Human-centered Manufacturing Research Articles

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.

Smart Logistics Facing Industry 5.0: Research on Key Enablers and Strategic Roadmap

While Industry 4.0 has played a significant role in advancing smart logistics, it has yet to provide adequate solutions for widespread concerns such as human centricity, sustainability, and resilience. The emergence of Industry 5.0 addresses and complements these shortcomings of Industry 4.0. However, there is currently a notable gap in the research regarding how Industry 5.0 can drive the transformation of smart logistics. To address this gap, this study develops a strategic roadmap that offers a solution to this issue. The research is initiated by conducting a comprehensive literature review with a focus on content, identifying 13 key enablers crucial for realizing smart logistics in Industry 5.0. Subsequently, this study establishes the hierarchical relationship among these key enablers through the application of the Fuzzy Interpretative Structural Model (FISM). Following this, the study employs the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) to compute the driving force and dependence of each enabler. The results underscore the significant roles of “Active support from the government” and “Human-centric manufacturing and logistics” as the most critical enablers for Industry 5.0. The strategic roadmap, informed by expert opinions, provides valuable insights for policymakers and implementers while explaining the methods and strategies needed to drive Industry 5.0 transformation in smart logistics. Furthermore, it determines the impact relationship between enablers and the optimal development order, facilitating their synergistic alignment. Ultimately, this strategic roadmap serves as an actionable guide for the logistics industry, steering it toward achieving smart logistics and fortifying competitiveness in the industry 5.0 era.

Digital ergonomic assessment to enhance the physical resilience of human-centric manufacturing systems in Industry 5.0

The emergence of Industry 5.0 promotes the creation of human-centric values. To fulfill this objective, Internet of Things (IoT) technologies are increasingly being exploited to digitize the human factor and monitor the ergonomics of manual manufacturing systems. These digital assessments, combined with computational algorithms, contribute to the establishment of socially inclusive workplaces while offering detailed insights to safeguard the health of the aging workforce. In this scenario, this study proposes a digital architecture for evaluating the European Assembly Worksheet (EAWS) in human-centric manufacturing systems. Three distinct enabling technologies are leveraged to acquire heterogeneous data streams. A radio-frequency-based smart glove detects the operator’s interactions with the surrounding environment, while a network of marker-less cameras and a four-channel surface Electromyography (sEMG) system capture body joint movements and muscular contractions of the upper limbs, respectively. The acquired data are processed by computational algorithms to define an EAWS-driven set of Key Risk Indicators (KRIs), embedded in an ergonomic decision support system. These risk metrics highlight operator-driven process weaknesses in musculoskeletal, muscular, and material handling dimensions. Finally, the validity of the proposed digital architecture is demonstrated in an industrial-related pilot environment, where an operator assembles a piece of home furniture.

Promoting human-centered manufacturing through Lean Ergonomics – a structural equation model for ergonomics and management data

ABSTRACT The Stress-Strain Concept (SSC) describes central mechanisms in Lean Ergonomics (LE) and Human Factors Engineering (HFE). The SSC can create additional value through content and methodological enrichment based on current economic, technological, and demographic constraints. This article uses the SSC to explore mechanisms of interactions of productivity and ergonomics in empirical shop floor data. Ergonomic (Borg, NASA-Task Load Index (N-TLX), Ergonomic Assessment Worksheet (EAWS), Health, Age) and business data (normalized and absolute standard deviations of work process times) were collected for 24 manual work processes operating large-scale chemical reactors. Data is analyzed using several Structural Equation Models (SEM). Stress (EAWS) and – depending on Health – Physical Strain with a model quality of Adjusted Goodness-of-Fit ≥0.9 were identified as statistically significant regressors for time data. In addition, carry-over effects from ergonomics to productivity were identified in an autoregressive model. Fit indices are in the good to acceptable range.

Deep learning based assembly process action recognition and progress prediction facing human-centric intelligent manufacturing

With the breakthroughs in cutting-edge technologies and the growing demand for personalized services, the approach of full automation alone is no longer sufficient to meet rapidly changing production needs. In light of this, a new human-centric manufacturing (HCM) paradigm has been put forward. Industrial human action recognition, as one of the key means of realizing HCM, has received widespread attention and research in recent years. In this paper, an end-to-end deep learning-based action recognition and progress prediction model was built, which can not only recognize action classes in real-time, but also accurately predict action completion times, to improve the efficiency and safety of HCM systems. First, a non-contact data acquisition system based on Azure Kinect sensor, MediaPipe and You-Only-Look-Once V5 (Yolo V5) algorithm was constructed, achieving comprehensive perception of human and objects in manufacturing system. Second, an Auto-encoder model was established to learn and compress high-dimensional human joint and object data into low-dimensional space to achieve efficient representation of the operation process. Finally, the human action recognition and progress prediction model was built and evaluated in a reducer assembly line, with an accuracy rate for action recognition of 99 %, and a time error for progress prediction of less than 6.9 %. This research aims to provide guidance for improving the level of intelligence in manufacturing and the application of AI technology in the industrial field.

Personalizing Human–Robot Workplace Parameters in Human-Centered Manufacturing

This study investigates the relationship between collaborative robot (CR) parameters and worker utilization and system performance in human–robot collaboration (HRC) environments. We investigated whether optimized parameters increase workplace efficiency and whether adapting these parameters to the individual worker improves workplace outcomes. Three experimental scenarios with different CR parameters were analyzed in terms of the setup time, assembly time, finished products, work in process, and worker utilization. The main results show that personalized CR parameters significantly improve efficiency and productivity. The scenario in which CR parameters were tailored to individual workers, balanced the workload, and minimized worker stress, resulting in higher productivity compared to non-people-centric settings. The study shows that personalization reduces cognitive and physical stress, promotes worker well-being, and is consistent with the principles of human-centered manufacturing. Overall, our research supports the adoption of personalized, collaborative workplace parameters, supported by the mathematical model, to optimize employee efficiency and health, contributing to human-centered and efficient HRC environments.

Requirements and Barriers for Human-Centered SMEs.

With the advantages of new technologies and rising demand from customers, it is necessary to improve the manufacturing process. This necessity was recognized by the industry; therefore, the concept of Industry 4.0 has been implemented in various areas of manufacturing and services. The backbone and main aspect of Industry 4.0 is digitalization and the implementation of technologies into processes. While this concept helps manufacturers with the modernization and optimization of many attributes of the processes, Industry 5.0 takes a step further and brings importance to the human factor of industry practice, together with sustainability and resilience. The concept of Industry 5.0 contributes to the idea of creating a sustainable, prosperous, and human-friendly environment within companies. The main focus of the article is to analyze the existing literature regarding what is missing from the successful implementation of human centricity into industry practice, namely in small and medium-sized factories (SMEs). These findings are then presented in the form of requirements and barriers for the implementation of human centricity into SME factories, which can serve as guidelines for implementing human-centered manufacturing using axiomatic design theory in SMEs, which can serve as a roadmap for practitioners.

Hybrid Model Based Autonomous System for Controlling Double Side Grinding Process

Manufacturing uncertainty is rising due to diverse customer demands and new technologies. These challenges are propelling us into the Industry 4.U (Industrial Revolution 4.0 Upgrade) and Industry 5.0 era, focusing on human-centric manufacturing and value creation, necessitating digital transformation. This transformation relies on the core technologies of the 4th Industrial Revolution (Data Analytics, Connectivity, Autonomy, and Collaboration) to perform value-creating processes autonomously and reliably. To adapt, Industry 5.0-oriented Smart Manufacturing architecture was designed. In this system, process planners collaborate with generative AI to generate optimal process plans based on new specifications, pursuing human-centered manufacturing. The manufacturing system is reconfigured with modular technology, and each process is autonomously controlled as a 4.U/5.0-oriented production system. This paper focuses on developing an autonomous process control system for Industry 4.U/5.0-oriented Smart Manufacturing. The study aims to stabilize the quality of the brake disc grinding process in an automotive parts company. The developed control system has been tested on a car’s brake disc manufacturing line, with experimental results showing the reliability of the DT and AI in controlling the grinding process.

Human-robot collaboration assembly personalised safety detection method based on digital twin

Human-robot collaboration (HRC) in assembly is becoming increasingly popular in the field of intelligent manufacturing. However, safety concerns remain the most critical issue to be addressed. The complex assembly environment and the individual differences of workers make it challenging to ensure safety effectively. Furthermore, the concept of human-centric manufacturing also places higher requirements on safety and efficiency of HRC assembly. The application of digital twin (DT) technology has bolstered the cognitive and decision-making capabilities in the context of HRC assembly. This technological advancement provides essential support for addressing safety concerns in HRC assembly processes guided by a human-centric approach. A personalised safety detection method for HRC assembly based on DT technology is proposed. This approach involves personalised modelling and simulation of real objects within the HRC process, establishing a collision detection model based on skeleton points. Subsequently, human motion prediction methods drive safety monitoring between humans and robots. Ultimately, personalised safety warning strategies are applied to ensure the safety of the assembly process.

Human in the loop: revolutionizing industry 5.0 with design thinking and systems thinking

AbstractThis study delves into Industry 5.0's Human Centric Manufacturing and Systems (HCM and HCS), emphasizing worker welfare and sustainability. Industry 5.0 advocates a human-centric approach, built upon three foundational pillars safety, inclusivity, and empowerment. The paper highlights the successful integration of Design and Systems Thinking in HCM and proposes a workshop at MADE COMPETENCE CENTRE proving the effectiveness in raising awareness and promoting Human-Centric principles throughout the system life cycle and in achieving Human-Centric Systems (HCS).

Towards a Human-Centric Digital Twin for Human-Machine Collaboration: A Review on Enabling Technologies and Methods.

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human-machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human-machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area.

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability.

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators' well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators' cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators' mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators' level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

Surveying the landscape of Human-Centric Manufacturing in Lombardy: insights from the practices and perspectives of Italian enterprises

The ongoing technological changes raise the need for socially sustainable paths of digital innovation, putting technology at the service of humans positioned at the centre of production processes. Indeed, in digital factories, where increasingly complex and high-performance models and technologies are strongly considered, humans have been almost neglected. In that sense, Industry 5.0 recognizes worker well-being as a driver for the transition toward smart factories. However, human-centric manufacturing is still an early-stage concept, thus lacking standardized terminology and frameworks. The development of a shared understanding is also hindered by stakeholders being concerned about job displacement, costs, or uncertainty about the benefits. This article aims to bridge that gap by mapping the state-of-the-art of several Italian companies in terms of technology readiness and human friendliness, thus providing best practices and solutions to foster the technological transition required by Industry 5.0 and the development of trustworthy human-machine synergies. A specific survey questionnaire was developed to evaluate the digitalization level of companies in terms of funds destination, technology development, and integration with operators. The data gathered through the survey allowed the definition of a model that highlights a general attitude toward innovation and human centrality, even though the latter still results far from maturity.

Enabling Professionals for Industry 5.0: The Self-Made Programme

Education in the Industry 5.0 context emphasizes a combination of technical and soft skills, multidisciplinary learning, and lifelong learning to empower individuals to leverage advanced technologies and drive innovation in the evolving industrial landscape. One of the critical challenges in education for professionals in Industry 5.0 is the rapid pace of technological advancements and the associated skill gap. Moreover, considering the interdisciplinary nature of Industry 5.0, professionals need to possess a combination of technical, business, and interpersonal skills to succeed in this context. Addressing these challenges requires a collaborative effort between educational institutions and industry stakeholders, involving the development of flexible and responsive educational programs. In this work, we present the Self-Made Programme, a novel blended training program designed to equip professionals with the necessary skills and knowledge to thrive in the dynamic landscape of Industry 5.0. The pedagogical component of this program consists of four different learning paths, offered by a consortium from five different European hubs. On the one hand, the program's focus lies in cultivating proficiency in Digitalization & Human-Centric Manufacturing, enabling professionals to navigate the convergence of physical and digital systems. On the other hand, the program equips professionals with essential skills in Data-driven Decision Making, enabling them to leverage vast amounts of data for strategic and operational insights. By combining theoretical foundations with practical applications, this training program empowers professionals to embrace the potential of Industry 5.0.

Safe Operator 5.0 digital architecture: towards resilient human-centric manufacturing systems

The current shift in European demographic pyramids and the increase in the retirement age are posing serious challenges to the manufacturing sector. To address these social challenges, the recent Industry 5.0 paradigm is promoting human-centric value creation to achieve resilient and socially sustainable industrial environments. In this evolving scenario, this work conceptualizes the Safe Operator 5.0 digital architecture to safeguard the system and physical resilience of human-centric manufacturing environments leveraging three main layers. While workers are digitized through positioning, proximity, and physiological sensors, a machine learning-based cyber layer processes these data streams as well as managerial data to evaluate the efficiency and physical well-being of the workforce. Finally, a central management system provides feedback and early warning to final users.

Introducing assistance systems in production - comparing management and operator’s perspectives

With the rise of Industry 5.0 and a shift to human-centric manufacturing, companies must successfully include their frontline workers when introducing assistance systems. While many frameworks address technical aspects or strategic management considerations of technology introductions, human- and organisational factors have been considered less. We contribute to the literature by comparing perspectives from workers and management on the introduction of two assistance systems. We uncover multiple findings, e.g. that a technology champion concept should be institutionalised or that the communication of the price point of technological systems can lead to intimidated employees and potentially slower technology adoption.

RHYTHMS: Real-time Data-driven Human-machine Synchronization for Proactive Ergonomic Risk Mitigation in the Context of Industry 4.0 and Beyond

Human-machine work system (HMWS) represents a composite system that integrates machine precision and strength with human cognitive abilities, flexibility, and adaptability, aiming to achieve enhanced performance in various tasks aligned with a common goal, which is getting smarter in the era of Industry 4.0 (I4.0) and has received extensive attention from academia and industry. However, the lack of real-time information sharing during HMWS implementation and operation, coupled with uncertainties stemming from human instability and the complexities introduced by smart networking environments in the context of I4.0, poses challenges to the synchronous coordination of this distributed human-centric manufacturing system. Failure in synchronization not only compromises overall system integrity, leading to unrealistic decision-making and potential harm to human health but also goes against the principles of Industry 5.0 (I5.0), which proposes placing human well-being at the center of the production process for human-centric smart manufacturing in the future. In this context, this research presents real-time data-driven hyper objects orchestrating for the human-machine synchronization (RHYTHMS) based on a service-oriented human-to-machine architecture (SOH2M) and a model reference adaptive fuzzy control for real-time information sharing and synchronous coordination of the smart HMWS. Based on these, a real-life assembly case is carried out by a full-scale HMWS prototype to quantitatively analyze the benefits of the proposed approach in proactive ergonomic risk mitigation (PERM), contributing to the human-centric value-oriented I5.0 era.

Task reallocation of human-robot collaborative production workshop based on a dynamic human fatigue model

As collaborative robots become more popular in industry, they are used to collaborate with human workers and free human workers from repeated and heavy work and reduce the risk of physical injuries. However, human workers need to finish tasks that require high cognitive abilities, which increases the risk of mental fatigue. Fatigue can negatively impact workers and even threaten the safety of workers. Currently, the dynamics of human fatigue model are rarely considered. Hence, it is crucial to develop a dynamic allocation method for human-robot collaborative tasks that considers dynamic human fatigue to ensure efficient and safe human-robot collaboration. This paper proposes a task reallocation method for human-robot collaborative production workshop based on a dynamic human fatigue model. A fatigue feature extraction method from multimodal data and a fatigue evaluation network are proposed to evaluate the current fatigue status of human workers. Based on the evaluation results, the human fatigue model is dynamically updated. If the worker’s fatigue state changes, a task reallocation scheme is generated by an improved genetic algorithm based on reinforcement learning and the dynamic human fatigue model. The experimental results show that the proposed model and algorithm are effective and efficient, and can provide innovative insights for realizing a human-centered manufacturing workshop.

A human-cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in Industry 5.0

Industry 5.0 characterized by human-centricity and sustainability has sparked a new wave of the industrial revolution. Human-cyber-physical system is the cornerstone of human-centric manufacturing while remanufacturing has gained considerable attention in fostering sustainability. Disassembly planning is a vital but challenging task in remanufacturing. In order to accommodate the frequent changes resulting from mass personalization, a human-robot collaboration cell is valued for its ability to achieve flexible disassembly. However, recent studies treated disassembly sequence and task allocation as independent aspects. And tasks are allocated by predefined metrics and thresholds without considering the ergonomics of operators. This paper proposes a human-cyber-physical framework for the human-robot collaboration cell by illustrating human-in-the-loop and human-on-the-loop paradigms. A multi-objective sequential disassembly planning model is presented by simultaneously considering disassembly sequence and task allocation. The task complexity and operator ergonomics are evaluated based on cloud models to handle the fuzziness and randomness in human cognition. Moreover, an improved multi-objective hybrid grey wolf optimization algorithm is proposed to obtain Pareto optimal disassembly plans. A case study on the disassembly planning of a control box is presented to illustrate the feasibility and practicability of the proposed approach. The results underscore the superiority of the proposed approach in handling rigorous constraints inherent in the sequential disassembly planning model, which acquires more convergent, diverse, and uniform disassembly plans along the Pareto front.

Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review

This paper presents a literature review on the different aspects of task allocation and assignment problems in human–robot collaboration (HRC) tasks in industrial assembly environments. In future advanced industrial environments, robots and humans are expected to share the same workspace and collaborate to efficiently achieve shared goals. Difficulty- and complexity-aware HRC assembly is necessary for human-centric manufacturing, which is a goal of Industry 5.0. Therefore, the objective of this study is to clarify the definitions of difficulty and complexity used to encourage effective collaboration between humans and robots to leverage the adaptability of humans and the autonomy of robots. To achieve this goal, a systematic review of the following relevant databases for computer science was performed: IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and ASME Digital Collection. The results extracted from 74 peer-reviewed research articles published until July 2022 were summarized and categorized into four taxonomies for 145 difficulty and complexity definitions from the perspectives of (1) definition-use objectives, (2) evaluation objectives, (3) evaluation factors, and (4) evaluation variables. Next, existing definitions were primarily classified according to the following two criteria to identify potential future studies on the formulation of new definitions for human-centric manufacturing: (1) agent specificity and (2) common aspects in manual and robotic assemblies.