Manufacturing Life Cycle Research Articles

Comprehensive review of additive manufacturing lifecycle optimization in a cloud environment: from distributed manufacturing to postprocessing

Purpose This paper aims to offer a comprehensive review and categorization of production optimization throughout the additive manufacturing lifecycle in a cloud environment. It aims to provide a structured approach to identifying and addressing issues. Design/methodology/approach This paper systematically reviews 75 technical papers on cloud manufacturing, nesting, scheduling and postprocessing in additive manufacturing. This includes a detailed discussion of the key issues. Findings This paper introduces a production framework for the entire lifecycle of additive manufacturing in a cloud environment. This framework aids in problem identification and decision-making based on the process flow. It provides an integrated view from cloud to postprocessing, examining decision interdependencies and enhancing problem identification and organization. Originality/value To the best of the authors’ knowledge, this paper is the first to review the complete lifecycle of additive manufacturing, emphasizing the often-overlooked aspects of postprocessing and cloud manufacturing. It offers a comprehensive study of lifecycle optimization challenges and suggests ways to streamline the production process.

AI-driven pharmaceutical manufacturing : Revolutionizing quality control and process optimization

The Integrating AI into pharmaceutical production processes represents a paradigm change for the pharmaceutical sector. This article examines the role of AI in pharmaceutical production, focusing on its potential to improve productivity, cut costs, and guarantee the highest standards of product quality and safety in quality control and process optimization. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly being employed to analyze large volumes of data generated throughout the pharmaceutical manufacturing lifecycle. Risks related to production anomalies can be reduced and regulatory compliance can be ensured with the help of these smart systems’ real-time monitoring, early identification of deviations, and predictive maintenance. AI-driven technologies are revolutionizing quality control processes by allowing for the automated screening of pharmaceutical items at lightning speeds and with pinpoint accuracy. Better product quality and fewer cases of batch rejection are the results of AI systems’ superiority over conventional approaches for detecting tiny faults, ensuring uniformity, and identifying potential contamination. In addition, AI is improving manufacturing processes by analyzing large data sets for trends, tweaking settings, and maximizing output. Pharmaceutical companies save money as a result of streamlined production processes, shorter cycle times, and better resource utilization.

Bibliometric Performance and Future Relevance of Virtual Manufacturing Technology in the Fourth Industrial Revolution

Virtual manufacturing (VM) technology emerged in the 1980s as a revolutionary strategy to optimize and streamline the entire product/service manufacturing lifecycle. However, over the years, its popularity appears to have waned. Further, the advent of the fourth industrial revolution (4IR) or Industry 4.0 brings with it other integrated digital technologies, including the Internet of Things (IoT), Blockchain, and digital twin (DT), among others. DT offers functions like VM plus other benefits, including intelligent manufacturing, to revolutionize future manufacturing operations activities and predictive capability using real-time data. This paper employs bibliographic metadata from publications indexed on SCOPUS to evaluate the recent trends in VM research and develop predictive models to forecast VM’s future trajectory and relevance in 4IR. The results of the bibliometric evaluation of VM-related scientific literature publications show a rapidly declining research productivity and highlight an exponential decline from the mid-2000s. This period of VM publication decline coincides with the advent of 4IR and DT technology, which are trending. The results of the predictive analytics using the quadratic regression model created in this study to forecast the future relevance of VM in the 4IR era suggest that VM publications show a similar conclusion. VM research output increased until 2009 and then started decreasing exponentially. The quadratic model implies an exponential decrease in yearly VM publications. Future works can evaluate DT and VM research trends from the last two decades.

Machining tool identification utilizing temporal 3D point clouds

The manufacturing domain is regarded as one of the most important engineering areas. Recently, smart manufacturing merges the use of sensors, intelligent controls, and software to manage each stage in the manufacturing lifecycle. Additionally, the increasing use of point clouds to model real products and machining tools in a virtual space facilitates the more accurate monitoring of the end-to-end production lifecycle. Thus, the conjunction of both, intelligent methods and more accurate 3D models allows the prediction of uncertainties and anomalies in the manufacturing process as well as reduces the final production costs. However, the high complexity of the geometrical structures defined by point clouds and the high accuracy required by the Quality Assurance/Quality control parameters during the process, pave the way for continuous improvements in smart manufacturing methods. This paper addresses a comprehensive analysis of machining tool identification utilizing temporal point cloud data. Specifically, we deal with the identification of machining tools from temporal 3D point clouds. To do that, we propose a process to construct and train intelligent models utilizing such data. Moreover, in our case study, we provide the research community with two labeled temporal 3D point cloud datasets, and we experiment with the pioneering PointNet neural network and three of its variants demonstrating an accuracy of 95% in the identification of the utilized machining tools in a machining process. Finally, we provide a prototype end-to-end intelligent system of machining tool identification.

Artificial Intelligence-Based Smart Quality Inspection for Manufacturing.

In today's era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.

Review of Responsiveness and Sustainable Concepts in Cellular Manufacturing Systems

Cellular manufacturing systems are widely used due to their advantageous capability of combining the flexibility of the job-shop and the productivity of the flow-shop. In recent years, the reduction of the product life cycle, variation in demand products, and introduction of new technologies, have driven the manufacturing companies to improve responsiveness, thus reducing the cellular manufacturing life cycle with a focus on re-designing the cells. Another issue concerns the sustainability of the manufacturing systems due to the introduction of energy costs in the design model of cellular manufacturing systems. This study is an overview of the more recent works on design approaches to improve the responsiveness and the models to support the sustainability of cellular manufacturing systems. The analysis of the literature review highlights the main findings and suggests future development paths considering the open problems in this field.

Identification of a monoclonal antibody clipping variant by cross-validation using capillary electrophoresis – sodium dodecyl sulfate, capillary zone electrophoresis – mass spectrometry and capillary isoelectric focusing – mass spectrometry

Critical quality attributes (CQAs) of recombinant monoclonal antibody therapeutics are constantly monitored throughout the life cycle of drug development and manufacturing. In the past few decades, numerous analytical techniques have been developed for the characterization of CQAs. In this regard, non-reduced and reduced capillary electrophoresis – sodium dodecyl sulfate (CE-SDS) methods have been widely adopted by the biopharmaceutical industry for the evaluation of size-related heterogeneities in biologics. In this work we demonstrate that, with recent development of capillary electrophoresis – mass spectrometry (CE-MS) technologies, a clipping variant of bevacizumab may be identified directly by both capillary zone electrophoresis – mass spectrometry (CZE-MS) and capillary isoelectric focusing – mass spectrometry (cIEF-MS) approaches, providing a powerful addition to the traditional CE-SDS analysis workflow. In this novel workflow, linear regression between the mobility and molecular weight first results in an approximate size range of this variant. The intact masses of all species in the bevacizumab are then obtained, after deconvolution of all features identified in the CZE-MS analysis. Subsequent CZE-MS analysis of the subunits of bevacizumab leads to the confirmation of a clipped heavy chain. Furthermore, cIEF-MS of the intact bevacizumab confirms the existence of this clipping variant. The cross-validation between CE-SDS, CZE-MS, and cIEF-MS, creates a comprehensive roadmap for monoclonal antibody size variants profiling. These CE-based analytical techniques are complementary to each other, leading to orthogonal verification for size heterogeneity characterization.

생산자동화장비 모델기반 시뮬레이터 설계에 관한 연구

The recent development of ICT technology arising from the Fourth Industrial Revolution has resulted in the digitization of the production system, which is changing the paradigm of the manufacturing site and is being presented as a new concept of the digital twin. Manufacturing and production systems can be categorized into information technology and operation technology systems. Owing to the advent of digital twins, sophisticated and accurate models for the manufacturing lifecycle are necessitated. To evaluate a digital twin-based simulation model, we propose a design technology that can pre-examine the feasibility of the work procedure by applying a production automation equipment control simulator at the engineering stage.

Research and development of public service platform for integrated circuit test

Integrated circuit (IC) test is a key technical part of IC chain, which works through the entire life cycle of IC design, manufacturing, packaging, and application. Based on the increasing requirements of IC test, this paper analyzes the breakthrough point to promote the development of IC test and puts forward an accurate and feasible programme of public test platform construction so as to achieve long-term sustainable development of IC industry.

Graph Learning for Cognitive Digital Twins in Manufacturing Systems

Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this article, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This article presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.

Environmental Impact Assessment of Reconditioning Titanium Alloy Powder

The environmental impact was assessed for the spheroidisation process to compare its advantages versus mining titanium from the ground. Energy consumption was used to compare the environmental impact. With the introduction of spheroidisation at Necsa, there was a need to investigate the environmental impact of the process. The environmental impact of plasma spheroidisation making use of the 15 kW Tekna plasma system was investigated. Environmental impact assessment is part of a bigger study to investigate the holistic impact of the spheroidisation of titanium powder at Necsa. The study was carried out using ASTM standards, ensuring that the results from the experiments are acceptable. The primary focus of the paper was the 15-kW spheroidisation system. The energy consumption of the reconditioning of titanium alloys was compared to conventionally producing titanium. The role spheroidisation plays in the additive manufacturing lifecycle was also assessed. This life cycle assessment also included the other processes in additive manufacturing to give an overview of how the spheroidisation process can fit in and improve the additive manufacturing value stream.

Ecolabelling in textile industry: A review

As informed decision making has become one of the key attributes in the modern economy which is driven by sustainable production and consumption, assessment of the present situation and providing future recommendations in the area of textile ecolabelling can be considered highly significant. Therefore, this review aimed at assessing the attributes of textile ecolabels that are currently applied around the world, identify current trends in textile ecolabelling, and propose recommendations for future research in the area. A comprehensive literature review on textile ecolabelling was performed by accessing the world’s largest ecolabelling database “Ecolabel Index” by reviewing 107 ecolabels that are currently applied in the textile industry. The present review highlights the importance of focusing on regional or ‘gate-to-gate’ ecolabelling frameworks for the textile industry, due to the outspread nature of the product manufacturing life cycle that covers a range of geographical regions.

Introducing Digital Twins of High-Speed Machining in the Manufacturing Life Cycle

Introducing Digital Twins of High-Speed Machining in the Manufacturing Life Cycle

The study and scientifical substantiation of critical control points in the life cycle of immunostimulating products such as pastila and marmalade

Based on the study results, this paper reports a technology to produce natural fruit and berry pastila and marmalade of improved nutritional and biological value with a long shelf life. Critical control points of product safety have been identified and scientifically substantiated. The technological process to prepare pastila and marmalade involves exposing the raw materials to high and low temperatures while maintaining certain indicators of the content of dry substances. The identification feature of marmalade products is the content of a certain type of fruit raw materials, which poses risks throughout the life cycle of product manufacturing despite the fact that the preparation and storage of the raw materials implies the temperature range from 0 °C to minus 15 °C. Problematic issues of mold occurrence have been considered. It was established that the moisture content in the product above 15 % leads to an increase in the content of mold fungi and significantly reduces its shelf life. This paper gives the results of a study to identify the preservation of vitamin C in products prepared from fresh berries and from berries subjected to freezing. It was revealed that in the process of storing the raw materials at a temperature of minus 15 °C, the amount of vitamin C in the resulting product decreased by 11.3 % compared to the product prepared from fresh raw materials. The maximum limit of the high preparation temperatures of 108 °C did not significantly affect the loss of vitamin C. Studies were conducted to determine the sugar content in products whereby a high fructose content was identified. Critical control points at all stages of production were determined, which has made it possible to choose the optimal technological modes and parameters for the safety and quality of the product

Unified adaptive deep classification for industrial real‐time situation awareness in edge environment

SummaryThe rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting‐edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi‐supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real‐time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework.

Comparative Life Cycle Analysis of Low Energy-consuming Materials, Case Studies: Concrete, Brick, Wood, System Boundary: Cradle to Gate

Recent researches all across the world emphasize the threat of the increasing consumption of energy. The undeniable role of energy consumption in all stages of the life cycle of materials, including extraction, factory manufacturing, and transportation has revealed the necessity of using sustainable methods to have lower energy consumed. The whole energy of all different steps of the life cycle is called "embodied energy" and the process of assessing this embodied energy input is called "life cycle assessment” (LCA). Despite the great importance of LCA, the quantitative test of such a hypothesis has been less of a concern for previous researchers in our country Iran, and due to the lack of organized information from industrial units, such a study has also faced the difficulties of data collection. In this regard, this paper evaluates the amount of embodied energy consumption of building materials at different stages of their life cycle. To reach this goal this research evaluates the initial energy quantitatively (including different stages). More precisely, the present study, based on life cycle assessment system, quantitatively evaluates and compares energy input in different stages of cradle to gate scope, in 3 case studies: Concrete, wood, and brick. The results finally show that per ton of concrete produced 110 (kw.h) electrical energy, 35 (ton) of gas, 170 (Mj) of human Energy, and 495 (g) of Gasoline is consumed, while these quantities for per ton of Brick are 35(kw.h), 18.2 (ton), 72 (Mj) and 250 (g) and For one ton of timber produced are 900 (Kw.h), no Gas used, 170 (Mj) and 495 (g).

Scale-Up of Pilot Line Battery Cell Manufacturing Life Cycle Inventory Models for Life Cycle Assessment

Even though the gate-to-gate production of lithium based battery cells is one of the main contributors to the environmental impacts of electric vehicles, primary data required for the estimation of environmental impacts of large-scale production of battery cells remains scarce. While Life Cycle Assessment for battery cells produced in research pilot lines can increase the understanding of related environmental impacts, the data is difficult to scale up to large-scale production systems. This paper presents a scale up methodology along with a Life Cycle Inventory and Life Cycle Assessment for battery cells manufactured in the Battery LabFactory Braunschweig (BLB). CO2-eq emissions of a single battery cell produced in a pilot line can be tenfold of comparable industrial cells. Material and energy efficiency, dry room sizing, lacking systemic efficiencies and unoptimized product design are the main reasons for this difference in the foreground system. Based on two scenarios, the paper elaborates how Life Cycle Inventories can be up-scaled from a pilot scale so that they can be used to estimate large-scale production environmental impacts.

Research on the Framework Design of NQI Service Cloud Platform in the Field of Smart Grid Measurement

The quality control of smart meters exists in production enterprises and power grid companies respectively. Their data are not connected, which leads to the lack of comprehensive control in the whole life cycle of smart meter design, manufacturing and operation scrap, which results in the overall quality level of smart meter industry is limited. The NQI service cloud platform in the field of smart grid measurement takes cloud computing technology as the core and NQI quality service as the process to mine and analyze the quality data of smart meters in the whole life cycle, so as to achieve the overall quality improvement of smart meter industry. In this paper, the service cloud platform architecture is designed by analyzing the service scenario, the quality evaluation index system and the data model of intelligent power meter.

Life Cycle Greenhouse Gas Implications of Multi Jet Fusion Additive Manufacturing

This study fills a gap in additive manufacturing life cycle assessments to improve decision-making in plastic product manufacturing. This assessment investigated cradle-to-gate life cycle energy co...

Digital Twin Driven Green Performance Evaluation Methodology of Intelligent Manufacturing: Hybrid Model Based on Fuzzy Rough-Sets AHP, Multistage Weight Synthesis, and PROMETHEE II

The design, planning, and implementation of intelligent manufacturing are mainly carried out from the perspectives of meeting the needs of mass customization, improving manufacturing capacity, and innovating business pattern currently. Environmental and social factors should be systematically integrated into the life cycle of intelligent manufacturing. In view of this, a green performance evaluation methodology of intelligent manufacturing driven by digital twin is proposed in this paper. Digital twin framework, which constructs the bidirectional mapping and real-time data interaction between physical entity and digital model, provides the green performance evaluation with a total factor virtual image of the whole life cycle to meet the monitoring and simulation requirements of the evaluation information source and demand. Driven by the digital twin framework, a novel hybrid MCDM model based on fuzzy rough-sets AHP, multistage weight synthesis, and PROMETHEE II is proposed as the methodology for the green performance evaluation of intelligent manufacturing. The model is tested and validated on a study of the green performance evaluation of remote operation and maintenance service project evaluation for an air conditioning enterprise. Testing demonstrates that the proposed hybrid model driven by digital twin can enable a stable and reasonable evaluation result. A sensitivity analysis was carried out by means of 27 scenarios, the results of which showed a high degree of stability.