69,217 research outputs found
Data Engineering for the Analysis of Semiconductor Manufacturing Data
We have analyzed manufacturing data from several different semiconductor
manufacturing plants, using decision tree induction software called
Q-YIELD. The software generates rules for predicting when a given product
should be rejected. The rules are intended to help the process engineers
improve the yield of the product, by helping them to discover the causes
of rejection. Experience with Q-YIELD has taught us the importance of
data engineering -- preprocessing the data to enable or facilitate
decision tree induction. This paper discusses some of the data engineering
problems we have encountered with semiconductor manufacturing data.
The paper deals with two broad classes of problems: engineering the features
in a feature vector representation and engineering the definition of the
target concept (the classes). Manufacturing process data present special
problems for feature engineering, since the data have multiple levels of
granularity (detail, resolution). Engineering the target concept is important,
due to our focus on understanding the past, as opposed to the more common
focus in machine learning on predicting the future
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection
Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear
Splitting hybrid Make-To-Order and Make-To-Stock demand profiles
In this paper a demand time series is analysed to support Make-To-Stock (MTS)
and Make-To-Order (MTO) production decisions. Using a purely MTS production
strategy based on the given demand can lead to unnecessarily high inventory
levels thus it is necessary to identify likely MTO episodes.
This research proposes a novel outlier detection algorithm based on special
density measures. We divide the time series' histogram into three clusters. One
with frequent-low volume covers MTS items whilst a second accounts for high
volumes which is dedicated to MTO items. The third cluster resides between the
previous two with its elements being assigned to either the MTO or MTS class.
The algorithm can be applied to a variety of time series such as stationary and
non-stationary ones.
We use empirical data from manufacturing to study the extent of inventory
savings. The percentage of MTO items is reflected in the inventory savings
which were shown to be an average of 18.1%.Comment: demand analysis; time series; outlier detection; production strategy;
Make-To-Order(MTO); Make-To-Stock(MTS); 15 pages, 9 figure
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