A technique for intrinsic and extrinsic calibration of a laser triangulation sensor (LTS) integrated in an articulated arm coordinate measuring machine (AACMM) is presented in this paper. After applying a novel approach to the AACMM kinematic parameter identification problem, by means of a single calibration gauge object, a one-step calibration method to obtain both intrinsic―laser plane, CCD sensor and camera geometry―and extrinsic parameters related to the AACMM main frame has been developed. This allows the integration of LTS and AACMM mathematical models without the need of additional optimization methods after the prior sensor calibration, usually done in a coordinate measuring machine (CMM) before the assembly of the sensor in the arm. The experimental tests results for accuracy and repeatability show the suitable performance of this technique, resulting in a reliable, quick and friendly calibration method for the AACMM final user. The presented method is also valid for sensor integration in robot arms and CMMs.

Traditionally, the single purpose of the design of a workpiece has been its final use. In some cases, this design is adapted to the manufacturing process, as for example casting. However, verification is not considered in the design. In this paper, a methodology is proposed to evaluate the friendliness of the design with the verification in a benchmark. The main topics in dimensional verification: uncertainty, sampling or cost are addressed, Three actual cases, on the same workpiece design, are used to illustrate the method, benchmarking every of them in an Efficacy and Efficiency scale. For each of them, suggestion regarding workpiece design and process design are made in order to improve the final result of the benchmarking.

In the last century, statistical process control has been considered a successful methodology for monitoring the variability and stability of a process over time. The analysis of variability using functional data analysis has become widespread during the early years of the 21st century. The present study proposes the implementation of a hybrid methodology called functional process control based on the functional data analysis methodology and combines its advantages with those of statistical process control, in order to provide a tool for detecting functional deviations. The phases are: (1) stabilization of processes through the identification and elimination of special causes of functional variability; (2) improvement of the process to be controlled by minimizing the common causes of functional variability; and (3) monitoring of the processes to ensure the maintenance or addition of further improvements.

The design of a certain workpiece lies mainly in its functionality. Nevertheless, in most cases the design of the workpiece has to be adapted to the characteristics of the manufacturing process, taking casting as the most representative example. Surprisingly, dimensional verification is kept out of the design, increasing in some cases substantially the final cost of the workpiece. In this paper, the friendliness between the design and the verification is evaluated. The main concerns in dimensional verification: sampling strategy and uncertainty are addressed. Additionally, cost in terms of equipment and time is covered. Four illustrative cases, on the same workpiece design, are used to explain the method, benchmarking each of them in an Efficacy and Efficiency scale. For each, suggestion regarding workpiece design and process design are made in order to improve the final result of the benchmarking.