Human-centred appraoches to control and information technology: European experiences

In this paper, the concept of Human-Centred Technology will be described with regard to the different dimensions of workplace, groupwork and networks and in terms of the frameworks of both society and the natural environment. These different aspects of Human-Centred Systems will be illustrated by a series of case studies representing several European countries. The report covers a wide range of research fields. The emphasis is on technology: the roles of control and information technology in enterprises today — including issues of applying AI — and the strategies of designing and implementing technology taking into account the specific aspects which characterize human-centred systems.     

Scheduling Optimization Modelling: A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job splitting is allowed so that the power of work stations can be utilized better. The final objective is to minimize the total completion time of all jobs. The results show a significant improvement since the new planning may save more than 60% of lead time compared to the current schedule. The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process. The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries. A practical case study demonstrates the effectiveness of the proposed model.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

基于机器视觉的隔热条尺寸测量系统

针对当前隔热条的尺寸测量仍停留在产品离线后,工作人员用卡尺测量,劳动强度大,工作效率低、测量精度易受人为因素影响的现状,本文提出将机器视觉技术引入到隔热条自动化生产线的在线尺寸检测系统中,这种非接触式测量既可以避免在测量过程中对隔热条的损坏,又可以提高测量精度和测量速度,从而达到提高测量效能的目的。     

致密油藏直井多层缝网压裂产量递减分析

垂向多层发育的致密油藏常采用缝网压裂进行开采,从而改善储层渗透率,提高油井产量。为此建立直井多层缝网压裂不稳定渗流数学模型,压裂后各层划分为人工裂缝区、改造区和未改造区。绘制分析Blasingame产量递减典型曲线,曲线分为7个流动阶段,总裂缝长度一定时,裂缝半长短的储层流体渗流率先到达未改造区,流体渗流阻力大,Blasingame曲线靠下;总改造体积一定时,各层改造体积差异越大,Blasingame曲线越靠下。用所建立的模型,对实测生产资料进行解释,获得各层裂缝半长、改造区渗透率等相关地层参数,该模型对进行多层缝网压裂直井产量分析具有指导意义。     

基于数据挖掘的底水油藏开发预测新方法

底水油藏开发过程中受强底水、油柱高度低、地层原油黏度大、隔夹层分布复杂等因素影响,导致单井开发效果差异大。为解决目前常规方法所存在的多因素数据分析量大、应用局限性大等缺点,提出基于BP神经网络数据挖掘算法的底水油藏水平井可采储量预测新方法,通过数模机理模型分析了该方法的可靠性。针对底水油藏静动态资料,充分挖掘隐含其中的有效信息,在完成基础数据集建立的基础上,构建了基于数据驱动的底水油藏可采储量预测模型。实际应用结果表明,该方法实现了底水油藏水平井开发的影响因素和技术参数界限的定量分析,可采储量预测最大误差低于8%,拟合效果较好,可进一步应用于底水油藏水平井生产动态、开发界限、井位设计等方面。     

LX—2型变频式喂线设备的研制及应用

根据炼钢生产的实际需要,分析了喂线技术的工艺要求,介绍了所研制的LX－2型变频式喂线机的结构特点,指出了影响喂线机使用效果的因素,并提出了若干措施。     

国内轴承钢的生产现状及发展

介绍了2002年国内高碳铬轴承钢的产量及品种结构，评述了国内轴承钢生产工艺装备、质量状况和发展方向。     

立方氮化硼(CBN)工具材料及其生产、应用现状与发展趋势

立方氮化硼（CBN）是一种适应现代加工技术要求的新型工具材料，由于它的独特而优良的切削性能，使之具有巨大的潜力与良好的发展前景．本文对立方氯化础工具材料的结构、性能、制造方法及应用现状进行了综述，并对其发展趋势进行了展望．