The Zero-Defect Manufacturing (ZDM) strategy, particularly the Zero Defect Zero Waste (ZDZW) methodology, emerges as a crucial approach to enhance sustainability in the global manufacturing supply chain. The European Union project 'ZDZW', focusing on diverse industrial scenarios, addresses challenges faced by industries such as plastics, metals, energy, ceramics, and consumer goods. Within this framework, the integration of ZDZW technologies, including Non-Destructive Inspection Technologies (NDIT) and artificial intelligence (AI), becomes pivotal. The industrial use case presented involves implementing ZDZW solutions in the thermoforming process for refrigerator inner body parts. This integration aims to automate quality assessment, reduce defects, and optimize production quality. The application of AI-enhanced thermal imaging and digital twin models provides real-time data for quality control, minimizing scrap rates and energy consumption. Sustainable Key Performance Indicators (KPIs) are defined to evaluate the impact of NDIT, emphasizing the reduction of scrap rates, carbon dioxide emissions, and overall environmental impact. The ZDZW methodology, positioned as part of the smart manufacturing ecosystem, contributes to innovative quality assurance, control, and sustainability services, aligning with the growing demand for sustainable production in the face of global challenges and disruptions.
The Zero-Defect Manufacturing (ZDM) strategy aims to minimize defects in industrial processes
by prioritizing first-time accuracy. ZDM integrates four main strategies: detection, prediction,
prevention, and repair [
The principal aim of the ZDZW European project is to test and validate solutions within six
distinct use cases, covering diverse industrial scenarios, from equipment manufacturers to the
production of
parts, including plastic, metal, energy, white goods, ceramics, and consumer goods, representing
multiple industrial sectors. The plastic industry and related manufacturing conforming processes are
crucial in the European manufacturing value chain and face challenges impacting sustainable
development. Disruptions from global events, such as China's growth, the COVID-19 pandemic, and
the Ukrainian conflict, increase raw material and product prices [
Under the current industrial pilot, the company is set to implement Zero-Defect and Zero-Waste (ZDZW) solutions to automate the quality assessment of inner body parts produced through thermoforming. Customer satisfaction is based on ensuring the high-quality production of a refrigerator's interior body through accurate dimensional precision and defect-free finished surfaces. High-impact polystyrene (HIPS) is the primary raw material employed to conform the refrigerator's inner body through thermoforming. The inner body of refrigerators is conformed by thermoforming, a production process where the thermoplastic is heated by electrical resistance to a specific value and later moulded in a vacuum chamber. The deviations in process parameters, such as the thickness of sheet plates, humidity, and ambient temperature, lead to the generation of defects by materials tearing and slimming. The process parameters are defined in the industrialization phase by employing sheet plates with mesh, with vacuum and resistance settings adjusted based on mesh structures for process reliability. Conventional thermoforming production lines rely on operators' experience to address these problems. Due to the intrinsic variability of industrial processes, the thermoforming process parameters should be adjusted daily to adapt to the raw material variations, reducing reliability risk. The flow diagram defined in Figure 1 summarizes the different manufacturing steps required to obtain the fridge's inner body part, the inspection methods employed to control the product quality, and the different data sources.
The lack of in-process control and decision-making tools in the thermoforming process increases the scrap rate, leading to higher plastic and energy consumption, increasing overall operational costs and environmental impact of the process. Integrating ZDZW Thermal Inspection solutions based on AI-enhanced thermal imaging and digital twin models will address and optimize current operations, reducing defects and enhancing overall production quality. The ZDZW methodology introduces a new thermal vision system and simulation tool that provides real-time data (Figure 2), which can automatically measure and calculate the quality-relevant properties of the semi-finished product and control the final product during production. This ZDM strategy aims to send feedback and close the loop of the thermoforming process by adjusting thermoforming parameters through artificial intelligent algorithms and digital twin, reducing the scrap rate and the related material and energy consumption.
The optimal process parameters will be defined by the Finite Element Analysis (FEA)-based AI model, considering the data gathered from different sources of sensors. The use FEM models, machine learning algorithms, and the acquisition of data through sensors in the thermoforming process enable the prediction of thickness and surface quality failures in real-time, optimizing material usage for sustainable production. Implementing ZDZW solutions will allow the real-time inspection and control required to adopt a sustainable manufacturing approach, where thinner sheets can be employed, reducing scrap ratio and energy consumption.
For the current research, several Key Performance Indicators (KPIs) are defined to provide
quantitative metrics to evaluate the impact of NDIT in the thermo-forming process to produce
refrigerator inner body parts. Baseline values for KPIs are determined using historical data from
production control records. Estimations are made based on experience or adaptation from similar
products. The ZDZW solution's effectiveness will be assessed by evaluating Sustainable KPIs
postimplementation, collecting data from new production batches or operations, and comparing it to the
baseline. Implementing ZDZW solutions must assure the reduction of scrap rates based on fast,
accurate, and preventive reactions to defect risk using a line monitoring and control system. In this
subsection, the Sustainable Key Performance Indicators (KPIs) are defined to monitor, control, and
improve the sustainable manufacturing of the thermoforming process. The ISO 22400 Automation
Systems and Integration KPIs for Manufacturing Operations Management serve as a framework for
defining, implementing, and visualizing the current article Sustainable KPIs [
The environmental sustainability KPI refers to the ecological impacts of the proposed
thermoforming process depending on their As-Is (Fig. 1) and To-Be scenarios (Fig. 2). The current
article has selected carbon dioxide emissions as the environmental sustainability to target and analyze
the impact of deploying non-destructive inspection solutions for in-process quality assurance. The
equation employed to calculate the CO2 emission is calculated considering the life cycle assessment
(LCA) approach which follows DIN EN ISO 14040:2006 [
Zero Defects Zero Waste (ZDZW), stands as a pivotal approach to address sustainability challenges in the manufacturing sector. The integration of ZDZW technologies, holds the promise of minimizing defects, reducing waste, and optimizing operational efficiency across diverse industrial sectors. The ZDZW European project's focus on six distinct use cases, spanning various industries, underscores the versatility and applicability of this methodology. The highlighted industrial use case, involving the implementation of ZDZW solutions in the thermoforming process for refrigerator inner body parts, exemplifies how advanced technologies can revolutionize quality control and contribute to sustainable manufacturing practices. By automating the quality assessment, employing AIenhanced thermal imaging, and utilizing digital twin models, the project aims to enhance overall production quality while simultaneously reducing material and energy consumption. The establishment of environmental Key Performance Indicators (KPIs) further emphasizes the commitment to measuring and evaluating the impact of ZDZW solutions on defective rates, material consumption, and environmental sustainability. As the project progresses, it is expected to bring about significant reductions in carbon dioxide emissions and operational costs, ultimately contributing to a more sustainable and efficient manufacturing ecosystem. The ZDZW methodology, with its focus on innovation, quality assurance, and environmental impact, represents a crucial step towards achieving a more sustainable future in manufacturing. sustainable manufacturing. Ec. 2 1 1000
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Other information Units of measure: Percentage (%).
Source of data: Enterprise Resource Planning software.
Measurement: Each manufacturing order.
Reviewing period: The Quality Assurance Engineers will conduct a monthly review of the KPI to analyze its behavior and determine if any adjustments to production parameters are necessary.
Range tolerances: A decrease in raw material usage between 85% and 95% will be acceptable.
Responsible: Supervisors.
Audience: Managers.
Units of measure: Percentage (%).
Source of data: Enterprise Resource Planning software.
Measurement: Each manufacturing order.
Reviewing period: The Quality Assurance Engineers will conduct a weekly review of the KPI to analyze its behavior and determine if any adjustments to production parameters are necessary.
Range tolerances: Anything less than five percent (<5%) is deemed acceptable. The presence of any noticeable defect classifies the product as defective. There is no discernible pattern in the occurrence of errors.
Responsible: Supervisors.
Audience: Managers.
Units of measure: Ton carbon dioxide equivalent (t CO2 eq.).
Source of data: Enterprise Resource Planning software.
Measurement each manufacturing order, monthly/yearly Reviewing period: Quality Assurance Engineer will review the KPI monthly to study behaviour and possibly change production parameters.
Range tolerances: A decrease in CO2 emission between 5% and 15% will be acceptable.
Responsible: Supervisors.
Audience: Managers. The ZDZW project has received funding from the European Union’s Horizon Europe programme under grant agreement No 101057404. Views and opinions ex-pressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
The author(s) have not employed any Generative AI tools.