diagnostic study of manufacturing process

Machine Learning Approaches for Fault Detection in …

of process monitoring techniques reported for metal etching process, which is a batch operation carried out in semiconductor manufacturing industry. V arious machine learning

Case Study: Fault Detection in Semiconductor Manufacturing Process

7. As we saw in this case study, the rate of correctly identified faulty wafers needs to be assessed with respect to the number of faulty wafers in the dataset. The number of non-faulty records is not that important. An overall success rate considers the faulty and the non-faulty records and is therefore misleading.

Real-time quality monitoring and diagnosis for manufacturing process

One reciprocating screw injection molding process from a Chinese manufacturing company motivated us to perform this study, and the screw injection molding machine is shown in Fig. 1. The temperature of the charging barrel and mold, and the pressure in the mold can be measured by the corresponding sensors over time.

A gray correlation based Bayesian network model for fault …

One of the key technologies to realize the quality control and continuous improvement of the complex product manufacturing process is to deeply study the forming principle of quality characteristic deviation and carry out the fault diagnosis of the complex product's multi-source and multi-level manufacturing process.

Machine learning-based techniques for fault diagnosis in the

Hence, we propose several methodologies and ML models for fault diagnosis for smart manufacturing process applications. A case study has been conducted on a real dataset from a semiconductor manufacturing (SECOM) process. However, this dataset contains missing values, noisy features, and class imbalance problem.

A Novel Quality Defects Diagnosis Method for the Manufacturing Process

Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality …

Multiple time-series convolutional neural network for

This study aims to propose a multiple time-series convolutional neural network (MTS-CNN) model for fault detection and diagnosis in semiconductor manufacturing. This study incorporates data ...

A guide to aid the selection of diagnostic tests

Diagnostic testing has become indispensable for diagnosing and monitoring disease, for providing prognoses and for predicting treatment responses. 1, 2 Today, over 40 000 products are available globally for the in vitro diagnostic testing of a wide range of conditions. 3 These include traditional laboratory-based tests, with samples being sent ...

An Overview of Drug Substance Manufacturing …

The two main components that make up the pharma-ceutical manufacturing process are those of drug substance and drug product manufacturing. Drug substance is an active ingredient that is intended to furnish pharmacolog-ical activity, directly impact the diagnosis, cure, mitigation, treatment, or prevention of disease, or to affect the structure ...

A Review of Current Machine Learning Techniques Used …

The aim of this paper is to review the recent application of machine learning tech‐niques to manufacturing process diagnosis. This review covers papers published from 2007 to 2017 that utilized machine learning techniques for manufacturing fault diag‐nosis. This review covers 20 articles. The keywords used in the search are "machine ...

Digital twin: current scenario and a case study on a manufacturing process

In the current scenario, industries need to have continuous improvement in their manufacturing processes. Digital twin (DT), a virtual representation of a physical entity, serves this purpose. It aims to bridge the prevailing gap between the design and manufacturing stages of a product by effective flow of information. This article aims to …

A Review of Current Machine Learning …

The aim of this paper is to review the recent application of machine learning tech‐niques to manufacturing process diagnosis. This review covers papers published from 2007 to …

Fault diagnosis in semiconductor manufacturing processes …

Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes.

Processes | Free Full-Text | A Review of Real-Time …

The design of process monitoring and fault diagnosis methods in industrial manufacturing has become a compelling research topic in recent years. With the rapid …

Smart manufacturing of paints and coatings

To study the applicability and performance of the RP method for large systems, the method was applied to the Tennessee Eastman (TE) chemical process [47], [49] and then conducted fault detection and diagnosis. The TE chemical process is a large-scale complex process, as it has 91 variables (12 manipulated variables, 38 state …

A review of diagnostic and prognostic …

A review of diagnostic and prognostic capabilities and best practices for manufacturing. Gregory W. Vogl1 Brian A. Weiss1 Moneer Helu1. ·. Received: 29 October 2015 / …

Processes | Free Full-Text | A Review of Real-Time Fault Diagnosis …

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data …

A Sequential Inspection Procedure for Fault Detection in …

2. Problem Description. Let us consider a MMP as shown in Figure 1, where the raw material starts at stage 1 and undergoes a series of manufacturing operations until the last stage, N.At each stage, critical process characteristics may affect the results on part quality, for instance, a fixture locator which plays a critical role in determining the …

A study on spectral characterization and quality detection

In the process of laser additive manufacturing, the transmission efficiency of laser energy and the forming quality are influenced by the plasma which plays a fundamental role in coupling the incident radiation to the material. The aim of this work is to present an effective spectral diagnosis method for quality research in laser additive …

Development and Commercialization of In Vitro

The typical development process of an IVD assay, from its initial design through validation, is a lengthy and tedious process as illustrated in Figure 24.2.The development process of the IVD is focused on demonstrating that the assay's analytical performance reflects the capability of detecting the analyte in an accurate and …

Companion and Complementary Diagnostics

The goal of a typical companion diagnostic (CDx) development program is to deliver a globally reproducible, robust, and reliable clinical test to match patients to a safe and effective therapeutic product. ... Predefined numerical performance metrics for the outcome of a study: ... manufacturing process validation, in-process and final release ...

Real-time quality monitoring and diagnosis for …

A deep-belief-network-based intelligent method is proposed for monitoring and diagnosing manufacturing process profiles. •. The proposed method is more sensitive …

Die-Casting Defect Prediction and Diagnosis System using Process …

This study aims to construct a system for predicting and diagnosing defects in casting products and their causes to improve the productivity of the casting process in the die casting industry. Three data analysis algorithms are proposed to predict defects and diagnose the causes of the defects. First, diagnosing the pre-heating state, which ...

Experimental study of the process failure diagnosis in …

DOI: 10.1016/J.MEASUREMENT.2018.12.067 Corpus ID: 115285597; Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission @article{Wu2019ExperimentalSO, title={Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission}, author={Haixi Wu …

How to implement new diagnostic products in low-resource …

During this phase, all components of the diagnostic product (eg, hardware, software, reagents, controls and other consumables) are finalised following the standard diagnostics product design optimisation and development process and then transferred to manufacturing. During the design process, the developer identifies an optimal product …

Fault diagnosis and self-healing for smart manufacturing: a …

Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to …

Assessing Manufacturing Process Robustness

Part B is a diagnostic study of the current process control (a seven-step product assessment process) that results in determining the adequacy of the current control strategy. ... The protocol-driven product assessment closely dissects the manufacturing process to determine the current state of control, to comprehend and …

A review of current machine learning techniques …

The aim of this paper is to review the recent application of machine learning tech-niques to manufacturing process diagnosis. This review covers papers published from 2007 to …

A review of current machine learning techniques used in …

niques to manufacturing process diagnosis. This review covers papers published from 2007 to 2017 that utilized machine learning techniques for manufacturing fault diagno-sis. This review covers 20 articles. The keywords used in the search are "machine learn-ing application in manufacturing process diagnosis". The search was filtered to focus

Artificial Intelligence in Process Fault Diagnosis: Methods …

A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, computer programs …