ABSTRACT Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

ABSTRACT Recurrent production changes due to shortened product lifecycles besides the increased complexity, that the industry 4.0 paradigm brings, highlight the need for tools that are able to seamlessly orchestrate intelligent resource agents and guarantee rapid commissioning times. This paper presents a complete package for the creation, debugging, editing, management and execution of complex work schedules of robotic systems, named Workcell Controller. The controller is capable of simultaneously orchestrating multiple resources and modules or managing errors based on the real-time shopfloor’s state. For greater applicability and compatibility, it is completely integrated with the ROS framework, meaning that standard ROS interfaces, functionalities and practices can be deployed. An intuitive web-based graphical user interface is designed for offering high usability even for novice users during programming and execution phases. The proposed package has been deployed and tested in two use cases inspired from the composites industry, demonstrating the controller’s applicability, usability and short ramp up times.

ABSTRACT Currently, manufacturing industries in many countries are grappling with an imbalance in the supply and demand of manufacturing resources, encompassing resource scarcity and idleness, as well as insufficient and surplus manufacturing capabilities. To address this issue, cloud manufacturing has emerged, integrating the Internet of Things, cloud computing, and advanced manufacturing technologies. This fusion enables the on-demand allocation of manufacturing resources and capabilities, facilitating efficient networked production. However, in the realm of the Internet, the challenge lies in efficiently aligning abundant information with a company’s manufacturing objectives to genuinely deliver an “on-demand service.” This represents a pivotal issue that demands immediate resolution within the operational framework of cloud manufacturing platforms. To effectively address the optimization challenge in Manufacturing Cloud Services (MCS), this study leverages two dimensions: the fundamental Quality of Service (QoS) of MCSs and the interrelated QoS among these services. A holistic QoS model for assembling MCSs is constructed. In this model, the timely relevance of fundamental QoS data is ensured through the incorporation of a temporal weighting function, while an enhancement in the precision of optimization is achieved by considering the interconnections between service combinations. The final simulation experiments showcase the effectiveness of the proposed approach in this paper.

ABSTRACT To make robotic welding more flexible and intelligent, artificial intelligence-based systems are one of the most important developments. This paper introduces a computer vision-based algorithm for weld path detection, gap measurement, and weld length calculation. The proposed innovative approach employs various image processing techniques and mathematical operations, accurately determining weld attributes at seam points. Using the YOLO-based object detection algorithm, the model attains a remarkable average precision of 99.5% in identifying atypical weld regions. The study also introduces an efficient boundary line elimination method based on the Probabilistic Hough transform and mathematical logic. Methodology for classifying weld lines with or without significant gaps has been proposed, followed by adapting distinct set of algorithms for weld line identification and gap measurement. Rigorous testing on butt joints of diverse shapes (e.g., straight, zig-zag, and curve) and sizes verifies the robustness of the algorithm, with errors well within ±1 mm for length measurements. In testing conducted at three different points along the individual weld profile, the maximum error in estimating the weld gap was observed to be 0.11 mm. Weld seam information can be extracted effectively with the proposed algorithm, which proves its viability for industrial applications.

ABSTRACT In this paper, an approach for computing online safety zones for collaborative robotics in a robust way, despite uncertain robot dynamics, is proposed. The strategy implements the speed and separation monitoring paradigm, and considers human and robot enclosed in bounding volumes. The human-robot collaboration is monitored by a supervisory controller that guides the robot to stop along a path-consistent trajectory in case of collision danger between human and robot. The size of the robot safety zone is minimized online according to the stop time of the manipulator, and the uncertain robot dynamics is considered using interval arithmetic to ensure compliance with the joint torques limits even in case of imperfect knowledge of the dynamic model parameters. The results verify the effectiveness of the proposed approach, and evaluate the influence of dynamics variations on human-robot collaboration.

ABSTRACT Disassembly of end-of-use products is critical to the economic feasibility of circular feedstock, reusing, recycling and remanufacturing loops. High-volume disassembly operations are constrained by disassembly complexity and product variability. The capability to buffer against timing, quantity and quality uncertainties of end-of-use products impacts the efficiency and profitability of demanufacturing systems. To achieve a competitive operation in the manufacturing life-cycle, disassembly systems need automated lines, however, the unpredictability of core supply challenges automation adaptability. Disassembly robot trajectories that are programmed manually or controlled by vision systems can be time intensive and subject to variability in lighting conditions and image recognition models. Alternatively, this paper presents a novel human-robot disassembly framework to systematically extract and generate robot trajectories derived from human-collaborative robot (cobot) disassembly. The collaborative training station proposed classifies trajectory segments and then adjusts trajectories to station-specific robots in a high-volume disassembly line. Virtual and physical collaborative disassembly case studies are presented and discussed. Results demonstrate the effectiveness of the disassembly data extraction method but indicate a disparity between the expected and ideal disassembly trajectories due to variability from human handling, which is further discussed in this paper.

ABSTRACT The present study addresses integrated manufacturing information systems (IMIS) and highlights importance of realizing ‘adaptability’ and ‘reconfigurability’ characteristics in the system architecture of IMISs, where engineering design for system adaptability is interested. This study develops a matrix-based probabilistic definition for the manufacturing information system (MIS) reconfigurability and proposes modularization of physical architecture of the system as an effective design solution for fulfilling system reconfigurability and adaptability requirements. To modularize the physical architecture, Design Structure Matrix (DSM) method is employed. Moreover, information axiom of Axiomatic Design (AD) theory is also utilized to develop helpful indicators for evaluating the adaptability characteristic for the architecture and support engineering designers to make sound decisions in design for adaptability. To verify the developed methodology, the MIS in a real case (Barez Industrial Group in Iran) is addressed and design of the physical architecture for this system is studied. A new optimally modularized design for the physical architecture of the MIS of interest is finally presented and examined. Results of the indicators reflecting reconfigurability and adaptability of the architecture indicate that the new proposed design of the concerned MIS is highly capable of coping with the rapidly changing manufacturing environment.

ABSTRACT The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are mostly the past heritage, with robot programmers reluctant to adopt new programming paradigms. This work aims to evaluate the impact on non-expert users of introducing a new task-oriented programming interface that hides the complexity of a programming framework based on ROS. The paper compares the programming performance of such an interface with a classic robot-oriented programming method based on a state-of-the-art robot teach pendant. An experimental campaign involved 22 non-expert users working on the programming of two industrial tasks. Task-oriented and robot-oriented programming showed comparable learning time, programming time and the number of questions raised during the programming phases, highlighting the possibility of a smooth introduction to task-oriented programming even to non-expert users.

ABSTRACT Surface roughness is primarily used to evaluate the surface finish of a workpiece, which significantly affects the quality performance of the finished components. Effective surface roughness prediction can reduce the time of trial-and-test, thus increasing productivity and reducing costs. Although earlier studies have proposed many different prediction approaches for different machining methods, few studies have been done to develop a generic approach for surface roughness prediction. Therefore, this paper presents a generic evolutionary ensemble learning (GEEL) framework for surface roughness prediction of different kinds of manufacturing process. The GEEL framework for surface roughness prediction consists of three modules, including pre-processing module, multi-algorithm regression module, and GA-based ensemble learning module. Validation experiments were conducted on fluid jet polishing (FJP) of 3D-printed Cobalt Chrome (CoCr) alloy and other seven cases of different machining methods. The results indicate that the mean square error (MSE) and mean absolute error (MAE), obtained by the proposed method were reduced to a certain extent than typical methods in predicting the surface roughness, exhibiting excellent prediction performance and robustness. At the same time, the method has good extensibility and is expected to become a generic framework for surface roughness prediction in manufacturing.

ABSTRACT As the last production link, the diesel engine assembly process (DEAP) significantly impacts the quality consistency of diesel engine products. Therefore, the quality consistency improvement of DEAP has become an urgent problem for academia and industry. The challenge is how to mine the causal relationship in DEAP and establish a reliable quality prediction model. This paper attempts to describe DEAP using a causal relationship network (CRN) and to provide an effective data-based scheme for improving quality consistency by integrating CRN with support vector regression (SVR). First, a two-step CRN learning method is proposed for describing the DEAP. In the first step, the association relationship network is developed by a hybrid direct association detection method of the maximal information coefficient and network deconvolution, which can accurately measure the data relations. In the second step, the information geometric causal inference is employed to determine the direction of the edges in the association relationship network, thus forming the CRN of DEAP. Then, an integrated CRN-SVR approach is proposed to realize the predictive modeling of the critical quality indicators in DEAP, which integrated SVR into CRN. At the same time, it also provides a feasible idea for the interpretability of existing machine learning techniques. Finally, the proposed approach is tested and verified in a real-world DEAP and the obtained RMSE is only 0.033. The results of this study provide theoretical support and technical guarantee for quality consistency improvement in DEAP.