Y-Mat: an improved hybrid finite-discrete element code for addressing geotechnical and geological engineering problems

PurposeContinua and discontinua coexist in natural rock materials. This paper aims to present an improved approach for addressing the mechanical response of rock masses based on the combined finite-discrete element method (FDEM) proposed by Munjiza.Design/methodology/approachSeveral algorithms have been programmed in the new approach. The algorithms include (1) a simpler and more efficient algorithm to calculate the contact force; (2) An algorithm for tangential contact force closer to the actual physical process; (3) a plastic yielding criterion (e.g. Mohr-Coulomb) to modify the elastic stress for fitting the mechanical behavior of elastoplastic materials; and (4) a complete code for the mechanical calculation to be implemented in Matrix Laboratory (MATLAB).FindingsThree case studies, including two standard laboratory experiments (uniaxial compression and Brazilian split test) and one engineering-scale anti-dip slop model, are presented to illustrate the feasibility of the Y-Mat code and its ability to deal with multi-scale rock mechanics problems. The results, including the progressive failure process, failure mode and trajectory of each case, are acceptable compared to other corresponding studies. It is shown that, the code is capable of modeling geotechnical and geological engineering problems.Originality/valueThis article gives an improved FDEM-based numerical calculation code. And, feasibility of the code is verified through three cases. It can effectively solve the geotechnical and geological engineering problems.

Influence of Longitudinal Scratch Defects on the Bendability of Titanium Alloy

Post-manufacturing induced defects in the form of scratches are sometimes inadvertently introduced onto sheet metal surfaces during either transportation, storage or handling. However, limited research has been previously carried out to establish the impact of such surface defects on sheet formability. Test trial results after press brake forming of Ti-3Al-2.5V showed that for longitudinal scratches oriented along the sheet rolling direction, scratch profiles with depth in the ranges of -1μm to -18μm and pile up height between 1μm to 16μm can be successfully formed; hence could be deemed acceptable during the sheet selection process. Failure of the coupons during the press brake forming trials was due to the impact of the scratch defects in their role as stress raisers and occurred primarily at the longitudinal scratch defect zones

Effects of Three-Directional Seismic Wave on Dynamic Response and Failure Behavior of High-Steep Rock Slide

The landslide triggered by earthquakes can cause severe infrastructure losses or even fatalities. The high-steep rock slide is the most common type of landslide in the earthquake area. In an earthquake, the ground moves randomly in all directions, two horizontal directions (East-West (EW) direction, North-South (NS) direction) and one vertical direction (Up-Down (UD) direction). Even though extensive studies have been carried out on the earthquake-triggered landslide, the effects of each single seismic wave and the three-directional seismic waves are not considered. This study aims to evaluate the effects of different types of the seismic waves on the dynamic response and failure behavior of the high-steep rock slide. To investigate the effects of each single seismic wave and three-directional seismic wave, this study presents a numerical model with four types of seismic waves, e.g., East-West (EW) direction, North-South (NS) direction, Up-Down (UD) direction, and three-directional wave (EW_NS_UD). The numerical results revealed that the types of the seismic waves have significantly different effects on the dynamic process, failure behavior, run-out distance, velocity, and deposition of the high-steep rock slide.

Design Rule for Constructing Buckling-Free Polymeric Stencil with Microdot Apertures

A polymeric stencil with microdot apertures made by using polydimethylsiloxane (PDMS) molds with pillar patterns has many advantages, including conformal contact, easy processability, flexibility, and low cost compared to conventional silicon-based membranes. However, due to the inherent deformability of PDMS materials in response to external pressure, it is challenging to construct structurally stable stencils with high structural fidelity. Here, we propose a design rule on the buckling pressure for constructing polymeric stencils without process failure. To investigate the critical buckling pressure (Pcr), stencils are fabricated by using different PDMS molds with aspect ratio variations (AR: 1.6, 2.0, 4.0, and 5.3). By observing the buckled morphology of apertures, the structures can be classified into two groups: low (AR 1.6 and 2.0) and high (AR 4.0 and 5.3) AR groups, and Pcr decreases as AR increases in each group. To investigate the results theoretically, the analysis based on Euler’s buckling theory and slenderness ratio is conducted, indicating that the theory is only valid for the high-AR group herein. Besides, considering the correction factor, Pcr agrees well with the experimental results.

Comparison of optimized Markov Decision Process using Dynamic Programming and Temporal Differencing – A reinforcement learning approach

Reinforcement learning is one of the promising approaches for operations research problems. The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtimes due to non-conforming raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a Markov model developed to estimate the probability of the raw material being accepted or rejected in an incoming inspection environment. The proposed forecasting model is further optimized for efficiency using the two reinforcement learning algorithms (dynamic programming and temporal differencing). The results of the two optimized models are compared, and the findings are discussed.

Markov Decision Process approach in the estimation of raw material quality in incoming inspection process

The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtime due to defective raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a raw material acceptance prediction model (RMAP) developed based on the Markov analysis. RFID tags are used to track the parts throughout the process. A secondary dataset can be derived from the raw RFID data. In this study, a dataset is simulated to reflect a typical incoming inspection process consisting of six substations (Packaging Inspection, Visual Inspection, Gauge Inspection, Rework1, and Rework2) are considered. The accepted parts are forwarded to the Pack and Store station and stored in the warehouse. The non-conforming parts are returned to the supplier. The proposed RMAP model estimates the probability of the raw material being accepted or rejected at each inspection station. The proposed model is evaluated using three test cases: case A (lower conformities), case B (higher conformities) and case C (equal chances of being accepted and rejected). Based on the outcome of the limiting matrix for the three test cases, the results are discussed. The steady-state matrix forecasts the probability of the raw material in a random state. This prediction and forecasting ability of the proposed model enables the industries to save time and cost.

High-resolution monitoring of VFA dynamics reveals process failure and exponential decrease of biogas production

The future of biogas production will be characterized by on-demand provision to compensate the unpredictability of solar and wind power. Such biogas production through feedstock management is a promising possibility but requires close monitoring. The dynamics of volatile fatty acid (VFA) formation and further degradation to methane production are of special interest when providing high portions of fast degradable carbohydrates. Their fast degradability can impair process stability. The correlation of VFA loading and the biogas process was tested in four anaerobic continuously stirred tank reactors supplied with maize silage hourly and with sugar beet silage twice a day at a 12-h interval. The reactors differed in the amount of sugar beet silage and thus in total organic loading rate from 2.0 to 3.5 kgVS m−3 day−1. The VFA concentrations increased immediately after each input of sugar beet silage but levelled down until the next feeding period. At the highest organic loading rate, successive VFA accumulation escalated after 25 days (50 feeding periods) at 3.5 kgVS m−3 day−1, causing process failure with propionic acid concentrations exceeding 3500 mg L−1. The data revealed a strong negative exponential relationship between VFA concentrations and biogas and methane yields, respectively. High-resolution monitoring showed the instant dynamics of VFA production after intermittent sugar beet silage supply and the cumulative impact during increasing process disturbance.

Making Sense of a Seemingly Random Walk

This chapter traces the origins of my research on corporate governance and describes the pitfalls and challenges that arose early in my career. Many of these pitfalls are characteristic of conducting interdisciplinary research more generally. They include criticism from discipline-based scholars, special challenges in negotiating the peer review process, failure to articulate a coherent theoretical framework in individual articles, and the struggle to articulate a coherent identity as a scholar. The lessons learned should apply broadly to conducting interdisciplinary research on virtually any topic in organization theory and strategic management.

Customer Experience: Extracting Topics From Tweets

The ubiquity of social media platforms facilitates free flow of online chatter related to customer experience. Twitter is a prominent social media platform for sharing experiences, and e-retail firms are rapidly emerging as the preferred shopping destination. This study explores customers’ online shopping experience tweets. Customers tweet about their online shopping experience based on moments of truth shaped by encounters across different touchpoints. We aggregate 25,173 such tweets related to six e-retailers tweeted over a 5-year period. Grounded on agency theory, we extract the topics underlying these customer experience tweets using unsupervised latent Dirichlet allocation. The output reveals five topics which manifest into customer experience tweets related to online shopping—ordering, customer service interaction, entertainment, service outcome failure, and service process failure. Topics extracted are validated through inter-rater agreement with human experts. The study, thus, derives topics from tweets about e-retail customer experience and thereby facilitates prioritization of decision-making pertaining to critical service encounter touchpoints.