A sustainable and environmentally friendly society is developing rapidly, in which pavement engineering is an essential part. Therefore, more attention has been paid toward waste utilization and urban noise pollution in road construction. The object of this study was not only to investigate the mix proportion of waste crumb modified asphalt mixtures with diatomite and basalt fiber but also to evaluate the comprehensive performances including sound and vibration absorption of modified asphalt mixtures. Firstly, the mix proportion scheme was designed based on Marshall indices and sound and vibration absorption properties according to the orthogonal experimental method. Considering the specification requirements, as well as better performances, the optimal mix proportion was determined as follows: diatomite content at 7.5%, basalt fiber content at 0.3%, and asphalt-aggregate ratio at 5.5%. The range and variance analysis results indicated that asphalt-aggregate ratio has the most significant influence on volumetric parameters, diatomite has the most significant influence on sound absorption, and basalt fiber has the most significant influence on vibration reduction. Furthermore, the conventional pavement performances and sustainable sound and vibration absorption performances of modified asphalt mixtures were also analyzed. The results showed that the performances of modified asphalt mixtures were improved to different extents compared to the base asphalt mixture. This may be attributed to the microporous structure property of diatomite and the spatial network structure formed by basalt fibers. The pavement as well as sound and vibration absorption performances of the waste crumb modified asphalt mixture with diatomite and basalt fiber would be a good guidance for asphalt pavement design.

Automated robot tool trajectory planning for spray forming is highly desirable for today’s automotive manufacturing. Generating a robot tool trajectory to manufacture an automotive part to satisfy material distribution requirements is still very challenging due to the complexity of the problems. An industrial part may need to be partitioned into multiple patches because of its complexity. The trajectories of all patches must be connected to form a complete trajectory for the industrial part in order to minimize the material waste and process cycle time. In this paper, the methodology for automated robot tool trajectory connection is developed. Experimental tests were carried out to generate trajectories for automotive parts and the results validate the developed approach. A user-friendly software packages were developed and used at the Ford Motor Company to generate robot tool trajectories to manufacture automotive parts. The developed algorithm can also be extended to other applications in surface manufacturing such as spray painting.

Small part assembly has been received more and more attention recently. It is very challenging to automate small part assembly processes because of the part positioning problems, part mating related issues, and part manipulability problems etc. To facilitate the small part assembly processes, dual-arm robots have been developed. They have the advantages of flexibility and collaboration and can be used for small part assembly. In this paper we propose a small part assembly system using a dual-arm robot. In order to identify the part location, vision systems are deployed. The vision system calibration process is described. The small part assembly process is analyzed. An experimental system is constructed and experiments were performed. The experimental results demonstrate the effectiveness of our proposed system.

Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R². After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.

Purpose: High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process. Design/methodology/approach: In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed. Findings: Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes. Research limitations/implications: The proposed method is practical without any limitation. Practical implications: The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production. Originality/value: This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstable which will cause an unacceptable weld. Therefore welding parameters must be optimized in order to achieve best weld quality. However current methods have many limitations in exploring optimal welding parameters. In this paper, Gaussian Process Regression is applied to model the relationship between the welding performance indices and welding parameters. Bayesian Optimization Algorithm is adopted to balance the modeling and optimization processes and optimize welding parameters. Experiments were performed for the Gas tungsten arc welding (GTAW) process and the results demonstrate the effectiveness of the proposed algorithm. Compared to the existing methods, the proposed method greatly improves the welding parameter optimization process; moreover it can be applied with fewer experiments compared with existing methods which will reduce the testing cost and effort.