Study on Intelligent Tool Optimal Selection System in Milling

2009 ◽  
Vol 626-627 ◽  
pp. 605-610 ◽  
Author(s):  
Xi Feng Fang ◽  
T.X. Lan ◽  
Sheng Wen Zhang ◽  
W. Jia ◽  
Tong Yue Wang

Rule-based reasoning (RBR) and weight decision making have been widely used in a lot of decision support systems. According to the waste phenomenon of manufacturing corporations due to the reasonless use of tools during production process, and considering the characteristics of former tool selection systems, develop a tool optimal selection system with Visual Basic 6.0 as development tool and SQL Server 2000 as database developing platform. The system adopts the rule-based reasoning and weight decision-making theory, combines fuzzy theory, artificial intelligent technology and production conditions of corporations. According to the information of workpiece and processing conditions imported by users, the system can select the reasonable tools for users. The system overcomes the limitation of single theory tool selection system, simplifies reasoning mechanism and structure of knowledge base, makes programmer easy to realize, promotes operation efficiency of system, and raises the accuracy and efficiency of tool selection in actual production. A developed prototype system and an example have verified some presented techniques and the research results are the basis of the future development.

2014 ◽  
Vol 635-637 ◽  
pp. 589-593 ◽  
Author(s):  
Hua Bing Ouyang

A framework of an intelligent tool selection system for milling based on STEP-NC machining features is proposed. The main objective of the research is to develop a procedure for the selection of cutting tools. This will help the planners to select the optimal cutting tools. The proposed system consists of the knowledge base module, the inference engine, the user interface and the database. The implementation of the presented system is developed in Solidworks. An example is given to demonstrate the feasibility and efficiency of the prototype system. As a result, this research shows a high potential to aid the development of tool selection and process planning milling system.


2021 ◽  
pp. 1-13
Author(s):  
Congdong Li ◽  
Yinyun Yu ◽  
Wei Xu ◽  
Jianzhu Sun

In order to better meet customer needs and respond to market demands more quickly, mounting number of manufacturing companies have begun to bid farewell to the traditional unitary manufacturing model. The collaborative manufacturing model has become a widely adopted manufacturing model for manufacturing companies. Aiming at the problem of partner selection for collaborative manufacturing of complex products in a collaborative supply chain environment, this paper proposes a multi-objective decision-making model that comprehensively considers the maximization of the matching degree of manufacturing capacity and the profits of supply chain, and gives the modeling process and application steps in detail. The method first uses fuzzy theory to evaluate the manufacturing capabilities of candidate collaborative manufacturing partners. Secondly, Vector Space Model (VSM) is used to calculate the matching degree of manufacturing capacity and manufacturing demand. Then, the paper studied the profit of the supply chain under the “non-cooperative” mechanism and the “revenue sharing” mechanism. Furthermore, the decision-making model is established. Finally, a simulation was carried out by taking complex product manufacturing of Gree enterprise as an example. The research results show the feasibility and effectiveness of the method.


2021 ◽  
Vol 31 (3) ◽  
pp. 1-26
Author(s):  
Aravind Balakrishnan ◽  
Jaeyoung Lee ◽  
Ashish Gaurav ◽  
Krzysztof Czarnecki ◽  
Sean Sedwards

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.


2021 ◽  
Vol 20 (01) ◽  
pp. 2150013
Author(s):  
Mohammed Abu-Arqoub ◽  
Wael Hadi ◽  
Abdelraouf Ishtaiwi

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Jaber Naeemah ◽  
Kuan Yew Wong

PurposeThe purpose of this paper is (1) to review, analyze and assess the existing literature on lean tools selection studies published from 2005 to 2021; (2) to identify the limitations faced by previous studies; and (3) to suggest future works that are necessary to facilitate the selection of lean tools.Design/methodology/approachA systematic approach was used in order to identify, collect and select the articles. Several keywords related to the selection of lean tools were used to collect articles from different Scopus indexed journals. Next, the study systematically reviewed and analyzed the selected papers to identify the lean tools' selection method and discussed its features and limitations.FindingsAn analysis of the results showed that previous studies have adopted two types of methods for selecting lean tools. First, there are various traditional methods being used. Second, multi-criteria decision-making (MCDM) methods were commonly used in previous studies, such as the multi-objective decision-making method (MODM), single multi-attribute decision-making (MADM) methods and hybrid (MCDM). Moreover, the study revealed that the lean tools' selection methods in previous studies were based on evaluating the relationship between either lean tools and performance metrics or lean tools and waste, or both.Research limitations/implicationsIn terms of its theoretical value, the study is considered as an extension of the previous researches performed on this topic by determining and analyzing the features of the most selection methods of lean tools. Unlike previous review papers, this review had considered discussing and analyzing the characteristics and limitations of these methods. Section 2.2 of this paper reviewed some of the categories of MCDM methods as well as some of the traditional methods used in the selected previous studies. Section 2.1 of this paper explained the concept of lean management and its application benefits. Further, only three sectors were covered by the previous studies in this review paper. This study also provided recommendations for future research. Therefore, it provided researchers with a good conception of how to conduct the studies on lean tools selection. Besides, knowing the methods used in previous studies can help researchers develop new methods to select the best set of lean tools. That is, this study provided and advanced the existing knowledge base for researchers concerning lean tools selection, especially there is limited availability of review papers on this topic. Moreover, the study showed researchers the importance of the relationship between lean tools and indicators or/and performance indicators to determine the appropriate set of lean tools so that the results of future studies will be more realistic and acceptable.Practical implicationsPractically, manufacturers face a significant challenge when selecting proper lean tools. This study may enhance managers, manufacturers and company's knowledge to identify most of the methods used to choose the best set of lean tools and what are the advantages, disadvantages and limitations of these methods as well as the latest studies that have been adopted in this topic. That means this study can direct companies to prioritize the application of lean tools depending on either the manufacturing performance metrics or/and manufacturing wastes so that they avoid incorrect application of lean tools, which will add more non-value added activities to operations. Therefore companies can decrease the time and cost losses and enhancing the quality and efficiency of the performance. Correctly implementing the best set of lean tools in companies will lead in general to correctly applying lean management in corporations. Therefore, these lean tools can boost the economic aspect of companies and society through reducing waste, improving performance indicators, preserving time and cost, achieving quality, efficiency, competitiveness, boosting employee income and improving the gross domestic product. The correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. Besides, the correct lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right selection of lean tools will have positive effects on both the environment and society. The study may also encourage manufacturers and researchers to adopt studies on lean tools selection in small- and medium-sized companies because the study referred to the importance and participation of these kinds of companies in a large proportion of the economy of developing countries. Further, the study may encourage some countries that have not previously adopted this type of study, academically and industrially to conduct lean tools selection studies.Social implicationsAs mentioned previously, the correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. The proper lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right choice of lean tools will positively affect both the environment and society.Originality/valueThe study expanded the efforts of previous studies concerning lean management features. It provided an accurate review of most lean tools selection studies published from 2005 to 2021 and was not limited to the manufacturing sector. It further identified and briefly described the selection methods concerning lean tools adopted in each paper.


Author(s):  
S. Minami ◽  
T. Ishida ◽  
S. Yamamoto ◽  
K. Tomita ◽  
M. Odamura

Abstract A concept for the initial stage of the mechanical design and its implementation in the computer-aided design (CAD) are presented. The process of decision making in design is: (1) determining an outline of the whole assembly using a 2-dimensional model that is easy to operate; (2) checking the outline using a 3-dimensional model in which it is easy to identify the spatial relationships; (3) determining details of its sub-assemblies or their components using the 2-dimensional model; and (4) checking the details using the 3-dimensional model. The CAD system must provide consistent relationships through all the steps. For that, following functions are implemented in our prototype system: (1) a 2D and 3D integrated model for consistency between 2- and 3-dimensional shapes, (2) a hierarchical assembly model with dimensional constraints for consistency within an assembly and their components, and (3) a check on constraints for consistency between shapes and designers’ intentions. As a result, the system can provide an environment well fitted to the designers’ decision making process.


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