Collaborative Manufacturing Research Articles

Collaborative manufacturing technologies of structure shape and surface integrity for complex thin-walled components of aero-engine: Status, challenge and tendency

Presently, the service performance of new-generation high-tech equipment is directly affected by the manufacturing quality of complex thin-walled components. A high-efficiency and quality manufacturing of these complex thin-walled components creates a bottleneck that needs to be solved urgently in machinery manufacturing. To address this problem, the collaborative manufacturing of structure shape and surface integrity has emerged as a new process that can shorten processing cycles, improve machining qualities, and reduce costs. This paper summarises the research status on the material removal mechanism, precision control of structure shape, machined surface integrity control and intelligent process control technology of complex thin-walled components. Numerous solutions and technical approaches are then put forward to solve the critical problems in the high-performance manufacturing of complex thin-wall components. The development status, challenge and tendency of collaborative manufacturing technologies in the high-efficiency and quality manufacturing of complex thin-wall components is also discussed.

Development of a Platform Business Model for Co-creation Ecosystems for Sustainable Furniture

Existing design platforms with multi-dimensional value chains currently have deficits in terms of their business models, resulting in insufficient attention to sustainability goals and individual requirements for products of these platforms. Co-creation approaches, such as the Do-It-Together (DIT) approach for furniture, involve customers and manufacturers as equal partners in the design and production process. This allows customers to have more influence on the sustainability and individualization of products. The existing literature addresses sustainability-oriented design principles for platform business models, but concrete platform business models for multidimensional DIT co-creation of furniture are still missing. Therefore, the objective of this paper is to develop a business model for a DIT co-creation platform for the furniture industry based on a four-step business model innovation framework. This method will then be applied to a specific project scenario to derive a project-specific DIT co-creation business model. This generates knowledge about the collaborative manufacture of sustainable and customized furniture and contributes to the cross-sectoral transfer of platform business models for the development of sustainable products.JEL Codes: L23, O31

Online merchant resource allocation and matching for open community collaborative manufacturing (OCCM) in mass personalization model

In order to meet the requirements of customers' pursuit of personalized and diversified needs, enterprises need to change from the traditional mass manufacturing model to mass personalization manufacturing model. On the other hand, collaborative manufacturing based on stakeholders will effectively solve the problems of information asymmetry, low operational efficiency and insufficient resource utilization in the process of manufacturing. Moreover, the new model of open community manufacturing is conducive to the planning, allocation, optimization and management of various networked and social resources. Therefore, combined with above three innovative models of mass personalization manufacturing, collaborative manufacturing and open community manufacturing, this paper studies the online merchant resource allocation and matching of open community collaborative manufacturing (OCCM) for mass personalization (MP) model to realize the innovation of design model, improve design efficiency and save design resource cost. Firstly, the workflow for OCCM in MP model based on system engineering analysis method is studied. Secondly, online merchant resource allocation for OCCM based on social network analysis (SNA) and online merchant resource matching for OCCM based on fuzzy clustering are proposed. At last, a case study for OCCM in the automotive industry is given to verify the effectiveness of the proposed model and methods.

Strategic planning of the collaboration between humans and artificial intelligence in production

Artificial intelligence (AI) holds a vast potential for various applications and benefits in production, particularly concerning the collaboration of humans and AI. Their complementary skills can lead to significant advantages. However, the implementation of AI presents technical, organizational, and employee-related challenges that must be addressed from a socio-technical perspective. Concrete actions are necessary, such as defining the roles and responsibilities between humans and AI and selecting appropriate AI applications. Given the extensive scope of potential changes and challenges, a systematic approach is required.Therefore, this paper presents an approach for strategically planning of the collaboration between humans and artificial intelligence in production. The approach is based on an extensive literature review and empirical surveys and includes use cases, design fields, and a procedure model. This model allows companies to assess their current situation in terms of relevant capabilities for human-AI collaboration in manufacturing. Additionally, it identifies any organizational capabilities required for human-AI collaboration that may be lacking for specific use cases, enabling the derivation of necessary measures. Some measures may be similar across different use cases, allowing for the creation of bundles of measures to integrate AI into different work processes. The approach is integrated into a company's strategic planning as a strategic initiative, ensuring alignment with other sub-strategies such as the IT strategy.

A Digital Twin-Assisted Collaborative Capability Optimization Model for Smart Manufacturing System Based on Elman-IVIF-TOPSIS

Smart manufacturing is one of the key elements to realizing Industry 4.0, which helps to improve the production quality and efficiency of collaborative manufacturing companies. With the rapid development of information technology such as the Internet of Things, the application of digital twin technology in smart manufacturing is becoming more and more widespread. In the manufacturing process, companies still face problems such as slow data flow and a serious waste of idle equipment resources. The purpose of this paper is to carry out a digital twin-based smart manufacturing system that combines the concept of value co-creation to achieve the smart manufacturing goal of using fewer manufacturing resources to create greater system value. The system builds a multi-objective optimization model to enhance the value of the shared supply chain and then helps collaborative manufacturing enterprises to optimize their production capacity and use population intelligence algorithms to solve the problem. The results of the combined case study show that the system is effective in achieving good operation of dynamic equipment resources while improving the overall profit of the system by 13.26%. This study proposes a smart manufacturing system based on the digital twin and considering the value of the supply chain will effectively help collaborative manufacturing enterprises to respond to the market environment quickly, reduce the loss of manufacturing resources while also significantly reducing the operating costs of enterprises, and help to realize the value of the shared supply chain.

Virtual reality platform for design and evaluation of human-robot collaboration in assembly manufacturing

Virtual reality platform for design and evaluation of human-robot collaboration in assembly manufacturing

Virtual reality platform for design and evaluation of human-robot collaboration in assembly manufacturing

Virtual reality platform for design and evaluation of human-robot collaboration in assembly manufacturing

Collaborative manufacturing operation mode and modeling simulation of manufacturing enterprise based on collective intelligence

Collaborative manufacturing operation mode and modeling simulation of manufacturing enterprise based on collective intelligence

Quantifying injury risks of collaborative robots in a manufacturing cell using a simulation approach

Safety remains an important impediment towards fully exploiting the potential of collaborative robots for executing manufacturing tasks. The ISO 15066 proposes requirements for safety assurance of collaborative robots. This includes safeguards such as force, torque, and safe distance limiting functions. More importantly, safe force (and impact pressure) limits depend on the human body region. The latter is based on biomechanical limits derived from the body model, which specifies maximum pressure and force limiting values on different body regions. However, the limiting values proposed in the body model seldom considers practical impact scenarios that may potentially occur while executing collaborative manufacturing tasks. For instance, the impact force/pressure thresholds are influenced by aspects such as the speed of the moving manipulator, or contact point, further influenced by the mass, and contact surface area of the end effector. Consequently, this influences operator safety and design of collaborative workspaces and manufacturing tasks. So far, these practical influences are under-explored in literature, discussing human-robot collaboration, while considering manufacturing tasks.In this paper, a simulation framework is proposed exploring a use case of assembly tasks in a manufacturing setting. The framework embeds a hazard and operability analysis to identify potential hazardous events during execution of the collaborative manufacturing tasks. The tasks further modelled in the ABB RobotStudio environment, where varying path configurations of the manipulator are mimicked during task execution. Force and pressure impact values based on the path configurations are quantified on different body regions. Transient and quasi static forces/pressure are considered for varying impact scenarios on the body regions. Finally, risk mitigation is proposed based on the impact scenarios and quantified injury severity. This includes task re-design or implementing alternative path configurations for the robotic manipulator.

Collaborative optimization for energy saving and service composition in multi-granularity heavy-duty equipment cloud manufacturing environment

<p style='text-indent:20px;'>Efficient service scheduling is an important technique supporting collaborative manufacturing platforms such as cloud manufacturing. To achieve a more efficient task execution, heavy-duty equipment manufacturing, an important field of cloud manufacturing, must be explored beyond parameters of cost and time. The manufacturing service composition problem of heavy-duty equipment has the characteristics of task complexity, high process energy consumption, and multi-granularity nature of service (MGNoS). In the manufacturing process of heavy-duty equipment, the energy consumption of required logistics accounts for 30% the total energy consumption. However, to date, research has investigated the problem almost always from the task level, and MGNoS has received little attentions, which may lead to redundant energy consumption in logistics during manufacturing execution. In this paper, the problem of manufacturing service scheduling with integrating energy saving and service composition in cloud manufacturing is considered. Based on the mathematical description, a cross-granularity task chain reconfiguration algorithm is presented for mitigating the adverse effects of MGNoS and developing the adaptive non-dominated sorting genetic algorithm Ⅲ for solving the service composition scheme to generate optimal scheduling solutions. The effectiveness and efficiency performances of typical optimization algorithms are compared with the proposed approach. The results show that the proposed method achieves significant energy savings for all tasks in different scenarios.</p>

Collaborative manufacturing operation mode and modelling simulation of manufacturing enterprise based on collective intelligence

Collaborative manufacturing operation mode and modelling simulation of manufacturing enterprise based on collective intelligence

Multi-objective collaborative assembly line design problem with the optimisation of ergonomics and economics

Manufacturing systems are socio-technical systems, with explicit interactions between humans and technologies in shared workspaces. These shared workspaces could also be called hybrid collaborative manufacturing systems, which involve workers as well as technological equipment and combine the benefits of human workers and new Industry 4.0 technologies, such systems are particularly useful in a context requiring flexibility and adaptability. Furthermore, the new Industry 5.0 approach has the objective to shift toward more human-centric and resilient manufacturing systems. The key problems to solve in the design of collaborative manufacturing systems are the combinatorial assembly line balancing problem and the equipment selection problem. An efficient and sustainable line requires a cost-effective choice of equipment while improving the ergonomics and the safety of workers. Both decisions of balancing workload and the assignment of equipment impact the ergonomics of a collaborative system and present conflicting criteria. To this end, we propose a multi-objective approach, the objectives are the optimisation of the investment costs and the ergonomics with a fatigue and recovery criterion. We propose to linearise the fatigue and recovery to formulate a new Mixed Integer Linear Programming formulation. We developed an exact multi-objective solving algorithm based on the ϵ-constraint to obtain the trade-off between these objectives. We conducted numerical experiments with different instances from the literature with promising results for instances with up to 45 operations. Finally, we discuss insightful managerial conclusions and future research perspectives.

Dynamic Model Informed Human Motion Prediction Based on Unscented Kalman Filter

Human motion prediction is the foundation stone of human–robot collaboration in intelligent manufacturing. The nonlinear and stochastic nature of human motion has made it challenging to predict the motion accurately. Many recent deep-learning-based approaches, e.g., convolutional neural networks or recurrent neural networks (RNNs), have been applied to address this challenge. On the other hand, existing works tend to ignore the importance of human dynamics in motion prediction, especially the effect of muscle force on the motion. This article proposes a novel dynamic model informed motion prediction method. It utilizes an unscented Kalman filter (UKF) to predict the state of the future arm dynamic model such that the future motion of the human arm can be obtained. In particular, the arm dynamic model is developed based on Lagrangian mechanics and represented by differential equations. Embracing the future muscle force predicted by RNN into the differential equations, such a dynamic model is capable of explicitly establishing the intrinsic relation between the future muscle force and the corresponding future arm motion. UKF is leveraged to predict the future joint position and velocity of the human arm based on the dynamic model. Experiments on three motion datasets validate that the proposed prediction method, compared with the traditional RNN-based prediction using skeleton vectors, significantly improves the prediction accuracy regarding elbow and wrist positions.

A fusion-based spiking neural network approach for predicting collaboration request in human-robot collaboration

• Compared with the traditional assembly method, a fusion-based spiking neural network approach for predicting collaboration request proposed in this paper has the following advantages. • A human collaboration request prediction method under the joint influence of multi-channel information is proposed. • The FSNN method for asynchronous monitoring is proposed to reduce the workload of non-time-series feature detection. • The coding methods of different input information, and the threshold adaptive updating method under the influence of different neurons are proposed. • Compared with different prediction methods, the superiority of the FSNN scheme is proved. In human-robot collaborative (HRC) manufacturing systems, how the collaborative robots engage in the collaborative tasks and complete the corresponding work in a timely manner according to the actual state has been a critical factor that hinders the efficiency of HRC. Inappropriate collaborative behaviors will result in a poor perceptual experience for human operators (e.g., robots starting action too early or too late). To address this issue, a fusion-based spiking neural networks (FSNNs) approach for collaboration request prediction is proposed, aiming to find the right collaboration timing for robots in HRC assembly system and to minimize human aversion without affecting human operation behaviors. By encoding human behavior, product state and robot pose into spiking signals that can be processed by FSNNs, the spatio-temporal coupling relationship between those three aspects can be comprehensively analyzed, and to solve the appropriate timing of robot participation in collaboration. Finally, demonstrative experiments are carried out on the HRC assembly of generator end caps in the lab environment. Compared with the baseline methods, the decision accuracy of the proposed one is improved by nearly 30%, which further proves its effectiveness.

Economic implications of equipment sharing under cloud manufacturing

In the development of new information and communication technologies, cloud manufacturing (CMfg) has emerged as a novel and collaborative manufacturing model that enables original equipment manufacturers (OEMs) and clients to share their manufacturing equipment with other clients who do not own equipment. This study focuses on business-to-business (B2B), OEM-to-business (O2B), and B2B&O2B sharing scenarios under CMfg and develops an analytical framework to explore the implications of equipment sharing on retail price, ownership, usage, and environment. Compared with the traditional selling model, the three sharing scenarios result in either higher or lower retail price, ownership, usage, and environmental impact depending on clients' operation cost. Specifically, the higher the operation cost, the lower the retail price. In the B2B scenario, higher ownership, usage, and environmental impact appear only if the operation cost and platform service level are synchronously high/low. Meanwhile, higher ownership and lower usage and environmental impact in B2B&O2B only come with low operation cost. O2B and B2B&O2B surprisingly have the same sharing implications as they involve the same number of equipment units, usage, and environmental impact. Furthermore, this study explores the influence of other crucial factors, such as commission fee, which has opposite effects on ownership in B2B and B2B&O2B, and sharing's effect on the disparity of ownership between heterogeneous clients. • We analyze the economic and environmental long-term implications of Cloud Manufacturing (CMfg) paradigm. • Three scenarios and several distinctive features of equipment sharing in CMfg are considered comprehensively. • Optimal retail price and sharing involvement strategies for the OEM, as well as market-clearing sharing price are proposed.

Analysis of a Collaborative Scheduling Model Applied in a Job Shop Manufacturing Environment

Collaborative Manufacturing Scheduling (CMS) is not yet a properly explored decision making practice, although its potential for being currently explored, in the digital era, by combining efforts among a set of entities, either persons or machines, to jointly cooperate for solving some more or less complex scheduling problem, namely occurring in job shop manufacturing environments. In this paper, an interoperable scheduling system integrating a proposed scheduling model, along with varying kinds of solving algorithms, are put forward and analyzed through an industrial case study. The case study was decomposed in three application scenarios, for enabling the evaluation of the proposed scheduling model when envisioning the prioritization of internal–makespan-or external–number of tardy jobs-performance measures, along with a third scenario assigning a same importance or weight to both kinds of performance measures. The results obtained enabled us to realize that the weighted application scenario permitted reaching more balanced, thus a potentially more attractive global solution for the scheduling problem considered through the combination of different kinds of scheduling algorithms for the resolution of each underlying sub problem according to the proposed scheduling model. Besides, the decomposition of a global more complex scheduling problem into simpler sub-problems turns them easier to be solved through the different solving algorithms available, while further enabling to obtain a wider range of alternative schedules to be explored and evaluated. Thus, contributing to enriching the scheduling problem-solving process. A future exploration of the application in other types of manufacturing environments, namely occurring in the context of extended, networked, distributed or virtual production systems, integrating an increased and variable set of collaborating entities or factories, is also suggested.

Trusted and secure composite digital twin architecture for collaborative ecosystems

AbstractDigitalisation creates new opportunities for businesses to implement and manage collaborative ecosystems both internally and externally. Digital twin (DT) is a rapidly emerging technology that can be used to facilitate new models of interaction and sharing of information. DT is the digital version of a physical process or asset that can be used to model, manage, and optimise its physical counterpart. Connecting multiple DTs is vital to provide a holistic integration and view across complex ecosystems. To create a DT‐based collaborative ecosystem architecture, the following concerns need to be addressed. Trust is a fundamental requirement because multiple parties will work together as part of a composite DT. Interoperability is essential, as DTs from various domains will be required to interconnect and operate seamlessly. Finally, the governance is challenging as different scenarios require various mechanisms and governance structures. This study presents an architecture to enable multiple DT‐based collaborative ecosystems, and example use case scenarios to demonstrate its applicability in collaborative manufacturing.

A hybrid chemical modification strategy for monocrystalline silicon micro-grinding: Experimental investigation and synergistic mechanism

For high performance manufacturing of micro parts and features, a hybrid chemical modification strategy is proposed to decrease critical energy barrier of mechanical removal of hard and brittle crystal material by refining localized machining condition. The strategy, namely UV-light and IR-laser hybrid chemical modification (UVIR-CM) strategy, includes two steps, an ultraviolet light (UV-light) catalytic advanced oxidation and an infrared laser (IR-laser) assisted selective modification based on Fenton liquid–solid reaction for monocrystalline silicon. The modification effects of UVIR-CM strategy were investigated by surface morphology micro-observation, cross-section transmission electron microscopy (TEM) observation, Raman spectroscopy analysis and nanoindentation test. Experimental results demonstrated that varied degrees of laser texturing appeared on different strategy samples’ IR-laser scanned area. And the IR-laser thermal damage has been successfully inhibited due to the refraction and reflection of energy by bubbles in liquid medium. But for the UVIR-CM strategy, a uniform and amorphous silicate layer is detected in a certain boundary. The UV-light promotes oxidation cycle ability of the chemical solution and ensures sufficient oxide modified layer for subsequent step. Attributing to synergism of photochemical, photothermal and kinetic effects induced by IR-laser, the modified layer displays layered structure with about 600 nm thickness, (2.7 ± 0.60) GPa nanohardness, and (93.7 ± 22.9) GPa indentation modulus. And the layered structure is amorphous layer, nanocrystal and micro-twins layer from the surface to the interior of sample. Consequently, it reveals that the subsequent mechanical removal will become easy due to decreasing energy barrier of monocrystalline silicon in selective area. Meanwhile, its original excellent mechanical properties also are maintained under a certain depth. The results contribute to develop a novel combined micro-machining technology to achieve collaborative manufacturing of structure shape and surface integrity for micro parts and feature.

Collaborative Manufacturing Techniques: A Review

The competitive nature of today's economies has forced the manufacturing sector, small and medium-sized manufacturing enterprises (SMMES), to collaborate with other sectors to achieve stability and consistency. Manufacturing businesses are putting a lot of effort into managing their goods and services to reach a high level of client satisfaction. This is accomplished with the highest quality while maintaining a competitive cost tag. To accomplish this, the collaborative manufacturing technique (CMT) is used. It entails information exchange and dialogue amongst business processes concerning internal or external stakeholders in the hierarchy of value. An active CMT model that incorporates these present collaboration networks should provide operational value savings and significantly increase competitiveness. Therefore, the recent evaluation that provided a detailed view of such inclusion is no longer in existence. To promote collaboration techniques in the successful development of software products, this article provides a complete study of current collaborative models, respective benefits, and their collaborative features. This paper outlines the most recent mechanisms, approaches, and application possibilities for CMTs. In addition, the review paper thoroughly examines the techniques currently in use for employing CMT to solve problems in both science and engineering. More specifically, it suggests a revolutionary method for enhancing the current CMT methods through the application of machine learning (ML), artificial intelligence, and metaheuristics like genetic algorithms and particle swarm optimization (AI). In summary, this research highlights certain areas where CMT may be used soon.

Technological robot-Machine tool collaboration for agile production.

The flexibility and efficiency in parts production can be significantly increased through the technological cooperation of industrial robots and machine tools. The paper presents an approach in which a robot, in addition to the classic handling tasks, enhance machine tools by additional manufacturing technologies and thus beneficially supports workpiece machining. This can take place in various configurations, starting with pre- and final machining by the robot outside the machine, through sequential cooperative machining of the workpiece clamped in the machine, to parallel, synchronized machining of a workpiece in the machine. The approach results in a novel type of collaborative manufacturing equipment for matrix production that will improve the versatility, efficiency and profitability in production.