Smart Manufacturing Concepts Research Articles

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.

Geometric feature extraction in manufacturing based on a knowledge graph

In times of global crises, the resilience of production chains is becoming increasingly important. If a supply chain is interrupted, a cost-effective solution must be established quickly. In the context of Industry 4.0, the concept of smart manufacturing offers a solution for fast and automated decision-making in production planning. The core idea of smart manufacturing is the digitalization of the product life cycle and the linking of individual phases of this cycle. Computer Aided Process Planning (CAPP) plays an important role as the connecting element between design and manufacturing. An important prerequisite for CAPP is the automated analysis of 3D models of components. The aim of this work is the development of an automatic feature recognition (AFR) -method to recognize geometric manufacturing features and their properties from 3D-models and then store them in a knowledge base. In that way, the result of the design can be automatically analysed and compared with manufacturing information afterwards in order to achieve an automated process planning. Geometric and topological information of a 3D model (STEP-AP242 format) generated by CAD systems is extracted by a Python-script developed and stored in an ontology-based knowledge base. The extracted product data is analysed using a Python-script to identify manufacturing features. To provide a comprehensive extensibility of the model, geometric features are defined according to a layered and hierarchical structure.

Smart Manufacturing Technology Adoption for Improving Productivity: A Systematic Literature Review

This paper is to present a review of the Smart manufacturing application as Industry 4.0, specifically on the conceptual approaches to define it. Two conceptual approaches to the application of the concept of smart manufacturing are distinguished. The method using the previous articles of scientific journals from 2017-2022 (thirty-two international paper) were selected based on previous works, with the scope to investigate and analyze in-depth the extent to which literature used to discuss results in the selected studies of applying the Smart Manufacturing Technology in manufacture enterprises. The results of the previous studies are discussed and investigated in a table format.

A Configuration Model of the Smart Manufacturing Company

The paper aims at illustrating the new developments of information technology applied in production management and at providing a conceptual model of the configuration of the intelligent or smart enterprise. After a brief overview of Industry 4.0 addressed in the introduction, the paper presents the concept of smart manufacturing, where the role of 5G connection, Internet of Things (IoT), artificial intelligence (AI), and digital twin is explained. Next, a conceptual model of the smart manufacturing company configuration is proposed, with examples from the Bosch company. The paper ends with conclusions highlighting the qualities and usefulness of a smart manufacturing company.

Lean, green, and smart manufacturing: An ingenious framework for enhancing the sustainability of operations management on the shop floor in industry 4.0

In the present competitive landscape industry, individuals have emphasized fulfilling the consumer needs in available resources and limited constraints. However, the industry individuals face several problems in achieving operational efficiency. These problems include ergonomics issues, environmental waste, work processes waste, inobservance on the shop floor, and workers’ apathy. The operations management teams use various approaches to improve operational excellence on the shop floor, including lean manufacturing, smart manufacturing, green manufacturing, and artificial neural network. These approaches are used to enhance productivity by eliminating problems in the operations management on the shop floor. The present research aims to develop a framework for improvement in operational excellence and sustainability in operations management on the shop floor in an industry 4.0 environment using lean, green, and smart manufacturing concepts. In the study, a strategic ergonomics assessment and environmental assessment have been used to assess working and operational performance on the shop floor. The adaptability of the developed framework has been investigated by implementing it in mining machinery manufacturing industry. The framework has been developed with insights gained from previous research, industry individuals, and brainstorming analysis on problems faced in operations management on the shop floor in industry 4.0 working environment. The novelty of the present study lies in the fact that no model has been developed for achieving operational excellence on the shop floor in industry 4.0 using the smart, lean green manufacturing concept. The developed framework significantly improved productivity by 85%, improved machinery utilization by 21%, reduced production time by 65%, reduced manufacturing defects by 96%, and production cost by 75%. The present research work strongly believe that the developed model would benefit industry individuals in enhancing sustainability in operational excellence on the shop floor in industry 4.0 environment by establishing a more competent and cleaner working environment with available resources.

Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.

A Sustainable Productive Method for Enhancing Operational Excellence in Shop Floor Management for Industry 4.0 Using Hybrid Integration of Lean and Smart Manufacturing: An Ingenious Case Study

In industry 4.0, industry individuals implement lean and smart manufacturing to improve shop floor management systems. Shop floor management is used to control operational performance and enhance production within limited constraints. Various shop floor management approaches are used in the present scenario of industry 4.0, and mainly include value stream mapping, total productive maintenance, Internet of Things, artificial intelligence, machine learning, and fuzzy logic. The present research aims to develop an open innovation method to achieve sustainability in shop floor management systems in industry 4.0 by using lean and smart manufacturing concepts. The proposed method has been validated by an enhancement obtained in a real case of the shop floor management system in industry 4.0. The authors are confident that the proposed method would provide sustainability in the shop floor management system within limited constraints in industry 4.0.

Development of a Data-Driven Decision-Making System Using Lean and Smart Manufacturing Concept in Industry 4.0: A Case Study

Nowadays, industries are emphasizing the implementation of a smart shop floor management method because of different types of problems faced in controlling the production activities in Industry 4.0. Several shop floor management methods are currently implemented in the present Industry 4.0 scenario, including lean manufacturing, logistics, Internet of things, smart manufacturing, cyber-physical system, and artificial intelligence. The present research work is focused on the development and Taguchi validation methodology of a data-driven decision-making system using L9 orthogonal array for smart shop floor management based on the relationship between production sustainability and constraints. The proposed system has been validated by a comprehensive investigation of a case of mining machinery manufacturing unit. The result of the investigation revealed that productivity has been enhanced by effective controlling of production activities on the shop floor. Taguchi L9 orthogonal array method of design of experiments is implemented to enhance flexibility for shop floor control and meanwhile minimize the production time due to inefficient operating conditions on the shop floor. Taguchi method was implemented for critical conditions affecting production lead time and resource utilization. The authors have detailed discussion on developing present novel hybrid integration of lean and smart manufacturing approaches to enhance operational excellence in production activities and other complicated manufacturing environment on the shop floor within available resources. The present finding demonstrates that the adopted digital technologies under smart manufacturing with lean manufacturing are found to be cost-effective approach under different environmental conditions. The proposed system has significantly improved the efficiency of production management and operational performance by using smart systems and has proved effective in improving the financial position by making a safer shop floor management approach. In this article, a robust problem-solving system is provided. The present work aims to introduce revolutionary methods for Industry 4.0 that would result in productivity enhancement and beneficial impact on industry persons by improving the smart shop floor management. The study also provides valuable perspective and sustainable guidelines to facilitate industry individuals to implement lean and smart manufacturing for productivity enhancement in the production environment of Industry 4.0.

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain.

CNN-Based Fault Detection for Smart Manufacturing

A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

Экономическая эффективность «умного производства»: от целевых установок к регламентации

The article deals with the problems of assessing the economic efficiency of smart production and ways to resolve them. It has been found that Smart Manufacturing is currently becoming a goal in itself and a guarantor of successful development of industrial business. A comparative analysis of scientific publications to clarify the content of the concept of Smart Manufacturing is carried out, it is revealed that it is necessary to distinguish between intelligent and smart production, which are currently used as synonyms. Analysis of the practice of organizing smart manufacturing has shown that often the process of creating Smart Manufacturing pursues the goal of participating in a popular, useful process with the expectation of future “blurry” positive results; but there are also the actual results in the form of cost savings (better organization of the production process), an increase in production and an increase in labor productivity. All enterprises and organizations quite clearly understand the process of creating a specific smart production with detailing of individual stages, therefore, the specific goals of Smart Manufacturing should be specified taking into account a specific enterprise, but at the same time, standardized for the possibility of a comparative assessment of economic efficiency. It has been proved that traditional economic indicators of efficiency are used to assess the economic efficiency of Smart Manufacturing; but for the organization it is necessary to form a special system of performance indicators based on the theory of management by goals. It is determined that the main factor of Smart Manufacturing is innovative capital, since smart manufacturing is innovative by definition. Possible options for the formation and combination of goals and objectives of Smart Manufacturing and innovation capital are considered. The content and structure of the regulation for assessing the economic efficiency of Smart Manufacturing are proposed

Smart data driven defect detection method for surface quality control in manufacturing

The use of machine vision systems for quality control of reflective metal surfaces has increased over the years with new systems that combine higher resolution cameras and better illumination of inspected objects. With the advances in artificial intelligence pattern recognition of images, the integration of machine vision systems in a manufacturing line for accurate automatic classification of defects would work towards the application of a smart manufacturing concept. To investigate the feasibility of such integration, a vision system that combines a 4K camera and chromatic confocal technology was employed to analyze surfaces of copper parts after the laser machining process. By the application of three machine-learning algorithms (decision trees, random forest and multi-layer perceptron) on features extracted from the Sobel edge detector, segmentation of defects has been performed using the Weka segmentation plugin. A simple convolutional neural network (CNN) was also applied for the classification of defects. Later on, using smart data rather than big data, transfer learning (TL) has been successfully performed with retraining the mobilenet-v1 model, which is based on CNN. This lean learning process can be implemented in devices that are limited by their computation resources. The maximum average of validation accuracy achieved using TL trained over 500 epochs was 90.5%. Whereas for the simple CNN classification models, the best validation accuracy was achieved by a model with a batch size equal to ten and with 40% of validation data with an average equal to 98.7% over 500 epochs.

Implementation of MQTT protocol based network architecture for smart factory

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

New Process Implementation to Enhance Cold Spray-Based Additive Manufacturing

Cold spraying (CS) is a solid-state coating technique that has been applied as a novel additive manufacturing (AM) method to fabricate freestanding metal components. Recent advances in cold spray additive manufacturing (CSAM) technology call for new process implementation to improve manufacturing accuracy and flexibility. In this study, a concept of modular systems is presented to design and implement a new CSAM framework. The current CSAM system is an open framework system composed of different modules. The flow of the proposed CSAM system is explained to understand the physical and functional relationships between the key elements of the entire system. This physical and functional modularity is useful to promote hybrid AM processes. In addition, based on the smart manufacturing concept of Industry 4.0, a novel approach is proposed to bring the perception and the decision-making abilities into the traditional CS system. Here, a 3D profiler is used to measure the morphology of deposited coatings online and to transfer data to the robot controller. The developed algorithm compares the obtained results with a theoretical model to ensure an adaptive adjustment of the robot trajectory. The experimental validation shows that this approach achieves closed-loop control for CSAM with high-precision by optimizing kinematic parameters in real time.

Smart Manufacturing Plant Implementation Using Industrial IoT

Industry 4.0 initiatives include the concept of smart manufacturing with the help of sensors and intelligent systems. Smart manufacturing optimizes the internal process and decision making with no/minor intervention from humans. Nevertheless, the adoption of IIoT in industry setting remains a challenge and very minimal work has been in progress. This work is aimed to showcase adoption of IIoT in the Slip Control System/Anti-Lock Braking System. It optimizes the monitoring and decision making of various processes with the help of intelligent systems and IoT in the perspective of Industrial IoT 4.0. It mainly focuses on the various conditions and parameters such as line breakdown notifications, pressure levels, etc. They are monitored through the cloud platform and an alert system is developed in the cloud to warn the user regarding the pressure level. The instant alert of line breakdown notifications reduces the downtime of the plant, improves production and monitoring of the pressure system in the plant and safety check of the plant is in place at any instant.

Smart Manufacturing and Industry 4.0: A Preliminary Approach in Structuring a Conceptual Framework

Engineering concepts usually, are complex concepts, thus many times are difficult for infusing into curriculums or to be comprehensive for practitioners. A concept that still now is not fully understandable is that of Industry 4.0, an approach that increases the complexity of production systems. Nowadays production systems are facing new challenges, as physical productions systems and internet technologies are directly linked, hence increasing the complexity but also the productivity of the systems. The paper introduces an approach of visualizing the concept of smart manufacturing in the context of Industry 4.0, as the term is not clearly specified, although has attracted attention both academicians and businesses. Concept mapping is a method of capturing and visualizing complex ideas. Concept maps are graphical tools for organizing, representing and communicating complex ideas by breaking them into more key concepts. As Industry 4.0 is a factor that can boost innovation and competitiveness of business, all parties involved in shaping the strategy of an organization, should perceive the issues to be covered. Furthermore, learners must be prepared to meet these challenges and knowledgebuilding activities may enhance their process of learning. The paper makes an interesting and valuable contribution, by identifying key concepts within the subject of smart manufacturing and Industry 4.0, using the method of concept mapping. Taking into consideration these concepts a conceptual framework will be introduced, by using the software tool CmapTools. The map can be used as a basis for future research in constructing a more comprehensive framework and identifying the concepts that describe smart manufacturing in the context of Industry 4.0, in a more thorough manner.

Industry 4.0 transformation initiatives in a small scale mill board unit through sustainable manufacturing: A case study

Small scale mill board manufacturing industries are shutting down shop at an enormous pace. Observation shows that three out of five such units close down operations within three years of its inception due to their inability to sustain in the market. These units have not been able to utilize resources effectively and have not been able to run profitably too. Further, they have been struggling to meet the norms stipulated by the state pollution control board. This article is a review of various problems identified in a mill board unit and the measures taken that made the unit self-reliant and transform into an Industry 4.0 enterprise through sustainable smart manufacturing concepts. This case study describes measures adopted to revive a struggling mill board unit and its success in terms of economic profitability and issues in incorporating Industry 4.0 concepts..

Implementation of Automation System to Control Machine Voltage of Electroplating Process

Technology has been proliferating and it has been creating a new term in the world of technology. One of the new terms that have emerged along with technological development is The Fourth Industrial Revolution. In line with the revolution, this research has a purpose of creating a smart manufacturing concept that involves several components such as software, machines, and certain tools such as RFID. This research will be designed and developed using software development method in the form of a waterfall method and this research will use C# programming language released by American multinational technology company, which is Microsoft to create the software. This research will be assisted by several hardware devices such as RFID Reader that will serve as a tool that will read information stored in RFID Tag and RFID Tag that will be used to store information needed by system. The output of this research is a software that is capable of automating voltage adjustment of electroplating machine, monitor the voltage and ampere of the machine, and able to aid users on the electroplating process

Edge Computing and Digital Twin Based Smart Manufacturing

Abstract This paper presents a novel approach to implement smart manufacturing concepts based on the edge computing paradigm using a distributed network of interconnected manufacturing nodes. These cyber-physical entities act as edge devices and combine the concepts of holons and digital agents together with local digital twins to enable local control, decision making and optimization. The network of manufacturing nodes is controlled globally by the global digital agent connected to a global digital twin for production order and logistics optimization, and locally by the local digital agents, digital twins, and information shared by the node network. The main contribution of this paper is a demonstration of the concept of edge computing based smart manufacturing with distributed control structure.

Smart manufacturing adoption in supporting technologies infrastructure in Indonesia: The case of South Sulawesi SMEs

This paper aims to give a unique view of looking at how SMEs in Indonesia applying the concept of smart manufacturing along with the challenges of smart manufacturing adoption in supporting technologies infrastructure in Indonesia and its current implementation. There are several challenges that were identified through the smart manufacturing adoption in Indonesia. The manufacturing industry has a great contribution to developing countries such as Indonesia. It is considered as the engine power for Indonesia’s economic growth. Moreover, by considering the value of SMEs in Indonesia, this research puts more attention on smart manufacturing implementation on SMEs. This study provides two cases from SMEs based on the SMEs scale and measuring their productivity from using the traditional method and after adopting the technology used on their business operations. The two cases indicate that the firms have an awareness of the importance of smart manufacturing implementation and its effect to the performance of the firm. However, direct migration to an advanced cyber-physical manufacturing system is not a practical option for the firms.