scholarly journals Data analytics to reduce stop-on-fail test in electronics manufacturing

2019 ◽  
Vol 9 (1) ◽  
pp. 200-211 ◽  
Author(s):  
Ana Elsa Hinojosa Herrera ◽  
Stoyan Stoyanov ◽  
Chris Bailey ◽  
Chris Walshaw ◽  
Chunyan Yin
Keyword(s):  
Cycle Time ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Test Sequence ◽  
Production Cycle ◽  
Production Test ◽  
Test Set ◽  
Electronic Manufacturing ◽  
Computational Procedures ◽  
Incomplete Datasets

AbstractThe use of data driven techniques is popular in smart manufacturing. Machine learning, statistics or a combination of both have been used to improve processes in electronic manufacturing. This paper presents the application of classical techniques to reduce production cycle time by compacting a production test sequence. This set of tests is run on stop-on-fail scenario for quality assurance of an electronical device. Data generated in the production test-set on stop-on-fail scenario challenges the traditional application of the data driven techniques, because of the missing data characteristic. The developed computational procedures handle this application-specific data attribute. The novelty of this work is in the algorithm developed, which applies classical techniques in an iterative environment, as a strategy to analyse incomplete datasets. Results show that the method can reduce a production test set with parametric and non-parametric tests by building an accurate prognostic model. The results can reduce production cycle time and costs. The paper details and provides discussions on the advantages and limitations of the proposed algorithms.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

scholarly journals A Novel Automated System for the Handling of Car Seat Wires on Plastic Over-Injection Molding Machines

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 141
Author(s):  
F. J. G. Silva ◽  
M. R. Soares ◽  
L. P. Ferreira ◽  
A. C. Alves ◽  
M. Brito ◽  
...  
Keyword(s):  
Cycle Time ◽  
Added Value ◽  
Automated System ◽  
Production Cycle ◽  
Labor Costs ◽  
Molding Process ◽  
Car Seats ◽  
Car Seat ◽  
Productivity And Competitiveness ◽  
Plastic Injection

The structure of car seats is becoming increasingly complex, with mixing of wire conformation and plastic injection. The plastic over-molding process implies some labor, which can be reduced if novel solutions are applied in this manufacturing area. The handling of the wires used in car seats is the main problem identified in the process, wasting time both in the feeding and in the extraction of the molds used in the wire over-molding process. However, these machines are usually extremely compact and the free space around them is too short. In classic molding injection machines, there are just two half-molds, the female, and the male. In the over-molding process of wires used in car seats, three half-molds are used in order to increase the cycle time. Thus, to solve this problem, the classic robotic solutions are not appliable due to lack of space and elevated cost. This work describes the development of an automated solution able to handle the wires in both the feeding and the extracting phases of the production cycle, avoiding the traditional labor costs associated with this type of machine. Departing from an industrial need, the developed novel solution is described in detail and can be successfully adapted to other situations of low added-value products where it is needed to increase the productivity and competitiveness of the product. The system developed uses mechanical and pneumatic solutions which, combined, can be used to solve the identified problem, occupying a restricted space and requiring a small budget. This solution can be translated into guidelines that will allow the analysis of situations where the same system can be applied.

scholarly journals Data-driven diagnostics of positioning deviations in multi-axis robots for smart manufacturing

2020 ◽  
Vol 53 (2) ◽  
pp. 10330-10335
Author(s):  
M. Soualhi ◽  
K. Nguyen ◽  
K. Medjaher ◽  
D. Lebel ◽  
D. Cazaban
Keyword(s):  
Data Driven ◽  
Smart Manufacturing

Analysis of production cycle-time distribution with a big-data approach

2020 ◽  
Vol 31 (8) ◽  
pp. 1889-1897 ◽  
Author(s):  
Xu Tan ◽  
Lining Xing ◽  
Zhaoquan Cai ◽  
Gaige Wang
Keyword(s):  
Big Data ◽  
Cycle Time ◽  
Time Distribution ◽  
Production Cycle

Logistic Performance Evaluation of Squeeze Casting Equipments Based on Simulation

2012 ◽  
Vol 468-471 ◽  
pp. 1936-1940
Author(s):  
Jian Xin Deng
Keyword(s):  
Performance Evaluation ◽  
Cycle Time ◽  
Processing Time ◽  
Squeeze Casting ◽  
Casting Machine ◽  
Utilization Rate ◽  
Production Cycle ◽  
Rate Performance ◽  
Curing Time ◽  
Simulation Results

The logistic performance of a squeeze casting equipment like production cycle time and the utilization rate of components, has impacts on the production rhythm , cast quality and it's lifecycle. So it is essential to analyze the logistic performance of squeeze casting equipments during design, buying and using. This paper deals with evaluating the logistic performance of squeeze casting equipments through simulation.Flexsim software was employed to simulate squeeze casting equipments, where the simulation model of a squeeze casting machine was constructed and the utilization rate was investigated by conducting four simulation samples of different cast products.It is indicated that simulation is an easy and effective method to evaluate the utilization rate performance of squeeze casting equipments and it is concluded that the utilization rate of all component cells except squeeze mechanism is very low and decreases with the curing time of casts increase, their value is very close to the ratio of the their processing time to each total cycle time. The simulation results can also help us find the bottleneck and optimize design of the squeeze casting equipment.

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