Multiple Dependent State Sampling Inspection Plan for Lindley Distributed Quality Characteristic

This article develops multiple dependent state (MDS) sampling inspection plans based on the mean of lifetime quality characteristic that follows non-normal distributions viz., exponential and Lindley distribution. In this plan, the lot quality is measured by the lot mean (𝜇). We have estimated the optimal plan parameters of the proposed technique by non-linear optimization approaches considering acceptable quality level and rejection quality level. We have compared the sample size between the MDS sampling inspection plan and the single sampling inspection plan for the variable. Finally, we have taken two examples to illustrate the proposed technique.

Optimization of Inspection Plan for Transformer Verification Line Under Uninterrupted State

At present, transformer verification line of metering centre adopts fixed cycle inspection method manually. This method requires downtime for detection, which costs a lot of time and cost. Moreover, the inspection cycle is determined based on experience and lacks rigorous basis. To solve this problem, a hybrid delivery of inspection devices is proposed to realize non-stop detection and reduce the cost of inspection time. Considering impact of cost and false detection risk on inspection cycle, a multi-objective optimization model of inspection cycle based on inspection and false detection cost is proposed. Based on NSGA-II algorithm, perturbation population is introduced to enhance the global search ability, which aims to minimize the cost of inspection and false detection. Taking the verification line’s inspection plan of the metering centre as an example. It is solved by ENSGA-II algorithm, and feasibility of hybrid delivery mode is verified, which reduced downtime by 14.58%. A more reasonable inspection cycle is obtained, inspection cost is reduced by 29.57%, and false detection cost is reduced by 6.34%. It provides a reference for the formulation of inspection plan in the actual production process.

A Blended Two-Sided Chain Sampling Plan Created on Process Potential Measure

This research article presents, a blended two-sided chain inspection plan with process potential measure. The Probability of acceptance and related measures are shown. Tables are prepared to find the parameters of the plan. In this plan the variable inspection sample size is obtained by using normal distribution and in the attribute inspection, two-sided chain sampling plan which yields small sample size is used the designed sampling plan is really used in production industries to study the product with respect to the specification measures and to defend the period and charge of inspection to impact on the end product.

Quality Inspection Plan for Imperfect Production System with Assembly Configuration

In this study, the model concerning a negative binomial sampling inspection plan is proposed and applied to an imperfect production system with assemble-to-order configuration, where the production system is subject to a Weibull deteriorating process and is operated under an in-control or an out-of-control state. The proposed model of this study contributes to developing an approach which can effectively integrate the considerations of the production system status, the defective rate, the working efficiency of employees, and the market demands with an aim to determine the optimal number of conforming items for inspection with minimum total cost, and the results can be practically applied to the assembly of products in various industries, especially for the prevalent Industry 4.0 in manufacturing.

Path Planning and Setup Orientation for Automated Dimensional Inspection Using Coordinate Measuring Machines

Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.