Automated Assembly Systems Research Articles

Fundamental Behaviors in Hybrid Deterministic Flow Lines With Applications

Since the mid-1960s, it has been known that regular or deterministic flow lines (DFLs) with an arbitrary arrival process possess three essential behaviors: 1) a customer exit time recursion; 2) a server reordering principle; and 3) a delay equivalence with prototype queueing systems. Even though these three fundamental behaviors were discovered decades ago, analogous results for hybrid DFLs (HDFLs)—possessing multiple servers for each stage—have not been unearthed. In this article, we prove the existence of a recursion for customer exit times in HDFLs. This result leads naturally to a server reordering principle. Furthermore, we identify specific conditions under which the total delay faced by a customer in an HDFL is equivalent to that in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G/D/c$ </tex-math></inline-formula> queue. Exploiting these results, we numerically determine the proportion of HDFL systems with a delay equivalent, study the computational burdens required for HDFL simulation, and assess the performance of approximations for HDFL systems. We hope these newly identified behaviors will lead to improved approximation methods and optimization models for use in modern automated manufacturing systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Many modern automated manufacturing systems, such as semiconductor wafer fabricators, use hundreds or thousands of highly complex, costly, and precise tools. These automated tools are often configured as a collection of processing modules served by material handling robots and clustered into a single chassis. One important example of such cluster tools is the clustered photolithography tool (CPT). For certain key performance metrics, including cycle time, residency time, and throughput time, such tools can be well-modeled as a tandem queueing system with random arrivals, regular or deterministic service times, multiple servers devoted to each stage, and finite internal buffers—a so-called HDFL. Such HDFLs also serve as a fundamental model of automated assembly systems. In this work, we prove that the behavior of HDFLs can be characterized by an exit recursion that requires significantly less computation than otherwise. Furthermore, HDFLs possess the surprising property that the customer queueing time is invariant to the order of the stages and, in some cases, is equivalent to a prototype queueing system. These results can be exploited to assess customer delay and to obtain simulation models with about an order of magnitude less computational complexity. We apply our results to a CPT model from the literature to highlight the properties of such tools. Finally, the theory introduced in this article enables a deeper understanding and intuition for such systems.

A novel silicone rubber key automatic assembly system using placement machine

Silicone rubber key is an on and off component assembled on PCB. However, the assembly of silicone rubber key has been by manual and it is low efficiency and higher labor cost. In order to solve this problem a new assembly method by using the surface mount placement machine is invented. The main improvement of this new products is that human work can be completely replaced. And the advantage of the new invention is that the production line does not need to change for the surface mount assembly. The only change is the placement nozzle which is to suck the component and then assembly the silicone rubber key on the PCB. Furthermore, the newly designed vibration materials loading device is equipped to increase the production rate.

Adaptive Automation Assembly Systems in the Industry 4.0 Era: A Reference Framework and Full–Scale Prototype

Industry 4.0 emerged in the last decade as the fourth industrial revolution aiming at reaching greater productivity, digitalization and operational efficiency standard. In this new era, if compared to automated assembly systems, manual assembly systems (MASs) are still characterized by wide flexibility but poor productivity levels. To reach acceptable performances in terms of both productivity and flexibility, higher automation levels are required to increase the skills and capabilities of the human operators with the aim to design next-generation assembly systems having higher levels of adaptivity and collaboration between people and automation/information technology. In the current literature, such systems are called adaptive automation assembly systems (A3Ss). For A3Ss, few design approaches and industrial prototypes are available. This paper, extending a previous contribution by the Authors, expands the lacking research in the field and proposes a general framework guiding toward A3S effective design and validation. The framework is applied to a full-scale prototype, highlighting its features together with the technical- and human-oriented improvements arising from its adoption. Specifically, evidence from this study show a set of benefits from adopting innovative A3Ss in terms of reduction of the assembly cycle time (about 30%) with a consequent increase of the system productivity (about 45%) as well as relevant improvements of ergonomic posture indicators (about 15%). The definition of a general framework for A3S design and validation and the integration of the productivity and ergonomic analysis of such systems are missing in the current literature, representing an element of innovation. Globally, this research paper provides advanced knowledge to guide research, industrial companies and practitioners in switching from traditional to advanced assembly systems in the emerging Industry 4.0 era matching current industrial and market features.

Life Cycle of a Digital Resource Twin: Meta-Modeling and Application Example

To provide a systematization for the emerging Digital Twin paradigm we recently introduced the Digital Twin Structure Model. In this contribution, we now utilize this model to elaborate further on the life cycle of a Digital Resource Twin: This covers the initial model generation, includes different simulation stages like software- and hardware-in-the-loop leading to virtual commissioning and the transition into serial production. The methodological considerations are accompanied by practical examples, which base on an automated assembly system. Thus, this contribution complements the Digital Twin Structure Model with an approach to design and operate a Digital Resource Twin.

A Skill Programming Method Based on Assembly Motion Primitive for Modular Assembly System

To improve the programming efficiency of automatic assembly system, a novel skill programming framework based on task learning is proposed for modular assembly system in this paper. In this framework, the motion sequence of assembly skills can be modeled by demonstration data. And the assembly task is represented hierarchically. A complete assembly process of a part is divided into several skills, and each skill is divided into several sequential assembly motion primitives (AMP) of multiple modules. Then, a learning method of assembly motion sequence based on Hidden Markov Model is proposed, and the maximum probability method is used to generate the optimal sequential AMP. Each AMP is input to the assembly system in the form of instruction to complete the assembly. Aiming at the problem of accurate positioning and trajectory planning, visual guidance and direct teaching method are used to settle this problem. To evaluate the viability of the proposed framework, a customized modular assembly system is used to acquire the demonstration data, and a graphical user interface (GUI) software is designed. Five assembly skills are learned. Experimental are conducted to validate the effectiveness of the proposed method.

The Machine Vision Measurement Module of the Modularized Flexible Precision Assembly Station for Assembly of Micro- and Meso-Sized Parts.

The machine vision measurement module is indispensable for the Modularized Flexible Precision Assembly Station (MFPAS), which is a fully automatic assembly system being developed at Dalian University of Technology (DUT). MFPAS consists of basic and additional modules, and are expected to be flexible, expandable, and re-configurable to adapt to a variety of parts with a large size range, requiring the machine vision measurement module to be able to achieve accurate measurement of position, as well as orientation of the parts with different size scale. An automatic zooming vision system was set up for evaluation and final integration in MFPAS. Pixel equivalent, principal point and orientation deviation of images were analyzed and experimentally studied using different magnifications of the lens. A new template with circular patterns of different diameters was designed for zoom-lens calibration. The experiments show that the measurement error caused by the variation of the pixel equivalent, principal point and orientation is estimated under 10 μm without online calibration. When high accuracy is required, online calibration can be employed during assembly. The evaluation results of the vision system with or without on-line calibration were given for a better trade-off between accuracy and efficiency during assembly.

Development of a novel integrated automated assembly system for large volume components in outdoor environment

In recent years, automatic assembly technology is widely used in the field of aerospace and automobile manufacturing and engineering. For large-scale components assembly, the pose alignment mechanism always occupies too much space and the cost of measurement system is also too expensive. In this paper, a new integrated automated assembly system is developed which costs much less and has a compact structure. Firstly, a novel three-layer pose adjustment mechanism consisting of two parallel mechanisms is designed, which we call a 6-DOF mixed linkage mechanism. The proposed mechanism is connected by a planar 3-DOF parallel mechanism and a 4-point lifting parallel mechanism in series. Secondly, a high-accuracy long-range measuring system composed of low-cost tilt sensor and laser sensors is proposed. The whole measuring process is divided into several major alignment steps to aid the automated assembly task. Finally, a prototype system that automatically assembles large radar antenna is set up. Various validation experiments are performed, and the results show that integrated large scale automated assembly system can achieve relatively high efficiency and accuracy for assembly task.

Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

Industrial cameras starting to play a significant role in current industrial environment and they represent a strong tool for robotics mainly in cases when they are combined with high-speed robots. However, there are still some difficulties in vision system integration. The capability of such system (e.g. assembly or technological system) depends on several factors, for instance, the camera position, lightning conditions, pattern recognition algorithms, precise setup, as well as well-trained programmer and engineer. The skills of the engineering staff and the precise analysis of conditions and process requirements seem to be crucial for successful solution, what was proved also in our experimental test. The main aim of the article is development and complex performance analysis of robotized screwing application with integrated vision system, concretely the case study of automated assembly system in automotive industry—bolting tightening robotized station as a part of car seat assembly process. The main key elements of the designed workplace are industrial robot FANUC M-20 iA/20M with integrated iRVision system containing the industrial camera Sony XC-56. The real influence of inaccuracies during the design process, gradual “step-by-step” refinement, and final influence of all changings on the final quality and efficiency of designed system was demonstrated. At the end, we reached the point where the total number of screwing operations with NOK results (not OK / negative results) is about six single negative results per day, which represents less than 0.1% of all recognitions (overall reliability is higher than 99.9%).

Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System

The fundamental process of predictive maintenance is prognostics and health management, and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment. A new data-driven predictive maintenance and an architectural impulse, based on a regularized deep neural network using predictive analytics, are proposed successfully for ring spinning technology. The paradigm shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages. The sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to them. We established a genetic algorithm based on multi-sensor performance assessment and prediction procedure for the spinning system. Results show that it operates with a relatively less amount of training data sets but takes advantage of larger volumes of data. This integrated system aims to prognosticate abnormalities, disturbances, and failures by providing condition-based monitoring for each component, which makes it more accurate to locate the defined component failures in the current spinning spindles by using smart agents during the operations through the neural sensing network. A case study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic, high-speed textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.

Virtual Commissioning Modeling and Simulation of Assembly System for Quasi Natural Language Programming

This paper focuses on a kind of quasi natural language programming for automatic assembly system, and studies the modeling and simulation of virtual assembly prototype, including the modeling of mechanical system, control system and the error model of assembly prototype. A multi-domain model is formed based on interfaces between different professional modeling and analysis software. Based on this model, the quasi natural language program is simulated and commissioned. At the same time, the script program is run on the physical prototype to verify the consistency of the assembly system prototype. Tested by experiments, the virtual commissioning modeling and simulation technology of the assembly system can approximately restore the assembly process of the real prototype, perform lifecycle management on the development of the equipment, and improve the commissioning speed and production quality.

Design, engineering and testing of an innovative adaptive automation assembly system

PurposeIndustry 4.0 emerged as the Fourth Industrial Revolution aiming at achieving higher levels of operational efficiency, productivity and automation. In this context, manual assembly systems are still characterized by high flexibility and low productivity, if compared to fully automated systems. Therefore, the purpose of this paper is to propose the design, engineering and testing of a prototypal adaptive automation assembly system, including greater levels of automation to complement the skills and capabilities of human workers.Design/methodology/approachA lab experimental field-test is presented comparing the assembly process of a full-scale industrial chiller with traditional and adaptive assembly system.FindingsThe analysis shows relevant benefits coming from the adoption of the adaptive automation assembly system. In particular, the main findings highlight improvements in the assembly cycle time and productivity, as well as reduction of the operator’s body movements.Practical implicationsThe prototype is applied in an Italian mid-size industrial company, confirming its impact in terms of upgrades of the assembly system flexibility and productivity. Thus, the research study proposed in this paper provides valuable knowledge to support companies and industrial practitioners in the shift from traditional to advanced assembly systems matching current industrial and market features.Originality/valueThis paper expands the lacking research on adaptive automation assembly systems design proposing an innovative prototype able to real-time reconfigure its structure according to the product to work, e.g. work cycle, and the operator features.

A Robotic Automatic Assembly System Based on Vision

At present, the production lines of mobile phones are mainly manual and semi-automatic. Robots are the most important tools used to improve the intelligence level of industry production. The design of an intelligent robotic assembly system is presented in this paper, which takes the assembly of screen components and shell components as examples. There are major factors restricting the application of robots, such as the calibration of diversified objects, the moving workpiece with incomplete information, and diverse assembly types. A method was proposed to solve these technological difficulties. The multi-module calibration method is used to transform the pose relationship between the robot, the conveyor belt, and the two cameras in the robot assembly system. Aiming at a workpiece moving with incomplete information, the minimum matrix method is proposed to detect the position. Then dynamic fetching is realized based on pose estimation of the moving workpiece. At last, the template matching method is used to identify the assembly area of the workpiece type. The proposed method was verified on a real platform with a LINKHOU LR4-R5 60 robot. Results showed that the assembly success rate was above 98%, and the intelligent assembly system of the robot can realize the assembly of mobile phone screens and back shells without any staff.

Modeling and optimization of assembly process of flexible accelerometer based on Petri net

With the continuous improvement of micro-machining technology, sensors such as flexible accelerometers are developing in the direction of miniaturization, integration and complexity. At present, the assembly of flexible accelerometers is mainly done by manual, which cannot meet the requirements of product consistency, and the low efficiency of the production cannot match the requirements. Therefore, based on the modular design method, this paper proposes an automatic assembly system for flexible accelerometer, then uses Petri net to display the assembly process of flexible accelerometer visually, and finds the constraints which restrict the improvement of production efficiency.

Quantifying the complexity of subassemblies in a fully automated assembly system

PurposeComplexity is the main challenge for present and future manufacturers. Assembly complexity heavily affects a product’s final quality in the fully automated assembly system. This paper aims to propose a new method to assess the complexity of modern automated assembly system at the assembly design stage with respect to the characteristics of both manufacturing system and each single component to be mounted. Aiming at validating the predictive model, a regression model is additionally presented to estimate the statistic relationship between the real assembly defect rate and predicted complexity of the fully automated assembly system.Design/methodology/approachThe research herein extends the S. N. Samy and H. A. ElMaraghy’s model and seeks to redefine the predictive model using fuzzy evaluation against a fully automated assembly process at the assembly design stages. As the evaluation based on the deterministic scale with accurate crisp number can hardly reflect the uncertainty of the judgement, fuzzy linguistic variables are used to measure the interaction among influence factors. A dependency matrix is proposed to estimate the assembly complexity with respect to the interactions between mechanic design, electric design and process factors and main functions of assembly system. Furthermore, a complexity attributes matrix of single part is presented, to map the relationship between all individual parts to be mounted and three major factors mentioned in the dependency matrix.FindingsThe new proposed model presents a formal quantification to predict assembly complexity. It clarifies that how the attributes of assembly system and product components complicate the assembly process and in turn influence the manufacturing performance. A center bolt valve in the camshaft of continue variable valve timing is used to demonstrate the application of the developed methodology in this study.Originality/valueThis paper presents a developed method, which can be used to improve the design solution of assembly concept and optimize the process flow with the least complexity.

Research on automatic assembly technology for final assembly of helicopter fuselage

The helicopter fuselage has the characteristics of compact structure space, complex coordination relationship and weak rigidity, etc. It is difficult to control the accuracy of the desired shape. According to the high precision and quality requirement of final assembly for helicopter fuselage, the application status of automatic assembly technology in aviation manufacturing was analyzed. Then the difficulties of automatic assembly technology for fuselage final assembly were also discussed. In this paper, an automatic assembly system for the final assembly of helicopter fuselage was designed, and relevant key technologies of the assembly system mentioned above were proposed. The designed system have the functions of automatic positioning, clamping, measuring, drilling, feeding and blind fastener riveting for fuselage components, which can improve the assembly quality and efficiency of helicopter fuselage during the final assembly process.

3-D Point Cloud Registration Using Convolutional Neural Networks

This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.

Auxiliary Drives for Automated Assembly Systems

Basic types of auxiliary drives in automated assembly systems are compared in terms of generalized characteristics. Regulation of the pneumatic drive by means of servomechanical positioning systems is considered. Diagrams are presented for assessment of the mean velocities and travel times of mechatronic modules in the angular positioning of loading drives on the basis of kinematic and mechanical characteristics.

Automatic precision robot assembly system with microscopic vision and force sensor

An automatic precision robot assembly system is established. The robot assembly system mainly consists of an industrial robot, three cameras, a micro force sensor, and a specific gripper. The industrial robot is a six-axis serial manipulator, which is used to conduct grasping and assembly subtasks. Two microscopic cameras are fixed on two high accuracy translational platforms to provide visual information in aligning stage for assembly. While one conventional camera is installed on the robotic end effector to guide the gripper to grasp component. The micro force sensor is installed on the robotic end effector to perceive the contacted forces in inserting stage. According to the characteristics of components, an adsorptive gripper is designed to pick up components. In addition, a three-stage “aligning–approaching–grasping” control strategy for grasping subtask and a two-stage “aligning–inserting” control strategy for assembly subtask are proposed. Position offset compensation is computed and introduced into aligning stage for assembly to make the grasped component in the microscopic cameras’ small field of view. Finally, based on the established robot assembly system and the proposed control strategies, the assembly tasks including grasping and assembly are carried out automatically. With 30 grasping experiments, the success rate is 100%. Besides, the position and orientation alignment errors of pose alignment for assembly are less than 20 μm and 0.1°.

Enhanced Processing and Testing Concepts for New Active Materials for Lithium‐Ion Batteries

Electrode manufacturing requires multiple process steps, e.g., dispersing and coating. In‐between these steps, intermediate products have to be transferred, stored, and handled. Especially for the development of new active materials or electrode formulations, the variety of parameters that need to be screened is enormous. In addition, these materials are initially tested in small batches, and it is not always possible to upscale the used processes. To evaluate the performance of different materials or differently processed materials, test cells are assembled. This usually requires manual work procedures, which are inherently sensitive to variations and untraceable errors. If the stochastic flaws are large enough, the effects of process variations are covered by these. It is therefore important to increase reproducibility in all process steps. Herein, new concepts for electrode production and automated sample preparation for highly reproducible production and more effective electrode development and screening of parameters are presented. A combined grinding and dispersion process for the production of silicon‐based anodes and an automated assembly system for efficient testing is presented. The processes are supported by methods of data mining to collect process data, ensure high reproducibility, and support research on new active materials.

Adaptive automation assembly: Identifying system requirements for technical efficiency and worker satisfaction

Manual assembly work systems bring high flexibility but low productivity in comparison to fully automated systems. To increase productivity but maintain flexibility, future systems need to incorporate greater levels of automation which complement or augment the capabilities of the human operators who provide the manual work. Future systems should be designed for social and economic sustainability within fluctuating conditions and for adaptive utilisation of operators’ individual capabilities to maintain levels of productivity and personal satisfaction. To successfully create such systems with greater adaptivity and interactivity between people and technology a comprehensive understanding of design requirements is needed; the current problem is that there is no standard valid framework. The work described in this paper employed a three-component investigation to identify the various key requirements that are needed to form such a design framework for future human-automation assembly systems. This involves separate activities with different methodologies involving literature reviews, surveys and business case analysis to define use case scenarios and requirements for creating adaptive automation assembly system demonstrators. The different methodological approaches and results for all of the three component studies are described, along with conclusions and implications for further research work and for industry in general.