Development of a Coordinate Measuring Machine—Based Inspection Planning System for Industry 4.0

Industry 4.0 represents a new paradigm which creates new requirements in the area of manufacturing and manufacturing metrology such as to reduce the cost of product, flexibility, mass customization, quality of product, high level of digitalization, optimization, etc., all of which contribute to smart manufacturing and smart metrology systems. This paper presents a developed inspection planning system based on CMM as support of the smart metrology within Industry 4.0 or manufacturing metrology 4.0 (MM4.0). The system is based on the application of three AI techniques such as engineering ontology (EO), GA and ants colony optimization (ACO). The developed system consists of: the ontological knowledge base; the mathematical model for generating strategy of initial MP; the model of analysis and optimization of workpiece setups and probe configuration; the path simulation model in MatLab, PTC Creo and STEP-NC Machine software, and the model of optimization MP by applying ACO. The advantage of the model is its suitability for monitoring of the measurement process and digitalization of the measurement process planning, simulation carried out and measurement verification based on CMM, reduction of the preparatory measurement time as early as in the inspection planning phase and minimizing human involvement or human errors through intelligent planning, which directly influences increased production efficiency, competitiveness, and productivity of enterprises. The measuring experiment was performed using a machined prismatic workpiece (PW).

Using Recognizable Fuzzy Analysis for Non-Destructive Detection of Residual Stress in White Light Elements

The phenomenon of residual stress in optical lens injection molding affects the quality of optical devices, with the refractive errors that are caused by geometric errors being the most serious, followed by the reduced accuracy and function of optical components; it is very important to ensure that the lens geometry remains intact and that the refractive index is reduced. This paper uses a photoelastic stress compensation method for measurement verification along with fuzzy theory to reorganize a set of processes that can be used to evaluate the residual stress of a product, whereby the use of corresponding theoretical formulas can effectively quantify and measure the residual stress of the product. A mold flow simulation is used to analyze the molded optical components and determine the feasibility of evaluating the quality of the lens. Through the measurement of the refractive stress value of the optical components, the molding quality of the lens can be improved, and its force distribution effects can be investigated. Geometric analysis and shear stress affect the performance of optical components, and these errors may also cause irreparable problems during secondary processing. Therefore, it is crucial to reduce the residual stress of optical components. When the stress distribution is uniform and the internal melting pressure is reasonably configured, the product’s shrinkage rate can be controlled; the method for determining the residual stress is the core theme of this research.

Measurement without Clear Incentives to Improve: The Impacts of the Higg Facility Environmental Module (FEM) on Apparel Factory Practices and Performance

Poor environmental and social practices are common across the apparel industry. Over the past 30 years, Non-governmental Organizations (NGOs) have steadily pressured buyers to make sustainability improvements in their supply chains. Yet, literally billions of dollars, and many attempts at new environmental standards, codes, monitoring, and capacity building programs have failed to drive significant progress in environmental performance. Against this pessimistic backdrop, an 11-year old initiative—the Sustainable Apparel Coalition (SAC)—has developed the leading strategy to drive sustainability within the global apparel industry. Its major initiative has been the Higg Index: a suite of six data tools. The Facility Environment Module (FEM), now in version 3.0, is the annual assessment of an apparel facility’s environmental management capabilities, procedures, and plans. This report is the output of a four-year analysis of the implementation and effectiveness of the FEM v2.0. This report analyzes whether the standards, measurement, verification, and learning processes advanced through the FEM improve the environmental performance of the apparel industry. The study covers quantitative data analysis of all FEM v2.0 data, a survey of a select sample of facilities, and case studies of eight facilities in Bangladesh and China. Our overall conclusion is that the FEM is having foundational, but not transformative impacts as it still lacks critical incentives to change factory practices. If factories were to receive more or fewer orders based on their FEM score, there might be sufficient internal incentives for improvement. Yet this internal mechanism is only likely to be successful if there is external transparency and accountability.