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This paper seeks to propose an auto body taillight assembly model using finite element analysis. Fitting variation induced by tighten‐up sequence is analyzed by the model to control the dimensional quality of auto body.

The taillight assembly model is constructed with finite elements to depict the assembly process of the taillight. The validity of the simulation model is proved by the consistence between the pressures obtained from the finite elements simulation results and the pressure sensitive paper. The fitting variation induced by tighten‐up sequences is analyzed with the proposed model. GAGE R&R method is employed to check the significant difference among four different sequences and a rational conclusion is obtained.

The proposed finite element model could be used for taillight fitting quality control. The results have shown that as far as the car taillight assembly structure discussed in the paper is concerned, the taillight final fitting quality has nothing to do with the tighten‐up sequence of connecting bolts.

The taillight assembly model is first constructed with finite elements to depict the assembly process of the taillight. For the first time, GAGE R&R method is employed to check the significant difference among the fitting qualities obtained by different sequences. The results of this research will enhance the understanding of the optimal fitting of car taillight, and help to systematically improve the productivity and the fitting quality in the automotive industry.

Fitting evenness is one key characteristic for three-dimensional objects' optimal fit. The weighted Gaussian imaging method is developed for fitting evenness of auto body taillight fitting optimization.

Fitting boundary contours are extracted from scanning data points. Optimal fitting target is represented with gap and flushness between taillight and auto body. By optimizing the fitting position of the projected boundary contours on the Gaussian sphere, the weighted Gaussian imaging method accomplishes optimal requirements of gap and flushness. A scanning system is established, and the fitting contour of the taillight assembly model is extracted to analyse the quality of the fitting process.

The proposed method accomplishes the fitting optimization for taillight fitting with higher efficiency.

The weighted Gaussian imaging method is used to optimize the taillight fitting. The proposed method optimized the fitting objects' 3-D space, while the traditional fitting methods are based on 2-D algorithm. Its time complexity is O(n3), while those of the traditional methods are O(n5). The results of this research will enhance the understanding of the 3-D optimal fitting and help in systematically improving the productivity and the fitting quality in automotive industry.

Optoforming is meant to be a potential substitution for the established Stereolithography (SLA) process. Its potential is that different ceramic‐filled photo‐curable epoxy‐resins can theoretically be used to manufacture highly loadable parts and tools. The stiffness as well as the thermal and chemical resistance of the material used (an epoxy resin named Tooling B) are higher than those of established SLA‐materials, such as SOMOS 7120. Automotive applications, in fields where the parts are directly used parts, such as lighting housings for prototype purposes, as well as tools for the veneering process in small batch production, were successfully tested. In order to enable precise and cost‐effective fabrication, optoforming has to be developed further in the field of secondary processes, such as inline‐filtration of the material and its feeding, as well as the machine software. Currently, another competitor offers a more mature process based on upgraded SLA machines, which use a ceramic‐filled epoxy‐resin also.

Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model.

This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter.

The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R.

The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.

The call to defund the police emerges globally from the voices of those who stand up to police brutality. Reallocating funds so that non-criminalising entities can meet community needs is crucial, but it does not address the problem in the system of policing. Further, the call for accountability cannot just emerge after a police-related death but must extend to the process of police work, beginning with police academy training. The author can identify and solve the most pressing issues by examining officer training as it is foundational to departmental work culture, organisational structure, and daily decision-making. Based on participant observation of academy lectures, scenario training, and informal conversations at two police academies in the Midwestern part of the United States, the author uncovers daily processes in which new recruits are socialised into their role as officers. Data reveal officers are taught a racialised decision-making logic that prioritises arrest, and perpetuates harm against its citizens. Training also devalues formal education, undermining knowledge that can expand officer thinking and critical self-reflection. Adhering to the goals of activist criminology, this chapter illuminates deeply rooted patterns of oppression and suggests reform aligned with critical social justice and anti-racist principles.

The purpose of this paper is to draw a better understanding of the potential impact of daylight in officer decision making. In order to this, the authors test the veil of darkness hypothesis, which theorizes that racial bias in traffic stops can be tested by controlling for the impact of daylight, while operating under the assumption that driver patterns remain constant across race.

Publicly available traffic-stop records from the Louisville Metro Police Department for January 2010–2019. The analysis includes both propensity score matching to examine the impact of daylight in similarly situated stops and coefficients testing to analyze how VOD may vary in citation-specific models.

The results show that using PSM following the VOD hypothesis does show evidence of racial bias, with Black drivers more likely to be stopped. Moreover, the effects of daylight significantly varied across citation-specific models.

The data are self-reported from the officer and do not contain information on the vehicle make or model.

This paper shows that utilizing PSM and coefficients testing provides for a better analysis following the VOD hypothesis and does a better job of understanding the impact of daylight and the officer decision-making on traffic stops.

Based on the quality of the data, the findings show that the use of VOD allows for the performance of more rigorous analyses of traffic stop data – giving police departments a better way to examine if racial profiling is evident.

This is the first study (to the researchers' knowledge) that applies the statistical analyses of PSM to the confines of the veil of darkness hypothesis.

Complicated workpiece, such as an engine block, has special rough locating datum features (i.e. six independent datum features) due to its complex structure. This locating datum error cannot be handled by current variation propagation model based on differential motion vectors. To extend variation prediction fields, this paper aims to solve the unaddressed variation sources to modify current model for multistage machining processes.

To overcome the limitation of current variation propagation model based on differential motion vectors caused by the unaddressed variation sources, this paper will extend the current model by handling the unaddressed datum-induced variation and its corresponding fixture variation.

The measurement results of the rear face with respect to the rough datum W and the pan face with respect to the hole Q by coordinate measuring machine (CMM) are −0.006 mm and 0.031 mm. The variation results for rear face and pan face predicted by the modified model are −0.009 mm and 0.025 mm, respectively. The discrepancy of model prediction and measurement is very small.

This paper modifies the variation propagation model based on differential motion vectors by solving the unaddressed variation sources, which can extend the variation prediction fields for some complicated workpiece and is useful in the future work for many fields, such as process monitoring, fault diagnosis, quality-assured setup planning and process-oriented tolerancing.

This chapter focuses on how people with a history of short-term incarceration engage with the criminal justice system. It is based on analysis of interview data with men and women who had been incarcerated in a county-level facility in Massachusetts; they were interviewed up to five times (once prerelease and four times postrelease). A primary goal of most was to be free of or minimize criminal justice system contact (not just incarceration), and this drove their approach to criminal justice system contact. In spite of this goal, they often remained ensnared for lengthy periods.