Complex Manufacturing Environments Research Articles

Optimizing Production Efficiency Through Time Study in UPS Manufacturing

Time studies have long been a proven method for improving operational efficiency, particularly in complex manufacturing environments like Uninterruptible Power Supply (UPS) production. By analyzing how tasks are performed and identifying opportunities for optimization, time studies help companies improve productivity, reduce costs, and ensure quality consistency. This article delves into how time studies can be implemented in UPS manufacturing, the benefits they bring, and the challenges that must be addressed.

Bill-of-materials visualization for aerospace & defense: A digital transformation retrospective

We look back at seven years of work to reduce test cost in complex aerospace manufacturing organizations using a bill-of-materials visualization, describing the evolution from a data integration team to a data analytics team, and several of the innovations we developed to be successful. We illustrate a maturity path for digital transformation using open-source tools that visualize over a hundred products, comprised of thousands of parts and bills-of-material to optimize factory flow. We aggregate past work for reducing test cost in aerospace manufacturing, reinforcing and extending work of Deming, and place it in the context of evolving data science capabilities, specifically so others can evaluate how they might accelerate their own digital transformations. We follow the development of a single product from its conception through its deployment and use, a production flow visualization of bills-of-materials plus operations, and discuss how it facilitates analytics and decision-making, depicting complementary visual analytics capabilities built to support it. Reducing cost of testing in complex manufacturing environments is our primary focus by understanding how testing contributes to product cost, and to gain insight into the testing process to reduce testing – and ultimately product – cost. Complex manufacturing organizations considering digital transformations can evaluate the principles, approach, and problems we encountered throughout the team's evolution, specifically, how to extend Deming's work complemented by standard bill-of-materials plus operations visualizations to make critical manufacturing optimizations.

Determining number of suppliers, duration of supply cycle and allocation to in-house production under supply uncertainty

Supply chain management has become an increasingly critical strategic capability to gain competitive advantage in the global business environment. The COVID-19 pandemic, coupled with geopolitical conflicts, has caused unprecedented disruptions to global supply chains. Suggested strategies to cope with the uncertain supply environment include better supplier selection, review of the number of demand periods in the supply cycle and the possibility of in-house production. This research proposes a novel approach that considers the aforementioned strategies by considering that both the suppliers and the manufacturer have the capability to increase supply and production, respectively. The proposed model is applicable in manufacturing environments characterized by mature products and a well-consolidated pool of preferred suppliers. The effectiveness of the proposed model is evaluated through a set of experiments, revealing significant trade-offs among decision variables and model parameters. These trade-offs provide valuable managerial insights, such as the optimal percent allocation of in-house manufacturing increasing when both supplier and in-house manufacturing flexibility increase up to a certain point, but then decreasing. Overall, this research presents a valuable contribution to supply chain management literature, highlighting important insights that can inform decision-making in complex manufacturing environments.

Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning

In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem.

Leveraging Blockchain to Support Collaborative Distributed Manufacturing Scheduling

The recent trend in collaborative distributed manufacturing scheduling (CDMS) has gained significant importance in extended, networked, and virtual manufacturing environments due to its adaptability and integration potential. In a distributed manufacturing environment, CDMS can occur within a single factory or across multiple companies in a dynamic and variable extended or virtual organization. For effective collaboration, the CDMS system must be secure, transparent, and trustworthy. This paper proposes a blockchain-based model for CDMS and discusses its implementation in the processing of manufacturing functions, specifically joint process planning and scheduling. An illustrative example is used to demonstrate the application of the proposed approach and its potential to enhance the management processes of CDMS enterprises. The results of the analysis indicate that the proposed blockchain approach can effectively facilitate communication and integration among CDMS enterprises. Additionally, the approach can be expanded to more complex manufacturing environments under different conditions.

Domain Models and Data Modeling as Drivers for Data Management: The ASSISTANT Data Fabric Approach

To develop AI-based models capable of governing or providing decision support to complex manufacturing environments, abstractions and mechanisms for unified management of data storage and processing capabilities are needed. Specifically, as such models tend to include and rely on detailed representations of systems, components, and tools with complex interactions, mechanisms for simplifying, integrating, and scaling management capabilities in the presence of complex data requirements (e.g., high volume, velocity, and diversity of data) are of particular interest. A data fabric is a system that provides a unified architecture for management and provisioning of data. In this work we present the background, design requirements, and high-level outline of the ASSISTANT data fabric - a flexible data management tool designed for use in adaptive manufacturing contexts. The paper outlines the implementation of the system with specific focus on the use of domain models and the data modeling approach used, as well as provides a generic use case structure reusable in many industrial contexts.

Fast tag identification for mobile RFID robots in manufacturing environments

Purpose In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection. Design/methodology/approach A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection. Findings The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission. Originality/value In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

Multidomain Simulation Model for Analysis of Geometric Variation and Productivity in Multi-Stage Assembly Systems

Nowadays, the new era of industry 4.0 is forcing manufacturers to develop models and methods for managing the geometric variation of a final product in complex manufacturing environments, such as multistage manufacturing systems. The stream of variation model has been successfully applied to manage product geometric variation in these systems, but there is a lack of research studying its application together with the material and order flow in the system. In this work, which is focused on the production quality paradigm in a model-based system engineering context, a digital prototype is proposed to integrate productivity and part quality based on the stream of variation analysis in multistage assembly systems. The prototype was modelled and simulated with OpenModelica tool exploiting the Modelica language capabilities for multidomain simulations and its synergy with SysML. A case study is presented to validate the potential applicability of the approach. The proposed model and the results show a promising potential for future developments aligned with the production quality paradigm.

Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems

Lean manufacturing is one of the most popular improvement agents in the pursuit of perfection. However, in today’s complex and dynamic manufacturing environments, lean tools are facing an inevitable death. Industry 4.0 can be integrated with lean tools to avoid their end. Therefore, the primary purpose of this paper is to introduce an Industry 4.0-based lean framework called dynamic value stream mapping (DVSM) to digitalize lean manufacturing through the integration of lean tools and Industry 4.0 technologies. DVSM with its powerful features is proposed to be the smart IT platform that can sustain lean tools and keep them alive and effective. This paper specifically tackles the scheduling and dispatching in today’s lean manufacturing environments, where the aim of this research is developing a smart lean-based production scheduling and dispatching model to achieve the lean target through optimizing the flow along the VSM and minimizing the manufacturing lead time. The developed model, called the real-time scheduling and dispatching module (RT-SDM), runs on DVSM. The RT-SDM is represented through a mathematical model using mixed integer programming. Part of the testing and verification process, a simplified IT-based software, has been developed and applied on a smart factory lab.

Flexible Job Shop Scheduling using Software

This present paper speaks about Flexible Job Shop scheduling Problem using a software. Flexible Job shop scheduling is most typical and complex manufacturing environments in production planning issues. A case study was conducted on a manufacturing business set in Vijayawada. Information collected from the producing company to get the time taken by the conventional scheduling method and gathered data for 5 jobs requiring 8 machines that arrived during the analysis period. The Project additionally speaks about the study of varied programming strategies, learning of LEKIN programming package is a software tool has a flexibility to develop new heuristic models to produce effective schedules be applied practically. Input data, taken from the manufacturing company, converted to tables and routing sequences. The LEKIN programming package [3], develops the different schedules for given data with reference to priority rules, such as First Come First Serve (FCFS), Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Due Date (EDD), Critical Ratio (CR) [6]. The schedules obtained from priority rules analysed through performance measures like Make Span, No of late jobs, Total Flow Time, Total Tardiness, Maximum Tardiness, Total Weighted Tardiness, Total Weighted Flow Time. Our goal is to come up with a optimized schedule with in the process of flexible job shop scheduling by using LEKIN scheduling software, using various priority rules as mentioned above and to minimize the make span i.e. the time length of the schedule, during which all the operations for all jobs is completed in an engineering company.

A Rule-Based Approach Founded on Description Logics for Industry 4.0 Smart Factories

This paper develops a formal framework, founded on description logics, to assist decision making in relation to the manufacturing operation and control in modern enterprises that stand to benefit from the transition to Industry 4.0. The objective is to provide sophisticated support to individuals making decisions in the area of production operations management and in particular, production scheduling and material requirements planning. Using this framework, this paper demonstrates an approach to encode the domain knowledge of human experts managing the production as sets of formal rules. These rules can be implemented in an intelligent system that can assist and empower human experts, reducing difficulty when making decisions in complex manufacturing environments.

A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment.

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR’s improvement in performance.

Optimization Framework for the Simultaneous Batching and Scheduling of Multisite Production Environments

In recent years, many companies have shifted from single-site production to more complex manufacturing environments involving multiple multiproduct facilities. At the operational level, the scheduling posed on each individual facility does not lead to efficient use of resources and consequently an optimal production management over the whole production system cannot be achieved. In this context, a multisite production scheduling framework is fundamental to respond appropriately to the market requirements. In this work, a mixed-integer linear programming (MILP) model for the simultaneous batching and scheduling of multisite multiproduct batch plants with nonidentical parallel units is proposed. Given a set of manufacturing sites with unit capacities and structure known and multiple orders with associated release and due dates, the proposed approach determines the plant where each customer order must be processed, the number and sizes of batches to satisfy each order, the assignment of these batches to units, as well as the sequencing and timing of batches on each unit of the plant that processes them, in order to minimize the global makespan related to the whole production system. The application and performance of the proposed model are highlighted through numerical examples.

Teaching Styles of Virtual Training Systems for Industrial Applications – A Review of the Literature

The demand for individualized and efficient production yields more capable but also more complex manufacturing environments. Virtual training systems were proposed to train the operators who control and maintain industrial machines. This article reviews such training systems with a focus on their teaching styles and identifies new research directions in the field of adaptive training systems. The review applies a classification scheme that describes the instructions, the presentation of the instructions, and the interaction possibilities of a virtual training system. Another aspect of the classification scheme are the capabilities to adapt a training system to the characteristics of the trainee. The review indicates that existing training systems apply similar teaching styles and provide limited adaptive capabilities. The development of training systems that adapt their teaching style to the disposition and the qualification of individual trainees is proposed, to address the increasingly diverse workforce.

Evaluating of dynamic service matching strategy for social manufacturing in cloud environment

As a new form of manufacturing industry in cloud environment, social manufacturing has its inherent “social–cyber” complexity: the source of enterprise services is social, and this sociality aggravates the diversity, uncertainty and dynamics of service supply. Researchers have done a lot to improve the adaptability of service matching strategies. However, it is difficult or even impossible to for traditional methods to evaluate the viability of these strategies because of the “social–cyber” complexity. This poses new challenges to how to evaluate and optimize these strategies in a complex social manufacturing environment. Aiming at the problem, this paper proposes a computational experiment-based evaluation framework, which can simulate all kinds of actual scenarios to verify the performance of service matching strategies. This method includes three parts: (1) design of supply & demand matching strategy; (2) construction of computational experiment system; (3) performance evaluation of service strategies in different experiment environments. A case study is given to verify the applicability of our method by means of comparing several adaptive service matching strategies (supply-oriented, demand-oriented, initial supply & demand-oriented, optimized supply & demand-oriented) in two kinds of market environments. The results demonstrate that our method has a substantial promise.

Quick health assessment for industrial robot health degradation and the supporting advanced sensing development

Robotic technologies are becoming more integrated with complex manufacturing environments. The addition of greater complexity leads to more sources of faults and failures that impact a robot system’s reliability. Industrial robot health degradation needs to be assessed and monitored to minimize unexpected shutdowns, improve maintenance techniques, and optimize control strategies. A quick health assessment methodology is developed at the U.S. National Institute of Standards and Technology (NIST) to quickly assess a robot’s tool center position and orientation accuracy degradation. An advanced sensing development approach to support the quick health assessment methodology is also presented in this paper. The advanced sensing development approach includes a seven-dimensional (7-D) measurement instrument (time, X, Y, Z, roll, pitch, and yaw) and a smart target to facilitate the quick measurement of a robot’s tool center accuracy.

Opportunities to apply manufacturing systems analysis techniques in genetic manufacturing systems

Breakthroughs in molecular and synthetic biology are pivotal to understanding the function of cells and creating new pharmaceutical applications. These advances in biological processing present a new class of manufacturing systems, defined here as genetic manufacturing systems, which produce a final product with a genetic construct. Genetic manufacturing systems rely on molecular events for success, and this aspect is a key difference from traditional manufacturing systems. Analysis techniques for manufacturing systems have been successful in providing valuable insights for complex manufacturing environments and have the potential to transform how genetic constructs are currently produced. This paper provides an introduction to the interdisciplinary field of genetic manufacturing systems and outlines the similarities and primary differences between traditional manufacturing systems and genetic manufacturing systems. Mathematical modeling and simulation opportunities are presented as they relate to reducing cost and time as well as increasing efficiency in genetic manufacturing systems. Finally, several challenges for genetic manufacturing systems are presented.

Hybrid Simulation for Complex Manufacturing Value-chain Environments

Hybrid simulation is nowadays a valid alternative for studying complex manufacturing environments. Some challenges exist in this context, as the ambiguous use of terms and definitions in the literature; and the demanding skills required for developing hybrid models. A structured literature review provides an overview of the use of hybrid simulation in manufacturing business performance and its most important advantages and drawbacks. A classification scheme for the 51 analysed papers is presented, including interfaced, sequential, enrichment, and integrated taxonomies.

Application of Virtual Reality in Side Clamp Fixture Assembly

Background/Objectives: The main objective of this paper is dealt with the application of virtual reality in side clamp fixture assembly. Methods/Statistical Analysis: The proposed methodology utilized the geometry and assembly details of side clamp assembly and makes the fixture assembly as close as possible to the real one. It includes two stages. The first stage demonstrates the procedure used for creating virtual prototype of an assembly and the second stage includes the creation of virtual fixture assembly. Findings: VRML coding is used to simulate virtual assembly process. Based on interference analysis errors can be rectified. Fixture assembly planning in virtual environment gives better understanding of the design engineer and the virtual assembly planning gives clear detail for virtual manufacturing. It enables intuitive assembly sequences which are then evaluated by Vizard inspector module. Application/Improvement: In this paper fixture assembly sequences are simulated using VIZARD software. Future research will investigate the fixture assembly plan in a complex virtual manufacturing environment.

A Multi-Perspective Performance Approach for Complex Manufacturing Environments

Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.