Machine Learning in Failure Analysis of Optical Transceiver Manufacturing Process

2021 ◽  
Author(s):  
Lai Mun Choong ◽  
Wei Kuang Cheng
Keyword(s):  
Machine Learning ◽  
Failure Analysis ◽  
Manufacturing Process

scholarly journals Machine Learning-Based on Assessment of the Impact of the Manufacturing Process on Battery Electrode Heterogeneity

Energy and AI ◽  
2021 ◽  
pp. 100090
Author(s):  
Marc Duquesnoy ◽  
Iker Boyano ◽  
Larraitz Ganborena ◽  
Pablo Cereijo ◽  
Elixabete Ayerbe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Battery Electrode ◽  
The Impact

scholarly journals Special Issue on Intelligent Computation in Design and Manufacturing

2018 ◽  
Vol 12 (3) ◽  
pp. 273-274 ◽  
Author(s):  
Roberto Teti ◽  
Pascal Le Masson ◽  
Mitsutaka Matsumoto ◽  
AMM Sharif Ullah
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Fuzzy Inference ◽  
Special Issue ◽  
Fuzzy Inference Systems ◽  
Design And Manufacturing ◽  
Inference Systems

To solve problems underlying design and manufacturing we often rely on methodologies of computational intelligence such as machine learning, artificial neural networks, fuzzy logic, fuzzy inference systems and smart optimization algorithms. In this Special Issue of the International Journal of Automation Technology, original articles are presented with reference to the engagement of intelligent computation in diverse application areas of design and manufacturing, including manufacturing process monitoring, manufacturing systems management, scheduling, design theory and methodology. The six research papers in this Special Issue propose the use of intelligent computation methodologies to deal with various topics related to manufacturing and design. In particular, the first three papers focus on manufacturing process monitoring with reference to different manufacturing technologies, including tool wear monitoring in drilling of composite materials, sensor monitoring in CNC turning and residual stress prediction in welding. Diverse intelligent approaches such as artificial neural networks and adaptive neuro-fuzzy inference systems are proposed to support manufacturing process monitoring. The fourth paper deals with the manufacturing system level, proposing the employment of a solution algorithm combining metaheuristics and operation simulation for scheduling of production processes. The fifth paper aims at developing tools to guide the manufacturers to manage the technology investment and cost saving target for customer satisfaction based on the application of internet of things. The last paper proposes a methodology to support the introduction of customer requirements in product and service design via a decision support system which exploits artificial intelligence algorithms (machine learning) based on inductive inference, allowing knowledge related to product/service to be mapped, structured and managed to design the service and product semantic model. The editors deeply appreciate all the authors and anonymous reviewers for their effort and excellent work to make this Special Issue unique. We hope that future research on intelligent computation in manufacturing and design will advance manufacturing technology and systems as well as design methodologies.

scholarly journals New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021 ◽  
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blast Furnace ◽  
Conceptual Framework ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Steel Company ◽  
Steel Manufacturing ◽  
New Business

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

scholarly journals Thermal field prediction in DED manufacturing process using Artificial Neural Network

2021 ◽  
Author(s):  
Seifallah Fetni ◽  
Quy Duc Thinh Pham ◽  
Van Xuan Tran ◽  
Laurent Duchêne ◽  
Hoang Son Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Manufacturing Process ◽  
Thermal Field ◽  
Activation Function ◽  
Tungsten Carbides ◽  
Artificial Neural ◽  
The Impact

In the last decade, machine learning is increasingly attracting researchers in several scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this technique remains as a black box technique for many researchers. Indeed, it allows obtaining novel insights to overcome the limitation of classical methods, such as the finite element method, and to take into account multi-physical complex phenomena occurring during the manufacturing process. This work presents a comprehensive study for implementing a machine learning technique (artificial neural network) to predict the thermal field evolution during the direct energy deposition of 316L stainless steel and tungsten carbides. The framework consists of a finite element thermal model and a neural network. The influence of the number of hidden layers and the number of nodes in each layer was also investigated. The results showed that an architecture based on 3 or 4 hidden layers and the rectified linear unit as the activation function lead to obtaining a high fidelity prediction with an accuracy exceeding 99%. The impact of the chosen architecture on the model accuracy and CPU usage was also highlighted. The proposed framework can be used to predict the thermal field when simulating multi-layer deposition.

An Advanced Diagnosis Flow for SRAMs

2017 ◽  
Author(s):  
Tien-Phu Ho ◽  
Eric Faehn ◽  
Arnaud Virazel ◽  
Alberto Bosio ◽  
Patrick Girard
Keyword(s):  
Failure Analysis ◽  
Manufacturing Process ◽  
Single Chip ◽  
Automated Diagnosis ◽  
Representative Case ◽  
Preliminary Results ◽  
Accurate Localization ◽  
Analysis Phase ◽  
Yield Learning

Abstract In modern electronic designs, more and more memories are embedded in a single chip. With the latest technologies, defects due to the manufacturing process are more prone to occur in the periphery of the memory. Obtaining a fast and accurate localization of such defects has become much more difficult with traditional diagnosis approaches that do not allow a fast-enough yield learning and improvement. This paper describes a new and automated diagnosis flow for SRAMs to determine the localization of any given defect and thus, to precisely guide the Failure Analysis phase. Based on the electrical and topological fault signatures obtained through traditional methods, each potential fault on the identified active nets is automatically simulated to retrieve the best defect candidates. This paper also presents preliminary results on a representative case study.

Failures in Soldering

2021 ◽  
pp. 326-337
Author(s):  
Qiming Zhang ◽  
Babak Kondori ◽  
Xing Qiu ◽  
Jeffry C.C. Lo ◽  
S.W. Ricky Lee
Keyword(s):  
Solder Joint ◽  
Failure Analysis ◽  
Manufacturing Process ◽  
Failure Modes ◽  
Solder Joints ◽  
Failure Detection ◽  
Cause Analysis ◽  
Root Cause ◽  
Integration Density ◽  
Root Cause Failure Analysis

Abstract Due to the recent requirement of higher integration density, solder joints are getting smaller in electronic product assemblies, which makes the joints more vulnerable to failure. Thus, the root-cause failure analysis for the solder joints becomes important to prevent failure at the assembly level. This article covers the properties of solder alloys and the corresponding intermetallic compounds. It includes the dominant failure modes introduced during the solder joint manufacturing process and in field-use applications. The corresponding failure mechanism and root-cause analysis are also presented. The article introduces several frequently used methods for solder joint failure detection, prevention, and isolation (identification for the failed location).

Application of Machine Learning Techniques in Additive Manufacturing: A Review

2022 ◽  
pp. 1-24
Author(s):  
Amithkumar Gajakosh ◽  
R. Suresh Kumar ◽  
V. Mohanavel ◽  
Ragavanantham Shanmugam ◽  
Monsuru Ramoni
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Control Process ◽  
Machine Learning Techniques ◽  
Review Of The Literature ◽  
Learning Techniques ◽  
Manufactured Products ◽  
And Control ◽  
Monitoring Quality

This chapter provides an analysis of the state-of-the-art in ML applications for optimizing the additive manufacturing process. This chapter primarily presents a review of the literature on the use of machine learning (ML) in optimizing the additive manufacturing process at various stages. The chapter identifies ML-researched areas in which ML can be used to optimize processes such as process design, process plan and control, process monitoring, quality enhancement of additively manufactured products, and so on. In addition, general literature on the intersection of additive manufacturing and machine learning will be presented. The benefits and drawbacks of ML for additive manufacturing will be discussed, as well as existing obstacles that are currently limiting applications.

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