A decision support system for factory automation: a case study

Economic justification for factory automation (FA) and development of a decision support system (DSS) have recently been two independent ‘hot’ research topics. This paper integrates the two issues by developing the Decision Support System for Factory Automation (DSSFA) which follows generally accepted DSS framework and considers critical factors in justifying FA. Then, the DSSFA is applied to a real plant which is considering factory automation.     

Automating the factory: theory versus practice

Considerable attention has been given to the problems associated with acquiring and implementing many of the advanced manufacturing technologies currently available to firms. Much of the information being published, however, is based on individual experiences under conditions that are frequently not reported, as is common in the trade journals and public media. Fifteen such generally-accepted ‘postulates’ for managing factory automation are identified here and analysed in terms of five in-depth case studies involving advanced manufacturing systems. In a few cases, the postulates are rejected. In some cases they are qualified, and in many cases they are reinforced with substantially more detail.     

An approach to the design of production systems giving a high quality of working life and production efficiency

People's social and economic desires are gradually becoming more versatile and fragmented. Industries should be developed in such a way that these versatile human desires are brought into harmony with the organizational needs of production engineering.

In this paper we report on the state of the desires and motivations of Japanese workers in 1974 and 1977 resulting from investigation and analysis using application methods of multivariate analysis and give an example of new production systems in which workers are in harmony with organizational needs.     

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 1: system stability and distribution of container starts

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material-handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. MR systems generally result in short lead times, low variability, and low line-side inventory levels. In the first part of this two-part paper, a basic, single-tugger MR system is described. Stability conditions are derived with respect to the ‘physical capacity’ of the tugger, and the ‘time utilisation’ of the driver/material handler, both of which are key criteria since the number of containers that can be delivered during each MR is limited, and each MR targets a prescribed cycle time to ensure timely and consistent deliveries. We also derive the distribution of the number of containers requested per MR, which allows the model to estimate the probability of exceeding the physical capacity of the tugger or the prescribed cycle time, under the assumption that sufficient kanban are available for each part type delivered by the MR system. The results of the study facilitate the design and analysis of MR systems used in industry.     

Human–machine interaction theories and lane departure warnings

A technological race toward always ever-increasing automation is engaged, a race in which automation users are hardly considered. This explains why more automation is not necessarily for the best. A variety of human–machine interaction (HMI) theories co-exist. First, those HMI theories and predictions for lane departure warnings systems (LDWS, assisted driving automation) are described. Second, a synthesis of the key questions currently addressed by empirical data on LDWS is offered. Lastly, a new model of human–machine cooperation modelling is proposed. The model has been inspired by previous theories and empirical data collected with LDWS-assisted driving. Interestingly, automation such as LDWS seems to avoid the ‘ironies of automation’ with no negative effects on human performance. However, a major issue to be addressed is poor automation acceptance. While the focus was set on LDWS to offer a complete overview for this type of device, the model may be extended to other warning assistance devices.     

Quality control information systems

As competition for world markets becomes more intense and as greater demands are placed on human and natural resources, manufacturers face a formidable challenge — to produce cost competitive products of the highest possible quality. Nowadays the quality control (quality assurance) department can spend incredibly large amounts of time just collecting the test data they need in order to do their job. Collating and analysing the data is similarly very demanding in terms of how much human time can be expended. Simplification and automation of the tasks of data collection, collation and analysis is possible with a quality control information system. A microprocessor can form the basis of a quality control information system if application to only a very small area of a manufacturing operation is desired. However, far greater benefits can be derived if a real-time minicomputer is used instead of a microprocessor. This is because quality control data can then be collected across the entire factory floor. Statistical quality control techniques can subsequently be applied to examine the effect of process conditions in some part of the factor on product malquality exhibited in any other part of the factory. ‘What-if’ type analyses can also be conducted factory wide. A quality control information system should be capable of (a) identifying the product quality problems (b) determining the causes of these product quality problems (c) helping to eliminate the causes (d) monitoring the altered process. These system functions enable the quality control department to progress from a ‘fix-the-product’ mode to a ‘fix-the-process’ mode and thereby concentrate on making the product right in the first place, by achieving better control of the manufacturing process. Application software packages are now available which have been purpose-designed for quality control information systems. One such example is the Quality Decision Management (QDM) software package from Hewlett-Packard, which runs on an industrial HP 1000 real-time minicomputer. On the data entry side a quality control information system should be capable of manual input from menu-driven terminals and bar-code wands. Automatic input of test data is also vitally important. This enables such devices as analysers, test instruments, data acquisition units. ATE and other computers to be interfaced to gather test data with no manual intervention. All collected data should be held in a database. The QDM database can be searched to generate a wide range of reports in tabular or colour graphic form, the latter include histograms, scattergrams, control charts (X-bar, sigma), P-charts and Pareto diagrams. The reports enable crucial quality control decisions to be taken. Quality control information systems can be linked to higher level production control computers to form a computer integrated manufacturing (CIM) network. Free movement of data around a CIM network offers enormous flexibility and efficiency in the overall manufacturing process, together with considerable savings in manpower. Costs incurred in the creation of a quality control information system can be easily won back by virtue of the significant savings that such a system brings about.     

Using the when/where rules in dual resource constrained systems for a hybrid push-pull control

This paper proposes a production control system, DRC-HPP, which uses the when/where rules in dual resource constrained (DRC) systems for a hybrid push-pull (HPP) control, to overcome some difficulties in modelling/implementing DRC/Kanban systems. These rules and the novel ‘process-or-transport’ and ‘whereto’ rules are embedded in some policies workers use to decide when to process (transport) parts, and where (whereto). Unlike most control systems, in which a group of workers is always responsible for transporting and another group is always responsible for processing parts, workers in DRC-HPP are responsible for both transporting and processing parts, as in the Toyota Sewn Products Management System (TSS). Yet, unlike TSS, DRC-HPP can be applied in any layout type. Workers transport parts when they are idle in part processing to enhance their utilisations and synchronise transportation. Since the transportation does not require special worker skills, the cost of training workers is not incurred. DRC-HPP is compared with different benchmarks through simulation experiments to evaluate its performance. It performs well under relatively short transportation times with respect to processing times. If they are relatively longer, the issue becomes to determine the number of workers to achieve a performance level. DRC-HPP also facilitates bottleneck management.     

Understanding reliance on automation: effects of error type,error distribution,age and experience

An obstacle detection task supported by ‘imperfect’ automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over-relying on it during non-alarm states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behaviour according to the characteristics of the automation similar to younger adults, although it took them longer to do so. The results of this study suggest that the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human–automation interaction can help designers of automated systems to make predictions about human behaviour and system performance as a function of the characteristics of the automation.     

Similarities and differences between human–human and human–automation trust: an integrative review

The trust placed in diagnostic aids by the human operator is a critical psychological factor that influences operator reliance on automation. Studies examining the nature of human interaction with automation have revealed that users have a propensity to apply norms of human–human inter-personal interaction to their interaction with ‘intelligent machines’. Nevertheless, there exist subtle differences in the manner in which humans perceive and react to automated aids compared to human team-mates. In the present paper, the concept of trust in human–automation dyads is compared and contrasted with that of human–human dyads. A theoretical framework that synthesizes and describes the process of trust development in humans vs automated aids is proposed and implications for the design of decision aids are provided. Potential implications of this research include the improved design of decision support systems by incorporating features into automated aids that elicit operator responses mirroring responses in human–human inter-personal interaction. Such interventions will likely facilitate better quantification and prediction of human responses to automation, while improving the quality of human interaction with non-human team-mates.     

Talking about automated vehicles: What do levels of automation do?

Automated vehicles have become a popular topic of conversation. Initially, these conversations were limited to technology developers, innovators and engineers, as they worked to progressed the various technologies and systems that are required to create automated vehicles. Then, over time, these conversations extended to other communities; lawyers, insurers, planners, policymakers, social scientists, and various publics all began hearing, and talking about automated vehicles – also known as ‘driverless’, ‘self-driving’, and ‘autonomous’ vehicles. Levels of automation emerged as a way to depict gradations or categories of autonomy, with tasks divided between those for the machine and those for humans. In this paper, we critically reflect upon the dominance of levels of automation – up to seven sequential ‘steps’ - proposed by a number of industry organisations. Focusing on the Society of Automotive Engineers (SAE) Standard J3016, we signal the intended and unintended performative effects of these levels. We argue that current discourses on automated vehicles have been underpinned by a techno-centric, expert-dominated logic, and point to the benefits of more dispersed, geographically contingent, and socio-technical perspectives in re-framing the dominant discourse and allowing for more nuanced spatial and temporal understandings on future systems of (automated) mobility.     

创新驱动发展 科技助推生产——记陕西秦川机械发展股份有限公司秦川塑料机械厂

秦川塑机是国内最早研发塑机产品的厂家之一。该公司43年的发展历程,始终坚持以技术自主研发为先导,向多层化发展,向自动化发展,向专业化发展。大胆提出从＂传统塑机向高端路线发展,走向高端产业多元化发展＂的创新思路,开拓出一条崭新的发展道路。该公司生产的大规格中空塑机产品至今仍保持高速成长,在国内同行业具有较高的市场占有率。     

On-line condition monitoring of servo motor drive systems by HHT in Industry 4.0

Abstract

Industry 4.0, the current trend of automation and data exchange in manufacturing technologies, includes the development of cyber-physical systems, the Internet of Things, and cloud computing, which create what has been called a ‘smart factory.’ In order to prevent digital manufacturing production lines in smart factories from shutting down, the development of self-diagnostic techniques is a significant issue in Industry 4.0. Thus, this paper presents a novel on-line condition monitoring method that is based on the measured line current of a brushless DC motor drive system. The characteristic component of line current is extracted by the time–frequency signal processing method, namely the ensemble empirical mode decomposition-based Hilbert Huang Transform (EEMD-based HHT), and then the mean value of characteristic component of current utilized for health monitoring is obtained. For the sake of increasing the reliability of the results, gauge repeatability and reproducibility is employed to evaluate the reliability of the computed mean value of the characteristic current. A healthy reference index, defined from the characteristic intrinsic mode function, is proposed for decision-making during on-line monitoring. Theoretical analysis and experiments are conducted to evaluate the effectiveness of the proposed health monitoring approach.     

Application of Neural Computing in Pharmaceutical Product Development: Computer Aided Formulation Design

Abstract

Most pharmaceutical products are complex systems designed to meet several compendial or other performance standards simultaneously. Ideal or ‘optimum’ product composition and the manufacturing process variables are generally established after extensive experimentation. Artificial Neural Networks are pattern recognition tools that allow the development of ‘expert’ systems without having to write computer programs. With this technology it may be possible to develop formulation ‘expert’ systems to predict the formulation composition and the manufacturing process conditions necessary to achieve the desired performance standards. This report introduces the concept of a formulation expert system to predict the in vitro drug release profile from hydrophilic matrix tablets. Formulation expert systems or Computer Aided Formulation Design has the potential to reduce the time and cost of the product development process.     

Toyota production system and Kanban system Materialization of just-in-time and respect-for-human system

The Toyota Production System and Kanban System introduced in this paper was developed by the Vice-President of Toyota Motor Company, Mr. Taiiohi Ohno, and it was under his guidance that these unique production systems have become deeply rooted in Toyota Motor Company in the past 20 years. There are two major distinctive features in these systems. One of these is the ‘just-in-time production ’, a specially important factor in an assembly industry such as automotive manufacturing. In this type of production, “ only the necessary products, at the necessary time, in necessary quantity ” are manufactured, and in addition, the stock on hand is held down to a minimum. Second, the System is the ‘respect-for-human’ system where the workers are allowed to display in full their capabilities through active participation in running and improving their own workshops.     

Application of a computer-based approach to designing real-time control software for an integrated robotic assembly and automated storage/retrieval system†

The typical factory automation system contains a number of programmable automation devices such as robots, automatic storage/retrieval systems (AS/RSs) and automatically guided vehicles (AGVs). Real-time control system software (RTCS) is needed for integrating these devices into a synchronized manufacturing system (SMS). However, development of this RTCS software could be hampered by excessively long software development backlogs which are currently averaging about 30 months in the typical US corporation. This paper presents a structured methodology for the development of the specifications for such software. This methodology is illustrated by applying it to an automated system for producing highway traffic signs. This system consists of an assembly robot with an automatic tool changer, an AS/RS and a reciprocating table     

Human behaviour with automated driving systems: a quantitative framework for meaningful human control

Abstract

Automated driving systems (ADS) with partial automation are currently available for the consumer. They are potentially beneficial to traffic flow, fuel consumption, and safety, but human behaviour whilst driving with ADS is poorly understood. Human behaviour is currently expected to lead to dangerous circumstances as ADS could place human drivers ‘out-of-the-loop’ or cause other types of adverse behavioural adaptation. This article introduces the concept of ‘meaningful human control’ to better address the challenges raised by ADS, and presents a new framework of human control over ADS by means of literature-based categorisation. Using standards set by European authorities for driver skills and road rules, this framework offers a unique, quantified perspective into the effects of ADS on human behaviour. One main result is a rapid and inconsistent decrease in required skill- and rule-based behaviour mismatching with the increasing amount of required knowledge-based behaviour. Furthermore, the development of higher levels of automation currently requires different human behaviour than feasible, as a mismatch between supply and demand in terms of behaviour arises. Implications, discrepancies and emerging mismatches this framework elicits are discussed, and recommendations towards future design strategies and research opportunities are made to provide a meaningful transition of human control over ADS.     

Worker allocation in lean U-shaped production lines

U-shaped lines are widely used in lean systems. In U-shaped production lines, each worker handles one or more machines on the line: the worker allocation problem is to establish which machines are handled by which worker. This differs from the widely-investigated U-line assembly line balancing problem in that the assignment of tasks to line locations is fixed. This paper address the worker allocation problem for lean U-shaped production lines where the objectives are to minimize the quantity of workers and maximize full work: such allocations provide the opportunity to eliminate the least-utilized worker by improving processes accordingly. A mathematical model is developed: the model allows for any allocation of machines to workers so long as workers do not cross paths. Walking times are considered, where workers follow circular paths and walk around other worker(s) on the line if necessary. A heuristic algorithm for tackling the problem is developed, along with a procedure representing the ‘traditional’ approach of constructing standard operations routines. Computational experiments considering three line sizes (up to 20 machines) and three takt time levels are performed. The results show that the proposed algorithm both improves upon the traditional approach and is more likely to provide optimal solutions.     

A framework for operator – workstation interaction in Industry 4.0

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the ‘Industry 4.0’ era. We name the proposed framework ‘Operator – Workstation Interaction 4.0’. The latter’s capabilities permit an adaptive, ongoing interaction that aims to improve operator safety, performance, well-being, and satisfaction as well as the factory’s production measures. The framework is composed of three subsystems: (1) the observation subsystem which observes the operator and the processes occurring in the workstation, (2) the analysis subsystem which generates understanding and implications of the observations output, (3) the reaction subsystem which determines if and how to respond. The paper describes these elements and illustrate them using an example of a fatigued worker. The contributions, implications, and limitations of the proposed framework are discussed, and future research directions are presented.