Flexible Assembly Systems Research Articles

Graduation-inspired manufacturing system and synchronization mechanism for hybrid assembly cell lines

This paper presents an innovative flexible assembly system – hybrid assembly cell lines (HACL) to adapt to small batch, high product variety and dynamic demands. HACL provides specially designed trolleys for assembly operations, materials and tools to form the workstations flexibly and quickly at low reconfiguration cost. To conduct accurate and effective control for this complex assembly system, this paper presents a modified Graduation-inspired Manufacturing System (GMS), a recently proposed new card-based control system. GMS for HACL designs three kinds of tickets and the ticketing mechanism to absorb the uncertainties of HACL. Job tickets are for workload control and synchronization of part deliveries; setup tickets are for cell line formation and synchronization of resources of tools and machines; operation tickets are for production execution and synchronization of workers and operations in cell lines. GMS for HACL develops Internet of Things-enabled (IoT-enabled) architecture for managing the smart tickets to achieve automatic and intelligent interoperability between GMS and the physical workstations of HACL. This work designs synchronization mechanism based on specific model and genetic algorithm to synchronize manufacturing resources under GMS for HACL

Reconfigurable flexible assembly model and implementation for cross-category products

As the production orders are becoming multi-category and small-batch in the era of product personalization, these require frequent reconfiguration of reconfigurable flexible assembly system for cross-category products (RFAS-CCP). However, there is no suitable theoretical assembly model and systematic implementation framework. We first propose a five-element assembly model (FAM) for RFAS-CCP, i.e. product, process, resource, knowledge, and decision. The product, process, and resource element describe the objects, steps to be assembled, and the tools, fixtures, and other equipment used for assembly, respectively. The knowledge element is a form representation of various heterogeneous data, such as a knowledge graph. The decision element includes various assembly methods to achieve assembly automation, flexibility, and intelligence. Then, in order to standardize and easy the frequent reconfiguration process, we reorganize various decision methods into a three-phase systematic implementation framework according to which stage they are used: design, configuration, and operation phases. The design phase methods primarily design various assembly modules for a product family, forming an assembly resource library. The configuration phase methods primarily configure suitable assembly lines for a specific product in the product family. The operation phase methods monitor the status of the assembly line and ensures its stable operation through health management. Finally, the effectiveness and practicality of the proposed five-element assembly model and three-phase systematic implementation framework are experimented with a pressure reducing valve product.

Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems

AbstractManufacturing systems are undergoing systematic change facing the trade-off between the customer's needs and the economic and ecological pressure. Especially assembly systems must be more flexible due to many product generations or unpredictable material and demand fluctuations. As a solution line-less mobile assembly systems implement flexible job routes through movable multi-purpose resources and flexible transportation systems. Moreover, a completely reactive rearrangeable layout with mobile resources enables reconfigurations without interrupting production. A scheduling that can handle the complexity of dynamic events is necessary to plan job routes and control transportation in such an assembly system. Conventional approaches for this control task require exponentially rising computational capacities with increasing problem sizes. Therefore, the contribution of this work is an algorithm to dynamically solve the integrated problem of layout optimization and scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal policy optimization and consists of a decoder and encoder, allowing for various-sized system state descriptions. A simulation study shows that the proposed algorithm performs better in 78% of the scenarios compared to a random agent regarding the makespan optimization objective. This allows for adaptive optimization of line-less mobile assembly systems that can face global challenges.

Fault Detection Algorithm for Pneumatic Measuring and Sorting Station

The aim of this paper is to present basic hardware components of a Flexible Assembly System (FAS) and investigate possible faults, classify the errors and implement real diagnosis. The mentioned system offers professional training according to the industrial reality, simulating a real assembly process and including different technologies from Industry 4.0. This equipment consists of an automated flexible assembly cell with five different stages: assembly, handling, quality inspection, transfer and warehouse. The pneumatic system repeats the identical programmed sequence during it operation. The data was collected when the pneumatic system worked perfectly and had some generated faults including empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. The faults are simulated according to developed algorithms in this investigation. The case study with fuzzy logic is used to develop a new sequence for one pneumatic module diagnosis. The analysis of the simulated faults is discussed and the obtained results can be applied in the similar pneumatic systems in industry.

Software architecture for adaptable assembly systems

Modern manufacturing methods demand highly flexible and reusable assembly systems. Such flexibility and reusability can be attained using systems that are modular. These modular systems have the ability to react faster to market forces and are adaptable beyond the limits of the so-called flexibility corridor. Also, placing the constraints of flexibility and reusability on software paves the way for design and usage of generic systems that have a relatively low initial cost.However, these advantages are often overshadowed by the complexity of these software systems. Especially, one faces many difficulties in software modularizing the assembly system so that each module can be integrated seamlessly into the entire system. Defining an interface in the development phase is practically impossible given that systems evolve a lot before reaching production phase.

Intelligent flexible assembly system for labor-intensive factory using the configurable virtual workstation concept

Intelligent flexible assembly system for labor-intensive factory using the configurable virtual workstation concept

FLEXIBILITY IN ASSEMBLY PLANNING THROUGH PRODUCT DESIGN IN INDUSTRIES

Manufacturing processes and equipment that can handle preconceived and emergency changes with low penalty in time, effort, cost and performance are required in the industries to accelerate introduction of new product, handle breakdown of systems and modification of already existing products. Though there are several attempts on this research, especially in development of equipment capable of handling industrial changes but the results are still not satisfactory. Contributing to achieving a Flexible assembly system. Compensative flexibility Model/Approach was carried out in this research to handle Flexible Assembly system through product design. The model developed performed: generation of assembly sequence, schedule and balancing for assembly planning at the product design stage, extracted product assembles requirements for product design at the conceptual stage to guide the designer; synchronized assembly system and product design and; modularized assembly system and product design. Transportation system of an Assembly system of an engine was simulated using ProModel Simulator and the result was remarkable. Also the design of IC engine using two variance; inline and V IC engines showed remarkable improvement in parts interchangeability and commonality with modularized design; 76% of the parts used in inline engine were also used in the V engine variance. Results from simulation runs with 8 automatic guided vehicle (AGV) in the system were observed, with the average time spent by parts in the system and the average time blocked were significant dropped as number of buffers and AGV were increased; the effect was more pronounced with 8 AGVs and 8 storage buffers in the system. The average (AVG) time blocked with 8 AGV and 1 storage buffer was 56.7 minutes and reduced to 0 minutes when the number of storage buffer increased to 8 with 8 AVGs, the percent reduction was 100 percent while AVG time in the system with one storage buffer was 59.34 minutes and reduced to 4.37 minutes with 8 storage buffers which is 96.9 percent reduction. The variation of number of vehicles and buffers reduced or increased the time component spent in the system. Thus, if correct workstation or assembly system resources are determined and designs are modularised, more articles will be introduced to the market, time to market will reduce, many variances will be produced and there will be reduction in the cost of production. The application of this technique is recommended for processing industry.

Integrating force and vision feedback for flexible assembly system

The Industrial Assembly Challenge at the World Robot Summit 2020 was a competition showcasing the latest technologies in robot assembly tasks. Teams had two days to set up an autonomous assembly system and had to manipulate surprise parts on the competition day. The manufacturing industry has trended towards high-mix low-volume production, which often requires frequent tool changes and can be difficult to set up with industrial robots. Small- and medium-sized enterprises (SMEs) may struggle to use robots for a variety of products, particularly in assembly tasks requiring high accuracy and specialized tools. To improve assembly performance, we propose an assembly method using multiple types of sensors, including an RGB camera and six-axis force-torque sensor. This system allows for flexible assembly tasks without strict alignment, does not require jigs or dedicated hands for each object, and can be easily adapted to different production scenarios. In this paper, we present the motion strategy of our proposed assembly system that participated in the World Robot Summit 2020 and demonstrate how it was able to robustly accomplish tasks such as screw tightening and peg-in-hole by combining information from the camera with information from force-torque sensors.

INDUSTRIAL ASSEMBLY LINE OPERATION

The idea of this paper is to present basic components of a flexible assembly system (FAS), discuss it characteristics, classify the systems and describe an application. This equipment offers professional training according to the industrial reality, simulating a real assembly process and including different technologies from Industry 4.0. The FAS consists of an automated flexible assembly cell with five different stages: assembly, handling, quality inspection, transfer and warehouse. It is the ideal answer to research and training in industrial automation. The main advantage is that it has fully modularity in keeping with industrial reality. The technologies included in its different assembly modules, as well as the assembly process with several variants, allow the user to develop the professional skills required by advanced automation. In this work the conventional control approach is presented with programmable logic controllers (PLC) and SCADA level.

Development of a Flexible Assembly System for the World Robot Summit 2020 Assembly Challenge

The assembly challenge of the World Robot Challenge (WRC) 2020, which was a part of the World Robot Summit (WRS) 2020, aimed to complete rapidly changing tasks in high mix/low volume production through building agile and lean production systems that can respond to one-off products. The authors of this paper participated in the challenge with the team PneuBot from the Industrial Robotics Facility of the Italian Institute of Technology by developing a flexible assembly system. The purpose of this work was to develop an assembly system able to handle variations of parts and tasks with a minimal changeover in hardware and software. In particular, assembly tasks were carried out, such as the assembly of a DC motor, pulleys, and a flexible belt on a plate, starting from pieces of unknown positions and orientations on a tray. The proposed work cell is light-weighted and can be fast deployed and replicated. It is composed of two Universal Robots; an RGB-D camera mounted on the wrist of the robot, able to detect both the position and orientation of the different objects to manage; a custom gripping system composed of 3D printed fingers for manipulation purposes and miniature force sensors for the grasping detection.

Collaborative force and shape control for large composite fuselage panels assembly

This study proposed a force and shape collaborative control method that combined method of influence coefficients (MIC) and the elitist nondominated sorting genetic algorithm (NSGA-II) to reduce the shape deviation caused by manufacturing errors, gravity deformation, and fixturing errors and improve the shape accuracy of the assembled large composite fuselage panel. This study used a multi-point flexible assembly system driven by hexapod parallel robots. The proposed method simultaneously considers the shape deviation and assembly load of the panel. First, a multi-point flexible assembly system driven by hexapod parallel robots was introduced, with the relevant variables defined in the control process. In addition, the corresponding mathematical model was constructed. Subsequently, MIC was used to establish the prediction models between the displacements of actuators and displacements of panel shape control points, deformation loads applied by the actuators. Following the modeling, the shape deviation of the panel and the assembly load were used as the optimization objectives, and the displacements of actuators were optimized using NSGA-II. Finally, a typical composite fuselage panel case study was considered to demonstrate the effectiveness of the proposed method.

Flexible Assembly System with Stiffness Switching Joint

Flexible Assembly System with Stiffness Switching Joint

An early-phase design process to enable long-term flexibility in assembly systems

Assembly systems require to be designed considering flexibility from a holistic perspective to produce the variety of current and future product generations. Reactive ad hoc changes after realizing both the product and assembly system designs require considerable effort and may even be impossible. A systematic collaborative approach that concurrently considers the early phases of product and assembly system development appears to be essential for working with long-term changes. To this end, a greater understanding of the working procedures and design activities concerning flexibility is required. In this paper, this is investigated based on a theoretical framework and a multiple case study at a world-leading manufacturer of heavy-duty vehicles. As a result, a developed early phase design process to enable long-term flexibility in assembly systems is presented. The theoretical implications provided and the findings are also relevant to those involved in the design process of flexible assembly systems.

Exploiting the technological capabilities of autonomous vehicles as assembly items to improve assembly performance

The automotive industry is on the brink of transitioning to autonomous vehicles (AVs). This will require highly flexible assembly systems. This paper focuses on exploiting the capabilities of the technology base, e.g., sensors and image recognition, of AVs as assembly items and employing their self-driving function in assembly systems. This fundamentally new approach to matrix manufacturing systems based on autonomously navigating automated guided vehicles (AGVs) and the elimination of set assembly sequences is a growing topic of discussion. This study develops a conceptual framework, based on a systematic literature review and interviews with fifteen experts from three carmakers, for exploring the field of research and assessing the feasibility of employing the technology base of autonomous driving instead of AGVs. This study is intended for assembly planners and researchers of assembly systems in automotive manufacturing.

Microforce Sensing and Flexible Assembly Method for Key Parts of ICF Microtargets

Microassembly is one of the key techniques in various advanced industrial applications. Meanwhile, high success rates for axial hole assembly of thin-walled deep-cavity-type items remain a challenging issue. Hence, the flexible assembly approach of thin-walled deep-cavity parts is investigated in this study using the assembly of the key components, the microtarget component TMP (thermomechanical package) and the hohlraum in ICF (inertial confinement fusion) research, as examples. A clamping force-assembly force mapping model based on multisource microforce sensors was developed to overcome the incapacity of microscopic vision to properly identify the condition of components after contact. The ICF microtarget flexible assembly system, which integrates multisource microforce sensing and a six degrees of freedom micromotion sliding table, is presented to address the constraint that the standard microassembly approach is difficult to operate once the parts contact. This method can detect contact force down to the mN level, modify deviation of the component posture efficiently, and achieve nondestructive ICF microtarget assembly at the end.

Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops.

Evaluation of Material Shortage Effect on Assembly Systems Considering Flexibility Levels

The global pandemic caused delays in global supply chains, and numerous manufacturing companies are experiencing a lack of materials and components. This material shortage affects assembly systems at various levels: process level (decreasing of the resource efficiency), system level (blocking or starvation of production entities), and company level (breaking the deadlines for the supplying of the products to customers or retailers). Flexible assembly systems allow dynamic reactions in such uncertain environments. However, online scheduling algorithms of current research are not considering reactions to material shortages.In the present research, we aim to evaluate the influence of material shortage on the assembly system performance. The paper presents a discrete event simulation of an assembly system. The system architecture, its behavior, the resources, their capacities, and product specific operations are included. The material shortage effect on the assembly system is compensated utilizing different system flexibility levels, characterized by operational and routing flexibility. An online control algorithm determines optimal production operation under material shortage uncertain conditions. With industrial data, different simulation scenarios evaluate the benefits of assembly systems with varying flexibility levels. Consideration of flexibility levels might facilitate exploration of the optimal flexibility level with the lowest production makespan that influence further supply chain, as makespan minimization cause reducing of delays for following supply chain entities.

Simulation-based evaluation of performance benefits from flexibility in assembly systems and matrix production

This paper contributes to a novel approach for analysing and quantifying the benefits for reconfigurable cellular manufacturing and assembly systems (RCMS/RCAS), also known as Matrix production, based on modelling and dynamic simulation.International market trends concerning consumer behaviour and demand result in increasing flexibility requirements, which conventional production lines struggle to meet, thus creating the need for flexible production and assembly systems such as Matrix production. The flexibility results in additional variables and thus a more complex system behaviour. To utilize its benefits, the design and setup of a matrix production is crucial and for potential real-world applications. However, methods to quantitatively assess beneficial features of a matrix production are only beginning to appear in the literature, making decision-making for a production system transformation challenging. This paper contributes a simulation-based approach to analyse the performance benefits of matrix production using modelling and dynamic simulation. The method is demonstrated with a simplified automotive assembly use case for a dynamic multi-variant production. The results confirm the feasibility of the method and quantify the potential for increased assembly system performance gained from specific flexibility features in a matrix production.

A flexible manufacturing assembly system with deep reinforcement learning

Traditional assembly line requires a significant amount of designs from engineers, especially in the case of multi-species and small-lot production. Recently, intelligent algorithms based on reinforcement learning are proposed to address this issue. However, the lower success rate and safety reasons limit their industrial applications. In this article, we proposed a systematic solution, including the automatic planning of assembly motions and the monitoring system of the production lines. In the planning stage, we built the digital twin model of the assembly line, then trained a deep reinforcement learning agent to assembly the workpieces. In the production stage, the digital twin model is used to monitor the assembly lines and predict failures. To validate the system we proposed, we conducted a peg-in-hole assembly experiment, and reached a 90% success rate for a single assembly attempt. During the whole experiment, no collision happens in the real world.

A Flexible Robotic Assembly System Combining CAD Based Localization, Compliance Control, and a Multi-Modal Gripper

As manufacturing trends shift towards customized production, the demand for agile automation systems capable of efficient adaptation to rapidly changing task requirements is rising. This work presents a flexible robotic assembly system that combines CAD based component localization, compliance control, and a multi-modal gripper to enable robust and efficient programming of complex tasks. The process can be easily configured for novel assemblies through a dedicated Graphical User Interface (GUI), which facilitates component identification and task sequencing. Component poses are extracted from 3D CAD models in reference to the assembly origin, while the active compliance scheme compensates for minor positioning errors. The gripper incorporates a parallel jaw element, a rotating module, and an electromagnet to minimize retooling delays. The first iteration of the proposed system placed first in the manufacturing track of the Robotic Grasping and Manipulation Competition of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), experimentally validating its efficiency.