Manufacturing Production Systems Research Articles

Enhancing Production in Robot-Enabled Manufacturing Systems: A Dynamic Model and Moving Horizon Control Strategy for Mobile Robot Assignment

Abstract This paper presents a dynamic mathematical model for a robot-enabled manufacturing system, where mobile robots independently manage workstation tasks. Each robot possesses one or multiple skills, enabling collaborative work at workstations. A real-time robot assignment problem is formulated to maximize production of the system, and a novel control strategy is developed to address this problem. Leveraging system properties derived from the model and moving window downtime prediction, the problem of maximizing system production is transformed into a more tractable control problem focused on identifying and achieving a defined ideal clean configurations. The proposed solution significantly outperforms various benchmarks, including a pure reinforcement learning-based strategy, underscoring the importance of system understanding and its crucial role in enhancing flexibility and productivity in manufacturing systems.

Learning-based production, maintenance, and quality optimization in smart manufacturing systems: A literature review and trends

With the introduction of manufacturing paradigms, including Industry 4.0, production research has shifted its focus to enabling intelligent manufacturing systems within industrial environments. These systems can efficiently schedule and control processes and operations using artificial intelligence methods, including machine learning and deep learning. Since 1995, relevant literature has presented several examples of such implementations, addressing topics, for example equipment fault diagnosis and quality inspections. To this end, the present paper strives to present a state-of-the-art review of the learning-based scheduling and control frameworks, which are exploited in the production research. The review is limited to the relevant research between the years 1995 and 2024, surveying approaches in the domains of manufacturing, maintenance, and quality control. To this end, the paper follows a meta-analysis method for the selection and evaluation of relevant research articles. Moreover, research questions are formulated to analyze the obtained findings and seek out insights on aspects of the relevant research, including the inclusion of decision-making models and dissemination of literature. The provided answers, among others, reveal trends and limitations of the state-of-the art research in relation to learning-based scheduling and control.

Advancing heavy manufacturing

Advancing heavy manufacturing The main objective of ENGINE is to develop a first-time-right (FTR) and zero-defect metal product design and manufacturing system, then demonstrate it on the marine engine supply chain. Our ambition is to increase the competitiveness of industry and SMEs, reduce manufacturing defects and waste, create new business cases and improve employee well-being.

Cycle time analysis for cluster tools with parallel process chambers, processing time variation, and chamber cleaning operations

Cluster tools are the key equipment in semiconductor manufacturing. For high-end chip manufacturing processes, to ensure the wafer quality, periodical cleaning operations for process chambers in such tools are required for eliminating the residual gas and chemicals. In operating a tool, the wafer processing time in a process chamber may vary within a range. It is meaningful to predict the cycle time of cluster tools with chamber cleaning operations and processing time variation. By doing so, automation material handling system can be told when a lot is completed by a cluster tool such that it can assign an overhead hoist transporter to transport this lot at the right time. This plays an important role in improving the productivity of a whole semiconductor manufacturing system. This work conducts the cycle time analysis of cluster tools with chamber cleaning operations and processing time variation. Specifically, it proposes a novel method to calculate the average cycle time of a single-arm cluster tool under which an optimal schedule can be obtained. Then, for a dual-arm cluster tool, an efficient algorithm is developed to approximate the average system cycle time under which an optimal schedule can be obtained. Experiments show that the gap between the average system cycle time obtained by simulations and the average system cycle time obtained by the proposed method is no more than 0.1% which demonstrate the effectiveness of the proposed method.

A Cyber-Physical Production System for the Integrated Operation and Monitoring of a Continuous Manufacturing Train for the Production of Monoclonal Antibodies.

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber-physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

Integrated scheduling of production and material delivery for the intelligent manufacturing system

This paper addresses the issue of integrated scheduling of production and material delivery in an intelligent manufacturing system. The system includes multiple flexible flowlines and a fleet of automated guided vehicles (AGVs), whose capacity is shared among the flowlines. Multiple orders from different customers are processed on the flowlines, with AGVs delivering materials in different batch sizes based on varying product quantities of orders and limited load capacity. The problem is formulated as a mixed-integer linear programming model to minimise total order delays and the number of used AGVs. To handle the complexity caused by the uneven temporal coupling relationship between production and material delivery, a bi-level neighbourhood search solution algorithm is proposed. This algorithm uses variable neighbourhood search for production scheduling and adaptive large neighbourhood search for material delivery scheduling. Extensive numerical experiments using real-case-based instances are conducted to demonstrate the satisfactory performance of our proposed algorithm. Furthermore, we emphasise the advantages of integrated scheduling compared to traditional sequential scheduling across various problem settings.

Research and Design of Denture Manufacturing Production System Based on Integrated 3D Printing Technology

Research and Design of Denture Manufacturing Production System Based on Integrated 3D Printing Technology

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Data-driven sustainability evaluation and manufacturing system enhancement from economic, environmental, social, and sustainability perspectives.

Sustainable manufacturing is crucial to achieving carbon neutrality targets. However, research on the sustainability of manufacturing systems is limited, and high consumption, low efficiency, and high emissions have resulted in high resource consumption and rapid environmental degradation. Therefore, it is of great importance to establish an evaluation and improvement indicator system conducive to sustainable development. To this end, this study developed a data-driven methodology for evaluating and enhancing the sustainability of manufacturing systems. Manufacturing system production process data, with data dimensions unified via the emergy method, were used to construct a sustainable development evaluation model that includes four perspectives: economy, environment, society, and sustainability. The model was applied to a flange production workshop in China to analyze the interrelation mechanisms among energy consumption, resource consumption, and environmental pollution, and identify optimization schemes to improve sustainability. After implementing these optimization schemes, the emergy yield rate (EYR) of the flange increased by 23.40%, the environmental load rate (ELR) decreased by 19.03%, the per capita emergy (EPP) increased by 6.88%, and the emergy-based sustainability index (ESI) increased by 52.76%. The method presented herein offers a novel and effective tool to analyze and visualize sustainable development in manufacturing systems and identify the relationship between technology and management in the manufacturing industry; however, this method is based on historical data and rules, and lacks of flexible response to unknown situations. The results provide a reference for enterprises to achieve sustainable and lean manufacturing.

A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

Allocating Robots/Cobots to Production Systems for Productivity and Ergonomics Optimization

Collaboration between humans and robots has great promise in manufacturing systems. The utilization of cobots in a manufacturing system can improve both productivity and ergonomics. In this paper, we study the problem of how to allocate limited cobot/robots to manufacturing systems with multiple workstations so that an integrated performance measure, considering both productivity and ergonomics is optimized. Previous work on cobot/robot allocation in manufacturing systems focus on the decomposition of tasks for a single workstation into multiple work elements, and then split them between human and robots, rather than studying multi-machine systems. To bridge this gap, we consider the allocation of cobots/robots to a multi-stage manufacturing system. Specifically, we establish an integrated performance measure and formulate cobot/robot allocation into a constraint integer programming problem. With this formulation, we obtain the optimal allocation of one available cobot/robot in simulated production systems, based on the integrated performance measure of productivity and ergonomics. Furthermore, the allocation problems of production systems with multiple cobots/robots is considered and solved with a scalable algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Collaborative robots and independent robots are increasingly applied to manufacturing production systems. However, how to optimally allocate both types of robots considering both productivity and ergonomics influence has not been well studied. In this article, we established a practical optimization method to allocate cobots/robots to different workstations and split the work between cobot and human in one workstation when there are multiple workstations and a limited number of available cobots/robots in the manufacturing systems. Based on various real-world scenarios, we inferred useful insights for the robot/cobot allocation problem. To deal with the computational load when the number of workstations is large, a scalable optimization algorithm is also adopted. The case study results demonstrated the effectiveness of the proposed approach.

Developing a Modular Continuous Drug Product Manufacturing System with Real Time Quality Assurance for Producing Pharmaceutical Mini-Tablets

The pharmaceutical industry has shown keen interest in developing small-scale modular manufacturing systems for producing medicinal products. These systems offer agile and flexible manufacturing, and are well-suited for use in situations requiring rapid production of drugs such as pandemics and humanitarian disasters. The creation of such systems requires the development of modular facilities for making solid oral drug products. In recent years, however, the development of such facilities has seen limited progress. This study presents a development of a prototype modular system that uses drop on demand (DoD) printing to produce personalized solid oral drug products. The system's operation is demonstrated for manufacturing mini-tablets, a category of pediatric drug products, in continuous and semi-batch modes. In this process, the DoD printer is used to generate molten formulation drops that are solidified into mini-tablets. These dosages are then extracted, washed and dried in a continuous filtration and drying unit which is integrated with the printer. Process monitoring tools are also incorporated in the system to track the critical quality attributes of the product and the critical process parameters of the manufacturing operation in real time. Future areas of innovation are also proposed to improve this prototype unit and to enable the development of advanced drug manufacturing systems based on this platform.

Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems

ABSTRACT Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework.

Design and Analysis of a High-Precision Horizontal Machine Tools

Abstract: The horizontal machine tool has an automatic exchange table, which can be combined with a flexible manufacturing system for automatic processing and production. Therefore, it requires higher performance stability than other machines. This study analyzes the static and dynamic characteristics of a horizontal machine tool structure. The finite element analysis (FEA) method is generally used to analyze the whole machine structure and improve the deformation and resonance of the horizontal machine tool. In this study, FEA was applied to the design process of the machine tool, including static deformation analysis, modal analysis, transient analysis, and harmonic analysis of the machine. The deformation of the whole machine due to acceleration of gravity and cutting force was analyzed. The modal shapes generated by the first and third modes directly affected the machining process of the machine tool. To further analyze the influence of vibration signal processing on processing quality, transient response analysis was carried out on the effect of axial cutting force during machining. Spectrum analysis of the machine was also carried out. This study is expected to help the structural design of a horizontal machine tool to improve the dynamic characteristics and stability of the horizontal machining system.

Order scheduling optimization in manufacturing enterprises based on MDP and dynamic programming

In the era of Industry 4.0, order scheduling is a crucial link in the production of manufacturing enterprises. In view of order scheduling in manufacturing enterprises, a finite horizon Markov decision process model is proposed in this work based on two sets of equipment and three types of orders with different production lead times to maximize the revenue in manufacturing production systems. Then, the dynamic programming model is incorporated into the optimal order scheduling strategy. Python is employed to simulate the order scheduling in manufacturing enterprises. Based on survey data, the superiority of the proposed model compared to traditional first come, first served order scheduling is verified by experimental cases. Finally, sensitivity analysis is conducted on the longest service hours of the devices and the order completion rate to explore the applicability of the proposed order scheduling strategy.

The integration of the business model canvas and the service blueprinting to assist the conceptual design of new product-service systems

The capacity to guide the evolution and creation of new products (manufactured goods or services) is crucial for the enterprise's profitability. Hence, enterprises have acquired the capacity to manage the future of their product portfolio. However, companies that offer a combination of manufactured goods and services as an integrated system or a Product-Service System confront challenging conditions to maintain or increase their market share due to the complex relationship between manufacturing and service production systems. The complexity of a PSS makes it challenging to adapt its physical products to new customer requirements, satisfy new standards, or develop/adopt new technologies because any modification in one part of the system will undoubtedly affect the other. Therefore, it is necessary to propose an approach for managing the development process of a PSS from a broad perspective. The approach presented in this article combines the advantages of the Business Model Canvas to define the crucial functions of a business model with the service blueprinting capacity to represent service processes. The proposal describes a five stages methodology: Conceptualization, Business Model Design, Product-Service System (PSS) Scenarios, Blueprint design, and Validation. The methodology helps the analysis of a PSS from three perspectives: product, use, and result, which are the typical PSS scenarios. A case study applied to a company that distributes purified water is helpful to illustrate the methodology. Finally, the methodology includes some aspects that favor implementing creative and dynamic business models, emphasizing the constant changes in the evolution of products and services.

Opportunities and Challenges to Integrate Artificial Intelligence Into Manufacturing Systems: Thoughts From a Panel Discussion [Opinion

Rapid advances in artificial intelligence (AI) have the potential to significantly increase productivity, quality, and profitability in future manufacturing systems. (Caveat: The panel did not attempt to disentangle <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">artificial intelligence</i> from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine learning</i> and used the two terms loosely interchangeably during the discussion.) Traditional mass production will give way to personalized production, with each item made to order, at the low cost and high quality consumers have come to expect. Manufacturing systems will have the intelligence to be resilient to multiple disruptions, from small-scale machine breakdowns to large-scale natural disasters. Products will be made with higher precision and lower variability. While gains have been made toward the development of these factories of the future, many challenges remain to fully realize the vision shown in Figure 1.

A novel T-S fuzzy fault tree hybrid method for failure risk and multi-state reliability analysis of integrated production manufacturing system based on CPS

A novel T-S fuzzy fault tree hybrid method for failure risk and multi-state reliability analysis of integrated production manufacturing system based on CPS

Development of framework integrating ergonomics in Lean’s Muda, Muri, and Mura concepts

Lean and ergonomics are two disciplines that shared common goals to improve manufacturing production systems. However, there are limited studies that specifically link ergonomics with Lean’s 3 M concept of Muda (waste), Muri (overburden), and Mura (inconsistency). The study employed semi-structured interview with 10 experienced practitioners on past lean projects to establish potential linkage between ergonomics and Lean’s 3 M. A conceptual framework integrating ergonomics components into the Lean’s 3 M components was then generated through group discussion, and validated using Fuzzy Delphi Method analysis. The interviews revealed four themes linking ergonomics to Lean’s 3 M: (1) Human energy waste, (2) Uneven workload distribution, (3) Overburden of worker’s capacity, and (4) Worker performance affects work performance. A conceptual framework integrating ergonomics components into the Lean’s 3 M components was then proposed, which redefines several components of Lean’s 3 M from ergonomics perspective. Fuzzy Delphi Method analysis on 12 practitioners showed overall agreement with the content, structure, and applicability of the proposed framework (threshold (d) value ≤0.2, expert consensus ≥75%, and fuzzy score ≥0.5). The findings may provide platform for lean and ergonomics practitioners to communicate and bridge the gaps between the two disciplines.

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a grey area for many engineering managers who are key players in the gravitation of manufacturing and industrial production towards the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via PEST ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> olitical, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> conomic, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> ocial, and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> echnological) considerations of the broad implications of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this paper. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.