Flexible Manufacturing Process Research Articles

Industry 4.0 techniques applied to the improvement of the cutting process of Lithium-Ion battery terminals

The transition to Industry 4.0 has brought significant advancements in production, especially in the manufacturing of lithium-ion batteries, through the application of Artificial Intelligence (AI). This study focuses on the development of an intelligent system to improve the cutting of battery terminals, using AI algorithms to optimize precision and reduce defects. The main challenge is the imprecision in the cutting process, which results in a high rate of defective products and increases production costs due to material waste. The application of machine learning and real-time data analysis enabled automatic adjustments in the cutting process, creating a flexible and adaptable manufacturing environment. As a result, there was a significant reduction in the defect rate of the batteries, along with an increase in the quality and uniformity of the terminals. These efficiency gains demonstrate the potential of AI to improve precision and reduce costs while meeting quality requirements. The integration of AI in the cutting process not only improves quality and sustainability but also opens up new possibilities for other applications in Industry 4.0. The study demonstrates that technological innovations in flexible manufacturing processes can generate a significant competitive advantage for the renewable energy and storage sector.

A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process

Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a dynamic scheduling method combining iterative optimization and deep reinforcement learning (DRL) is proposed to address the impact of uncertain disturbances. A real-time DRL production environment model is established for the flexible job scheduling problem. Based on the DRL model, an agent training strategy and an autonomous decision-making method are proposed. An event-driven and period-driven hybrid dynamic rescheduling trigger strategy (HDRS) with four judgment mechanisms has been developed. The decision-making method and rescheduling trigger strategy solve the problem of how and when to reschedule for the dynamic scheduling problem. The data experiment results show that the trained DRL decision-making model can provide timely feedback on the adjusted scheduling arrangements for different-scale order problems. The proposed dynamic-scheduling decision-making method and rescheduling trigger strategy can achieve high responsiveness, quick feedback, high quality, and high stability for flexible manufacturing process scheduling decision making under sudden disturbance.

ON THE BENEFITS OF USING FLEXIBLE MANUFACTURING PROCESSES BY MANUFACTURERS TO PRODUCE HIGH-QUALITY AND COMPETITIVE PRODUCTS

ON THE BENEFITS OF USING FLEXIBLE MANUFACTURING PROCESSES BY MANUFACTURERS TO PRODUCE HIGH-QUALITY AND COMPETITIVE PRODUCTS

Can the Manufacturing Process Be Improved by FMEA? A Study on X Company in Plastic Packaging Industry in Vietnam

This study based on the FMEA method, provides a systematic approach, which include identifying potential errors in the production process, tracking priority issues, proposing improvement solutions, and putting those solutions into practice in order to improve the flexible plastic packaging manufacturing process at a typical X company in the plastic packaging industry in Vietnam. It is found that four large errors – GP8, SM4, LM4, and BF4 – stand out as having an RPN index higher than 250. The aforementioned issues are also analyzed, and their root causes are tracked down using techniques like brainstorming, cause-effect diagram, and 5 Whys. Then, appropriate actions are taken to deal with these causes. Overall, the findings can serve as an insightful reference for Vietnamese manufacturing companies using the FMEA method to identify any potential errors in their production processes. After that, the company is able to organize proper inquiries and make recommendations for potential failure prevention strategies as well.

Experimentation and FE analysis of low carbon steel-based laser welded tailored blanks in case of single point incremental forming

This study explores the formability of laser-welded tailored blanks in the context of single-point incremental forming (SPIF), a flexible and dieless manufacturing process. Two approaches, variation in materials and thicknesses, were used to fabricate tailor-welded blanks (TWB) with CRCA and DD steel sheets. The quality of the weld was evaluated by metallographic studies and tensile tests. The ductility of the welded joint was reduced by 81%, and the hardness was increased by 133% compared to the base metal. SPIF was employed using a hemispherical tool to form a ‘D’ shape geometry on fabricated TWBs. Fracture was observed only at the weld line, irrespective of thickness and material combination in the TWB. The forming height achieved from the SPIF of TWBs was reduced by 24% and 34% for both differences in thickness and material when compared to their corresponding base metals. Finite Element simulations were performed in Abaqus software using a dynamic explicit approach employing von Mises and Hill48 yield functions. Further, the FE model was validated by comparing the thickness and surface major/minor strains at the same forming height as observed from the experiments. Reduced formability and no weld line movement were the main observations made in this study, and the same was manifested by the FE simulation.

Additive manufacturing of polyamide 12 bulk structures using directed energy deposition with a thulium laser: Effect of process conditions on morphology and mechanical performance

Laser-based directed energy deposition of polymers (DED-LB/P) is a highly flexible additive manufacturing process capable of fabricating three-dimensional structures with a high degree of customization on free-form surfaces. In this article, the manufacturability of polyamide 12 bulk structures using DED-LB/P in combination with a thulium laser operating at a wavelength of 1.94 μm is investigated for the first time. The typical absorption bands at this wavelength eliminate the need for additives such as carbon-based nanoparticles, which would limit the range of applications. The generated structures were analyzed regarding porosity, thermal behavior, and mechanical properties to evaluate the potential of DED-LB/P with a thulium laser. As a consequence of the evaporation of absorbed moisture or ethanol residues resulting from powder preparation, a thermal pretreatment of the polymer powder reduced the porosity of the DED-LB/P structures to 0.9%. Depending on the processing parameters, the crystallinity of the produced structures ranged from 25.6% to 29.9%. For the tensile tests, the required geometries were milled from the DED-LB/P bulk structures. The results show that an ultimate tensile strength of up to 52 MPa, an elongation at break of up to 58%, and Young's modulus of up to 1590 MPa can be achieved. These remarkable mechanical properties are primarily attributed to the complete melting of the powder particles in DED-LB/P and the improved surface quality resulting from the milling process.

Green pharma supply chain: a review of existing practices and future directions

PurposeThe pharmaceutical industry is facing significant pressure to tackle antimicrobial resistance (AMR). Other ecological, societal and regulatory pressures are also driving the industry to “go green”. While such a (green) transition could be possible through appropriate green practices’ implementation, the present understanding about it is superficial and vague. A key reason is the lack of green practices’-related studies on pharmaceuticals, which are also insufficiently comprehensive. This knowledge gap is sought to be addressed.Design/methodology/approachA systematic literature review (SLR) was conducted with 73 carefully selected articles, then subjected to thematic content analyses for synthesising the relevant themes and sub-themes.FindingsAround 76 operational-level green practices covering all key stakeholders across the drug lifecycle were identified. It was revealed that designing drugs having accelerated environmental degradability is important to combat AMR. Also, redesigning existing drugs is environmentally more resource-intensive than developing new ones with significant cost-saving potential in solvent recycling and flexible manufacturing, both of which are not common at present. With regards to green-related barriers, stringent quality requirements on drugs (and therefore risks in making relevant green-oriented modifications) and time-consuming and costly regulatory approvals were found to be the key ones.Practical implicationsThe operational green practices’ framework developed for individual pharmaceutical supply chain stakeholders could help practitioners in benchmarking, modifying and ultimately, adopting green practices. The findings could also assist policymakers in reframing existing regulations, such as Good Manufacturing Practices or GMP-related, to promote greener drug development.Originality/valueThis work is the first systematic attempt to identify and categorise operational-level green supply chain practices across stakeholders in the pharmaceutical sector.Highlights•Biodegradability of drugs is more important than environmental degradability.•Flexible manufacturing process design (or quality by design) reduces resource wastage.•Ecopharmacovigilance is effective in combating PIE and AMR-related issues.•Upstream and downstream coordination is key to greening pharma operations.•Costly and time-consuming regulatory approval is a key barrier to greening pharma processes.

Modelling and control of tension in a flexible pipe tensile armour manufacturing process

Modelling and control of tension in a flexible pipe tensile armour manufacturing process

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

Using tensile and compressive stress superposition during incremental forming to manipulate martensitic transformation in SS304

Single point incremental forming (SPIF) is a flexible manufacturing process that has applications in industries ranging from biomedical to automotive. In addition to rapid prototyping, which requires easy adaptations in geometry or material for design changes, control of the final part properties is desired. One strategy that can be implemented is stress superposition, which is the application of additional stresses during an existing manufacturing process. Tensile and compressive stresses applied during SPIF showed significant effects on the resulting microstructure in stainless steel 304 truncated square pyramids. Specifically, the amount of martensitic transformation was increased through stress superposed incremental forming. Finite element analyses with advanced material modeling supported that the stress triaxiality had a larger effect than the Lode angle parameter on the phase transformation that occurred during deformation. By controlling the amount of tensile and compressive stresses superposed during incremental forming, the microstructure of the final component can be manipulated based on the intended application and desired final part properties.

ADAPTIVE PROCESS MANAGEMENT USING DEEP LEARNING ON A PROGRAMMABLE LOGIC CONTROLLER (PLC)

In the era of Industry 4.0, the integration of advanced digital technologies into manufacturing processes has become paramount for enhancing operational efficiency and adaptability. This study introduces a groundbreaking approach to adaptive process management through the integration of deep learning algorithms within Programmable Logic Controllers (PLCs), thus addressing the limitations of traditional PLCs in dynamically adjusting to new operational conditions without manual intervention. By leveraging the inherent capabilities of deep learning for realtime data analysis and decision-making, this research develops a novel framework that enables PLCs to autonomously learn from process data, adapt control strategies in real-time, and optimize manufacturing operations. The methodology encompasses the design and implementation of deep learning models tailored for PLC environments, the development of a data-driven learning mechanism directly on the PLC, and a comprehensive evaluation of the system’s adaptability, efficiency, and performance in real-world industrial settings. The findings reveal significant improvements in process efficiency, reduction in downtime, and enhanced adaptability to changing operational conditions, demonstrating the potential of combining deep learning with PLC-based systems for fostering intelligent and flexible manufacturing processes. This study not only provides a viable solution to the challenges of static PLC programming but also opens new ways for research and development in smart manufacturing technologies, offering insights into the practical implications of deploying intelligent automation systems in Industry 4.0.

Designing a System to Facilitate the process of Connecting 4.0-Generation Industrial Machines to the internet

Industry 4.0 aims to create more efficient and flexible manufacturing processes that can respond quickly to changes in demand and customer requirements. The integration of digital technologies and physical systems allows data to be collected, analysed, monitored and controlled in real time. This can increase productivity, reduce downtime and improve product quality. In this paper, remote control, monitoring and online data storage of industrial machines based on human-machine interface (HMI) module are facilitated. HMI integrates the control system with the Industrial Internet of Things (IIOT). On the other hand, it can be controlled and monitored anywhere in the world via the Internet using a Virtual Private Network (VPN) to open different navigation private channel and HMI interface. It was clear from the collection of recorded data and the results of practical testing that the designed system facilitated the process of accessing the industrial machine

Towards smart manufacturing – a case study

The Industry 4.0 model of manufacturing transformation based on the application of various communication technologies, models for the informational connections between machines and parts, with the possibility of big data analysis facilitated by Artificial Intelligence (AI) tools and techniques. These technologies provide the users with powerful data-driven tools to create, control and improve flexible and profitable manufacturing processes. In order to truly pave the way forward to Industry 4.0 and beyond, manufacturing must evolve into a Smart Manufacturing (SM) concept, as a base model, which is fundamentally monitored and managed by AI. This paper presents a case study from a real factory in which elements of Industry 4.0 paradigm were applied, in the construction of a digital manufacturing model, as a basic step in the development of the SM model.

Numerical Modelling for Efficient Analysis of Large Size Multi-Stage Incremental Sheet Forming

Incremental sheet forming (ISF) is an advanced flexible manufacturing process to produce complex 3D products. Unlike the conventional stamping process, ISF does not require any high cost dedicated dies. However, numerical computation for large-size ISF processes is time-consuming, and its accuracy for spring back due to unclamping tools after ISF cannot satisfy industrial demand. In this paper, an advanced numerical model considering complicated forming tool paths, trimming, and spring back was developed to efficiently simulate the multi-stage deformation phenomena of incremental sheet forming processes. Numerical modeling accuracy and efficiency are investigated considering the influence of tool path, material properties of the blank, mesh size, and boundary conditions. Through a series of case studies and comparisons with experimental results, it is observed that the numerical model with kinematics material properties and a moderate element size (5 mm) may reproduce the deformation characteristics of ISF with good accuracy and can obtain practical efficiency for a large-size ISF part.

In-Process Integration of Reinforcement for Construction Elements During Shotcrete 3D Printing

The current state of the art for additive manufacturing often utilises horizontal layer printing approaches for a variety of materials and applications. However, it imposes restrictions on the integration of utilities, mounting fixtures, installations, and reinforcement. Particularly the integration of reinforcement into 3D concrete printing still faces many challenges. It is currently restricted by the nozzle to strand distance, the lack of bond quality, automation, and geometric limitations of the respective 3D concrete printing techniques. The following research presents a case study on additively manufactured concrete construction elements utilising the Shotcrete 3D Printing (SC3DP) technique, focusing on interlayer- and short rebar reinforcement. To demonstrate the potential benefits for an automated reinforcement integration and to uncover further challenges and research questions, a wall segment was produced using a unique combination of Interlayer Reinforcement (ILR) and Short Rebar Insertion (SRI). By incorporating these methods, it was possible to generate three-dimensional continuous reinforcement structures within the wall. The innovative approach showcased takes full advantage of the SC3DP technique, enabling the integration of reinforcement during the printing process itself, thus utilising the geometric freedom, the fast build up rate and the kinetic energy during application. This eliminates the need for premanufactured reinforcement structures, enabling a more efficient and flexible manufacturing process. Furthermore, the discussion includes the potential for surface finishing and attainment of geometrical accuracy through the direct integration of reinforcement. An outlook is given as future construction elements can be produced structurally reinforced without formwork and with a high degree of geometric freedom.

Development of Screw-Based 3D Printing Machine and Process Experiments for Short Fiber Reinforced Polymer Composites

3D printing is one of the flexible additive manufacturing (AM) processes that can be used to fabricate parts from various types of materials such as polymers, metal, and ceramic. 3D printing process is one of the famous techniques for printing the product from the filament causing material degradation. Granule-based 3D printing or screw-based material extrusion 3D printing is an alternative process that can create the parts from plastic or composite granule raw materials. However, there are limited use and study in the designation of granule-based 3D printing and process parameters including material temperature, heat bed temperature, nozzle size, and printing speed. These process parameters play a significant role in the properties of 3D printing parts. Some parameters cannot be adjusted in the commercial 3D printing process. Thus, the purposes of this study are to develop a screw-based material extrusion 3D printing machine that can freely adjust the process parameters and to investigate the effect of 3D printing parameters on the appearance and mechanical properties of printed parts. Pellets of neat acrylonitrile butadiene styrene (ABS) and short glass fiber/ABS composites are used in the experiments. Six process parameters were studied, including % fiberglass, printing temperature, printing speed, nozzle size, % Infill, and heat bed temperature. Each parameter has 3 levels, which were designed by the Taguchi L18 method. The results were evaluated by the main effect plot method and showed that the printing speed, nozzle size, and %fiberglass are the top 3 parameters that affect tensile strength. The nozzle size, %infill, and %fiberglass are the top 3 parameters that affect Young’s modulus. The granule-based 3D printing machine was completely developed; however, the extruded plastic line from the nozzle was difficult to control resulting in poor product quality. Thus, the feedback control for controlling the screw-extruder speed and temperature will be developed in future work.

Chemical etching enhanced nanosecond pulsed laser micromachining: An experimental and numerical investigation

Nanosecond pulsed laser micromachining (PLM) under liquid confinement is a highly precise, flexible, efficient, and non-contact manufacturing process being used in widespread applications. The process efficiency of the PLM as determined by the laser ablation rate, however, often quickly reaches saturation with the increase of laser energy. This is ascribed to the screening effect of laser-induced plasma that absorbs part of incident energy. In this study, a chemical etching enhanced PLM (CE-PLM) is developed to tackle this challenge. By replacing water (H2O) with an environmentally friendly and active liquid confinement - hydrogen peroxide (H2O2), single-shot laser ablation experiments were firstly performed on different metallic materials to evaluate the considerably enhanced ablation rate. Then, the beneficial effects of H2O2 on PLM applications including micro-drilling and micro-grooving were investigated. The enhanced ablation rate and PLM efficiency were attributed to the synergistic effect of laser ablation and ultrafast chemical etching. Moreover, a physics-based model was developed to elucidate the process mechanism, with focus on revealing the contribution of chemical etching to the material removal during CE-PLM as affected by laser processing parameters. The findings of this study will contribute to enhancing the efficiency and capability of PLM techniques towards broader industrial applications.

Improved crow search algorithm for optimal flexible manufacturing process planning

The enhancement of manufacturing processes due to the growing demand for products and services is a problem that must be constantly updated. Several schemas have been conducted to improve the manufacturing processes. These schemas are based on the flexible use of machines, tools, and tool access directions to determine the best process planning (PP) for a specific piece or part. The combination of different tools and machines leads to the formulation of complicated combinatory problems that can be treated as optimization problems. On the other hand, bioinspired optimization techniques have been widely employed for solving several complex engineering and real-life problems showing competitive results. The crow search algorithm (CSA) is an optimization technique based on the intelligent foraging behavior of crows. Our work uses an improved CSA (ICSA) to determine the optimal flexible process planning (FPP) with a low computational effort. Several AND/OR networks FPP problems are employed in the experimentation stage, where the production cost and production time are considered as fitness functions. To determine the performance and accuracy of the proposed technique, different results previously reported in the literature are used for a comparison showing that ICSA-FPP is able to outperform similar approaches.

Design and Research of Intelligent Assembly and Welding Equipment for Three-Dimensional Circuit

The processing of the three-dimensional circuit on the surface of conformal antennas is mainly performed via manual processing. At present, there is no automatic intelligent equipment for the processing of a similar small-sized circuit with variable curvature in China. Therefore, a high-precision, automated, full-process manufacturing method for three-dimensional circuits with flexible surfaces on conformal antennas of radar equipment has been proposed to improve processing quality and manufacturing efficiency. The processing relationship between solder paste spraying, resistor mounting, and laser welding in the flexible three-dimensional circuit manufacturing process was analyzed. The structure of the new conformal antenna three-dimensional circuit intelligent manufacturing equipment was determined, and simulation verification of the three-dimensional circuit processing was performed using Vericut. The optimal processing parameters were selected based on solid experiments. This method meets the electronic assembly requirements of radar equipment and fills the domestic gap.

Supply network resilience capabilities: a social–ecological perspective

PurposeThis study aims to investigate supply network (SN) resilience capabilities across the organizational, supply chain (SC) and industry levels by drawing on the complex adaptive systems (CASs) theory and the social–ecological perspective of resilience. An empirically grounded framework operationalizes the concept of social–ecological resilience by expounding resilience capabilities across phases of the CAS adaptive cycle.Design/methodology/approachThis research uses a qualitative multiple case study approach. It draws on the case of the Australian Defence Manufacturing SN (ADM SN) during COVID-19 disruptions. A total of 28 interviews with senior decision makers from 17 companies, complemented by 5 interviews with the Australian Defence SC organizations and secondary data analysis, support the findings.FindingsIndividual organizations’ SC visibility and flexibility enabled by effective risk management and collaboration enhance the ability of the SN to anticipate and prepare for disruption. At the same time, the strength of SC relationships reduces resilience. SN disruption response velocity is enabled by inventory redundancy, process flexibility at the organizational level and visibility and collaboration at the SC level. Institutional support at the national industry level, development of value-adding capabilities and manufacturing process flexibility at the organizational level enhances the SN’s ability to re-organize. The transition from hierarchical to decentralized collaborative governance enhances SN resilience.Practical implicationsFrom a practitioner’s perspective, the findings highlight the need to embrace a broader view of SC beyond immediate tiers. Decision-makers in multinational companies must recognize the long-term impact of their procurement decisions on the supplier ecosystem. Developing local supplier capabilities rather than relying on established global SCs will pay off with future resilience. It, however, demands substantial investment and radical changes across all SC tiers. The lesson for smaller firms is not to over-rely on the existing relationships with supply partners. Although trust-based relationships and collaboration are essential, over-commitment can be counterproductive during global disruptions. With a lack of visibility and control over the SC, operational flexibility is critical for small firms to adapt to shifts in supply and demand.Originality/valueTo the best of the authors’ knowledge, this empirical research is one of the first attempts to operationalize the social–ecological perspective of SN resilience. Evidence-based theoretical propositions contribute to the emerging conversation about the CAS nature of resilience by demonstrating the multi-level effects of resilience capabilities.