A virtual collaborative maintenance architecture for manufacturing enterprises

This paper presents a virtual collaborative maintenance architecture aimed at improving the performance of manufacturing systems. The proposed architecture incorporates maintenance elements such as operational reliability, maintenance economics, human factors in maintenance, maintenance program, and maintenance optimization in a virtual collaborative architecture. An analytical model is proposed to measure the relative performance of the proposed virtual collaborative architecture as well as that of the manufacturing enterprise. A numerical example is also presented to demonstrate the application of the proposed approach.     

A framework to design a human-centred adaptive manufacturing system for aging workers

The so-called smart manufacturing systems (SMS) combine smart manufacturing technologies, cyber-physical infrastructures, and data control to realize predictive and adaptive behaviours. In this context, industrial research focused mainly on improving the manufacturing system performance, almost neglecting human factors (HF) and their relation to the production systems. However, in order to create an effective smart factory context, human performance should be included to drive smart system adaptation in efficient and effective way, also by exploiting the linkages between tangible and intangible entities offered by Industry 4.0. Furthermore, modern companies are facing another interesting trend: aging workers. The age of workers is generally growing up and, consequently, the percentage of working 45–64 years old population with different needs, capabilities, and reactions, is increasing. This research focuses on the design of human-centred adaptive manufacturing systems (AMS) for the modern companies, where aging workers are more and more common. In particular, it defines a methodology to design AMS able to adapt to the aging workers’ needs considering their reduced workability, due to both physical and cognitive functional decrease, with the final aim to improve the human-machine interaction and the workers’ wellbeing. The paper finally presents an industrial case study focusing on the woodworking sector, where an existing machine has been re-designed to define a new human-centred AMS. The new machine has been engineered and prototyped by adopting cyber-physical systems (CPS) and pervasive technologies to smartly adapt the machine behaviour to the working conditions and the specific workers’ skills, tasks, and cognitive-physical abilities, with the final aim to support aging workers. The achieved benefits were expressed in terms of system usability, focusing on human-interaction quality.     

Designing and evaluating a crew resource management training for manufacturing industries

This article presents the evaluation of crew resource management (CRM) training specifically designed for employees within manufacturing industries. The central objective of this training program was to improve teamwork, communication, and stress management skills as well as to increase the workers' situational awareness of potential errors that can occur during the manufacturing process. Eighty employees, all of whom were working in a production unit for gearbox manufacturing, participated in the training program in this study. Effectiveness of the CRM training course was evaluated. The results showed a significant improvement in a wide range of CRM‐relevant categories, especially in teamwork‐related skills, in addition to an increase in the workers' situational awareness after the training program. When comparing the data regarding human error occurring in gear production that were derived from the trained group and a control group, mixed results were produced. Based on the results, it can be stated that CRM training, which was originally developed for the aviation industry, can be transferred to the manufacturing industry. © 2010 Wiley Periodicals, Inc.     

A Bayes-SLIM based methodology for human reliability analysis of lifting operations

Investigations have shown that human error (HE) is the most common cause of accidents involving lifting operations. So it is essential to conduct human reliability analysis (HRA) in lifting operations. The estimation of human error probabilities (HEPs) is a key to HRA. In this paper, five useful performance shaping factors (PSFs) of lifting operations were introduced and an approach to combine Bayesian methodology and the success likelihood index method (SLIM) was applied to determine the HEPs of various lifting operation errors. A numerical example was used to illustrate the application of the proposed methodology.Relevance to industryReliability analysis of crane lifting operation has traditionally been difficult without considering human error. The present study suggests that managers could use the Bayesian-SLIM methodology to conduct human reliability analysis and minimize human error in crane lifting operation.     

Reinforcement learning for facilitating human-robot-interaction in manufacturing

For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.     

Key human factors and their effects on human centered assembly performance

Today in the increasingly competitive market, consumers prefer to have a great variety of products to choose from; this preference is often coupled with demands for a relatively smaller lot size, shorter lead time, higher quality and lower cost. Consequently, manufacturing companies are being forced to consistently increase flexibility and responsiveness of their production systems in order to accommodate changes of the fluctuating market. Among various forms of production systems, human-centred manufacturing systems can offer such a capability in dealing with product variations and production volumes as human workers can always adapt themselves to perform multiple tasks after a learning process. However, human performance can also be unpredictable and it may alter due to varying psychological and physiological states, which are often overlooked by researchers when designing, implementing or evaluating a manufacturing system. This paper presents a study aiming to address these issues by exploring human factors and their interactions that may affect human performance on human-centred assembly systems. The study was carried out based on a literature review and an industrial survey. Critical system performance indicators, which are affected by human factors, were evaluated and the most significant human factors were identified using the fuzzy extent analysis method. The research findings show that experience is the most significant human factor that affects individual human performance, compared to age and general cognitive abilities in human-centred assembly. By contrast, both human reaction time and job satisfaction have the least effect on human performance. The significance of ageing on human performance was also studied and it was concluded that average assembly time of human workers rises by average 1% per year after the age of 38 years old.     

Simulation and optimization of tool-life in manufacturing centers

This paper presents the development of an optimization and a computer-simulation model to evaluate the process plans of a manufacturing center by analyzing the effect of tool failure on system performance. The GPSS/PC simulation program that is used in this study has been built with variables, functions and matrices so that many experiments could be conducted with the model. Sensitivity analysis is presented and the developed model has proven to be useful in determining optimum sequencing of parts for various operating policies.     

An investigation of a human material handler on part flow in automated manufacturing systems

This paper presents a formal approach to resolve an important question concerning changes in the control of computerized manufacturing systems when a human operator is involved as a task-performing agent. It requires building a model of human functional specifications used in executing tasks and integrating it into a control scheme for the model. More importantly, analysis of control complexity needs to be conducted to build an effective control mechanism. In this paper, a human material handler is considered, and an assessment of part flow complexity affected by human tasks in a highly automated manufacturing system is presented. For this purpose, a formal model of human task-performing processes is proposed in terms of a part and location(s) of a task. A classification for human material handling tasks is presented based on the proposed model. Furthermore, human errors and the impact of human errors on part flow are considered. Part flow complexity of a manufacturing system from the control perspective is then investigated in terms of the human tasks and errors. A shop floor control example where a human operator performs material handling tasks is provided to illustrate the proposed model.     

Errors enacted during endoscopic surgery-a human reliability analysis

The aim of the study was to document the nature and incidence of surgical errors enacted during laparoscopic surgery in order to direct future research and surgical training. A modified Human Reliability Analysis (HRA) approach, based on direct observation, was adopted to categorise and record errors encountered during the practice of laparoscopic cholecystectomy. This study confirmed the applicability and usefulness of an observational methodology in the assessment of human error in endoscopic surgical performance. The study identified aspects of the design and usage of instruments, surgical training and the differences between tasks which needed further directed research in order to identify underlying performance shaping factors (PSFs) and so reduce error rates.     

考虑运输因素的网络化制造资源优化配置方法

针对网络化制造环境下制造资源的优化配置问题,为提高配置结果的实用性,在考虑加工时间和加工成本等因素的基础上,加入企业间运输时间和运输成本因素,建立了综合优化模型。采用带精英保留策略的遗传算法对该问题进行求解。通过实例验证了以上模型和求解算法。研究结果表明,考虑运输因素的网络化制造资源优化配置方法能够有效降低总运输成本和时间,配置方案更贴合实际。     

Workplace and organizational accident causation factors in the manufacturing industry

Labor inspectors investigate accidents to identify possible accident causes, initiate prosecution, and plan future accident prevention. The Method of Investigation for Labor Inspectors (MILI) was designed to help them to identify workplace and organizational factors in addition to immediate factors and legal breaches. The present study analyzes the impact of workplace (work design and provision of unsafe equipment) and organizational factors (training and employee involvement) on accident causation and validates MILI on real accident cases. Accident data from the manufacturing sector are analyzed with LISREL structural equation modeling. Results confirm the relationship between work design and training as well as between provision of unsafe equipment and employee involvement. The present study provides evidence that MILI is a structured accident investigation method allowing multiple accident causation factors to be revealed and that it could help all interested parts (not only labor inspectors, but companies as well) to thoroughly investigate occupational accidents. © 2009 Wiley Periodicals, Inc.     

Transforming failure into success through organisational learning: an analysis of a manufacturing information system

This paper describes the idiosyncracies of a case study company, through highlighting issues and problems experienced during their attempts to evaluate, implement and realise the holistic implications of a manufacturing information system. Although the Information System (IS) was operational for a period of time, it was eventually deemed a failure. The reason for this was that a range of human and organisational factors prevented the organisation from embracing the full impact of the system. The eventual success of their information system was realised through a bespoke implementation, based upon a traditional systems development lifecycle that indirectly addressed learning issues following the earlier failed deployment. The paper highlights key issues relating to business success and failure, and then contrasts them alongside the presented case study. In doing so, the authors conclude by proposing methods through which manufacturing information systems can be transformed for business success. This is described achievable through both a realisation in the positioning of the organisation relative to technology management, and the related mapping of human and technological constructs that support information systems related success.     

Minimizing staircase errors in the orthogonal layered manufacturing system

The factors that generally affect the slicing error in layered manufacturing (LM) processes are first analyzed, and issues pertaining to the current methods to deal with the slicing error are discussed in this paper. A method based on a recently developed and implemented orthogonal LM system to reduce the overall slicing error is presented. In this method, the flat region is separated from the stereolithography (STL) model and different processing methods are applied to the different areas in the part geometry. In addition, the mathematical model for calculating the slicing error is derived and an approach based on a genetic algorithm has been developed to optimize the build orientation in terms of minimizing the slicing error. Case studies are given to demonstrate the effectiveness and efficiency of the method. Note to Practitioner-The staircase effect has been the major concern for industry to widely adopt rapid prototyping technologies. It will not only worsen the surface quality but also create errors on the parts built. This paper introduces a novel approach to minimizing staircase errors based on a multidirectional deposition approach. A mathematical method combined with a generic algorithm is used to minimize the slicing errors. From the case study given, the approach has been proven to be effective in minimizing staircase errors and thus improving the rapid prototyping (RP) built part quality.     

Quantitative relationship between time margin and human reliability

Operator reliability in complex systems is influenced by various performance shaping factors (PSFs). Time is a particularly important PSF; however, empirical studies of human reliability analysis (HRA) are rarely focused on modeling the effect of time PSF on human error probability (HEP). This study contributes to HRA literature by investigating the empirical relationship between time margin and HEP. Time margin is defined as the difference between the time available to complete a task and the time required to successfully complete the task, divided by the required time. We investigate and compare two models (logistic and linear) to explain HEP based on time margin. The empirical HEP data for model testing were extracted from a microworld simulator (Study 1) and a full-scope simulator (Study 2) in two existing studies relevant to procedural tasks in nuclear power plants. For Study 1, both models exhibited an acceptable, equivalent explanatory power; for Study 2, although both models exhibited an acceptable explanatory ability, the logistic model explained more variance in HEP. Our findings indicate the potential of the logistic model in explaining and predicting HEP based on time margin in time-critical tasks.     

Psychophysiological evaluation of seafarers to improve training in maritime virtual simulator

Over the years, safety in maritime industries has been reinforced by many state-of-the-art technologies. However, the accident rate hasn’t dropped significantly with the advanced technology onboard. The main cause of this phenomenon is human errors which drive researchers to study human factors in the maritime domain. One of the key factors that contribute to human performance is their mental states such as cognitive workload and stress. In this paper, we propose and implement an Electroencephalogram (EEG)-based psychophysiological evaluation system to be used in maritime virtual simulators for monitoring, training and assessing the seafarers. The system includes an EEG processing part, visualization part, and an evaluation part. By using the processing part of the system, different brain states including cognitive workload and stress can be identified from the raw EEG data recorded during maritime exercises in the simulator. By using the visualization part, the identified brain states, raw EEG signals, and videos recorded during the maritime exercises can be synchronized and displayed together. By using the evaluation part of the system, an indicative recommendation on “pass”, “retrain”, or “fail” of the seafarers’ performance can be obtained based on the EEG-based cognitive workload and stress recognition. Detailed analysis of the demanding events in the maritime tasks is provided by the system for each seafarer that could be used to improve their training. A case study is presented using the proposed system. EEG data from 4 pilots were recorded when they were performing maritime tasks in the simulator. The data are processed and evaluated. The results show that one pilot gets a “pass” recommendation, one pilot gets a “retrain” recommendation, and the other two get “fail” results regarding their performance in the simulator.     

Functions required for advanced flexible manufacturing systems

This paper describes the concept and functions of multilevel supervisory systems aimed at a more efficient operation of advanced flexible manufacturing systems. A supervisory system consists of a control system, an adaptation system, a process monitoring system and a machine failure diagnosis system. Thus supervisory system assure the control of machining accuracy, the detection of human errors and tool breakage, and the identification of machine failures. This paper presents some successful results of the implementation of these subsystems.     

团购模式下云制造服务资源组合优化模型与算法

在云制造服务环境中,为了进一步降低需求者的服务成本,提出了一种团购模式下云制造服务资源组合优化模型与算法。在云制造平台发展的初期阶段,以服务需求者的视角分析云制造服务资源组合优化管理问题,通过团购模式研究了资源组合优化模型与算法,模型中考虑团购定价、团购信任度等关键影响因素,对云制造资源组合优化进行综合决策;设计改进的遗传算法进行模型求解,进一步对团购模式下云制造服务资源组合模型进行仿真分析。通过不同规模问题的仿真实验验证了模型与算法的有效性和可行性,仿真结果表明,在团购规模逐渐增大的情况下,团购模式比个体模式更具有成本优势。     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

An experimental study on estimating human error probability (HEP) parameters for PSA/HRA by using human model simulation

A framework of Human Error Probability (HEP) parameters, which is needed for Human Reliability Analysis (HRA) within a practice of Probabilistic Safety Assessment (PSA) of Nuclear Power Plant is first proposed. Then a laboratory experiment was conducted in order to construct a computer simulation model (human model) that describes human cognitive behaviour on detecting and diagnosing plant anomaly causes. An inter-comparison between experimental data and human model simulation was performed to estimate Human Cognitive Reliability (HCR) curves, in order to confirm the applicability of a human model for estimating these HEP parameters for PSA/HRA practice.     

Contribution of simulation to the optimization of inspection plans for multi-stage manufacturing systems

In this paper we develop a new mathematical model to optimize inspection plans for multi-stage manufacturing systems with possible misclassification errors. The presented model minimizes total inspection related costs while still assuring a required output quality. Because of the complexity of the proposed mathematical model, a simulation algorithm is presented to model the multistage manufacturing system subject to inspection and to estimate the resulting inspection costs. We use the popular Arena simulation software to implement our simulation algorithm and then we utilize OptQuest, Arena’s built-in optimization package, to find the optimal inspection plan. Finally, a numerical example is presented and simulation experiments are also conducted in order to examine the effects of several model parameters.