Towards Modular and Plug-and-Produce Manufacturing Apps

Industry 4.0 redefines manufacturing systems as smart and connected systems where software solutions provide additional capabilities to the manufacturing equipment. However, the connection of manufacturing equipment with software solutions is challenging due to poor interoperability between different original equipment manufacturers (OEMs), making it difficult to integrate into the manufacturing system. Hence, there is a need for a methodology to develop modular "plug-and-produce" applications in the manufacturing domain to meet the requirements of Industry 4.0. This work investigates the "appification" of manufacturing processes where the goal is to subdivide the process into independent, re-configurable digital manufacturing applications. In this context, "appification" means separating the digital implementation from the physical implementation of the system by making the former modular and independent so that digital implementations can be re-used without depending on the physical parts of the system. In this paper a framework for the development of such manufacturing "apps" is presented. This framework consists of four main elements: a modular plug-and-produce architecture, a manufacturing apps development kit, a communication protocol, and a construction methodology. The modular plug-and-produce architecture is developed using the recent advances in microservices, containerization, and communication technologies. The manufacturing apps development kit (MAPPDK) has been developed to facilitate the implementation of manufacturing apps using high-level programming languages. MAPPDK allows to control manufacturing equipment from external computational devices. The methodology for developing different modules for different types of manufacturing processes is also provided. The proof of concept is shown experimentally by the "appification" of a sorting process using an industrial robot arm, a gripping end-effector, a third-party vision camera, and an intelligent vision module

Optimal Manufacturing Configuration Selection: Sequential Decision Making and Optimization using Reinforcement Learning

In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations

A maturity model for the autonomy of manufacturing systems

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model

A holistic methodology for manufacturing systems configuration

The manufacturing sector is undergoing rapid transformations driven by global economic factors, technological advancements, and fluctuating market demands. These dynamics necessitate continuous innovation and adaptability in manufacturing systems to maintain competitiveness. Therefore, this research addresses the manufacturing systems configuration (MSC) problem, aiming to develop a methodology to make manufacturing systems resilient and adaptable that balances resource management, production efficiency, and costs. Despite the critical nature of the MSC problem, existing solutions are fragmented and suffer from significant limitations, including underutilisation of data models, software integration issues, and the inadequacy of traditional optimisation and decision-making methods in dealing with uncertainties and multiple objectives. Therefore, this research formulates the problem as ``developing a holistic solution for addressing the MSC problem for adapting manufacturing systems to rapidly changing manufacturing requirements" to address these gaps. In this context, a holistic solution synergistically combines data modelling, software integration, adaptive optimisation and decision-making algorithms. The research objectives include the development of adaptable data models that encapsulate the complexities of manufacturing systems, plug-and-produce manufacturing software solutions that address system scalability and adaptability, and adaptive optimisation algorithms capable of navigating complex solution spaces. The research employs a multi-staged validation approach, initially testing the proposed methodologies in two distinct manufacturing processes with unique challenges: sorting cylinders and bin-picking parts of industrial pipe couplers. These processes serve as a comprehensive testing ground for the proposed solutions. Three research hypotheses were sequentially assessed, focusing on the adaptability of object-oriented data models, the effectiveness of manufacturing apps in achieving interoperability, and the efficiency of optimisation and decision-making algorithms in managing multiple objectives and uncertainties. Each hypothesis was successfully validated, confirming the research contributions. Subsequently, empirical validation was extended to real-world industrial settings, focusing on aerospace and custom product manufacturing sectors. In the aerospace sector, the task was to find optimal manufacturing system configurations for changing and multiple conflicting manufacturing costs for assembling a generic hinged product. In the custom product manufacturing sector, the task involved planning a machining process that required balancing multiple manufacturing costs. These validations substantiate the research hypotheses and demonstrate the proposed methodology's generalisability and adaptability. By developing a holistic approach, this research contributes significantly to the field. It addresses the limitations of existing fragmented solutions and provides a robust, adaptable, and holistic framework for manufacturing systems. The research has practical implications for manufacturing entities aiming to be agile and responsive to market changes, fulfilling the main aim of developing a holistic solution to the MSC problem

A holistic methodology for manufacturing systems configuration

The manufacturing sector is undergoing rapid transformations driven by global economic factors, technological advancements, and fluctuating market demands. These dynamics necessitate continuous innovation and adaptability in manufacturing systems to maintain competitiveness. Therefore, this research addresses the manufacturing systems configuration (MSC) problem, aiming to develop a methodology to make manufacturing systems resilient and adaptable that balances resource management, production efficiency, and costs. Despite the critical nature of the MSC problem, existing solutions are fragmented and suffer from significant limitations, including underutilisation of data models, software integration issues, and the inadequacy of traditional optimisation and decision-making methods in dealing with uncertainties and multiple objectives. Therefore, this research formulates the problem as ``developing a holistic solution for addressing the MSC problem for adapting manufacturing systems to rapidly changing manufacturing requirements" to address these gaps. In this context, a holistic solution synergistically combines data modelling, software integration, adaptive optimisation and decision-making algorithms. The research objectives include the development of adaptable data models that encapsulate the complexities of manufacturing systems, plug-and-produce manufacturing software solutions that address system scalability and adaptability, and adaptive optimisation algorithms capable of navigating complex solution spaces. The research employs a multi-staged validation approach, initially testing the proposed methodologies in two distinct manufacturing processes with unique challenges: sorting cylinders and bin-picking parts of industrial pipe couplers. These processes serve as a comprehensive testing ground for the proposed solutions. Three research hypotheses were sequentially assessed, focusing on the adaptability of object-oriented data models, the effectiveness of manufacturing apps in achieving interoperability, and the efficiency of optimisation and decision-making algorithms in managing multiple objectives and uncertainties. Each hypothesis was successfully validated, confirming the research contributions. Subsequently, empirical validation was extended to real-world industrial settings, focusing on aerospace and custom product manufacturing sectors. In the aerospace sector, the task was to find optimal manufacturing system configurations for changing and multiple conflicting manufacturing costs for assembling a generic hinged product. In the custom product manufacturing sector, the task involved planning a machining process that required balancing multiple manufacturing costs. These validations substantiate the research hypotheses and demonstrate the proposed methodology's generalisability and adaptability. By developing a holistic approach, this research contributes significantly to the field. It addresses the limitations of existing fragmented solutions and provides a robust, adaptable, and holistic framework for manufacturing systems. The research has practical implications for manufacturing entities aiming to be agile and responsive to market changes, fulfilling the main aim of developing a holistic solution to the MSC problem

Online and Modular Energy Consumption Optimization of Industrial Robots

Industrial robots contribute to a considerable amount of energy consumption in manufacturing. However, modeling the energy consumption of industrial robots is a complex problem as it requires considering components such as the robot controller, fans for cooling, the motor, the friction of the joints, and confidential parameters, and it is difficult to consider them all in modeling. Many authors investigated the effect of operating parameters on the energy consumption of industrial robots. However, there is no prescriptive methodology to determine those parameter values because of the challenges in the modeling of industrial robots. This work investigates an industrial robot and the manufacturing process together and proposes a black-box model-based energy consumption optimization approach. Our contribution to the research is the new online and data-efficient methodology, prescriptive algorithm, and the analysis of operating parameters' effects on industrial robots' energy consumption. The proposed methodology was tested using two real FANUC industrial robots in three industrial settings

Integration of cutting-edge interoperability approaches in cyber-physical production systems and industry 4.0

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.publishersversionpublishe

Big Data Life Cycle in Shop-floor – Trends and Challenges

Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components - Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented