller which guarantees boundedness and reversibility simultaneously. The designed controller also guarantees T -liveness for the largest possible subset T of the set of transitions. Therefore, boundedness, reversibility, and liveness are enforced simultaneously whenever possible. Furthermore, the designed controller is maximally permissive in the sense that no transitions are disabled unnecessarily and the reachability set of the controlled system is the largest possible set in which boundedness and reversibility can be enforced simultaneously. Controller design for an automated manufacturing system example is also presented to demonstrate the proposed approach.

Designing of Tooling Solution for Workpiece Handling in Highly Automated Manufacturing System

A flexible manufacturing system (FMS) is an integral part of a smart factory of Industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, workpiece handling for such an automated production system is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and are not suitable for highly automated production lines. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation of workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, and universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.

Tools and Process of Defect Detection in Automated Manufacturing Systems

INTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems.
 OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound systems.
 METHODS: The utilization of AI and ML algorithms has gained significant traction in the realm of defect detection. This is attributed to their ability to process vast amounts of data and discern patterns that may have otherwise eluded detection. The aforementioned tools have the capability to anticipate potential flaws and implement pre-emptive measures to avert their occurrence. 
 RESULTS: The detection of defects in automated manufacturing systems is a continuous process that necessitates meticulous observation and examination to guarantee prompt and effective identification and resolution of defects. CONCLUSION: The utilization of suitable tools and technologies is imperative for manufacturers to guarantee optimal production quality and operational success.

Adaptive Neural Self-Triggered Bipartite Fault-Tolerant Control for Nonlinear MASs With Dead-Zone Constraints

An adaptive neural bipartite tracking control approach is proposed for nonlinear multi-agent systems in this article. In contrast to previous results, it is worth noting that this paper considers a cooperative-competitive relationship in multi-agent systems, which stands for a more common situation. In this paper, a distributed self-triggered communication strategy is designed to improve the transmission efficiency of the whole system. In addition, the designed controller can compensate the actuator failure and dead-zone nonlinearity, and increases the system fault-tolerance. The proposed method ensures the boundedness of all signals of the closed-loop system and the bipartite tracking performance. The effectiveness of the proposed method is verified by two simulation examples. <i>Note to Practitioners</i>&#x2014;Since complex modern engineering systems are difficult to be controlled by a single component, the cooperative control mode of multi-agent systems has become the mainstream trend. For multi-agent systems with cooperative-competitive relationships, the unique bipartite consensus will allow each agent to better complete the control objectives according to their respective advantages. In addition, for engineering systems such as automated manufacturing systems and transportation systems, fault problems are becoming more commonplace. These faults may make the system difficult to operate normally, and then affect the project progress. Therefore, how to guarantee the normal work of the control system when subject to faults has become a key topic. On the other hand, the channel bandwidth of the actual communication system is limited, and frequent updating of control signals will produce huge communication pressure in the traditional control scheme. Hence, it is challenging to design a control strategy that can achieve system stability and reduce communication resources simultaneously. This paper discusses the bipartite fault-tolerant control problem for nonlinear multi-agent systems. Meanwhile, a distributed adaptive self-triggered mechanism is designed to save communication resources.

Maximally Permissive Robustness Analysis of Automated Manufacturing Systems With Multiple Unreliable Resources

For real-world automated manufacturing systems (AMSs), the failures of multiple unreliable resources are very common. Maximal permissiveness is a key aspect for evaluating the performance of robust control strategies of failure-prone AMSs. However, there exist fewer works in the existing literature that can achieve maximally permissive robustness analysis. This may impede the applicability of existing robust control strategies in realistic AMSs. This article tackles the issue in the paradigm of Petri nets (PNs). We present an innovative mechanism to formalize unreliable resource failures by preventing all ingoing transitions of unreliable resource places from firing. Both reachability graph-based and integer linear programming-based methods are proposed to analyze the robustness of AMSs with either single or multiple unreliable resources. Several examples are proposed to illustrate the approach.

BIO27: Automated PDMS Engraving and Assembly of a Prototype Microfluidic Artificial Lung

Purpose: The ability to cost-effectively produce high-surface area microfluidic devices would bring many small-scale technologies from the realm of research to clinical and commercial applications. In particular microfluidic artificial lungs (µALs) promise improved gas exchange efficiency through micron-scale feature sizes. However, most µALs have only a single layer which limits their rated flows. Efforts to scale up these devices, such as by stacking multiple flat µALs have been labor intensive and resulted in bulky devices. Here, we present an automated roll-to-roll (R2R) manufacturing system and a cylindrical multi-layer µAL. Methods: The R2R system (Fig 1A) begins with a roll of biocompatible silicone rubber (Silpuran 2030). A heavily-modified CO2 laser engraver (Omtech K40 with custom vacuum engraving bed, N2 purge, and custom motherboard) creates patterns in the silicone sheet (Fig 1B). The sheet is then plasma-activated using two CeraPlas F-series cold-plasma wands to bond the PDMS to itself in an irreversible bond. Finally, it is rerolled into a processed device around a 30 mm PDMS core. This entire process from unrolling to rerolling is performed simultaneously and synchronously, allowing the creation of very large area microfluidic devices. Further, it is automated (Arduino Mega 1680), eliminating human error over this repetitive process. Results: Using our automated manufacturing system, we fabricated a 27-layer cylindrical prototype µAL, consisting of 1) an 8-layer base, 2) 11 layers of alternating blood and gas channels, and 3) an 8-layer protective cover. Laser-engraved fluidic channels were triangular, 60 µm deep and 180 µm wide (Fig 1B). If unrolled, the microfluidic part of this system would stretch 1.1 m, and the total length would stretch 2.7 m. This was placed into a custom 3D printed housing which routes blood and gas to their respective microfluidic channels (Fig 2A,B; filled with dyed water for visualization). Empirical pressure drop data were gathered for flows from 1 mL/min to 10 mL/min, which were similar to theoretical values (Fig 3). While the R2R manufacturing system successfully created the prototype 27-layer µAL, the device exhibited some internal fluid pooling and layer-to-layer leaks during fluidic testing. Both issues are believed to be caused by inadequate localized layer-to-layer adhesion. We plan to remedy these issues in the coming months. Conclusion: This roll-to-roll manufacturing system is designed to manufacture the first large surface-area microfluidic devices that are impractical to manufacture by conventional means. This can be employed for large-scale membrane gas exchange such as human-scale µALs.Figure 1. (A) An automated roll to roll manufacturing system for silicone. The path of the silicone is shown in green. The silicone comes on a protective PET backing, which is removed and highlighted in cyan. (B) Confocal scan of engraved Silpuran 2030. Channels were 60 µm deep, and 180 µm wide.Figure 2. (A) Finished device, with custom housing (B) Device with colored water being routed through. Red arrows depict blood connections, and blue arrows depict gas connections. Two of the blood outlets filled with blue dye, and there is a dark blue region indicating imperfect layer-to-layer bonding resulting in leaking.Figure 3. Experimental vs theoretical pressure drop across the μAL (water)

Modeling of Fault Recovery and Repair for Automated Manufacturing Cells with Load-Sharing Redundant Elements Using Petri Nets

Failure of resource in automated manufacturing systems could cause a complete system shutdown. This paper addresses the issue of unreliable resource failure in manufacturing cells through the use of load-sharing redundant resources (LSRRs). The aim is to use more than one type of a failure-prone resource to share tasks between a failure-prone resource, called a target resource, and reliable ones called load-sharing redundant resources (LSRRs). Both an unreliable resource and its LSRR perform the same tasks, and there is normally a system that assigns tasks to them. If the target resource fails, all the tasks will be performed by the LSRRs. After the faulty target resource is fixed and restored, its assigned tasks are automatically returned to it. This way the system can continue to produce or process parts. Thus, a total system shutdown due to unreliable resource failures is eliminated. The proposed method is tested using real examples. The results, compared with those obtained by the studies in the literature, show that the proposed method has an outstanding performance and outperforms some of the existing studies.