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The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study's purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modelling (STM) was utilised to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development. [ABSTRACT FROM AUTHOR]

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line. [ABSTRACT FROM AUTHOR]

In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynamic analysis remains unexplored compared with the well-established theoretical system of steady-state analysis. Therefore, in this study, we propose a fast algorithm for predicting the production process performance in the delayed differentiation-based flexible production line under operation control. We first formulate practical problems existing in the auto, food, and furniture industries into a mathematical formation. Then, we offer closed-form formulae for predicting the production process performance using the built stochastic model in the production line with three machines. We also propose an algorithm to predict the performance of a production line having more than three machines. The proposed methods were verified to be highly accurate through comparison experiments. In terms of theoretical contributions, this study offers a research foundation for other transient-based studies. From a practical perspective, the proposed algorithms can be employed to predict the production process performance of processing lines under production control in advance. [ABSTRACT FROM AUTHOR]

Smart honeycombs are becoming the novel structural strategy for next‐generation devices and equipments. Unfortunately, traditional surface‐coating, structural immersing, and 3D printing approaches still face significant challenges in combining structural customization and performance multifunctionality. Herein, a brand‐new processing strategy is developed to construct graphene‐based smart honeycombs by stacking and deploying laser‐induced graphene (LIG) layers with alternatively inserted adhesives. Through tuning key parameters, various LIG‐enabled smart honeycombs (LIG‐HC) can be assembled with scalable areal dimension and thickness, variable cell sizes and shapes, as well as patternable graphene clusters. By further understanding the process‐dependent structural stability and electrical conductivity, multifunctional characteristics are systematically explored, including anisotropic mechanical, electrical, piezoresistive, and electromagnetic performance. To finally demonstrate the unique honeycomb multifunctionally applied in fields of aviation, an intelligent LIG‐HC enabled aircraft‐wing model is representatively constructed for performing anti‐/de‐icing, high‐temperature warning, flame retardancy, pressure and vibration monitoring, as well as electromagnetic shielding and stealth. [ABSTRACT FROM AUTHOR]

Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations. [ABSTRACT FROM AUTHOR]

Flexible manufacturing systems (FMS) should be able to respond to changing manufacturing requirements and environments. From the layout point of view, FMS need to be rearranged to fit the new requirements. However, rearranging the layout is often undesirable due to its unpredicted high costs and production disruption. This paper proposes a practical approach to mitigate the effects and repercussions of changing environments and avoid rearranging the layout. A robust layout approach is presented, where changes in product demand and mix are absorbed by altering product routes and not rearranging the layout. In this approach, the problem is decomposed into two sub-problems: sub-problem 1 (SP1) where a robust layout is constructed, and sub-problem 2 (SP2) to obtain the best routes of products. To solve SP1, design of experiments is used to find a critical period, which is the period most affected under demand changes. Then, the layout for the critical period is determined using a hybridized genetic-tabu search algorithm. Then SP2 is solved by a branch and cut algorithm to obtain the optimal routes of the products in each period. The performance of the proposed methodology is illustrated using a case study and is benchmarked against rival ones from the literature. [ABSTRACT FROM AUTHOR]

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system. [ABSTRACT FROM AUTHOR]

Robotic hole-making systems are widely used in aerospace, automotive and other fields, mainly for diverse and structurally complex parts processing tasks. However, the industrial robot automatic hole-making system is an open-chain multi-bar tandem mechanism, which is prone to quality problems such as unstable cutting process, large hole diameter deviation and hole wall surface defects when performing tasks such as hole-making. This article constructs a stiffness enhancement model for the axial and horizontal directions of the robot presser foot based on the robot end stiffness enhancement method and analyzes the impact of pressure changes on hole-making quality under the action of the presser foot. Through experimental data analysis, the aperture deviation decreased by 32.91% compared to before the pressure foot and the average height of outlet burrs decreased by 85.3%, verifying the feasibility of the method. [ABSTRACT FROM AUTHOR]

This paper addresses modular construction as an efficient method for improving technological processes in obtaining parts made of Makrolon. By adopting a modular approach, the study explores the benefits of implementing a flexible system capable of responding to variable market and technological demands. The research focuses on the design and implementation of standardized modules, facilitating adaptability to diverse production requirements. A detailed analysis of the advantages of modular construction emphasizes the reduction of production time, operational costs, and resource optimization. Additionally, the impact of this model on the quality of finished products and the durability of the manufacturing process is examined. Through an evaluation of current practices and trends in modular construction, the paper proposes innovative solutions for optimize and streamline the molding process with Makrolon 2407. The obtained results underscore the importance of adopting a modular approach in the context of Makrolon parts, contributing to increased flexibility, efficiency, and sustainability of the overall technological process. [ABSTRACT FROM AUTHOR]

The ever-shorter product life cycle entails a shortening of the life cycle of the production site and production systems as well. One of the biggest advantages of flexible production systems is that new products can be introduced to them continuously, so that their life cycle can be significantly extended. Flexible manufacturing systems, on the other hand, also require flexible control, the logic of which must be defined already in the design phase. The main elements of the design methodology of the production and logistics layout that make up the research are the information relationship chart and the related decision tables, which are suitable for speeding up the design and later for facilitating the creation of a digital twin and control logic programming. The paper covers an extended layout planning method which can be used to design flexible manufacturing lines. [ABSTRACT FROM AUTHOR]

The integration of mobile robots in material handling in flexible manufacturing systems (FMS) is made possible by the recent advancements in Industry 4.0 and industrial artificial intelligence. However, effectively scheduling these robots in real-time remains a challenge due to the constantly changing, complex, and uncertain nature of the shop floor environment. Therefore, this paper studies the robot scheduling problem for a multiproduct FMS using a mobile robot for loading/unloading parts among machines and buffers. The problem is formulated as a Markov Decision Process, and the Q-learning algorithm is used to find an optimal policy for the robot's movements in handling different product types. The performance of the system is evaluated using a reward function based on permanent production loss and the cost of demand dissatisfaction. The proposed approach is validated through a numerical case study that compares the proposed policy to a similar policy with different reward function and the first-come-first-served policy, showing a significant improvement in production throughput of approximately 23%. [ABSTRACT FROM AUTHOR]

The purpose of this research was to identify trending topics, emerging themes, and future research areas in flexible manufacturing in the digital era. It includes a synthesis of 51 published articles; 38,671 tweets; and expert opinions from various social media platforms, including blogs and forums on flexible manufacturing. Using latent Dirichlet allocation, the study highlights six flexible manufacturing in the digital era themes: (1) smart factories, digital twins, and flexible manufacturing; (2) flexible manufacturing and sustainability; (3) supply chain flexibility and resilience; (4) the dark side of flexible manufacturing; (5) flexible and lean manufacturing; and (6) infrastructure challenges and the realm of flexible manufacturing. Rather than focusing exclusively on aggregate impacts, this study combines the outcome of multiple databases and presents a comprehensive framework that covers the drivers, processes, and impacts of each theme, suggesting avenues for future research. The findings will help managers be better equipped to identify possible issues and areas for improvement by giving them a thorough understanding of the drivers, processes and outcomes indicated in the framework. The insights gained from this study are valuable to researchers and policymakers, as they enable them to leverage the latest developments in flexible manufacturing in the digital era and effectively address current and future challenges. [ABSTRACT FROM AUTHOR]

Traditional sheet metal processing enterprises are facing problems such as upgrading manufacturing systems, lower material utilization rate, and lower efficiency of CNC machining. In this paper, we design a flexible manufacturing production line for sheet metal processing of complex parts, use the DQN algorithm to optimize the scheduling of each part of the work in the production process of the production line, and optimize the adaptability of the DQN algorithm by using the action selection strategy and the reward function and so on. Then, the digital twin simulation system is constructed to simulate the process of a flexible manufacturing production line for sheet metal processing. The results show that the DQN algorithm can effectively assign the corresponding processing machines and processes to the workpieces in the production line, and the digital twin simulation system can also accurately simulate the actual situation of the production line. Meanwhile, it is found that the optimized production line can reach 87.52% of the production beat balance rate, which is 23.64% higher than the original production line balance rate, and it can improve the average utilization rate of the main operating units in the production line. The optimized production line proposed in this paper can effectively solve the practical problems in sheet metal production lines, improve the mobility of the production line and corporate efficiency, and provide new ideas for the lean production management of large-scale machinery manufacturing enterprises.

In this paper, we first use particle swarm optimization to improve the BP neural network algorithm to optimize the neural network connection weights and thresholds and then apply it to the flexible manufacturing system to build a complete, flexible manufacturing process of mass customization. Based on the nonlinear mapping relationship between customer demand and product structure, a three-layer network is used to identify the effective demand of customers as well as the actual risk of the enterprise in order to satisfy the common users who do not care about the product structure and the optimization of industrial structure. After completing the configuration, the system is applied to an automobile company and a manufacturing company for testing purposes. After a long time of use, the average accuracy error is 0.0061. The value of the body module ignored by the customer is as high as 0.129, and the weight of the indicator system of the traveling control module is as low as 0.072, but it has become one of the most important indicators of the potential demand of the customer. Supply chain risk has a maximum difference of 6% when compared to the actual values. The manufacturer’s expectation value of 3.8967 for SCRF11 is “low”.

For a complex product production, any flexible manufacturing system with a work-in-process inventory is recommended for a supply chain management (SCM) system. Building a flexible manufacturing system increases the total cost of the supply chain; for this reason, a discrete investment is important. For flexible production systems, production rate within a finite specific interval of production rate as work-in-process inventory is calculated. The aim of the supply chain is to reduce the total cost when demand during the lead time is a random variable with a normal distribution. A crashing cost is utilised to reduce the duration of lead time within the supply chain system. A model is proposed to obtain the optimal flexible production rate with the reduced total cost of the supply chain. A classical optimisation technique is employed to obtain the closed-form and quasi-closed-form solutions of the decision variables. An improved algorithm is designed to obtain the global minimum cost of SCM under the framework of a flexible production system. An illustrative numerical example and sensitivity analysis are given to test the model. A numerical study proves that this model obtains the minimum cost with the optimal decision variables. [ABSTRACT FROM AUTHOR]

Reconfiguration activities remain a significant challenge for automated Vision Inspection Systems (VIS), which are characterized by hardware rigidity and time-consuming software programming tasks. This work contributes to overcoming the current gap in VIS reconfigurability by proposing a novel framework based on the design of Flexible Vision Inspection Systems (FVIS), enabling a Reconfiguration Support System (RSS). FVIS is achieved using reprogrammable hardware components that allow for easy setup based on software commands. The RSS facilitates offline software programming by extracting parameters from real images, Computer-Aided Design (CAD) data, and rendered images using Automatic Feature Recognition (AFR). The RSS offers a user-friendly interface that guides non-expert users through the reconfiguration process for new part types, eliminating the need for low-level coding. The proposed framework has been practically validated during a 4-year collaboration with a global leading automotive half shaft manufacturer. A fully automated FVIS and the related RSS have been designed following the proposed framework and are currently implemented in 7 plants of GKN global automotive supplier, checking 60 defect types on thousands of parts per day, covering more than 200 individual part types and 12 part families. [ABSTRACT FROM AUTHOR]

With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity. Reasonable scheduling optimization is an effective way to reduce carbon emissions, which motivates us to work on this research. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a new low-carbon scheduling problem of flexible manufacturing and crane transportation considering multi-state collaborative configuration (LSP-FM&CT-MCC). First, an integrated energy consumption model based on multi-state machining machines and cranes is established to optimize the overall energy efficiency of the production process. Then, a new hybrid differential evolution algorithm and firefly algorithm with collaborative state optimization strategy (DE-FA-CSOS) is proposed to solve the proposed MIP model. In DE-FA-CSOS, the differential evolution algorithm (DE) is used for global search, and the firefly algorithm (FA) is used for local search. The collaborative state optimization strategy (CSOS) is proposed to guide the search direction of the DE-FA algorithm, which greatly improves the performance of the hybrid algorithm. Finally, the practicality and superiority of the solution method are verified by examples. The results show that machining and transportation energy consumption is reduced by 25.17% and 34.52%, respectively. In the context of traditional optimization methods and manual scheduling modes facing failure, the method has a broad application background for manufacturing process optimization in such workshops, which is of guiding significance for promoting the low-carbon development of traditional heavy industry manufacturing. [ABSTRACT FROM AUTHOR]

To address the multi-flexible integrated scheduling problem with setup times, a multi-flexible integrated scheduling algorithm is put forward. First, the operation optimization allocation strategy, based on the principle of the relatively long subsequent path, is proposed to assign the operations to idle machines. Second, the parallel optimization strategy is proposed to adjust the scheduling of the planned operations and machines to make the processing as parallel as possible and reduce the no-load machines. Then, the flexible operation determination strategy is combined with the above two strategies to determine the dynamic selection of the flexible operations as the planned operations. Finally, a potential operation preemptive strategy is proposed to judge whether the planned operations will be interrupted by other operations during their processing. The results show that the proposed algorithm can effectively solve the multi-flexible integrated scheduling with setup times, and it can also better solve the flexible integrated scheduling problem.

Several industries are facing many challenges in their production systems due to increasing customer demand. Customer demand is growing for products with innovative features that are flexible, good quality, and appealing. This paper presents a flexible production-inventory system that produces multiple parts of a product. Defective products may be produced during the production process. Those defective products are remanufactured immediately after inspection. Limited budget and space constraints are considered, along with product assembly. Based on different distribution functions, non-linear equations are calculated using the Kuhn–Tucker optimization technique. Numerical examples, a graphical representation, and sensitivity analysis are presented in this paper. The solution procedure evaluates the minimization of the total investment based on the χ 2 distribution. This study examines electronic products those are more likely to be defective rather than perfect during production. [ABSTRACT FROM AUTHOR]

This paper provides a classification of manufacturing types in terms of new technological tools provided in the Industry 5.0 framework. The manufacturing types agile, holonic, flexible and reconfigurable benefit from and are potentially changed by Industry 4.0 technologies and the human-centric focus of Industry 5.0. Furthermore, the use of Lifecycle Analysis (LCA) provides a holistic method for estimating the true value of emissions emitted during the carrying out of manufacturing decisions. As a result, LCA may be used as a central guiding framework, in addition to the use of Circular Economy metrics, for decisions in manufacturing whose results could be presented to humans as part of a scenario-generation system using visualisations within a Digital Twin environment. This enables a decision maker to make informed decisions regarding current and future production needs. Regardless of the size of production facility, this integrated approach is perhaps the most significant gap in research identified by this survey of manufacturing types and systems when viewed through the lens of Industry 5.0. This paper makes the contribution of providing an assessment of the major manufacturing types in the context of Industry 5.0, highlighting the gaps in the current research and providing a sustainable and human-centric agenda supported by LCA use with modern production methodologies. [ABSTRACT FROM AUTHOR]

This paper presents an innovative grasping system for pick-and-place applications, capable of handling a wide range of objects with varying characteristics, including size, shape, weight, rigidity, and fragility. The system uses a four-finger design to incorporate multiple grasping modes, including inner gripping, outer gripping, suction, and pinch-gripping. The paper details the development of a soft actuator system that drives a Watt's Type I link chain for mechanical compliance and passive safety. Additionally, an end-to-end grasping policymaker using depth vision is proposed to determine the optimal grasping mode and finger trajectory. Results from experiments demonstrate the system's versatility and state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this study, we analyse the value creation of Industry 4.0 (I40) technologies in flexible manufacturing (FM) under a sustainability perspective. I40 is a popular strategy that Western manufacturing organizations adopt to face competition from low-cost producers. Organizations adopting I40 use advanced digital technologies to make production processes more flexible and increasingly automated. Several pieces of evidence confirm how I40 leads to higher productivity and higher-quality products, improving the economic performance of organizations. However, increasing automation may also lead to the reduction of human labour in the production process, which may contribute to the disappearance of jobs, the reduction of expertise and the loss of know-how in manufacturing organizations. While the literature acknowledges the technical and economic advantages of I40, the sustainability of the value created through these technologies deserves further investigation. To address the gap, we complement the IT value theory with the concept of sustainability, including the three dimensions of economic, environmental and social sustainability. We perform a multiple case study analysis of four Italian manufacturing organizations that have successfully implemented I40 technologies in FM. The cases show that I40 technologies support sustainable organizational value when they are deployed with a worker-centric approach. In this condition, the organization leverages workforce activities to continuously fine-tune the technologies and to exploit the adaptive features of the technologies to continuously improve processes. [ABSTRACT FROM AUTHOR]