Assembly Manufacturing System Research Articles

A Discrete Cooperative Control Method for Production Scheduling Problem of Assembly Manufacturing System

With a sharp decrease in resource utilization of the complex production process, integrated scheduling methods are urgently needed in assembly manufacturing industries. To this end, this paper presents an integrated scheduling with jobs processing and assembly sequence (ISJPAS) problem where jobs and assemblies can be processed simultaneously. As the first contribution to this work, we develop a mixed integer nonlinear programming model (MINLP) that aims at minimizing maximum completion time and determining the earliness and tardiness of jobs and resulting inventory time. The structured property of the optimal scheduling solution is analyzed with regard to job sequencing and assembly sequencing. The NP-hard nature of the problem is proved, which provides upper and lower bounds on the optimal solution. Second, an efficient discrete assembly time and arrival time control (DAATC) method is presented based on continuous time variable control models. From a modified set of benchmark problems, the proposed method is tested by comparing four assembly association levels in real applications. The comparisons indicate the potentiality of our method to satisfy the due date. Lastly, relevance of practical applications are discussed, and several future research avenues are emphasized.

Genetic Transfer Learning for Optimizing and Balancing of Assembly Lines

In this paper, the genetic transfer learning (GTL) method is proposed for knowledge transfer of sustainable assembly manufacturing systems. Existing methods for the assembly line balancing problem (ALBP), such as genetic algorithms (GAs) suffer from three significant limitations: tedious ‘trial and error’ processes, no utilization of existing system solutions, and no consideration of new constraints on system reconfiguration. To address these problems, we propose GTL to migrate the knowledge of the GA setup in system reconfiguration. The contributions are as follows: 1) transfer pretreatment is performed to formulate knowledge of existing systems and adapt to the new constraints of future systems; 2) the similarity of existing and future systems is defined quantifiably to determine transfer conditions and avoid weak and negative transfer for maximizing knowledge transfer; 3) the transfer strategy is made to determine the method and knowledge to be transferred. The case study of a computer assembly line shows that adopting transfer learning has helped to improve assembly line efficiency and sustainability.

Methodology for systematic design of cleanroom assembly workstations

Given the growth of cleanroom manufacturing for high-tech products and the high costs of operating within a cleanroom environment, an accompanying efficient manufacturing system is important. While abundant examples of efficient assembly manufacturing systems exist today, implementation of these into a sensitive cleanroom manufacturing environment has proven to be difficult. Against this background, this paper presents a transferable methodology to support the design of cleanroom assembly workstations which brings together aspects like cleanliness, ergonomics, modularity/flexibility and flow in an integrated manner. A set of guidelines was developed and embedded into a systematic procedure. The methodology was implemented and validated in a case at a high-tech precision manufacturing company, with high-mix-low-volume characteristics. The case study underlines the feasibility of the approach and the multicriterial improvement of workstation design towards the current state of the art. It also lead to a transferable cleanroom workstation design.

Virtual Engineering and Commissioning to Support the Lifecycle of a Manufacturing Assembly System

Prior to the physical build of the industrial automation system, some challenges arise, such as processes’ cycle times calculations, ergonomics and safety evaluation, and the integration of separate machines to the complete production shops. This, in turn, requires reconfiguring the processes and component parameters. As a result, the lifecycle of the system development is prolonged, and the potential for erroneous performance increases. In modern digital manufacturing environments, virtual engineering (VE) and virtual commissioning (VC) serve as effective tools to tackle the aforementioned problems and their consequences. The virtual models developed for VE and VC not only assist system developers in the physical build stage but also in the following stages of the system lifecycle by providing a common virtual model, a digital twin (DT), of the manufacturing processes and the product. This developed model should possess the ability to simulate the system behaviour, e.g., the mechanics, kinematics, speed and acceleration profiles. Three stakeholders are involved in the development process: the machine builder, system integrator and end user. The current work focuses on the virtual engineering approach to support the entire lifecycle of a manufacturing system from the machine builder, system integrator and end user perspectives. For this purpose, it puts forward a systematic methodology of implementing VC and VE using a toolset developed by the Automation Systems Group at the University of Warwick within an industrial project. The suggested methodology is illustrated in a case study where a digital twin of a physical station was modelled, developed and tested in parallel with the physical machine development and build. Finally, the benefits and limitations are highlighted based on the gained outcomes and the implemented activities.

Health states assessment and prediction of manufacturing system based on brittleness

Accurate recognition of system health states is the key to ensure the safe operation of the system. In view of the shortcomings of the existing methods, a new method of manufacturing system health states assessment and prediction based on brittleness is proposed. Firstly, according to the real-time effective performance parameters, the brittle risk entropy model of equipment is constructed, and the brittleness of corresponding equipment on each station is calculated; Secondly, based on the structural characteristics of manufacturing system, the calculation model of system brittleness is constructed by analysing from equipment to system step by step, and the mapping relationship between system brittleness and health states is established to complete the assessment of system health states; Thirdly, based on the historical data samples, the quintic polynomial regression model of system brittleness and time is constructed to predict the future health states of system and the time nodes that need to be maintained. Finally, an assembly manufacturing system is taken as an example to verify the correctness and effectiveness of the proposed method.

Analysis of assembly-time performance (ATP) in manufacturing operations with collaborative robots: a systems approach

Reducing station processing times has a significant importance in manufacturing assembly systems. In recent years, there has been a growing interest in using collaborative robots to assist human operators in many manufacturing systems, which can not only improve ergonomics measures but also reduce processing time and increase throughput. In this paper, a system-theoretic approach is introduced to analyse the assembly-time performance (ATP) of assembly systems with collaborative robots, where ATP is defined as the probability to finish all the assembly operations in a station within a desired time interval. Specifically, the assembly operations are described by stochastic processes with both individual (human operator and robot) preparation tasks and joint collaboration tasks, characterised by general or arbitrary distributions of task times. Then an efficient algorithm is presented by using gamma distributions to approximate task times and aggregate multiple interacting tasks to calculate ATP. High accuracy in ATP evaluation is obtained through such an approximation method. In addition, system properties, such as monotonicity and sensitivity, i.e. bottlenecks, are investigated. Finally, a case study at an automotive powertrain assembly plant is introduced to illustrate the applicability of the method and the effectiveness for assembly time reduction through using collaborative robots.

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.

Integrated Capacity Planning and Production Control of an Assembly Manufacturing System

For an assembly manufacturing system, its capacity planning problem and production control problem are usually considered separately. However, production capacity cannot exert its maximum productivity without employing optimal production control; and optimal production control may not satisfy the demand without sufficient production capacity. Therefore, it is important to jointly optimize capacity planning and production control. First, the mathematical formulation of integrated production control and capacity planning problem for an assembly manufacturing system with multiple upstream production lines and one downstream assembly line is proposed in this paper. Second, we develop a method to obtain the marginal probability distribution of the inventory level in the assembly system, which can be employed to calculate the average production cost under an approximate optimal production control policy. Since optimal control policy is subject to production capacity (i.e., machine number), we employ the integer programming method to optimize the machine number (i.e., production capacity), which consequently influences the control policy. Numerical experiments are conducted to verify the correctness of the method of calculating steady-state probability distribution, and the effectiveness of the algorithm of integrated optimization of production control and production capacity.

On the relationships between the design of assembly manufacturing and inspection systems and product quality

This study is a response to the observation that little attention has been paid to the relationship between production (manufacturing) system design and product quality. In particular, only very limited work exists analyzing assembly system quality when multiple products are produced in the same system. Here, a computational scheme to estimate material, capacity and resource requirements in assembly manufacturing systems with imperfect quality is proposed. Accurate estimation of these requirements is vital to system design. The proposed scheme integrates the design of both production and inspection systems, accounts for inspection errors of both types—missing defective units and false rejection—and enables the comparison of alternative designs. In addition, the structure of the inspection system significantly affects both the volumes of material flows and the structure of the material flow network. These ramifications of imperfect quality have been overlooked thus far, but are also examined and discussed in this study.

Iterative Widen Heuristic Beam Search Algorithm for Scheduling Problem of Flexible Assembly Systems

Scheduling of assembly manufacturing systems has received much attention in recent years, but the scheduling problem of deadlock-prone flexible assembly systems (FASs) is rarely studied. Based on the Petri net models of FASs, this article studies the scheduling problem of deadlock-prone FASs, and the heuristic beam search (HBS) algorithm is proposed to minimize the makespan. In HBS, the state space of the FAS is searched level by level, and two types of evaluation functions are proposed to ensure that only the most promising states are explored. To find a better solution in an acceptable time, the HBS algorithm is integrated with the iterative policy, and the iterative widen heuristic beam search (IWHBS) algorithm is obtained. Experimental and comparison results validate the effectiveness and superiority of the IWHBS algorithm over other heuristic search algorithms.

A data-driven robust optimization method for the assembly job-shop scheduling problem under uncertainty

ABSTRACT This paper studies the production scheduling problem in an assembly manufacturing system with uncertain processing time and random machine breakdown. The objectives of minimizing makespan and the performance deviation of the actual schedule from the baseline schedule are simultaneously considered. Specifically, a boosting radial basis function network constructed using the data generated by Monte Carlo method, is used as the surrogate model to approximate the performance deviation. After that, a modified master-apprentice evolutionary algorithm (MAE) is developed for robust scheduling. In the design of MAE, we employ an extended adjacency matrix of subassemblies to cope with the sequential constraints of operations in AJSSP. Based on this, effective neighbourhood structures and distance metric of solutions are designed for tabu search and path relinking operators to generate feasible schedules. To evaluate the effectiveness of the proposed method, a series of computational experiments are conducted. The results indicate that, compared with several commonly used algorithms, the suggested method shows good performance in dealing with AJSSP under uncertainty.

Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management

With advances in smart grid technologies, demand response has played a major role in improving the reliability of grids and reduce the cost for customers. Implementing the demand response scheme for industry is more necessary than for other sectors, because its energy consumption is often considered the largest. This paper proposes a multi-agent deep reinforcement learning based demand response scheme for energy management of discrete manufacturing systems. In this regard, the industrial manufacturing system is initially formulated as a partially-observable Markov game; then, a multi-agent deep deterministic policy gradient algorithm is adopted to obtain the optimal schedule for different machines. A typical lithium-ion battery assembly manufacturing system is used to demonstrate the effectiveness of the proposed scheme. Simulation results show that the presented demand response algorithm can minimize electricity costs and maintain production tasks, as compared to a benchmark without demand response. Moreover, the performance of the multi-agent deep reinforcement learning approach against a mathematical model method is investigated.

Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept

This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line.

CPuS-IoT: A cyber-physical microservice and IoT-based framework for manufacturing assembly systems

Today's customers are characterized by individual requirements that lead the manufacturing industry to increased product variety and volume reduction. Manufacturing systems and more specifically assembly systems (ASs) should allow quick adaptation of manufacturing assets so as to respond to the evolving market requirements that lead to mass customization. Meanwhile, the manufacturing era is changing due to the fourth industrial revolution, i.e., Industry 4.0, that will change the traditional manufacturing environment to an IoT-based one. In this context, this paper introduces the concept of cyber-physical microservice in the Manufacturing and the ASs domain and presents the Cyber-Physical microservice and IoT-based (CPuS-IoT) framework. The CPuS-IoT framework exploits the benefits of the microservice architectural style and the IoT technologies, but also utilizes the existing in this domain huge investment based on traditional technologies, to support the life cycle of evolvable ASs in the age of Industry 4.0. It provides a solid basis to capture domain knowledge that is used by a model-driven engineering (MDE) approach to semi-automate the development, evolution and operation of ASs, as well as, to establish a common vocabulary for assembly system experts and IoT ones. The CPuS-IoT approach and framework effectively combines MDE with IoT and the microservice architectural paradigm. A case study for the assembly of an everyday life product is adopted to demonstrate the approach even to non-experts of this domain.

A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing

Industry 4.0 rose with the introduction of cyber-physical systems (CPS) and Internet of things (IoT) inside manufacturing systems. CPS represent self-controlled physical processes, having tight networking capabilities and efficient interfaces for human interaction. The interactive dimension of CPS reaches its maximum when defined in terms of natural human-machine interfaces (NHMI), i.e., those reducing the technological barriers required for the interaction. This paper presents a NHMI bringing the human decision-making capabilities inside the cybernetic control loop of a smart manufacturing assembly system. The interface allows to control, coordinate and cooperate with an industrial cobot during the task execution.

Performance guaranteed human-robot collaboration with POMDP supervisory control

Abstract Human-Robot Collaboration (HRC) studies how to achieve effective collaborations between human and robots to take advantage of the flexibility from human and the autonomy from robots. Many applications involving HRC, such as joint assembly manufacturing systems and advanced driver assistance systems, need to achieve high-level tasks in a provably correct manner. These applications motivate the requirements of HRC to have the performance guarantee to assure the task completion and safety of both human and robots. In this paper, a correct-by-design HRC framework is built to enable a performance guaranteed HRC. To model the uncertainties from human, robots and the environment, partially observable Markov decision process (POMDP) is used as the model. Based on the POMDP modeling, a supervisory control framework is applied and designed to be adaptive to modeling uncertainties. To reduce the model checking complexity involved in the supervisor synthesis process, an abstraction method for POMDP is integrated to find a quotient system with a smaller size of state space. Based on the abstraction method, a verification adaptation technique is developed with simulation relation checking algorithms to deal with possible online model changing. If the verification adaptation indicates the necessity to update the supervisor, supervisor adjustment methods are given. Altogether, it leads to a semi-online adaptation approach for system model changing. Examples are given to illustrate this framework.

Vulnerability evaluation method applied to manufacturing systems

The safe operation requirement for an enterprise depends significantly on the uninterrupted and reliable functioning of its manufacturing system (MS). However, this system is frequently subjected to different disturbances and destructions, such as safety accidents, equipment fault, and misoperation. Therefore, a sophisticated evaluation mechanism and an effective alleviation design for the vulnerability of a MS are of great importance for the reliable functioning of the MS. This study proposes a detailed methodology to comprehensively assess the vulnerability of a MS subject to different disturbances by using complex networks and simulation technology. The proposed methodology includes a complex network expression for the MS, a description of the equipment failure scenarios, a method to analyze the vulnerability of the MS, and an evaluation method to quantify the vulnerability value. The vulnerability of a MS is evaluated in terms of its work, including the topological structure vulnerability and function vulnerability. Different vulnerability metrics are described, and the vulnerability information is quantified by the multidimensional spatial computing method. Subsequently, the effectiveness of the proposed method is validated in the engineering assembly manufacturing system (EAMS).

An Integrated Product-Process Hierarchical Modeling Method for Development of Complex Assembly Manufacturing Systems

The development of assembly manufacturing systems of complex products, such as automotive vehicles, needs systematic approaches. This paper proposes a new development method based on hierarchical modeling of a complex product and its assembly processes. The method integrates product and process for assembly system development by decomposing a complex product according to its hierarchical architecture in a product domain and creating the respective assembly flows in a process domain. Based on the integrated product-process hierarchical model, this paper presents three steps of manufacturing system development: 1) to plan overall assembly system flow, 2) to configure subassembly lines, and 3) to design the functionality and processes of main assembly workstations. The paper demonstrates and discusses the first two steps of assembly system development with examples. This method can make assembly manufacturing development more systematic, standardized, and efficient. The method also promotes product design variety and modularity for DFM.

The Impact of the Availability of Resources, the Allocation of Buffers and Number of Workers on the Effectiveness of an Assembly Manufacturing System

AbstractThis paper proposes the application of computer simulation methods in order to analyse the availability of resources, buffers and the impact of the allocation of workers on the throughput andwork-in-progressof a manufacturing system. The simulation model of the production system is based on an existing example of a manufacturing company in the automotive industry. The manufacturing system includes both machining and assembly operations. Simulation experiments were conductedvis-à-visthe availability of the different manufacturing resources, the various allocations of buffer capacities and the number of employees. The production system consists of three manufacturing cells –each cell including two CNC machines– and two assembly stations. The parts produced by the manufacturing cells are stored in buffers and transferred to the assembly stations. Workers are allocated to the manufacturing cells and assembly stations, but the number of workers may be less than number of workplaces and are thus termed ‘multi-workstations’. Using computer simulation methods, the impact of the availability of resources, the number of employees and of the allocation of buffer capacity on the throughput andwork-in-progressof the manufacturing system is analysed. The results of the research are used to improve the effectiveness of manufacturing systems using a decision support system and the proper control of resources. Literature analysis shows that the study of the impact of buffer capacities, availability of resources and the number of employees on assembly manufacturing system performance have not been carried out so far.

Discussion on Application of Intelligent Manufacturing Technology in SMT product assembly manufacturing system

Intelligent manufacturing techniques has attracted much attention in SMT manufacturing system in recent years. A brief introduction to the basic idea of introducing intelligent manufacturing techniques to form SMT products intelligent manufacturing systems is described. Based on the analysis of SMT manufacturing system technical characteristics and problems, the basic composition and key technologies of SMT products intelligent manufacturing systems and technical system architecture are discussed.