About
37
Publications
12,161
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
317
Citations
Introduction
Manufacturing systems provide a wealth of data. Finding unknown relations between the many parts of the systems is key to better process understanding and control. My main focus is on semiconductor manufacturing and chemical process industry.
Additional affiliations
July 2017 - September 2020
Publications
Publications (37)
Optimizing semiconductor manufacturing processes is needed to solve the current shortage of computer chips. Discarding unfinished chips based on data-driven predictions models can significantly reduce time and resources otherwise spent on finishing faulty chips. The current paper presents the value proposition of predictive discarding at different...
Two of the most important extensions of the basic regression model are moderated effects (due to interactions) and mediated effects (i.e. indirect effects). Combinations of these effects may also be present. In this work, an important, yet missing combination is presented that can determine whether a moderating effect itself is mediated by another...
The complexity of manufacturing process data has made it more challenging to extract useful insights. Data-analytic solutions have therefore become essential for analyzing and optimizing manufacturing processes. Path modeling, also known as structural equation modeling, is a statistical approach that can provide new insights into complex multivaria...
Rwanda has been a notable player in sustainable development in the last decades, particularly in its agricultural practices. Based on extensive literature study and recent fieldwork, this paper offers a multifaceted exploration of Rwanda's specialty coffee sector. It highlights the challenges of optimizing the domestic supply chain and the effects...
To safeguard the quality of river water, a comprehensive approach is required within the European Water Framework Directive. It is vital to conduct non-target screening of the complete chemical fingerprint of the aquatic ecosystem, as this will help to identify chemicals of emerging concern and uncover their unusual dynamic patterns in river water....
The 2000 European Union Water Framework Directive (WFD) states that “Member States shall ensure the necessary protection for the bodies of water identified with the aim of avoiding deterioration in their quality in order to reduce the level of purification treatment required in the production of drinking water.” However, it does not specify how to...
A comprehensive approach to protect river water quality is needed within the European Water Framework Directive. Non-target screening of a complete chemical fingerprint of the aquatic ecosystem is essential to identify chemicals of emerging concern, to reveal dynamic patterns of suspect river water pollution. Dedicated data processing of ongoing GC...
Surface water of rivers like the Rhine is a highly relevant environmental and an important source of the Dutch drinking water. To improve protection of the environment and drinking water supply, it is important to have a continuous overview of the chemical composition of the river. Such an overview may be obtained with contemporary, untargeted anal...
Two of the most important extensions of the basic regression model are moderated effects (due to interactions) and mediated effects (i.e. indirect effects). Combinations of these effects may also be present. In this work, an important, yet missing combination is presented that can determine whether a moderating effect itself is mediated by another...
Surface water of rivers like the Rhine is a highly relevant environmental and an important source of the Dutch drinking water. To improve protection of the environment and drinking water supply, it is important to have a continuous overview of the chemical composition of the river. Such an overview may be obtained with contemporary, untargeted anal...
Optimizing semiconductor manufacturing processes is needed to solve the current shortage of computer chips. Discarding unfinished chips based on data-driven predictions models can significantly reduce time and resources otherwise spent on finishing faulty chips. The current paper presents the value proposition of predictive discarding at different...
Photolithography is a process used in the manufacturing of dies, which are at the core of complex integrated circuits. During this process several layers of semi-conducting material are stacked on top of each other. Precise alignment of the layers is crucial to the performance of a die. Upon completion, each die is subjected to several electrical t...
Chemical production processes benefit from intelligent data analysis. Previous work showed how process knowledge can be included in a structural equation modelling framework. While predictive models increase process value, currently available methods have limitations that hinder applicability to many (industrial) processes. This paper describes the...
published version at: https://doi.org/10.1016/j.procs.2022.01.352
Photolithography is a process used in the manufacturing of dies, which are at the core of complex integrated circuits. During this process several layers of semi-conducting material are stacked on top of each other. Precise alignment of the layers is crucial to the performance of a...
Understanding how different units of an industrial production plant are operationally related is key to improving production quality and sustainability. Data science has proven indispensable in obtaining such understanding from vast amounts of historical process data. Path modelling is a valuable statistical tool to obtain such information from his...
Statistical modelling of industrial production data can lead to improved understanding of the process to benefit process monitoring and control routines. The production data required for such models need however to be synchronized in time, a topic sparsely covered in literature. We propose a strategy for data-driven automated optimization of dynami...
Chemical production processes benefit from intelligent data analysis. Previous work showed how process knowledge can be included in a structural equation modelling framework. While predictive models increase process value, currently available methods have limitations that hinder applicability to many (industrial) processes. This paper describes the...
Quality control of liquid raw materials arriving on an industrial manufacturing site is typically performed in a dedicated laboratory using time-and chemicals-consuming analytical methods. Herein, we report the successful development of a handheld near-infrared spectroscopy method for the rapid, low-cost testing of organic solvents. Our methodology...
We report the successful development of a low-cost handheld near-infrared method for organic solvents of industrial relevance. Our methodology enables the classification of organic solvents with 100% accuracy and the quantification of water in methyl ethyl ketone with a precision of ~0.01 wt% in the 0-0.25 wt% range.
Preprocessing of near-infrared (NIR) spectra is an essential part of multivariate calibration. It mainly aims to remove artefacts caused during measurement to improve prediction performance or interpretation. However, preprocessing can have undesired side-effects. Additionally, calibration algorithms can learn to deal with artefacts by themselves w...
Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was firstly used to differentiate Angelicae Sinensis Radix (ASR) from other four similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR),...
By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets accor...
To make industrial processes lean, inclusion of technical process information is required into statistical modelling. Understanding how parts of a process are related to other parts and to output quality is key to understanding and controlling processes. In this work, we show how PLS path modelling can be used to incorporate process knowledge into...
Diffuse reflectance near-infrared (NIR) data (908 – 1676 nm) of chicken breast fillets was recorded in a non-destructive way using a portable miniaturised NIR spectrometer. The NIR data was used to discriminate between fresh and thawed breast fillets and to determine the birds’ growth conditions. NIR data was recorded of 153 chicken fillet commerci...
By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented in this paper shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, but even to correctly classify chicken f...
The 2000 European Union Water Framework Directive (WFD) states that 'Member States shall ensure the necessary protection for the bodies of water identified with the aim of avoiding deterioration in their quality in order to reduce the level of purification treatment required in the production of drinking water'. However, the WFD does not state how...
In order to make industrial processes lean, inclusion of technical process information is needed in statistical modelling. Knowing how the parts of a process are related to other parts and to output quality is key to understanding and controlling processes. We show how the PLS path model can be used to incorporate process knowledge into predictive...
In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by seque...
The latent class model is a powerful unsupervised clustering algorithm for categorical data. Many statistics exist to test the fit of the latent class model. However, traditional methods to evaluate those fit statistics are not always useful. Asymptotic distributions are not always known, and empirical reference distributions can be very time consu...
In this PhD thesis we compare different p values with which one can assess statistical model fit. We compare asymptotic, parametric bootstrap and posterior predictive p values for various levels of sparseness in the data. Then we improve the interpretability of the Bayesian posterior predictive p value by calibrating it with respect to the posterio...
Background:
Limited information exists on hour-by-hour physical activity (PA) patterns among adults aged 45-65 years. Therefore, this study aimed to distinguish typical hour-by-hour PA patterns, and examined which individuals typically adopt certain PA patterns.
Methods:
Accelerometers measured light and moderate-vigorous PA. GIS-data provided p...
Goal orientation is an important predictor of motivation at work. This study introduces goal orientation profiles in the work domain, evaluates their stability over time and assesses the impact of managerial coaching behavior on change in employees' goal orientation profiles. We hypothesize that coaching managers inspire, facilitate, and guide empl...
In order to accurately control the Type I error rate (typically .05), a p value should be uniformly distributed under the null model. The posterior predictive p value (ppp), which is commonly used in Bayesian data analysis, generally does not satisfy this property. For example there have been reports where the sampling distribution of the ppp under...
The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values are not valid when the sample size is not large and...
Binary data latent class analysis is a form of model-based clustering applied in a wide range of fields. A central assumption of this model is that of conditional independence of responses given latent class membership, often referred to as the “local independence” assumption. The results of latent class analysis may be severely biased when this cr...
