Ensuring high-quality production during commissioning and ramp-up: A case study at Northvolt

Ensuring high-quality production during commissioning and ramp-up

A case study at Northvolt

Henrik Eklund Jacob Engström

Industrial and Management Engineering, master's level 2021

Luleå University of Technology

Department of Social Sciences, Technology and Arts

Ensuring high-quality production during commissioning and ramp-up

A case study at Northvolt

Säkerställa högkvalitativ produktion under driftsättning och upprampning

En fallstudie vid Northvolt

Master thesis in Quality Technology and Management

at Luleå University of Technology and Northvolt Ett in Skellefteå Examensarbete utfört inom ämnesområdet kvalitetsteknik

vid Luleå tekniska universitet och Northvolt Ett i Skellefteå By / Av

Henrik Eklund Jacob Engström Luleå 2021-06-06

Supervisors / Handledare Sean Stephenson, Northvolt AB

Jens Aldenlöv, Luleå University of Technology

Preface

This study is the master thesis of our education in Industrial Engineering and Management at Luleå University of Technology (LTU). The master thesis was conducted as a case study at Northvolt AB in the area of Quality Technology and Management during the spring of 2021.

As a consequence of the COVID-19 pandemic, the entire thesis was performed on distance instead of on site at Northvolt Ett in Skellefteå. Unfortunately, we never got the opportunity to physically meet with the Northvolt Production Quality team, which we have been a part of during the entire spring. Nonetheless, the team has provided us with tremendous support along the way through daily online meetings, alleviating the negative effects of working from distance.

The master thesis gave us the amazing opportunity to work with the commissioning of Northvolt Ett in Skellefteå, currently one of the largest industry projects in Europe. Being a part of Northvolt has given us valuable insights on the complex process of large-scale battery manufacturing and the long road from product concept to finished product. We have also learned the importance of collaboration and network-building to enable efficient work and problem solving, especially when working on distance.

On behalf of us both, we would like to thank all Northvolters that have helped us along the way.

Special thanks to our Northvolt supervisor Sean Stephenson for all the many hours of helping us

align the study and making us feel welcome in the team. Another special thanks to our LTU

supervisor Jens Aldenlöv for your continuous input and advice on writing this report. Finally, we

would like to thank our opponents John Larsson, Fredrik Moreira Boman, David Hjälte, and Alvina

Ahlqvist for regularly reviewing the report and highlighting areas for improvement.

Abstract

Rechargeable lithium-ion batteries (LIBs) have generated a shift in the automotive industry towards electric vehicles (EVs) instead of vehicles powered by fossil fuels. As a result, the demand for LIBs is only expected to grow in the future due to an increased demand for passenger EVs.

Consequently, LIB manufacturers have to increase their production to meet the increasing demand.

Northvolt is a Swedish LIB manufacturer founded in 2016, aiming to start the production of LIBs at the Northvolt Ett factory in Skellefteå during 2021. The Northvolt Ett factory will be one of the largest battery plants in Europe, supplying battery cells for both commercial and domestic use.

Poorly manufactured battery cells can potentially cause hazardous events, such as fires or explosions, further supporting the need for high quality batteries. Consequently, requirements from customers and industry standards are high in terms of product quality control through e.g.

measurement system analysis (MSA), statistical process control (SPC), and capability analysis.

Furthermore, previous research has highlighted issues during commissioning and ramp-up of production, potentially occurring at Northvolt Ett.

The purpose of this study has been to describe how high-quality production can be ensured and maintained during and after commissioning. The study has been conducted as a qualitative case study at Northvolt Ett, focusing on qualification of the coating process. The basis for the study was to examine previous research on quality assurance from other industries, analyze automotive standards, and gather learnings from the pilot production at Northvolt Labs in Västerås.

Unstructured interviews were conducted with Northvolt staff to understand what had previously been done related to quality assurance for Coating.

The learnings from Northvolt Labs highlighted a clear focus on preventive actions, such as establishing a Design-FMEA, Process-FMEA, and a Control Plan for the coating process.

However, room for improvement was identified in terms of process improvement and control,

since the lack of SPC has yielded unreliable results from the performed capability analysis. In

addition, previous research has shown that preventive actions should be combined with actions for

process improvement to reach full-scale production quickly. Thus, recommendations have been

made for Northvolt to implement a clear strategy for product qualification through SPC and

capability analysis, as a complement to the preventive actions. The recommendations include

specific propositions for validation of the coating process and a general framework for process

validation through MSA, SPC, and capability analysis. The presented recommendations can help

Northvolt perform successful commissioning of the processes at Northvolt Ett and can also be

useful for process validation in other manufacturing industries.

Sammanfattning

Laddningsbara litium-jon-batterier (LIB:s) har skapat en omställning i bilindustrin mot eldrivna fordon istället för fordon som drivs av fossila bränslen. Som en konsekvens väntas efterfrågan av LIB:s bara att öka i framtiden på grund av en ökad efterfrågan på eldrivna passagerarfordon. LIB- tillverkare måste därför öka sin produktion för att möta den växande efterfrågan. Northvolt är en svensk LIB-tillverkare som grundades 2016, med sikte på att starta produktionen av LIB:s vid fabriken Northvolt Ett i Skellefteå under 2021. Fabriken Northvolt Ett kommer att vara en av de största batterifabrikerna i Europa och leverera battericeller för både kommersiell och privat användning. Dåligt tillverkade battericeller kan potentiellt orsaka allvarliga händelser som bränder eller explosioner, vilket vidare stödjer behovet av batterier med hög kvalitet. Till följd av detta är kraven från standarder och tillverkare inom bilindustrin höga i termer av kvalitetskontroll av produkter genom t.ex. mätsystemanalys (MSA), statistisk processtyrning (SPS), och duglighetsanalys. Vidare visar tidigare forskning på problem som kan uppstå under driftsättning och upprampning av produktion, vilka potentiellt kan uppstå för Northvolt Ett.

Syftet med denna studie har varit att beskriva hur högkvalitativ produktion kan säkerställas och upprätthållas under och efter driftsättning. Studien har genomförts som en kvalitativ fallstudie vid Northvolt Ett med fokus på kvalifikation av coating-processen. Utgångspunkten för studien har varit att undersöka tidigare forskning inom kvalitetssäkring från andra industrier, analysera standarder från bilindustrin, och hämta in lärdomar från pilotproduktionen vid Northvolt Labs i Västerås. Ostrukturerade intervjuer genomfördes med anställda på Northvolt för att öka förståelsen för vad som tidigare gjorts relaterat till kvalitetssäkring för Coating.

Lärdomarna från Northvolt Labs visade ett tydligt fokus på förebyggande åtgärder, som upprättande av en Design-FMEA, Process-FMEA, och en kontrollplan för coating-processen.

Dock identifierades ett förbättringsområde inom åtgärder för processförbättring och kontroll, då avsaknaden av SPS har genererat opålitliga resultat från den genomförda duglighetsanalysen.

Vidare har tidigare forskning visat att förebyggande åtgärder borde kombineras med åtgärder för

processförbättring för att snabbt uppnå fullskalig produktion. Rekommendationer har därför tagits

fram till Northvolt för att implementera en tydlig strategi för produktkvalifikation genom SPS och

duglighetsanalys, som ett komplement till de förebyggande åtgärderna. Dessa rekommendationer

inkluderar specifika förslag för validering av coating-processen samt ett generellt ramverk för

processvalidering genom MSA, SPS, och duglighetsanalys. De presenterade rekommendationerna

kan hjälpa Northvolt att genomföra en framgångsrik driftsättning av processerna på Northvolt Ett

och kan även vara användbara för processvalidering i andra tillverkningsindustrier.

Table of Contents

1. Introduction ... 1

1.1 Background ... 1

1.2 Problem description ... 2

1.3 Purpose ... 2

1.4 Delimitations ... 3

2. Literature review ... 4

2.1 The importance of high-quality production ... 4

2.2 Controlling production quality ... 6

2.2.1 Critical to quality characteristics ... 6

2.2.2 Measurement system analysis ... 7

2.2.3 Statistical process control ... 7

2.2.4 Capability analysis ... 8

2.3 Quality during production ramp-up ... 9

2.3.1 Production ramp-up performance ... 10

2.3.2 Ensuring effective throughput during ramp-up... 10

2.3.3 Ramp-up environments ... 12

2.4 Monitoring production quality ... 14

3. Methodology ... 15

3.1 Overall approach ... 15

3.2 Literature review ... 15

3.3 Case study ... 16

3.3.1 Document review ... 16

3.3.2 Interviews ... 16

3.4 Credibility of the study ... 18

4. Case study at Northvolt ... 19

4.1 General procedure of commissioning and ramp-up ... 19

4.2 Overview of the coating process ... 20

4.3 Product characteristics for Coating ... 21

4.4 Current validation of Coating at Northvolt Labs ... 24

4.4.1 Measurement system analysis ... 24

4.4.2 Statistical process control ... 25

4.4.3 Capability analysis ... 25

5. Analysis ... 27

5.1 Cost of Quality and maturity... 27

5.2 Current methods for controlling production quality ... 28

5.3 Prerequisites for successful commissioning and ramp-up ... 29

6. Conclusions and recommendations ... 32

6.1 Conclusions ... 32

6.2 Recommendations ... 32

6.2.1 Recommended use of MSA ... 33

6.2.2 Recommended use of SPC ... 34

6.2.3 Recommended use of capability analysis ... 35

7. Discussion... 38

7.1 Generalizability ... 38

7.2 Choice of methodology ... 38

7.3 Limitations and future research ... 39

References ... 40

Appendix I - Framework for process validation at NV Ett ... I

Appendix II - Checklist for process validation at NV Ett ... II

List of Figures

Figure 1.1: Production timeline at Northvolt

Figure 1.2: Overview of the LIB production process

Figure 2.1: Tradeoff between preventive and corrective costs Figure 2.2: The different phases of production

Figure 2.3: Visualization of effective throughput loss during ramp-up Figure 2.4: Mitigation of effective throughput loss

Figure 2.5: Ramp-up environments

Figure 4.1: Coating process at NV Ett (tandem coating) Figure 4.2: Coating process at NV Labs (single-line coating) Figure 4.3: Visualization of the SCs for Coating

Figure 4.4: Collection of reference data for Beta-Gauge MSA Figure 4.5: Collection of reference data for camera MSA Figure 5.1: Tradeoff between preventive and corrective costs Figure 5.2: Northvolt’s ramp-up environment

Figure 5.3: Strategies for Northvolt to decrease ramp-up time

List of Tables

Table 2.1: Performance maturity levels

Table 2.2: Quality system maturity level versus CoQ Table 2.3: Overview of inspection types

Table 3.1: Compilation of the interviews Table 4.1: Summary of the SCs for Coating Table 5.1: Potential maturity level for Northvolt

Table 6.1: Focus areas for process validation of Coating

Table 6.2: Recommendations for initial SPC on the coating process

Table 6.3: Recommended sampling for capability analysis of the coating process

1. Introduction

This chapter starts with presenting a background on the main topic from a general perspective, before describing the specific problem and how it relates to Northvolt. The purpose and objectives of this study are then described together with the selected delimitations.

1.1 Background

The development of rechargeable lithium-ion batteries (LIBs) has led to a shift in the automotive industry, where vehicles powered by fossil fuels are being replaced with electric vehicles (EVs) (Grey & Tarascon, 2016). The global annual production of rechargeable LIBs is expected to grow from 300 to 2000 GWh by 2030 (Energy Central, 2020). The increasing production of LIBs using sustainable materials and renewable energy would further drive the phasing-out of fossil fuels, having a positive impact on the global climate (Grey & Tarascon, 2016). The largest share of the demand for LIBs comes from the production of passenger EVs, which is expected to grow more than 800 percent until 2030 (Energy Central, 2020). As a consequence, LIB manufacturers have to increase their production capacity to meet the increasing demand of LIBs (Väyrynen &

Salminen, 2012). Moreover, LIB manufacturers have to ensure that they can produce high-quality LIBs to the lowest production cost possible (Kwade et al., 2018). Establishing high-quality production is especially important during the ramp-up phase of production, to quickly meet the target level of throughput (Colledani et al., 2018). This implies the need for new manufacturers in the LIB industry to focus on achieving high product quality directly from the start of production.

Previous research has focused on the importance of conducting proper quality management and

quality assurance during production ramp-up. Colledani et al. (2018) highlight two methods for

increasing the initial level of throughput and shortening the ramp-up time. The two methods build

upon the use of preventive work and production data (Colledani et al., 2018). Leffakis (2016) has

developed a framework that categorizes different ramp-up environments based on product scope

and process technology. The framework can be used to identify relevant quality management

practices during ramp-up (Leffakis, 2016). Furthermore, Westermeier et al. (2014) present

methods for quality management from the design phase up until the start of production, focusing

on the application within the LIB industry. Despite an extensive amount of previous research

conducted in quality assurance during production ramp-up, there is a lack of research on quality

assurance in the LIB industry according to the authors' awareness. With a continuous increase in

demand for LIBs, it is crucial to integrate quality assurance elements with the requirements of the

LIB industry.

1.2 Problem description

Northvolt is a Swedish LIB manufacturer established in 2016. Pilot production is currently being performed at Northvolt Labs in Västerås (hereafter referenced as NV Labs). At NV Labs, the processes are constantly adjusted to optimize the output, causing large variations in the rate of manufacturing (Figure 1.1). During 2021, the Northvolt Ett factory in Skellefteå (hereafter referenced as NV Ett) will be commissioned to enable mass production of battery cells, with the potential to expand the production capacity to 60 GWh in the future (Northvolt, 2021). Directly after the commissioning, the production will be ramped up to reach a high and stable rate of manufacturing. Consequently, proper quality assurance and inspection is crucial to ensure that the battery cells meet the customer requirements (Schnell & Reinhart, 2016). Lacking quality assurance during the LIB production process can have dire consequences, since a wrongly produced battery cell can result in fires or explosions (Hendricks et al., 2015). With the short timeframe until commissioning, it is crucial for Northvolt to quickly establish a plan for quality assurance (S. Stephenson, personal communication, January 20, 2021).

Figure 1.1: Production timeline at Northvolt (inspired by Almgren, 2000).

1.3 Purpose

The purpose of this study is to describe how high-quality production can be ensured and

maintained during and after commissioning. Due to the lack of research with specific focus on

quality assurance in the battery production industry, this study will build on general elements of

quality assurance. Lessons learned from the previous commissioning at NV Labs will also be

reviewed to understand what needs to be considered during the commissioning at NV Ett. The

practical contribution of this study is to give Northvolt a high-level overview of the quality

assurance and validation that needs to be performed at NV Ett. The purpose will be fulfilled

through completing the following four objectives:

1. Examine previous research on quality assurance and ramp-up performance.

2. Describe and analyze the planned commissioning procedure at NV Ett.

3. Develop a general framework applicable for validation of all processes at NV Ett.

4. Formulate recommendations for validating a specific process step at NV Ett.

1.4 Delimitations

The first process to be commissioned at NV Ett is Coating (see Coating & Drying in Figure 1.2), which will begin in July 2021. Hence, this study will focus on quality assurance of the coating process during commissioning and ramp-up to reach a stable level of production. However, since the commissioning starts after this study has ended, analysis of actual process data will not be possible during this study. Thus, the result of this study will rather be a guideline for Northvolt to use during the commissioning and ramp-up. The findings should also be possible to modify for validation of the other processes at NV Ett. Furthermore, this study will be focusing on the coating of battery cells for the automotive industry (called prismatic cells), as it is one of Northvolt’s largest customer segments. The first battery cells are scheduled to be delivered to automotive customers in the end of 2021, implicating the urgency of ramping up the production.

Figure 1.2: Overview of the LIB production process (inspired by Smekens et al., 2016).

2. Literature review

This chapter provides an overview of previous research on topics related to product and production quality. It starts with discussing production quality in general, before moving on to methods for controlling production quality, such as statistical process control and capability analysis. Lastly, suggested practices for quality management in specific phases of production and inspection types for monitoring production quality are presented.

2.1 The importance of high-quality production

Due to the increased competitiveness of various markets, quality is regarded as a key factor for success, especially in the manufacturing industry (Battini et al., 2012; Farooq et al., 2017). From a manufacturing perspective, quality can be defined as the proportion of products that are manufactured correctly for the first time (Battini et al., 2012). Low quality contributes to a reduced confidence in the product and decreased credibility of the manufacturing company (Battini et al., 2012; Chen, 2013). In recent years, customer expectations on quality at low cost have only increased (Farooq et al., 2017). As manufacturers try to meet these expectations, they come to a point where tradeoffs must be made between increasing quality and lowering manufacturing- related costs. The term Cost of Quality (CoQ) is described as the sum of conformance and non- conformance costs, where costs of conformance are costs for preventing poor quality, and costs of non-conformance are costs of poor quality caused by product failure (Schiffauerova & Thomson, 2006). There are various models for describing CoQ, and one of the most commonly used is the Prevention-Appraisal-Failure (PAF) model (Sower et al., 2007). Prevention costs are the costs for preventing non-conformance, such as maintenance and calibration of production and inspection equipment (Farooq et al., 2017). Appraisal costs refer to costs for attempting to detect non- conformance products through testing or inspection (Farooq et al., 2017). Meanwhile, failure costs are divided into internal and external failure costs. Internal failure costs occur due to scrap, rework and repair, while external failure costs include costs for product recalls and warranty (Farooq et al., 2017).

Surange (2015) defines CoQ based on the PAF model, where the four cost categories instead are

being divided into two categories: cost of good quality and cost of poor quality. Cost of good

quality refers to prevention and appraisal costs, while cost of poor quality refers to internal and

external failure costs (Surange, 2015). As shown in Figure 2.1, good and poor quality costs have

an inverse relationship (Omachonu & Suthummanon, 2004). A high rate of product failures implies

a lacking focus on failure prevention during production. Hence, focusing on failure prevention by

achieving high-quality production will lower the need for corrective costs associated with failures

from the finished product. However, trying to eliminate all failures to reach a zero percent defect

rate is not economically feasible. This implies a risk-benefit tradeoff between good and poor

quality, where the optimal tradeoff is achieved when costs of good quality are prioritized whilst

still allowing a certain rate of potential defects (Surange, 2015).

Figure 2.1: Tradeoff between preventive and corrective costs (inspired by Surange, 2015).

The tradeoff between cost of good and poor quality varies depending on the maturity level of the quality system for preventing failures (Sower et al., 2007). As shown in Table 2.1, the level of maturity can vary between 1 to 5, where level 1 describes a highly immature system and level 5 describes a fully mature system.

Table 2.1: Performance maturity levels (inspired by Sower et al. 2007).

Maturity level Performance level Guidance

1 No formal approach No clear systematic approach. No results, poor results or unpredictable results.

2 Reactive approach Problem- or corrective-based approach. Minimum data or improvement results available.

3 Stable formal system approach

Systematic process-based approach, early stage of systematic improvements. Data available on conformance to objectives and existence of improvement trends.

4 Continual improvement emphasized

Improvement process in use. Good results and signs of improvement trends.

5 Best-in-class performance Strongly integrated improvement process. Best-in-class

benchmarked results demonstrated.

An immature quality system puts major focus on end-of-line inspection, neglecting the need for preventive actions earlier in the production process (Montgomery, 1996). Hence, the total CoQ for a level 1 system consists of high costs from poor quality and low costs from good quality (Table 2.2). On the contrary, a level 5 system is characterized by process control and design of experiments (Montgomery, 1996). Here, major emphasis is put on preventive actions, subsequently lowering the costs of failure. In summary, as the maturity level of a quality system increases, the CoQ is redistributed from cost of poor quality to cost of good quality, implying a more predictable manufacturing system (Sower et al., 2007). As the costs of good quality usually are lower than costs of poor quality, this leads to a decreased CoQ for fully mature systems (Sower et al., 2007). As manufacturers are constantly striving to meet customer requirements with minimal costs, there is a need of achieving a high level of maturity where the CoQ is minimized (Schiffauerova & Thomson, 2006; Surange, 2015).

Table 2.2: Quality system maturity level versus CoQ (inspired by Sower et al. 2007).

Cost component Level 1 Level 2 Level 3 Level 4 Level 5

Prevention Very low Low Moderate High High

Appraisal Low Low-moderate Moderate Low-moderate Low

Internal failure High Very high Moderate-high Low-moderate Very low

External failure High High Moderate Low Very low

Total CoQ High Very high Moderate-high Low-moderate Low

2.2 Controlling production quality

This section begins by explaining the term critical to quality characteristics, followed by a description of methods used to control production quality, namely measurement system analysis, statistical process control, and capability analysis.

2.2.1 Critical to quality characteristics

Critical to quality characteristics (CTQs) are the key measurable characteristics of a product that significantly impact its quality (He et al., 2009). CTQs are usually interpreted from a qualitative statement from an internal or external customer, converted to a quantitative specification that includes an upper and lower specification limit (He et al., 2009). To meet customer specifications and improve product quality, manufacturers have to identify the CTQs that significantly impact product quality and manage to monitor and control them (Zhou et al., 2020). However, monitoring and inspection of other quality characteristics in addition to CTQs are costly (Zhou et al., 2020).

Therefore, identifying CTQs can lead to a reduction of unnecessary inspections, meaning that costs

will decrease since resources will be used where they are needed the most (Zhou et al., 2020).

2.2.2 Measurement system analysis

Before a manufacturing process can be validated, the measurement system’s capability must also be taken into account. The measurement system’s capability can be determined by performing measurement system analysis (MSA), where the variability of the different components used in the measurement system is analyzed (Montgomery, 2013). MSA commonly divides the capability of the measurement system into precision and accuracy. Precision can be measured through Gauge R&R, analyzing the repeatability (internal variation of the measurement gauge) and reproducibility (variation from different operators performing the same measurement) (AIAG, 2010; Al-Refai & Bata, 2010). Accuracy consists of three sub-components: bias, linearity, and stability. Bias is the difference between the true value (reference value) and the observed average of measurements on the same part. Linearity is the variation of bias throughout the expected operating range of the measurement system, which can be summarized as the change of bias to the size of the measurement. Stability is the change in bias over time, which is the total variation in the measurements obtained with a measurement system on the same parts over an extended period (AIAG, 2010). Moreover, the resolution of each instrument should also be taken into account.

According to AIAG (2010), the resolution should be evaluated through the “Rule of Tens”, meaning that the resolution of an instrument should divide the specifications into ten parts or more.

2.2.3 Statistical process control

Statistical process control (SPC) is a methodology for monitoring and controlling processes, and it has been frequently applied in several industries (Avakh Darestani & Nasiri, 2015). SPC should be used to improve the stability and capability of the most critical processes according to the customers (Antony & Taner, 2003). In SPC, control charts are a commonly used quality assurance tool to monitor important product characteristics in a process (VDA, 2005). A control chart contains a centerline (CL) that represents the average value of the measured CTQ characteristic, and an upper and lower control limit (UCL and LCL) that observe whether the process is in statistical control or not (Montgomery, 2013). A process in statistical control is characterized by having only random causes of variation (inherent to the process). In contrast, a process that is not in statistical control is affected by assignable causes (Montgomery, 2013).

Control charts can be used to monitor product characteristics (CTQs) such as weight, dimension

or volume (Montgomery, 2013). The process average is often monitored with the control chart for

means (X-bar chart) (Montgomery, 2013; VDA, 2005). In contrast, a control chart can monitor the

process variability for the range (R chart) or a control chart for the standard deviation (s chart)

(Senturk & Erginel, 2009; VDA, 2005). The X-bar and R charts require at least m = 20 to 25

samples, each containing a sample size of n = 4, 5, or 6. The same amount of samples m are

required for the X-bar and s charts, but the difference is that the sample size should be n ≥ 10

(Montgomery, 2013). Moreover, if the sample size is n > 1 and large shifts want to be detected,

the X-bar and R charts or the X-bar and s charts can be used. Meanwhile, suppose the same sample

size n > 1 is used, and small shifts want to be detected. In that case, the cumulative sum (CUSUM)

control chart and the exponentially weighted moving average (EWMA) control chart can be used (Montgomery, 2013).

2.2.4 Capability analysis

When a process has been proven to be in statistical control, capability analysis can be performed.

If the process is not in statistical control, it means that its parameters are unstable and the future value of these parameters is uncertain, making it difficult to make a reliable estimate of the process capability (Montgomery, 2013). Capability analysis is a common tool used to validate the capability of a process or machine given certain levels of specification (Kaya & Kahraman, 2011;

Avakh Darestani & Nasiri, 2015). A prerequisite for capability analysis is to perform a normality test, as the analysis builds upon the assumption that the analyzed process data comes from a normal distribution. If the process data is not normally distributed, it should be transformed to get a normal distribution appearance before a capability analysis can be conducted (Montgomery, 2013).

Process capability can be calculated through the C p and C pk indices and is based on the influence of the 4M; man, machine, material, and method (Hong, 2013; VDA, 2005). The C p describes the short-term capability of the process based on upper and lower specification limits (USL and LSL).

Meanwhile, the C pk describes the centration of the process variation compared to the target value (Montgomery, 2013). Both the C p and C pk value are desired to be as high as possible, signaling a low-spread and well-centered process. A process exhibiting a C p or C pk of 1.67 or above is considered to be highly capable, while a process with C p or C pk below 1.33 is considered to be barely capable (Hong, 2013). In addition to examining the process capability, the long-term process performance can be analyzed with the P p and P pk indices by utilizing every observation across several samples (Avakh Darestani & Nasiri, 2015). Furthermore, the samples used to calculate the P p and P pk indices should be collected over a period of 20 production days (VDA, 2005).

The machine capability index (C m ) is one component of the process capability and focuses on the short-term capability of one specific machine in the manufacturing process (VDA, 2005). The C m

index tells whether the machine can produce within the given specifications during normal operating conditions, isolated from the influence of other factors (Hong, 2013; Pristavka & Bujna, 2014; VDA, 2005). Just like the C pk and P pk , the C mk index tells the centration of the observed values compared to the target value. Similar to the C p and C pk , a machine with C m or C mk of 1.67 or above is considered highly capable, and a value below 1.33 is considered barely capable (Hong, 2013). Furthermore, the equations for calculating the machine capability, process capability, and process performance are identical. The difference lies in how the standard deviation is calculated.

The standard deviation for machine and process capability is calculated based on samples collected

during a shorter period. Meanwhile, the process performance is based on samples collected over

an extended period to represent the long-term process performance. When calculating these

capability indices, it is important to remember that these measures are point estimates based on

certain sample sizes (Montgomery, 2013). Thereby, the capability measures may be more or less representative for the entire population of values, creating a statistical fluctuation with a certain level of confidence for each measure. The width of the confidence interval will vary depending on the sample size and the selected significance level, resulting in a wider interval for small sample sizes and low significance levels (Montgomery, 2013).

2.3 Quality during production ramp-up

The production timeline can be divided into three main phases, as shown in Figure 2.2: pilot production, production ramp-up, and stable production (Almgren, 2000; Colledani et al., 2018).

During the pilot production phase, the manufacturing rate is generally low and varying, creating disturbances that overload the organization and affect the final verification performance (Almgren, 2000). During the ramp-up phase, the objective is to scale up the manufacturing rate from the small batches in pilot production to stable production, which is characterized by a constant manufacturing rate at a high volume (Terwiesch & Xu, 2004). As manufacturers strive for a first- mover advantage, they must both shorten the time for product development and the time to reach full utilization of production capacity (Leffakis, 2016). Hence, it is important for manufacturers to put extra emphasis on shortening the ramp-up phase to reach stable full-capacity production quickly.

Figure 2.2: The different phases of production (inspired by Almgren, 2000).

2.3.1 Production ramp-up performance

Two sets of parameters are used to determine the production ramp-up performance: parameters regarding throughput time and parameters regarding efficiency. Throughput time measures how quickly a company can perform the activities required from the start of pilot production to reach the output targets related to quantity and quality. Thus, throughput time is operationalized as time- to-quantity and time-to-quality. Efficiency is determined in relation to four performance measures:

quantity performance, product conformance, product quality and production efficiency. Quantity performance is measured as the number of produced products compared to the number of planned products, assessing to which extent quantity targets are achieved. Product conformance is measured as the number of faults per product of production. It takes quality performance from a customer point of view into account, as it confirms whether the product manages to meet the customer requirements or not. Product quality is measured as the number of products without known defects of the total number of products that leave the production system, focusing on the production system's capacity to convert inputs into outputs. Lastly, production efficiency is measured as the relation between standard cost and actual cost, referring to the planned cost of producing a product compared to the actual cost of producing the same product. (Almgren, 2000) In addition to what Almgren (2000) presents, other factors affect production ramp-up performance.

Fjällström et al. (2009) discuss the most critical events that emerge during production ramp-up, categorizing them into six categories: suppliers/supply, product/quality, equipment/technique, process, personnel/education, and organization. Moreover, Almgren (1999b) presents four independent variables that affect efficiency during production ramp-up: work method, work pace, process disturbances, and conformance. All variables have an effect on production output, which in turn is related to production efficiency (Almgren, 1999b).

2.3.2 Ensuring effective throughput during ramp-up

System effective throughput (TH ^Eff ) is a measure that combines the effects of quality, production and maintenance control (Colledani et al., 2018). The measure is defined as the production rate of conforming products produced by the system in a certain period (Colledani et al., 2018). The effective throughput rate is often low during the production ramp-up phase, as configurations and tunings of equipment halts both production speed and conforming quality of products (Figure 2.3).

Production systems should run at operating speed as quickly as possible to make problems visible,

enabling fast corrective actions (Almgren, 1999a). However, this is seldom the case in real

production. The variation in throughput causes an effective throughput loss compared to the target

level, generating costs from lacking conformance and utilization of capacity. During every

reconfiguration of the system, the effective throughput is zero, demanding a new ramp-up to reach

the target level of effective throughput again once the system is restarted (Colledani et al., 2018).

Figure 2.3: Visualization of effective throughput loss during ramp-up (Colledani et al., 2018).

There are several potential causes for effective throughput loss during the ramp-up phase, both internal and external. The causes are often related to a mismatch between assumptions made during system design and the actual conditions of the real system in use (Colledani et al., 2018). Internal causes are generated from a mismatch inside the system and include varying equipment behavior, part variability, poor design of the manufacturing system, human error, and poor design of the control system (Colledani et al., 2018). The external causes have an indirect effect on the system and include mismatch in quality of input raw materials, and mismatch in maintenance conditions.

Some of the most severe effects of the causes listed above are unforeseen quality issues and equipment failure, potentially generating high maintenance costs (Colledani et al., 2018).

Colledani et al. (2018) suggest two different strategies for mitigating the effective throughput loss

during ramp-up. Strategy 1 is to anticipate the potential ramp-up disturbances already when

designing the manufacturing system. This anticipation could be made through modeling potential

machine failures, modeling real variable parts instead of ideal parts and tracking the effect of

control logics (Colledani et al., 2018). Strategy 2 is to collect and analyze system data from the

very start of production, enabling continuous improvement (Colledani et al., 2018). In summary,

Strategy 1 is preventive and focuses on optimizing the system robustness before the start, while

Strategy 2 is corrective and focuses on quickly identifying the real problems once the system is up

and running. As shown in Figure 2.4, Strategy 1 will increase the initial level of effective

throughput, while Strategy 2 will shorten the ramp-up time. Hence, combining the two strategies

will generate a robust system with shorter time to the target level of throughput (Colledani et al.,

2018).

Figure 2.4: Mitigation of effective throughput loss (Colledani et al., 2018).

2.3.3 Ramp-up environments

Leffakis (2016) suggests a theoretical framework that highlights potential types of production ramp-up environments (Figure 2.5). The framework is based upon the fact that all production ramp-ups feature either a completely new or modified version of a product concept. Each environment is described by two factors: scope of product and orientation of process technology.

The scope of a product depends on the product’s newness, whether the ramp-up features a completely novel product that has not been developed before or a modified concept of an existing product (Leffakis, 2016). Similarly, the orientation of process technology depends on the newness of the process technology, whether completely novel or existing equipment is used during the ramp-up (Leffakis, 2016).

The combination of these factors leads to four possible environments during production ramp-up.

Each environment possesses different levels of uncertainty, complexity and operational characteristics. A production ramp-up featuring a novel product generates a higher level of uncertainty and complexity compared to the ramp-up of a modified product, as there is no previous knowledge on the interaction between materials, components and equipment in production (Leffakis, 2016). Similarly, new production technology increases the uncertainty and the complexity of production, as operators are less familiar with the new technology (Leffakis, 2016).

Additionally, each environment requires unique practices and techniques to improve performance

during production ramp-up (Leffakis, 2016). The characteristics and needs of each environment

are explained below.

Figure 2.5: Ramp-up environments (Leffakis, 2016).

Experimenters are ramping up the production of a novel product, using novel technology in the manufacturing process. Hence, no previous knowledge can be used as foundation during the ramp- up, creating the highest level of complexity and uncertainty (Leffakis, 2016). Suitable practices and techniques for managing quality in the Experimenters environment include process planning and design, failure modes and effect analysis (FMEA), value stream mapping, design of experiments, and benchmarking (Leffakis, 2016).

Like Experimenters, Tuners are ramping up the production featuring a novel product, but with the utilization of existing technology. Subsequently, the existing knowledge around the product is limited, while knowledge around the technology is available. Thereby, the complexity and uncertainty are high in product scope, but low in process technology (Leffakis, 2016). Suitable practices and techniques for the Tuners environment include quality conformance, continuous process improvement, and the PDCA cycle (Leffakis, 2016).

Testers have the opposite scenario than Tuners, with the ramp-up of an existing and modified product using new technology. Hence, the product knowledge is high and the technical knowledge is limited, causing uncertainty and complexity especially related to the use of technology (Leffakis, 2016). Recommended practices and techniques in the Testers environment include quality prevention, process improvement, and gamification learning (Leffakis, 2016).

Lastly, Extenders has the lowest level of uncertainty and complexity, as ramp-up is centered

around an existing and modified product through the utilization of existing technology (Leffakis,

2016). Thus, a good understanding exists of both the product and the technology. Here,

recommended practices and techniques include quality control, SPC, capability analysis, and

DMAIC (Leffakis, 2016).

2.4 Monitoring production quality

Various inspection types can be used for collecting data and monitoring quality during commissioning and ramp-up, as well as stable production. Two of the most common types are on- line and off-line inspection (Tzimerman & Herer, 2009). On-line inspection is performed during the manufacturing process, while off-line inspection is performed when the process is completed (Tzimerman & Herer, 2009). Off-line inspection is often used when the required time for inspection is longer than the processing time (Colledani & Tolio, 2009). Furthermore, off-line inspection is often more thorough and carried out by experts in a laboratory, requiring several days to complete (Dagge et al., 2009). On-line inspection is often considered to be more cost- and time- efficient than off-line inspection, since it is performed automatically where products are temporarily redistributed to a separate line for inspection before returning to the main process (AAVOS International, 2017).

Another method for monitoring production quality is at-line inspection, which is performed either manually or semi-automatically just beside the production line (Dagge et al., 2009). The at-line inspection is a more optimized and faster alternative than off-line inspection, as the analysis equipment is located close to the process (Dagge et al., 2009). A fourth and final method is in-line inspection, which is an even more efficient method than the on-line inspection (AAVOS International, 2017). The in-line inspection does not require samples to be prepared for the inspection, making it significantly different from the other three inspection types. As the in-line inspection equipment is located directly in the process, it can automatically inspect all products passing through the system (Dagge et al., 2009; AAVOS International, 2017). In summary, both in-line and on-line inspection enables continuous process control, while at-line and off-line inspection requires a number of units to be removed from the process (AAVOS International, 2017). In-line inspection is often desirable for manufacturers as it enables continuous inspection without disturbing the process flow. However, the requirements for robustness of in-line inspection systems are much higher than for the other inspection types, demanding expensive technology to be utilized (Dagge et al., 2009). Table 2.3 shows an overview of the characteristics of each inspection type.

Table 2.3: Overview of inspection types.

Inspection type Performed Location for inspection Inspection frequency

Off-line Manually Outside of process (e.g. lab) Very low (single units) At-line Semi-automated / Manually Next to process Low (samples) On-line Automatically In the process (separate line) High (samples)

In-line Automatically In the process (same line) Very high (all units)

3. Methodology

This chapter presents the methodology that was used to fulfill the purpose of this study. The overall approach of the study is introduced, followed by a description of how the literature review and case study were conducted. Lastly, the credibility of the study is discussed in regard to reliability and validity.

3.1 Overall approach

This study was conducted through a qualitative case study at the Swedish LIB manufacturer Northvolt. As none of the authors were familiar with the LIB production process at the start of the study, the opportunity was given to regularly participate in meetings with the Production Quality team at NV Ett. In parallel, a literature review was conducted to develop fundamental knowledge on commissioning and ramp-up approaches in the manufacturing industry. The literature review was then followed by the case study, examining the overall quality assurance procedure performed during commissioning and production ramp-up. More specifically, focus was put on examining the quality assurance and validation needed for Coating. The case study was conducted through unstructured interviews with Northvolt staff and by reviewing internal documents. The findings from the case study were then analyzed with the help of the featured literature to highlight potential areas for improvement. The analysis laid the foundation for conclusions and recommendations for improving the quality assurance process. Finally, specific recommendations for Coating were developed together with a general framework for quality assurance on process level.

3.2 Literature review

A review of previous research on the topic of production quality and quality assurance was made

to create a foundational knowledge in the area. After an initial search, the amount of relevant

literature from the LIB industry was determined as limited. Therefore, the review was based on

findings from other manufacturing industries, e.g. the automotive industry. The featured literature

consisted of research articles that were found through systematic searching on databases such as

Google Scholar, Scopus and Web of Science. The searches were made using combinations of the

following keywords and phrases: quality assurance, commissioning, production ramp-up,

measurement system analysis, statistical process control, capability analysis, machine capability,

inspection types. In addition to the research articles, the automotive standards AIAG (2010) and

VDA (2005) were also reviewed to highlight the specific requirements for automotive suppliers

like Northvolt. Automotive suppliers and manufacturers have to follow the requirements of these

standards, regardless of what products or components they produce.

3.3 Case study

This section describes the two steps of the conducted case study, namely the document review and the conducted interviews. Observations were initially planned to be performed to provide a visual picture of the coating process at NV Ett, acting as a complement to the document review and the interviews. However, the use of observations was prevented due to the COVID-19 pandemic.

3.3.1 Document review

A review of relevant documents was performed to gain more detailed knowledge on related topics that have to be considered during commissioning and ramp-up. The documents consisted of Northvolt’s own descriptions of the product, manufacturing processes, and process validation.

Documents giving detailed descriptions of the coating process were also reviewed, including Design-FMEAs, Process flow charts, Process-FMEAs, and Control Plans. The Northvolt documents contained confidential information and are thereby not referenced in this study.

3.3.2 Interviews

Interviews with Northvolt employees were used to supplement the document review to obtain additional information regarding product characteristics, process design and validation methods.

Due to the COVID-19 pandemic, the interviews were conducted through online video communication. The interviews were not recorded or transcribed, however, notes were taken frequently during the interviews to ensure that the answers were documented. Furthermore, since the respondents of the interviews were all Northvolt employees, there was an opportunity to quickly contact them again to clarify any potential uncertainties.

Qu and Dumay (2011) describe three strategies for conducting interviews: unstructured, semi-

structured and structured interviews. Unstructured interviews are often informal conversations

where the interviewee feels relaxed and unassessed. Unstructured interviews take shape to the

individual situation and context, meaning that the interviewer must be able to develop, adapt and

generate follow-up questions that reflect the purpose of the research (Qu & Dumay, 2011). Semi-

structured interviews are based on prepared questions guided by identified themes in a consistent

and systematic manner during the interviews to elicit more elaborate responses from the

interviewees (Qu & Dumay, 2011). Lastly, structured interviews are characterized by the

interviewer asking the same predetermined questions in the same order to all interviewees, limiting

the number of response categories (Qu & Dumay, 2011). Due to the authors’ lack of previous

knowledge in the field, unstructured interviews were considered suitable for this study. The

unstructured interviews enabled the respondents to provide in-depth answers on predetermined

subjects, depending on the purpose of each interview. Thus, the interviews became less formal and

became a learning opportunity where the respondents shared information on the specific subject.

The respondents from the interviews included members from the Production Quality team at NV Ett and NV Labs employees with expert knowledge of the coating process and the related manufacturing equipment (see Table 3.1). The interviews conducted with NV Labs employees also became an internal benchmarking since the pilot production is already operating at NV Labs.

Through the internal benchmarking, important lessons learned were obtained that will be useful during the large-scale commissioning and ramp-up at NV Ett. In addition to the interviews, valuable knowledge was obtained from daily meetings with the NV Ett Production Quality team.

Table 3.1: Compilation of the interviews.

Role Date Purpose with interview Result of interview

Process Quality Engineer 2021-02-04 Understand the coating process at NV Labs and NV Ett

Clear understanding of both processes Cell Design Team 2021-02-24 Identify product characteristics for the

coating process

Identified the SCs for the coating process

Process Quality Engineer 2021-03-16 Understand how MSA is performed for the coating process at NV Labs

Gained an understanding of how MSA is conducted for the coating process

Project Quality Manager 2021-03-17

Discuss the capability analysis for the coating process based on customer requirements

Agreement on the capability targets Data Analytics Quality

Engineer 2021-03-25 Understand the SPC and capability analysis done at NV Labs

Learned that SPC has not been done

Process Quality Engineer 2021-03-26 Discuss capability analysis conducted at NV Labs

Insight on how the capability analysis was conducted

Quality Engineer 2021-04-12 Discuss how the Beta-Gauges operate

Knowledge on how Beta-

Gauges collect data and

how loading level is

calculated

3.4 Credibility of the study

The following two definitions by Saunders et al. (2009) were used to ensure a high level of credibility of the study:

● Reliability - the extent to which the data collection techniques or analysis procedures provide consistent findings.

● Validity - whether the findings are about what they appear to be about.

Daily meetings with the Production Quality team at NV Ett, including the Northvolt supervisor, were held during the entire study. These meetings primarily focused on the planning and commissioning at NV Ett, which helped keep the study in scope and allowed for continuous input.

Advice regarding relevant people to contact for the unstructured interviews and documents to review was frequently provided during the team meetings. During the daily meetings with the Production Quality team, there could sometimes be ambiguities about information obtained from other employees at Northvolt, which was important to understand to proceed with the study.

Therefore, the unstructured interviews were conducted to clarify and confirm that the information raised at the meetings was correct, strengthening the validity of the acquired information. As previously mentioned, an opportunity was given to contact the respondents after the interviews to clarify any uncertainties, which helped strengthen both the reliability and validity of the findings.

Furthermore, separate reconciliations with the Northvolt supervisor and the Project Quality Manager were held weekly, focusing on this study. Both the team meetings and the reconciliations helped strengthen the case study, ensuring that the right focus was kept. The Luleå University of Technology supervisor also provided continuous support to help improve the content of the study.

The study was also regularly reviewed by opponents, resulting in constructive feedback and highlighted areas for improvement.

The research articles featured in the literature review were complemented with some textbooks to

strengthen the reliability of the literature findings. The featured literature describes quality

assurance on a general level and is combined with well-established methods for MSA, SPC, and

capability analysis. Thus, the recommendations from this study could likely be generalizable to fit

in other manufacturing industries. For example, the suggested framework could be modified by

other automotive manufacturers and suppliers for quality assurance of their specific processes.

4. Case study at Northvolt

This chapter starts with presenting the plans for commissioning and ramp-up that will be executed at NV Ett during 2021. An extensive description of the coating process is then presented, which is the first process to be commissioned at NV Ett. Lastly, the current validation procedure of the coating process at NV Labs is presented, including methods for MSA, SPC, and capability analysis.

4.1 General procedure of commissioning and ramp-up

Northvolt is currently preparing and planning for commissioning and ramp-up of the manufacturing processes at NV Ett in Skellefteå. Based on customer requirements and specifications from the Customer Quality team, the Cell Design team develops a Design-FMEA containing all product characteristics. The Design-FMEA describes the potential failure modes and effects that are related to each product characteristic, ranking the characteristics based on the severity of the failures and effects. The characteristics are then transferred into a Process-FMEA by Process Engineers to link each characteristic to specific steps in the manufacturing process.

Like the Design-FMEA, the Process-FMEA describes each process steps’ potential failure modes and effects, highlighting the effect that each process step can have on each product characteristic.

Based on the Process-FMEA, the Process Quality Engineers then create a Control Plan for each process step. The Control Plan combines the process steps from the Process-FMEA with the product specifications from the Design-FMEA, working as a manual for operating the specific process.

When the manufacturing equipment has been delivered to NV Ett, it is tested during Cold commissioning, where the manufacturing equipment is being run without processing any material.

Here, process characteristics such as operating speed, temperature, and pressure are tested. When

the Cold commissioning is completed, the Hot commissioning is performed by processing material

in smaller batches and inspecting the product characteristics. During the Hot commissioning at

NV Ett, all the process steps will have to undergo individual validation in MSA, SPC, and

capability analysis. The validation ensures that everything is operating according to plan and the

processed material conforms with the specific performance, safety, and legal requirements,

originating from both internal and external stakeholders. The Control Plan is utilized and tested

during the entire hot commissioning to have a comprehensive and well-working Control Plan for

the ramp-up and stable production. As mentioned in 1.4 Delimitations, Coating will be the first

process to be commissioned at NV Ett. The following two sections describe the coating process at

NV Ett and highlight the important product characteristics that need to be validated during the

commissioning and ramp-up.

4.2 Overview of the coating process

In Coating, the active material (slurry) is coated on a roll of metal foil to create electrode rolls, which are later pressed, notched, and assembled into either cylindrical or prismatic battery cells in the upcoming steps. Different control parameters can be adjusted in the coating process, e.g. foil speed and tension, dryer temperature, and slurry pressure at the coating head. Figure 4.1 provides an overview of the coating process at NV Ett, where both sides of the foil are coated in separate lines (tandem coating). The coating process at NV Ett is different from the coating process at NV Labs (Figure 4.2), where the foil passes through the same coating line twice to coat both sides (single-line coating). Furthermore, six tandem coating lines will be used at NV Ett (three for cathode and three for anode), compared to two single-line coaters being used at NV Labs (one for cathode and one for anode).

Figure 4.1: Coating process at NV Ett (tandem coating).

Figure 4.2: Coating process at NV Labs (single-line coating).

The coating process at NV Ett (Figure 4.1) operates as follows: First, a roll of 500- to 800-meter-

long current collector foil is loaded and unwound at the in-feeding station. Aluminum foil is used

for the cathode side and copper foil for the anode side. The foil is passed through Beta-Gauge 1 to

measure its bare weight. The Beta-Gauge (hereafter BG) is a fully automated inspection

instrument, configured to measure a minimum of six data points per minute. Once the foil is

prepared for coating, slurry is pumped from the storage tanks to the coating head and is

continuously applied on the first side of the foil (A-side) through a slot die. Different types of slurry are used for cathode and anode coating. Afterwards, the coated foil is passed through magnetic bars to remove any loose metal particles. The coated foil is then passed through BG 2 that inspects the loading level of the wet coating and Camera 1 for visual inspection of the wet coated surface. The cameras are continuously scanning the entire surface of the foil, measuring the dimensions and identifying potential defects.

Next, the coated foil is then passed through an electric oven to dry the applied slurry. The drying oven has two important mechanisms: solvent evaporation from the wet coated surface, and solvent diffusion through the dried coated surface. However, the drying step differs for the cathode and anode side. For the cathode side, the evaporated chemical solvent NMP is recovered. For the anode side, the evaporated water is removed and the moisture-rich air goes through a condenser. When the foil has dried in the oven, the loading level of the dried coating is measured by BG 3. The foil is then passing by Camera 2 for visual inspection of the dried coated surface. The B-side of the foil is then undergoing the same procedure as the A-side. When the B-side of the foil has been treated, a printer marks the foil with a QR-code to enable traceability. Then, the foil is cut into two foils in the half-slitting step. Finally, the two foils are rewound to electrode rolls and unloaded, then undergoing a final off-line inspection before the next process step (Calendaring).

4.3 Product characteristics for Coating

The customers require certain product quality characteristics with individual specifications, which

are affected in different steps of the manufacturing process. The characteristics are incorporated

into a Design-FMEA and assigned with their specifications. The most important characteristics are

individually labeled as a Significant Characteristic (SC) or Critical Characteristic (CC). The SCs

are defined as the characteristics that significantly affect product performance (e.g. lifetime and

charging capacity), meanwhile CCs are defined as the characteristics with significant effect on

product safety or legal requirements. Due to the safety and legal aspects, the CCs are ranked higher

than the SCs. The SCs and CCs are also included in the Process-FMEAs and linked to the specific

process steps in which they are affected. Thus, the most critical process steps can be highlighted

to ensure high production quality in relation to the characteristics. Based on customer requirements

and specifications, five characteristics were ranked as SCs for Coating (Table 4.1). However, no

characteristics were ranked as CCs based on the interview with the Cell Design team. Worth

emphasizing is that this study only focuses on the identified SCs in Table 4.1, and not different

defects that might occur in the coating process.

Table 4.1: Summary of the SCs for Coating.

SCs Description Unit Inspection (NV Ett)

Loading level Amount of slurry coated per square centimeter of the

foil (area density) mg/cm

Beta-Gauge 2, 3, 4, 5

At-line Dimension of coated

area Width of the area that has been coated with slurry mm Camera 1, 2, 3, 4 Off-line Dimension of

uncoated areas

Width of the areas that have not been coated with

slurry mm Camera 1, 2, 3, 4

Off-line Misalignment of side

A and B Parallelity of the coated areas on each side of the foil mm Camera 4 Off-line NMP content

(cathode only)

Portion of NMP (N-Methyl-2-Pyrrolidone) in coated

cathode slurry ppm Method under

development

Figure 4.3 shows a visualization of the SCs for Coating. The loading level, the dimension of the coated area, the dimension of the uncoated areas, and the misalignment of side A and B are all related to both anode and cathode, while NMP content is only applicable for cathode. The loading level, dimensions, and misalignment are measured several times throughout the coating process and are inspected off-line once the foil has been rewound. The NMP content will only be inspected off-line once a measurement method has been developed.

Figure 4.3: Visualization of the SCs for Coating.

Loading level

The loading level refers to the amount of slurry coated per square centimeter of the foil (see Figure

4.3). It is measured for side A and B through in-line inspection, performed continuously by BG 2,

3, 4, and 5 in Figure 4.1. BG 1 measures the bare foil weight while the other BGs measure the

loading level of the coating (including the bare foil weight). Thus, the BG 1 measurement needs

to be subtracted from the loading level measurements to withhold the loading level of the coating

itself. The loading level of the dry coating (measured by BG 3 and 5) is considered to be the most

important to analyze according to Northvolt. The BGs measuring the wet coating (BG 2 and 4) are