在材料处理系统中认知自动化对制造灵活性的影响外文文献翻译、中英文翻译

目 录1 英文文献翻译41.1英文文献原文题目4Effect of cognitive automation in a material handling system on manufacturing flexibility4Abstract41.Introduction52.Literature review82.1.Manufacturing flexibility and measurement82.2.Material handling flexibility102.3.Measuring levels of automation112.3.1.Cognitive automation111.2中文翻译14在材料处理系统中，认知自动化对制造灵活性的影响14摘要142文献综述152.1制造灵活性和测量152.2材料处理的灵活性162.3测量的自动化水平172.3.1认知自动化172.3.2自动化水平(贷款)172专业阅读书目192.1针织物热定型质量多变量控制系统研究192.2熔纺氨纶织物湿热定型工艺及防脱散性能的研究192.3一种液态成型工艺用定型-阻燃纤维织物及其制备方法212.4棉氨针织物热定型工艺的建模与优化设计212.5染整热定型过程织物克重的检测与控制研究222.6印染热定型机风道性能分析及结构优化222.7织物定型机242.8纯棉织物抗皱整理方法242.9纺织机械概论242.10现代工程制图252.11材料力学262.12机械原理271 英文文献翻译1.1英文文献原文题目Effect of cognitive automation in a material handling system on manufacturing flexibilityAbstract Manufacturing flexibility has become a competitive strategy to deal with market uncertainty. The application of advanced automation technology in manufacturing systems has tremendously increased manufacturing flexibility; however, this creates significant mental pressure for operators who must deal with a series of decisions, and this decreases their job satisfaction. In this study, we primarily investigated how cognitive automation and mechanical automaton in the material handling system affect manufacturing flexibility. Cognitive automation is defined as a computerized system that provides relevant information to operators, thereby reducing the cognitive workload; mechanical automation refers to an automated system to reduce the physical workload.The case of a truck-body production line of a truck company was investigated by applying modified DYNAMO+ for the material handling system. Then, a simulation program, Any Logic 6.9.0 was used to investigate the effect of the cognitive and mechanical automation in the material handling system on manufacturing flexibility. The research results showed that the levels of cognitive and mechanical automation of the material handling system were increased by 52.4% and 48.0%, respectively, which resulted in improving manufacturing flexibility by 14.2% in cycle time, 53.3% in downtime, and 26.3% in the number of tasks. Cognitive automation, in particular, contributed total improvements in cycle time and downtime of 64.2% and 74.1%, respectively, which showed that cognitive automation has a very critical effect on manufacturing flexibility in the material handling system.1.IntroductionTodays global competition, advancement of technology, and unpredictable customers demands are the primary factors that make the market highly competitive. Increasing numbers of customers have highly-personalized demands based on their functional, design, quality, and lifestyle needs, so manufacturing industries have high demand for shorter product lifecycles and high degrees of flexibility. Since the emergence of the flexible manufacturing system (FMS), manufacturers have paid closer attention to manufacturing flexibility to cope with uncertain demands.However, most attention has been given to the automation of assembly systems even though material handling systems are equally important in achieving effective manufacturing flexibility. Material handling, as one of the basic components in manufacturing, occurs in the assembly process and across the production cycle. So the automation of material handling is particularly important because it provides the significant benefits of shortening lifecycles and increasing productivity. Therefore, it is a good strategy in the effort to improve manufacturing flexibility.The application of computer numerical control (CNC) technology and production variations driven by emerging requirements creates a great deal of information to support the operators who work in the material handling system. In complex automated systems, operators must conduct physical tasks and also perform a series of cognitive tasks, such as supervision, decision making, and control based on the available information. Thus, the automation of cognitive activities has become as significant as the automation of mechanical activities in todays material handling systems. A high level of cognitive automation can improve the operators performance and decrease their mental workload. Thus, an increased level of cognitive automation and improved information flow can provide better support material-handling operators, thereby enhancing manufacturing flexibility.However, system designers usually are focused on mechanical automaton in manufacturing systems, and the limited interest in cognitive automation in manufacturing systems has been directed primarily to assembly systems)or manual material handling. Thus, the importance of cognitive automation in material handling systems has not been fully recognized.In this study, we investigate how the levels of cognitive automation and mechanical automaton in the material handling system affect manufacturing flexibility. First, we provide the methodology used to measure the level of automation (Lo A) (for both mechanical and cognitive automation) in the material handling system. DYNAMO+ was tailored to be suitable for the material handling process in a truck-body manufacturing company. Then, we simulated the material handling process using a computer program to study the effect of the Lo A on manufacturing flexibility.2.Literature review2.1.Manufacturing flexibility and measurementIn recent years, manufacturers have faced an increasingly uncertain market environment resulting from changes in customers requirements, global competition, and the advancement of technology (Oke, 2013). Manufacturing flexibility has been proposed as a practical solution to deal with the uncertainty (Hayes and Wheelwright, 1984). Manufacturing flexibility is the ability of manufacturers or manufacturing systems to cope with customers requirements while facing uncertainties (Beskese et al., 2004; Gerwin, 1993; Kathuria and Partovi, 1999; Koste and Malhotra, 1999; Wahab and Stoyan, 2008).SinceBrowne et al. (1984)established the taxonomy of manufacturing flexibility, many other taxonomies of manufacturing flexibility have been offered. Among them, the taxonomy offered bySethi and Sethi (1990)gives a comprehensive overview of several flexibilities in manufacturing systems. They classified manufacturing flexibility into 11 types at three levels (component level, system level, and aggregate level). The flexibility at the component level includes machine, material handling, and operational flexibility; the flexibility at the system level includes process, product, routing, volume, and expansion; the flexibility at the aggregate level includes program, production, and market flexibility. Because of this multi-dimensional aspect of flexibility, measuring manufacturing flexibility is fuzzy and complex , hence indirect measurements instead of direct measurements are used often to evaluate manufacturing flexibility .Indirect measurements do not determine manufacturing flexibility directly; rather, they measure some of the effects that are influenced by manufacturing flexibility. The number of parts (or part families), changeover time, downtime, and the number of tasks often provide an indirect perspective concerning the degree of manufacturing flexibility (Browne et al., 1984; Ettlie and Penner-Hahn, 1994; Fasth et al., 2007).2.2.Material handling flexibilityMaterial handling systems transport parts or subassembly parts between workstations (Shivanand et al., 2006). As a basic component of a flexible manufacturing system, flexible material handling is crucial in achieving manufacturing flexibility.An operator of a material handling system performs cognitive tasks (e.g., supervision, control, planning, and decision making) as well as physical tasks (e.g. loading/unloading materials, moving parts, packing, etc.). Cognitive tasks are usually classified into skill-based, rule-based, and knowledge-based levels based on how operators process information . Thus, inappropriate information would lead to the operators use of improper controls in the material handling system, thereby resulting in an adverse impact on manufacturing flexibility. In addition, considering that an operator has limitations in processing information easily can make errors in cognitive tasks, the presentation of relevant information in the material handling system would be an efficient way to improve the performance of the system by reducing downtime and cycle time, resulting in higher flexibility in material handling.2.3.Measuring levels of automation2.3.1.Cognitive automationAutomation is a cost-efficient way of production in the manufacturing of discrete parts or in the process industry (Satchell, 1998). Automation augments the efficient use of resources by partially or completely replacing workers with machinery (Parasuraman et al., 2000; Satchell, 1998). In the context of manufacturing, automation often refers to the mechanization and integration of environmental variables (Lindstrm and Winroth, 2010). Replacing laborers physical work, automation still requires the presence of a different type of worker, i.e., operators, who usually perform cognitive work, such as data processing, interpreting information, and decision making. In this regard, automation has been studied from the perspective of the cognitive aspects of workers (Wickens et al., 2004). In this study, we focused on cognitive automation and flexibility in relation to operators cognitive factors in the material handling system.Basile et al. (2012b, 2012c)showed the efficiency of the cognitive automation of material handling in an automated warehouse system. Cognitive automation for the operator of a material handling system, e.g., technical support, which provides such information as what sub-assembly part and how much of the part should be sent to each workstation, reduces the operators cognitive workload and improves situation awareness (Wickens et al., 2004). We define cognitive automation as a digitalized or computerized system that provides relevant information to operators for effective and efficient material handling. We assumed that well-designed cognitive automation in the material handling system would minimize the operators mental workload and increase productivity, which contribute to improving situation awareness (Wickens et al., 2004) and enhancing material handling flexibility.2.3.2.Levels of automation (LoA)To optimize the efficiency of the automation, companies often want to measure and decide the level of automation (Bengtsson and Olhager, 2002).Frohm (2008)defined the level of automation (LoA) as “the allocation of physical and cognitive tasks between humans and technology”.Frohm (2008)also defined mechanical LoA as the LoA for physical activities and cognitive LoA (or information LoA) as the LoA for cognitive activities. To increase manufacturing flexibility efficiently, both mechanical LoA and cognitive LoA should be considered (Fasth et al., 2008). Conceptually, the LoA ranges between “totally manual” and “totally automatic” (Frohm, 2008). However, measuring the LoA of manufacturing systems is not straightforward, because any automated system still requires a lot of cognitive work, which is difficult to measure. So, cognitive LoA often has been ignored in measuring the LoA . As a trial to measure cognitive work in a system, the allocation of tasks has been offered for humanmachine interactions that focus on cognitive tasks . Other trials to measure the levels of both mechanical and cognitive automation were made with some simple systems , but these approaches might not be sufficiently relevant for application to todays complex systems that process huge amounts of information. For this reason, DYNAMO was used to measure the Lo A of modern manufacturing systems.1.2中文翻译在材料处理系统中，认知自动化对制造灵活性的影响 摘要制造业的灵活性已经成为应对市场不确定性的一种竞争策略。先进的自动化技术在制造系统中的应用，极大地提高了制造的灵活性; 然而，这给那些必须处理一系列决策的运营商带来了巨大的心理压力，这降低了他们的工作满意度。在本研究中，我们主要研究了在材料处理系统中认知自动化和机械自动机如何影响制造的灵活性。认知自动化被定义为一种计算机化的系统，为操作人员提供相关信息，从而减少认知负荷;机械自动化指的是一个自动化的系统来减少体力劳动。通过对某卡车公司卡车车身生产线的应用，采用改进的电机+对物料处理系统进行了研究。然后，通过仿真程序，采用Any Logic 6.9.0对材料处理系统中认知和机械自动化对制造柔性的影响进行了研究。研究结果表明，材料处理系统的认知和机械自动化水平分别提高了52.4%和48.0%，这使得在生产周期中提高了14.2%的生产灵活性，在停机时间中提高了53.3%，在任务数量上提高了26.3%。尤其是认知自动化，在周期时间和停机时间上分别贡献了64.2%和74.1%，这表明认知自动化对材料处理系统的灵活性有非常重要的影响。简介 今天年代全球竞争,促进技术和不可预测的客户需求的主要因素是市场竞争激烈。越来越多的客户基于他们的功能、设计、质量和生活方式需求，拥有高度个性化的需求，因此制造行业对更短的产品生命周期和高度的灵活性有着很高的需求(Koren et al.， 1999;Chryssolouris,2005)。由于柔性制造系统(FMS)的出现，制造商更加关注制造的灵活性，以应对不确定的需求。然而，大多数注意力都集中在装配系统的自动化上，尽管材料处理系统在实现有效的制造灵活性方面同样重要(Sethi和Sethi, 1990)。材料处理，作为制造中的一个基本部件，发生在装配过程和整个生产周期。因此，材料处理的自动化尤其重要，因为它提供了缩短生命周期和提高生产力的重要好处。因此，提高制造业的灵活性是一个很好的策略。计算机数控技术的应用和新兴需求驱动的生产变化，创造了大量的信息来支持在材料处理系统中工作的操作人员。在复杂的自动化系统中，操作人员必须执行物理任务，并根据可用的信息执行一系列的认知任务，如监督、决策和控制。因此，认知活动的自动化已经变得和当今的机械活动的自动化一样重要。高水平的认知自动化可以提高运营商性能和减少他们的心理工作负荷(Fasth Stahre,2010)。因此，认知自动化水平的提高和信息流的改进可以提供更好的支持材料处理操作，从而提高生产的灵活性。然而，系统设计者通常专注于制造系统中的机械自动机(Parasuraman et al.， 2000)，而制造系统中对认知自动化的有限兴趣主要集中在装配系统(Fasth et al.， 2011;Lindstrom Winroth,2010;梅耶等，2011)或手工材料处理(Basile et al.， 2012a)。因此，认知自动化在材料处理系统中的重要性还没有得到充分的认识。在本研究中，我们研究了物料搬运系统中的认知自动化程度和机械自动机对制造弹性的影响。首先，我们提供了用于测量材料处理系统中的自动化程度(包括机械和认知自动化)的方法。DYNAMO+适合于卡车车身制造公司的物料处理流程。然后，利用计算机程序对材料处理过程进行了模拟，研究了LoA对制造柔性的影响。2文献综述 2.1制造灵活性和测量 近年来，由于客户需求变化、全球竞争和技术进步(Oke, 2013)，制造商面临着越来越不确定的市场环境。制造灵活性被提出作为解决不确定性的实际解决方案(Hayes和Wheelwright, 1984)。制造柔性是指制造商或制造系统在面对不确定性时能够应付客户需求的能力(Beskese et al.， 2004;Gerwin,1993;Kathuria Partovi,1999;Koste和Malhotra 1999;华和斯托亚,2008)。 由于Browne等人(1984)建立了制造灵活性的分类，提供了许多其他的制造灵活性的分类。其中，Sethi和Sethi(1990)提供的分类法全面概述了制造系统的几种灵活性。他们将制造的灵活性分为11种类型，分为3级(组件级、系统级和聚合级)。组件级别的灵活性包括机器、材料处理和操作灵活性;系统级的灵活性包括过程、产品、路由、容量和扩展;总体水平上的灵活性包括项目、生产和市场灵活性(Bengtsson, 2001;Sethi Sethi,1990)。由于灵活性的多维度，测量制造的灵活性是模糊和复杂的(Beskese et al.， 2004;Gerwin,1993;Gerwin和Tarondeau, 1989)，因此间接测量而不是直接测量被用来评估制造的灵活性(De Toni和Tonchia, 1998)。 间接测量并不直接决定生产的灵活性;相反，他们衡量的是受制造灵活性影响的一些影响。零件(或部分家庭)的数量、转换时间、停机时间和任务数量通常为制造灵活性的程度提供了间接的视角(Browne et al.， 1984;Ettlie Penner-Hahn,1994;Fasth et al .,2007)。2.2材料处理的灵活性 物料搬运系统在工作站之间运输部件或部件(Shivanand et al.， 2006)。柔性制造系统作为柔性制造系统的一个基本组成部分，柔性材料的处理对于实现制造柔性至关重要(Didem Batur et al.， 2012;D索萨和威廉姆斯,2000;Koste和Malhotra 1999;汀,2006)。物料搬运系统的操作员执行认知任务(例如,监督、控制、规划和决策)以及物理任务(如装卸材料,移动部件,包装,等等)。认知任务通常分为技能、基于规则和基于知识的水平(1983年Rasmussen)基于运营商如何处理信息(韦森特,1999)。因此,不恰当的信息会导致运营商年代使用的不当控制材料处理系统,从而导致生产灵活性造成负面的影响。此外,考虑到一个运营商在处理信息很容易可以限制错误认知任务(Oborski,2004;Wickens et al .,2004),表示材料的相关信息处理系统将是一个有效的方法来提高系统的性能,减少停机时间和周期时间,导致更高的材料处理的灵活性。2.3测量的自动化水平2.3.1认知自动化自动化是一种成本效率的生产方式，在制造离散零件或在加工工业(Satchell, 1998)。自动化增加了部分或完全用机器取代工人的有效利用(Parasuraman et al.， 2000;萨切尔,1998)。在制造方面，自动化通常指的是环境变量的机械化和集成(Lindstrom和Winroth, 2010)。自动化取代劳工体力劳动,仍然需要一种不同类型的工人的存在,即通常执行认知工作的操作人员，例如数据处理、解释信息和决策。在这方面，从工人的认知方面研究了自动化(Wickens et al.， 2004)。在本研究中，我们关注的是认知的自动化，以及在材料处理系统中与操作员的认知因素有关的灵活性。Basile et al. (2012b, 2012c)展示了自动化仓库系统中物料处理的认知自动化的效率。认知自动化操作的物料搬运系统,例如,技术支持,提供这样的信息分部装配部分和多少的部分应该被发送到每个工作站,减少了操作员工作负荷年代认知,提高态势感知能力(Wickens et al .,2004)。我们将认知自动化定义为一种数字化的或计算机化的系统，为操作人员提供有效和有效的材料处理信息。我们假设设计良好的认知自动化材料处理系统将减少操作员年代精神工作量,提高工作效率,有助于提高态势感知(Wickens et al .,2004)和增强材料处理的灵活性。2.3.2自动化水平(贷款)为了优化自动化的效率，公司常常想要衡量和决定自动化程度(Bengtsson和Olhager, 2002)。Frohm(2008)定义了自动化水平(LoA)作为“人与技术之间的物理和认知任务的分配”。Frohm(2008)也将“机械LoA”定义为身体活动的LoA，以及“认知LoA”(或信息LoA)作为认知活动的LoA。为了有效地提高生产的灵活性，应考虑机械LoA和认知LoA 。从概念上讲，LoA介于“完全手动”和“完全自动”之间(Frohm, 2008)。然而，测量制造系统的LoA并不简单，因为任何自动化系统仍然需要大量的认知工作，这是很难衡量的。因此，认知LoA在测量LoA (Kotha和Orne, 1989;挖槽机,2007)。作为一项测试系统中认知工作的试验，任务的分配被提供给专注于认知任务的人机交互(Lehto和Buck, 2008;谢里登,1980;比林斯,1996;Endsley,1997)。其他一些测量机械和认知自动化水平的试验是用一些简单的系统(Bright, 1958;Marsh和Mannari, 1981)，但是这些方法可能对应用到目前处理大量信息的复杂系统没有足够的相关性。由于这个原因，发电机被用来测量现代制造系统的LoA。参考文献1 吴宗泽.高等机械设计.北京:清华大学出版社, 1991.2 美 AH伯尔.机械分析与机械设计.机械工业出版社,1988.3 许镇宇, 邱宣怀.机械零件.北京:人民教育出版社, 1981.4 R.E.彼德逊. 设计中的应力集中系数.2专业阅读书目2.1针织物热定型质量多变量控制系统研究 内容摘要： 门幅、克重和含水率是染整热定型加工过程中的重要质量指标,建立门幅、克重和含水率与热定型过程温度、加热时间、超喂和拉幅量工艺参数间的模型是实现热定型工艺参数精确定量设计,门幅、克重和含水率闭环控制的前提,对提高热定型产品质量和生产效率具有重要作用。本文主要研究内容如下:1.分析了热定型过程中弹力针织物热塑性机理,从热定型过程中弹力针织物弹性动力学和热力学方程得到门幅、克重和含水率与热定型过程的经向与纬向拉伸量、温度和加热时间的数学模型,并用实际热定型实验验证了模型的有效性。2.针对现有热定型装置,建立了拉幅量控制系统、车速控制系统、超喂控制系统和克重控制系统的数学模型,设计了各个控制系统的PID控制器,并运用仿真与实验方法验证了模型和控制方案的有效性和合理性。3.分析热定型过程工艺参数与质量指标的相关性,研究变量间耦合关系,将热定型过程简化成超喂和温度为操纵变量,克重和含水率为被控变量的多变量控制系统,建立了多变量控制系统的数学模型,运用相对增益分析方法正确配对变量,并设计动态前馈解耦控制器,实现质量多变量系统的解耦控制,有效消除了克重和含水率控制回路间的关联,提高热定型控制系统的稳定性。4.开发热定型质量控制系统。以组态王为开发平台,设计热定型机质量控制系统的人机交互界面,实现热定型机工艺参数和质量指标的可视化监控。本文的研究成果,可为热定型质量多变量控制系统建模与控制技术的研究提供有效参考,对于提高产品质量、降低生产能耗具有重要意义。2.2熔纺氨纶织物湿热定型工艺及防脱散性能的研究 内容摘要： 人们生活水平不断提高的同时,消费观念也在不断提升,不仅要求服装舒适、美观,也越来越注重健康、环保。氨纶作为高弹性纤维,其制成的衣服柔软舒适,具有“第二皮肤”的美称。但是传统干纺氨纶在生产过程中有溶剂残留,不仅对环境造成污染,残留的溶剂还有害人体健康。传统纬编针织物具有易脱散的特性。当抽拉织物边缘的纱线时,织物的整个边缘将会沿着线圈横列的逆编织方向脱散,有些织物甚至可沿着顺编织方向脱散,因此,在制作成衣时,传统纬编针织物必须进行缝边或拷边;当织物中间的某处纱线断裂时,线圈会沿着纵向从纱线断裂处分解脱散,在外力的作用下,织物的破洞将会越来越大,严重影响织物外观及服用性。而熔纺氨纶的问世,无疑为人们带来了既舒适健康,又具有防脱散性能的弹性面料。本课题主要从熔纺氨纶丝入手,分析研究湿热定型后熔纺氨纶的粘合性、力学性能,进一步研究湿热定型工艺以及织物裁剪角度对于熔纺氨纶织物防脱散性能的影响,具体研究内容如下:1.利用扫描电镜,观察熔纺氨纶的纵面与横截面,初步了解熔纺氨纶结构;2.将熔纺氨纶与锦纶进行不同温度、不同时间的湿热定型,用扫描电镜观察其试样,对比分析熔纺氨纶与锦纶的粘合性;3.测试分析干热定型条件下和湿热定型条件下,熔纺氨纶的弹性回复性能和拉伸断裂性能,研究定型工艺对熔纺氨纶的力学性能的影响;4.观察不同定型条件下,熔纺氨纶织物经过30次洗涤后的毛边情况,分析定型工艺对于熔纺氨纶织物防脱散性能的影响;5.观察洗涤30次后,经过适当条件热定型处理的熔纺氨纶织物不同角度裁剪边的脱散情况,研究裁剪角度对于熔纺氨纶织物防脱散性能的影响。通过一系列的研究、测试、对比、分析,得到:湿热状态下,熔纺氨纶与锦纶的定型温度为115-125,定型时间为20s-60s,温度过低,时间过短,则熔纺氨纶与锦纶不粘合;温度过高,时间过长,则熔纺氨纶遭到破坏。与未经过定型的熔纺氨纶相比,经过湿热定型处理的熔纺氨纶弹性恢复率、应力松弛率降低,永久变形率升高,强力、伸长率和强度都有所提高,并且其强力和伸长率高于干热定型后的熔纺氨纶。湿热定型后的熔纺氨纶,其各项指标的变异系数普遍小于干热定型时熔纺氨纶的各项指标变异系数,经过湿热定型的熔纺氨纶性能更稳定。湿热条件下,定型温度为125时,熔纺氨纶织物裁剪边平整光滑,防脱散性能好。对于经过适当热定型条件处理的熔纺氨纶织物,0o裁剪的布边防脱散性能最佳,经过长时间洗涤后边缘仍然平整,未有脱散现象出现;随着裁剪角度的增大,织物毛边现象越来越明显,慢慢出现轻微的脱散现象;裁剪角度增加至90o时,熔纺氨纶织物边缘脱散现象最为严重。 2.3一种液态成型工艺用定型-阻燃纤维织物及其制备方法本发明属于阻燃复合材料制造技术领域，涉及一种适用于液态成型工艺的定型-阻燃双功能纤维织物及其制备方法，选择具有优异阻燃性能的苯并噁嗪树脂作为载体树脂，将其与含磷阻燃剂混合、预聚，得到苯并噁嗪树脂/